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19,801 results on '"Basu P"'

1. An entropy based comparative study of regional and seasonal distributions of particulate matter in Indian cities

Author: Banerjee, Suchismita, Basu, Urna, and Basu, Banasri
Subjects: Physics - Physics and Society
Abstract: Particulate matter (PM), especially $\text{PM}_{2.5}$, is a critical air pollutant posing significant risks to human health and the environment in India. This study, using six years (2018-2024) of daily $\text{PM}_{2.5}$ data, investigates the seasonal characteristics of the distributions of $\text{PM}_{2.5}$ concentrations across eleven Indian cities, selected from different regions of the country. We find that, while each city has its own unique seasonal patterns, all of them show a universal exponential decay in the tail of the $\text{PM}_{2.5}$ distribution for all the seasons. However, the decay rates of this tail vary across cities, highlighting regional and seasonal disparities in pollution levels. To quantitatively characterize the {\it randomness} of the seasonal $\text{PM}_{2.5}$ concentration distributions, we compute Shannon entropy, a key information theoretic measure. This allows for classifying cities into different groups, according to the level of randomness observed in their seasonal distributions. To further explore the inter-city relationships, we employ Jensen-Shannon divergence (JSD), a symmetric measure of relative entropy, to quantitatively assess the degree of similarity in the $\text{PM}_{2.5}$ distributions among different cities. Remarkably, we find that several cities show very similar distributions in the winter months, which helps us to categories them into several groups. The groups obtained from these entropy based measures, namely, individual Shannon entropy and the JSD estimate, are consistent with each other, providing a robust framework for efficient air quality management and policy-making in India., Comment: 22 pages, 10 figures and 8 tables
Published: 2025

2. On Fair Ordering and Differential Privacy

Author: Cohen, Shir, Basu, Neel, Basu, Soumya, and Alvisi, Lorenzo
Subjects: Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: In blockchain systems, fair transaction ordering is crucial for a trusted and regulation-compliant economic ecosystem. Unlike traditional State Machine Replication (SMR) systems, which focus solely on liveness and safety, blockchain systems also require a fairness property. This paper examines these properties and aims to eliminate algorithmic bias in transaction ordering services. We build on the notion of equal opportunity. We characterize transactions in terms of relevant and irrelevant features, requiring that the order be determined solely by the relevant ones. Specifically, transactions with identical relevant features should have an equal chance of being ordered before one another. We extend this framework to define a property where the greater the distance in relevant features between transactions, the higher the probability of prioritizing one over the other. We reveal a surprising link between equal opportunity in SMR and Differential Privacy (DP), showing that any DP mechanism can be used to ensure fairness in SMR. This connection not only enhances our understanding of the interplay between privacy and fairness in distributed computing but also opens up new opportunities for designing fair distributed protocols using well-established DP techniques.
Published: 2025

3. Probing the PeV Region in the Astrophysical Neutrino Spectrum using $\nu_\mu$ from the Southern Sky

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S. N., Babu, R., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brisson-Tsavoussis, Z., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Coleman, P., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Díaz-Vélez, J. C., Dierichs, P., DiKerby, S., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Esmail, W., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamacher, L., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hmaid, R., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kobayashi, Y., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kowalski, M., Kozynets, T., Krieger, N., Krishnamoorthi, J., Krishnan, T., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Lauber, F., Lazar, J. P., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Mand, A., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Mitchell, J., Molchany, L., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Parrish, V., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Pyras, L., Raab, C., Rack-Helleis, J., Rad, N., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reistroffer, I., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Schwirn, S., Sclafani, S., Seckel, D., Seen, L., Seikh, M., Seo, M., Seunarine, S., Myhr, P. A. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thakuri, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, F., Yu, S., Yuan, T., Zegarelli, A., Zhang, S., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: IceCube has observed a diffuse astrophysical neutrino flux over the energy region from a few TeV to a few PeV. At PeV energies, the spectral shape is not yet well measured due to the low statistics of the data. This analysis probes the gap between 1 PeV and 10 PeV by using high-energy downgoing muon neutrinos. To reject the large atmospheric muon background, two complementary techniques are combined. The first technique selects events with high stochasticity to reject atmospheric muon bundles whose stochastic energy losses are smoothed due to high muon multiplicity. The second technique vetoes atmospheric muons with the IceTop surface array. Using 9 years of data, we found two neutrino candidate events in the signal region, consistent with expectation from background, each with relatively high signal probabilities. A joint maximum likelihood estimation is performed using this sample and an independent 9.5-year sample of tracks to measure the neutrino spectrum. A likelihood ratio test is done to compare the single power-law (SPL) vs. SPL+cutoff hypothesis; the SPL+cutoff model is not significantly better than the SPL. High-energy astrophysical objects from four source catalogs are also checked around the direction of the two events. No significant coincidence was found.
Published: 2025

4. Complex LLM Planning via Automated Heuristics Discovery

Author: Ling, Hongyi, Parashar, Shubham, Khurana, Sambhav, Olson, Blake, Basu, Anwesha, Sinha, Gaurangi, Tu, Zhengzhong, Caverlee, James, and Ji, Shuiwang
Subjects: Computer Science - Artificial Intelligence
Abstract: We consider enhancing large language models (LLMs) for complex planning tasks. While existing methods allow LLMs to explore intermediate steps to make plans, they either depend on unreliable self-verification or external verifiers to evaluate these steps, which demand significant data and computations. Here, we propose automated heuristics discovery (AutoHD), a novel approach that enables LLMs to explicitly generate heuristic functions to guide inference-time search, allowing accurate evaluation of intermediate states. These heuristic functions are further refined through a heuristic evolution process, improving their robustness and effectiveness. Our proposed method requires no additional model training or fine-tuning, and the explicit definition of heuristic functions generated by the LLMs provides interpretability and insights into the reasoning process. Extensive experiments across diverse benchmarks demonstrate significant gains over multiple baselines, including nearly twice the accuracy on some datasets, establishing our approach as a reliable and interpretable solution for complex planning tasks.
Published: 2025

5. Seasonal Variations of the Atmospheric Muon Neutrino Spectrum measured with IceCube

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S. N., Babu, R., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brisson-Tsavoussis, Z., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Coleman, P., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Díaz-Vélez, J. C., Dierichs, P., DiKerby, S., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Esmail, W., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamacher, L., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hmaid, R., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kobayashi, Y., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kowalski, M., Kozynets, T., Krieger, N., Krishnamoorthi, J., Krishnan, T., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Lauber, F., Lazar, J. P., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Mand, A., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Mitchell, J., Molchany, L., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Parrish, V., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Pyras, L., Raab, C., Rack-Helleis, J., Rad, N., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reistroffer, I., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Schwirn, S., Sclafani, S., Seckel, D., Seen, L., Seikh, M., Seo, M., Seunarine, S., Myhr, P. A. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thakuri, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, F., Yu, S., Yuan, T., Zegarelli, A., Zhang, S., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment
Abstract: This study presents an energy-dependent analysis of seasonal variations in the atmospheric muon neutrino spectrum, using 11.3 years of data from the IceCube Neutrino Observatory. By leveraging a novel spectral unfolding method, we explore the energy range from 125 GeV to 10 TeV for zenith angles between 90{\deg} to 110{\deg}, corresponding to the Antarctic atmosphere. Our findings reveal that the seasonal variation amplitude decreases with energy reaching ($-4.6 \pm 1.1$)\% during Austral winter and increases ($+3.9 \pm 1.2$)\% during Austral summer relative to the annual average at 10TeV. While the unfolded flux exceeds the model predictions by up to 30\%, the differential measurement of seasonal variations remains unaffected. The measured seasonal variations of the muon neutrino spectrum are consistent with theoretical predictions using the MCEq code and the NRLMSISE-00 atmospheric model.
Published: 2025

6. Invariance principle for the Gaussian Multiplicative Chaos via a high dimensional CLT with low rank increments

Author: Chowdhury, Mriganka Basu Roy and Ganguly, Shirshendu
Subjects: Mathematics - Probability, Mathematical Physics, Mathematics - Statistics Theory
Abstract: Gaussian multiplicative chaos (GMC) is a canonical random fractal measure obtained by exponentiating log-correlated Gaussian processes, first constructed in the seminal work of Kahane (1985). Since then it has served as an important building block in constructions of quantum field theories and Liouville quantum gravity. However, in many natural settings, non-Gaussian log-correlated processes arise. In this paper, we investigate the universality of GMC through an invariance principle. We consider the model of a random Fourier series, a process known to be log-correlated. While the Gaussian Fourier series has been a classical object of study, recently, the non-Gaussian counterpart was investigated and the associated multiplicative chaos constructed by Junnila in 2016. We show that the Gaussian and non-Gaussian variables can be coupled so that the associated chaos measures are almost surely mutually absolutely continuous throughout the entire sub-critical regime. This solves the main open problem from Kim and Kriechbaum (2024) who had earlier established such a result for a part of the regime. The main ingredient is a new high dimensional CLT for a sum of independent (but not i.i.d.) random vectors belonging to rank one subspaces with error bounds involving the isotropic properties of the covariance matrix of the sum, which we expect will find other applications. The proof relies on a path-wise analysis of Skorokhod embeddings as well as a perturbative result about square roots of positive semi-definite matrices which, surprisingly, appears to be new., Comment: 34 pages, 1 figure
Published: 2025

7. Accretion bursts in young intermediate-mass stars make planet formation challenging

Author: Das, Indrani, Vorobyov, Eduard, and Basu, Shantanu
Subjects: Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We investigate the occurrence of accretion bursts, dust accumulation, and the prospects for planetesimal formation in a gravitationally unstable magnetized protoplanetary disk with globally suppressed but episodically triggered magnetorotational instability (MRI), particularly in young intermediate-mass stars (YIMSs) but with a brief comparison to low-mass counterparts. We use numerical magnetohydrodynamics simulations in the thin-disk limit (FEOSAD code) to model the formation and long-term evolution of a gravitationally unstable magnetized protoplanetary disk, including dust dynamics and growth, since the collapse of a massive slowly-rotating prestellar cloud core. Massive gas concentrations and dust rings form within the inner disk region owing to the radially varying efficiency of mass transport by gravitational instability (GI). These rings are initially susceptible to streaming instability (SI). However, gradual warming of the dust rings, thanks to high opacity and GI-induced influx of matter increases the gas temperature above a threshold for the MRI to develop via thermal ionization of alkaline metals. The ensuing MRI bursts destroy the dust rings, making planetesimal formation via SI problematic. In the later evolution phase, when the burst activity starts to diminish, SI becomes inefficient because of growing dust drift velocity and more extended inner dead zone, both acting to reduce the dust concentration below the threshold for SI to develop. Low-mass objects appear to be less affected by these adverse effects. Our results suggest that disks around young intermediate-mass stars may be challenging environments for planetesimal formation via SI. This may explain the dearth of planets around stars with $M_\ast > 3.0 \,$$M_\odot$., Comment: Accepted for Publication in Astrophysical Journal, 24 Pages, 13 Figures
Published: 2025

8. Unraveling Enhanced Superconductivity in Single-layer FeSe through Substrate Surface Terminations

Author: Zou, Qiang, Kim, Gi-Yeop, Kang, Jong-Hoon, Oli, Basu Dev, Ge, Zhuozhi, Weinert, Michael, Mandal, Subhasish, Eom, Chang-Beom, Choi, Si-Young, and Li, Lian
Subjects: Condensed Matter - Superconductivity
Abstract: Single-layer FeSe films grown on (001) SrTiO3 substrates have shown a significant increase in superconducting transition temperature compared to bulk FeSe. Several mechanisms have been proposed to explain such enhancement, including electron doping, interfacial electron-phonon coupling, and strong electron correlations. To pinpoint the primary driver, we grew FeSe films on SrTiO3 substrates with coexisting TiO2 and SrO surface terminations. Scanning tunneling spectroscopy revealed a larger superconducting gap of 17 meV for FeSe on TiO2 compared to 11 meV on SrO. Tunneling spectroscopy also showed a larger work function on SrO, leading to reduced charge transfer, as confirmed by angle-resolved photoemission spectroscopy. Scanning transmission electron microscopy revealed distinctive interfacial atomic-scale structures, with the Se-Fe-Se tetrahedral angle changing from 109.9{\deg} on SrO to 105.1{\deg} on TiO2. Compared to dynamical mean field theory calculations, these results suggest optimal electron correlations in FeSe/TiO2 for enhancing high-temperature superconductivity.
Published: 2025

9. A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models

Author: Lin, Zihao, Basu, Samyadeep, Beigi, Mohammad, Manjunatha, Varun, Rossi, Ryan A., Wang, Zichao, Zhou, Yufan, Balasubramanian, Sriram, Zarei, Arman, Rezaei, Keivan, Shen, Ying, Yao, Barry Menglong, Xu, Zhiyang, Liu, Qin, Zhang, Yuxiang, Sun, Yan, Liu, Shilong, Shen, Li, Li, Hongxuan, Feizi, Soheil, and Huang, Lifu
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: The rise of foundation models has transformed machine learning research, prompting efforts to uncover their inner workings and develop more efficient and reliable applications for better control. While significant progress has been made in interpreting Large Language Models (LLMs), multimodal foundation models (MMFMs) - such as contrastive vision-language models, generative vision-language models, and text-to-image models - pose unique interpretability challenges beyond unimodal frameworks. Despite initial studies, a substantial gap remains between the interpretability of LLMs and MMFMs. This survey explores two key aspects: (1) the adaptation of LLM interpretability methods to multimodal models and (2) understanding the mechanistic differences between unimodal language models and crossmodal systems. By systematically reviewing current MMFM analysis techniques, we propose a structured taxonomy of interpretability methods, compare insights across unimodal and multimodal architectures, and highlight critical research gaps., Comment: 30 pages, 4 Figures, 10 Tables
Published: 2025

10. Fundamental Survey on Neuromorphic Based Audio Classification

Author: Basu, Amlan, Chaudhari, Pranav, and Di Caterina, Gaetano
Subjects: Computer Science - Sound, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditional methods frequently depend on intricate signal processing algorithms and manually crafted features, which may fall short in fully capturing the complexities of audio patterns. Neuromorphic computing, inspired by the architecture and functioning of the human brain, presents a promising alternative for audio classification tasks. This survey provides an exhaustive examination of the current state-of-the-art in neuromorphic-based audio classification. It delves into the crucial components of neuromorphic systems, such as Spiking Neural Networks (SNNs), memristors, and neuromorphic hardware platforms, highlighting their advantages in audio classification. Furthermore, the survey explores various methodologies and strategies employed in neuromorphic audio classification, including event-based processing, spike-based learning, and bio-inspired feature extraction. It examines how these approaches address the limitations of traditional audio classification methods, particularly in terms of energy efficiency, real-time processing, and robustness to environmental noise. Additionally, the paper conducts a comparative analysis of different neuromorphic audio classification models and benchmarks, evaluating their performance metrics, computational efficiency, and scalability. By providing a comprehensive guide for researchers, engineers and practitioners, this survey aims to stimulate further innovation and advancements in the evolving field of neuromorphic audio classification., Comment: 24 Pages, 1 Table
Published: 2025

11. Measurement of the inelasticity distribution of neutrino-nucleon interactions for $\mathbf{80~GeV<E_{\nu}<560~GeV}$ with IceCube DeepCore

Author: IceCube Collaboration, Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S. N., Babu, R., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brisson-Tsavoussis, Z., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Coleman, P., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Esmail, W., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamacher, L., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hmaid, R., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kobayashi, Y., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kowalski, M., Kozynets, T., Krieger, N., Krishnamoorthi, J., Krishnan, T., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Lauber, F., Lazar, J. P., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Mand, A., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Parrish, V., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Pyras, L., Raab, C., Rack-Helleis, J., Rad, N., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Schwirn, S., Sclafani, S., Seckel, D., Seen, L., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, F., Yu, S., Yuan, T., Zegarelli, A., Zhang, S., Zhang, Z., Zhelnin, P., Zilberman, P., Zimmerman, M., and Aushev, V.
Subjects: High Energy Physics - Experiment
Abstract: We report a measurement of the inelasticity distribution in the scattering of neutrinos of energy $80-560$ GeV off nucleons, which is sensitive to the inclusive differential cross section. This analysis is based on a sample of atmospheric muon neutrinos detected in the IceCube sub-array DeepCore during 2012-2021, and is the first such measurement in this energy range. Our measurement extends to energies where accelerator data is not available, hence we compare our results to predictions from perturbative QCD calculations, finding good agreement., Comment: 17 pages, 19 figures
Published: 2025

12. Personalized Top-k Set Queries Over Predicted Scores

Author: Nia, Sohrab Namazi, Ghosh, Subhodeep, Roy, Senjuti Basu, and Amer-Yahia, Sihem
Subjects: Computer Science - Databases, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: This work studies the applicability of expensive external oracles such as large language models in answering top-k queries over predicted scores. Such scores are incurred by user-defined functions to answer personalized queries over multi-modal data. We propose a generic computational framework that handles arbitrary set-based scoring functions, as long as the functions could be decomposed into constructs, each of which sent to an oracle (in our case an LLM) to predict partial scores. At a given point in time, the framework assumes a set of responses and their partial predicted scores, and it maintains a collection of possible sets that are likely to be the true top-k. Since calling oracles is costly, our framework judiciously identifies the next construct, i.e., the next best question to ask the oracle so as to maximize the likelihood of identifying the true top-k. We present a principled probabilistic model that quantifies that likelihood. We study efficiency opportunities in designing algorithms. We run an evaluation with three large scale datasets, scoring functions, and baselines. Experiments indicate the efficacy of our framework, as it achieves an order of magnitude improvement over baselines in requiring LLM calls while ensuring result accuracy. Scalability experiments further indicate that our framework could be used in large-scale applications.
Published: 2025

13. A Pathwise Coordinate Descent Algorithm for LASSO Penalized Quantile Regression

Author: Kim, Sanghee and Basu, Sumanta
Subjects: Statistics - Computation, Statistics - Methodology, Statistics - Machine Learning
Abstract: $\ell_1$ penalized quantile regression is used in many fields as an alternative to penalized least squares regressions for high-dimensional data analysis. Existing algorithms for penalized quantile regression either use linear programming, which does not scale well in high dimension, or an approximate coordinate descent (CD) which does not solve for exact coordinatewise minimum of the nonsmooth loss function. Further, neither approaches build fast, pathwise algorithms commonly used in high-dimensional statistics to leverage sparsity structure of the problem in large-scale data sets. To avoid the computational challenges associated with the nonsmooth quantile loss, some recent works have even advocated using smooth approximations to the exact problem. In this work, we develop a fast, pathwise coordinate descent algorithm to compute exact $\ell_1$ penalized quantile regression estimates for high-dimensional data. We derive an easy-to-compute exact solution for the coordinatewise nonsmooth loss minimization, which, to the best of our knowledge, has not been reported in the literature. We also employ a random perturbation strategy to help the algorithm avoid getting stuck along the regularization path. In simulated data sets, we show that our algorithm runs substantially faster than existing alternatives based on approximate CD and linear program, while retaining the same level of estimation accuracy., Comment: 33 pages, 12 figures, 5 tables
Published: 2025

14. A co-segmentation algorithm to predict emotional stress from passively sensed mHealth data

Author: Kim, Younghoon, Basu, Sumanta, and Banerjee, Samprit
Subjects: Statistics - Applications
Abstract: We develop a data-driven co-segmentation algorithm of passively sensed and self-reported active variables collected through smartphones to identify emotionally stressful states in middle-aged and older patients with mood disorders undergoing therapy, some of whom also have chronic pain. Our method leverages the association between the different types of time series. These data are typically non-stationary, with meaningful associations often occurring only over short time windows. Traditional machine learning (ML) methods, when applied globally on the entire time series, often fail to capture these time-varying local patterns. Our approach first segments the passive sensing variables by detecting their change points, then examines segment-specific associations with the active variable to identify co-segmented periods that exhibit distinct relationships between stress and passively sensed measures. We then use these periods to predict future emotional stress states using standard ML methods. By shifting the unit of analysis from individual time points to data-driven segments of time and allowing for different associations in different segments, our algorithm helps detect patterns that only exist within short-time windows. We apply our method to detect periods of stress in patient data collected during ALACRITY Phase I study. Our findings indicate that the data-driven segmentation algorithm identifies stress periods more accurately than traditional ML methods that do not incorporate segmentation.
Published: 2025

15. Medical Applications of Graph Convolutional Networks Using Electronic Health Records: A Survey

Author: Hoyt, Garrik, Chatterjee, Noyonica, Battaglia, Fortunato, and Basu, Paramita
Subjects: Computer Science - Machine Learning
Abstract: Graph Convolutional Networks (GCNs) have emerged as a promising approach to machine learning on Electronic Health Records (EHRs). By constructing a graph representation of patient data and performing convolutions on neighborhoods of nodes, GCNs can capture complex relationships and extract meaningful insights to support medical decision making. This survey provides an overview of the current research in applying GCNs to EHR data. We identify the key medical domains and prediction tasks where these models are being utilized, common benchmark datasets, and architectural patterns to provide a comprehensive survey of this field. While this is a nascent area of research, GCNs demonstrate strong potential to leverage the complex information hidden in EHRs. Challenges and opportunities for future work are also discussed., Comment: 5 pages, 4 figures
Published: 2025

16. Search for Heavy Neutral Leptons with IceCube DeepCore

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S. N., Babu, R., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brisson-Tsavoussis, Z., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Coleman, P., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Esmail, W., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamacher, L., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hmaid, R., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kobayashi, Y., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kowalski, M., Kozynets, T., Krieger, N., Krishnamoorthi, J., Krishnan, T., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Lauber, F., Lazar, J. P., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Mand, A., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Parrish, V., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Pyras, L., Raab, C., Rack-Helleis, J., Rad, N., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Schwirn, S., Sclafani, S., Seckel, D., Seen, L., Seikh, M., Seo, M., Seunarine, S., Myhr, P. A. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, F., Yu, S., Yuan, T., Zegarelli, A., Zhang, S., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: High Energy Physics - Experiment
Abstract: The observation of neutrino oscillations has established that neutrinos have non-zero masses. This phenomenon is not explained by the Standard Model of particle physics, but one viable explanation to this dilemma involves the existence of heavy neutral leptons in the form of right-handed neutrinos. This work presents the first search for heavy neutral leptons with the IceCube Neutrino Observatory. The standard three flavor neutrino model is extended by adding a fourth GeV-scale mass state allowing mixing with the $\tau$ sector through the parameter $|U_{\tau4}|^2$. The analysis is performed by searching for signatures of heavy neutral leptons that are directly produced via up-scattering of atmospheric $\nu_\tau$'s inside the IceCube detection volume. Three heavy neutral lepton mass values, $m_4$, of 0.3 GeV, 0.6 GeV, and 1.0 GeV are tested using ten years of data, collected between 2011 and 2021. No significant signal of heavy neutral leptons is observed for any of the tested masses. The resulting constraints for the mixing parameter are $|U_{\tau4}|^2 < 0.19$ ($m_4 = 0.3$ GeV), $|U_{\tau4}|^2 < 0.36$ ($m_4 = 0.6$ GeV), and $|U_{\tau4}|^2 < 0.40$ ($m_4 = 1.0$ GeV) at the 90% confidence level. This analysis serves as proof-of-concept for heavy neutral lepton searches in IceCube. The heavy neutral lepton event generator, developed in this work, and the analysis of the expected signatures lay the fundamental groundwork for future searches thereof.
Published: 2025

17. The low-frequency flattening of the radio spectrum of giant HII regions in M 101

Author: Gajović, L., Heesen, V., Brüggen, M., Edler, H. W., Adebahr, B., Pasini, T., de Gasperin, F., Basu, A., Weżgowiec, M., Horellou, C., Bomans, D. J., Dénes, H., and Vohl, D.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: In galaxies, the flattening of the spectrum at low radio frequencies below 300 MHz has been the subject of some debate. A turnover at low frequencies could be caused by multiple physical processes, which can yield new insights into the properties of the ionised gas in the interstellar medium. We investigate the existence and nature of the low-frequency turnover in the HII regions of M 101. We study the nearby galaxy M 101 using the LOw Frequency ARray (LOFAR) at frequencies of 54 and 144 MHz, Apertif at 1370 MHz, and published combined map from the Very Large Array (VLA) and Effelesberg telescope at 4850 MHz. The spectral index between 54 and 144 MHz is inverted at the centres of HII regions. We find a significant low-frequency flattening at the centres of five out of six HII regions that we selected for this study. The low frequency flattening in HII regions of M 101 can be explained with two different free-free absorption models. The flattening is localised in a region smaller than 1.5 kpc and can only be detected with high resolution (better than 45''). The detection of low frequency flattening has important consequences for using radio continuum observations below 100 MHz to measure extinction-free star-formation rates., Comment: 15 pages, 13 figures, 5 tables, accepted to A&A
Published: 2025

18. Uniform confidence bands for joint angles across different fatigue phases

Author: Bastian, Patrick, Basu, Rupsa, and Dette, Holger
Subjects: Statistics - Methodology, 62R10 62M10
Abstract: We develop uniform confidence bands for the mean function of stationary time series as a post-hoc analysis of multiple change point detection in functional time series. In particular, the methodology in this work provides bands for those segments where the jump size exceeds a certain threshold $\Delta$. In \cite{bastian2024multiplechangepointdetection} such exceedences of $\Delta$ were related to fatigue states of a running athlete. The extension to confidence bands stems from an interest in understanding the range of motion (ROM) of lower-extremity joints of running athletes under fatiguing conditions. From a biomechanical perspective, ROM serves as a proxy for joint flexibility under varying fatigue states, offering individualized insights into potentially problematic movement patterns. The new methodology provides a valuable tool for understanding the dynamic behavior of joint motion and its relationship to fatigue.
Published: 2025

19. On Mechanistic Circuits for Extractive Question-Answering

Author: Basu, Samyadeep, Morariu, Vlad, Wang, Zichao, Rossi, Ryan, Zhao, Cherry, Feizi, Soheil, and Manjunatha, Varun
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Large language models are increasingly used to process documents and facilitate question-answering on them. In our paper, we extract mechanistic circuits for this real-world language modeling task: context-augmented language modeling for extractive question-answering (QA) tasks and understand the potential benefits of circuits towards downstream applications such as data attribution to context information. We extract circuits as a function of internal model components (e.g., attention heads, MLPs) using causal mediation analysis techniques. Leveraging the extracted circuits, we first understand the interplay between the model's usage of parametric memory and retrieved context towards a better mechanistic understanding of context-augmented language models. We then identify a small set of attention heads in our circuit which performs reliable data attribution by default, thereby obtaining attribution for free in just the model's forward pass. Using this insight, we then introduce ATTNATTRIB, a fast data attribution algorithm which obtains state-of-the-art attribution results across various extractive QA benchmarks. Finally, we show the possibility to steer the language model towards answering from the context, instead of the parametric memory by using the attribution from ATTNATTRIB as an additional signal during the forward pass. Beyond mechanistic understanding, our paper provides tangible applications of circuits in the form of reliable data attribution and model steering.
Published: 2025

20. Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning

Author: DUNE Collaboration, Abud, A. Abed, Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Alemanno, F., Alex, N. S., Allison, K., Alrashed, M., Alton, A., Alvarez, R., Alves, T., Aman, A., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anjarazafy, F., Antic, D., Antoniassi, M., Antonova, M., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Gómez, D. Ávila, Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Baigarashev, D., Balasubramanian, S., Balboni, A., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Basu, D., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, B., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolini, E., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bezawada, Y., Bezerra, A. T., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bogart, B., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Booth, A., Boran, F., Merlo, R. Borges, Bostan, N., Botogoske, G., Bottino, B., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M. B., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chalifour, M., Chappell, A., Chatterjee, A., Chauhan, B., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen, Z., Cherdack, D., Chhibra, S. S., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Choi, G., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conrad, J. M., Convery, M., Conway, K., Copello, S., Cova, P., Cox, C., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., De Jong, P., Sanchez, P. Del Amo, De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Di Silvestre, V., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Dolan, S., Dolce, M., Dolek, F., Dolinski, M. J., Domenici, D., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Emark, W., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Felix, J., Feng, Y., Fernandez-Martinez, E., da Silva, M. Ferreira, Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fucci, M., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., Gago, A. M, Galizzi, F., Gallagher, H., Galli, M., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghosh, A., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonzalez-Diaz, D., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Haegel, L., Hagaman, L., Hahn, A., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Handley, M., Happacher, F., Harris, D. A., Hart, A. L., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hawkins, S., Hays, J., He, M., Heavey, A., Heeger, K. M., Heindel, A., Heise, J., Hellmuth, P., Henderson, L., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Zink, J. Hoefken, Hoff, J., Holin, A., Holvey, T., Hong, C., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Hosokawa, R., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Huang, X., Hulcher, Z., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Oliveira, M. Ismerio, Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Jung, K. Y., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kalikulov, O., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasetti, S. P., Kashur, L., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Keloth, R., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., Kim, S., King, B., King, M., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kordosky, M., Kosc, T., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kroupova, T., Kubota, S., Kubu, M., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kus, V., Kutter, T., Kvasnicka, J., Labree, T., Lackey, T., Lalău, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Larkin, J., Lasorak, P., Last, D., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Miotto, G. Lehmann, Lehnert, R., Leitner, M., Lemoine, H., Silverio, D. Leon, Lepin, L. M., Li, J. -Y, Li, S. W., Li, Y., Liao, H., Lima, R., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Luo, X., Luppi, E., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Malige, A., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Manzanillas-Velez, L., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, Martinez-Casales, M., López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Mastbaum, A., Masud, M., Matichard, F., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mayhew, F., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., McNulty, C., Meazza, L., Meddage, V. C. N., Mehmood, M., Mehta, B., Mehta, P., Mei, F., Melas, P., Mellet, L., Mena, O., Mendez, H., Méndez, D. P., Mendonca, A. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Minotti, A., Miralles, L., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mladenov, D., Mocioiu, I., Mogan, A., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, M., Moore, Z., Moreno, D., Moreno-Granados, G., Moreno-Palacios, O., Morescalchi, L., Moretti, R., Morris, C., Mossey, C., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mukhamejanov, Y., Mukhamejanova, A., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Naples, D., Narita, S., Nava, J., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nielsen, A., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, A., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panareo, M., Panda, P., Pandey, V., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papadopoulou, A., Papaleo, R., Papoulias, D., Paramesvaran, S., Parke, S., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Camargo, G. Patiño, Paton, J. L., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paul, J., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Peake, A., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pfaff, M., Pia, V., Pickering, L., Pierini, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Pletcher, K., Plows, K., Pollack, C., Pollmann, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paixão, L. G. Porto, Potekhin, M., Pozzato, M., Pradhan, R., Prakash, T., Prest, M., Psihas, F., Pugnere, D., Pullia, D., Qian, X., Queen, J., Raaf, J. L., Rabelhofer, M., Radeka, V., Rademacker, J., Radics, B., Raffaelli, F., Rafique, A., Raguzin, E., Rahe, A., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralaikoto, M. A., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Randriamanampisoa, S. S., Rappoldi, A., Raselli, G., Rath, T., Ratoff, P., Ray, R., Razafinime, H., Razakamiandra, R. F., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reichenbacher, J., Reitzner, S. D., Renner, E., Repetto, S., Rescia, S., Resnati, F., Restrepo, Diego, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rikalo, A., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Robert, A., Roberts, A., Robles, E., Rocha, J. L. Rocabado, Roda, M., Rodrigues, M. J. O., Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Roy, N., Roy, P., Rubbia, C., Rudik, D., Ruggeri, A., Ferreira, G. Ruiz, Rushiya, K., Russell, B., Sacerdoti, S., Saduyev, N., Sahoo, S. K., Sahu, N., Sakhiyev, S., Sala, P., Salmoria, G., Samanta, S., Samios, N., Sanchez, M. C., Bravo, A. Sánchez, Sánchez-Castillo, A., Sanchez-Lucas, P., Sanders, D. A., Sanfilippo, S., Santoro, D., Saoulidou, N., Sapienza, P., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Schwartz, S., Segade, A., Segreto, E., Senise, C. R., Sensenig, J., Seppela, D., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Poudel, S. Sharma, Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Shi, J., Shi, W., Shin, S., Shivakoti, S., Shmakov, A., Shoemaker, I., Shooltz, D., Shrock, R., Siden, M., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Nunes, M. Soares, Sobel, H., Soderberg, M., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Soto-Oton, J., Sousa, A., Soustruznik, K., Correia, D. Souza, Spinella, F., Spitz, J., Spooner, N. J. C., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stewart, J., Stillwell, B., Stock, J., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Sweeney, C., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thomas, S., Thompson, A., Thorn, C., Thorpe, C., Timm, S. C., Tiras, E., Tishchenko, V., Tiwari, S., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Muñoz, D. Torres, Torti, M., Tortola, M., Torun, Y., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valdiviesso, G. A., Vale, V., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Auccalla, T. Vargas, Oliva, D. Vargas, Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Vences, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Vizarreta, R., Hernandez, A. P. Vizcaya, Vlachos, S., Vorobyev, G., Vuong, Q., Waldron, A. V., Wallach, M., Walsh, J., Walton, T., Wan, L., Wang, B., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Westerdale, S., Wetstein, M., Whalen, K., White, A., Whitehead, L. H., Whittington, D., Wieler, F., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wresilo, K., Wrobel, M., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Wynne, B. M., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, T., Yankelevich, A., Yates, L., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zettlemoyer, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures
Published: 2025

21. Self-similar Features in Sub-secondary Breakup of a Droplet and Ligament Mediated Fragmentation under Extreme Conditions

Author: Rao, Saini Jatin and Basu, Saptarshi
Subjects: Physics - Fluid Dynamics
Abstract: Droplet formation is relevant in many applications spanning natural and artificial settings. Comprehending droplet aerobreakup or air-assisted secondary atomization is challenging, especially in high-speed flow scenarios. This entails multi-scale interface deformations with intricate wave dynamics that conform to a non-linear cascade. In the present study, we look into shockwave-induced breakups and associated intermediate processes happening at smaller spatiotemporal scales across the disintegrating droplet interface at different Weber numbers ($We \sim 10^3$). We observe the undulations to follow breakup patterns that resemble a scaled-down version of a secondary atomization event. These sub-secondary breakup processes end with corrugated ligaments that generate the final daughter droplets. The size distribution of these droplets is estimated using a Depth from Defocus (DFD) technique. These illustrate the transient nature of aerobreakup, where the normalized statistics in subsequent time periods and different $We$ are observed to follow a universal distribution. This conforms to a gamma distribution where the associated fit parameters agree well with the coefficients determined from ligament shape factors, corresponding to the limit associated with most extreme corrugations. Scaling laws based on $We$ are deduced for the averaged statistics using a high energy chaotic breakup mechanism. These observations reinforce the idea of a self-similar mechanism for catastrophic aerobreakup of a droplet.
Published: 2025

22. VERITAS and multiwavelength observations of the Blazar B3 2247+381 in response to an IceCube neutrino alert

Author: Acharyya, Atreya, Adams, Colin B., Bangale, Priyadarshini, Bartkoske, J. T., Benbow, Wystan, Buckley, James H., Chen, Yu, Christiansen, Jodi, Chromey, Alisha, Duerr, Anne, Errando, Manel, Godoy, Miguel E., Falcone, Abe, Feng, Qi, Foote, Juniper, Fortson, Lucy, Furniss, Amy, Hanlon, William, Hanna, David, Hervet, Olivier, Hinrichs, Claire E., Holder, Jamie, Humensky, Thomas B., Jin, Weidong, Johnson, Madalyn N., Kaaret, Philip, Kertzman, Mary P., Kherlakian, Maria, Kieda, David, Kleiner, Tobias K., Korzoun, Mx. Nikolas, Krennrich, Frank, Kumar, Sajan, Lang, Mark J., Lundy, Matthew, McGrath, Conor, Meyer, Eileen T., Millard, Matthew J., Millis, John, Mooney, Connor, Moriarty, Patrick, Mukherjee, Reshmi, Ning, Wenmeng, O'Brien, Stephan, Ong, Rene A., Pohl, Martin, Pueschel, Elisa, Quinn, John, Rabinowitz, Pazit L., Ragan, Ken, Reynolds, Paul, Ribeiro, Deivid, Roache, Emmet Thomas, Ryan, Jamie L., Sadeh, Iftach, Sadun, Alberto, Saha, Lab, Santander, Marcos, Sembroski, Glenn H., Shang, Ruo-Yu, Splettstoesser, Megan, Tak, Donggeun, Talluri, Anjana K., Tucci, James V., Valverde, Janeth, Williams, David A., Wong, Sam L., Woo, Jooyun, Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S. N., Babu, R., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brisson-Tsavoussis, Z., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Coleman, P., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Esmail, W., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamacher, L., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hmaid, R., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kobayashi, Y., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kowalski, M., Kozynets, T., Krieger, N., Krishnamoorthi, J., Krishnan, T., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Lauber, F., Lazar, J. P., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Mand, A., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Parrish, V., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Pyras, L., Raab, C., Rack-Helleis, J., Rad, N., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Schwirn, S., Sclafani, S., Seckel, D., Seen, L., Seikh, M., Seo, M., Seunarine, S., Myhr, P. A. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, F., Yu, S., Yuan, T., Zegarelli, A., Zhang, S., Zhang, Z., Zhelnin, P., Zilberman, P., Zimmerman, M., Drake, Pablo, Spira-Savett, Elizabeth, Lusen, Piatra, and Mori, Kaya
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: While the sources of the diffuse astrophysical neutrino flux detected by the IceCube Neutrino Observatory are still largely unknown, one of the promising methods used towards understanding this is investigating the potential temporal and spatial correlations between neutrino alerts and the electromagnetic radiation from blazars. We report on the multiwavelength target-of-opportunity observations of the blazar B3 2247+381, taken in response to an IceCube multiplet alert for a cluster of muon neutrino events compatible with the source location between May 20, 2022 and November 10, 2022. B3 2247+381 was not detected with VERITAS during this time period. The source was found to be in a low-flux state in the optical, ultraviolet and gamma-ray bands for the time interval corresponding to the neutrino event, but was detected in the hard X-ray band with NuSTAR during this period. We find the multiwavelength spectral energy distribution is well described using a simple one-zone leptonic synchrotron self-Compton radiation model. Moreover, assuming the neutrinos originate from hadronic processes within the jet, the neutrino flux would be accompanied by a photon flux from the cascade emission, and the integrated photon flux required in such a case would significantly exceed the total multiwavelength fluxes and the VERITAS upper limits presented here. The lack of flaring activity observed with VERITAS, combined with the low multiwavelength flux levels, and given the significance of the neutrino excess is at 3$\sigma$ level (uncorrected for trials), makes B3 2247+381 an unlikely source of the IceCube multiplet. We conclude that the neutrino excess is likely a background fluctuation., Comment: 26 pages, 5 figures. Accepted for publication in the Astrophysical Journal (ApJ)
Published: 2025

23. Investigating Corporate Social Responsibility Initiatives: Examining the case of corporate Covid-19 response

Author: Basu, Meheli, Dutta, Aniruddha, and Shah, Purvi
Subjects: Computer Science - Information Retrieval
Abstract: In todays age of freely available information, policy makers have to take into account a huge amount of information while making decisions affecting relevant stakeholders. While increase in the amount of information sources and documents increases credibility of decisions based on the corpus of available text, it is challenging for policymakers to make sense of this information. This paper demonstrates how policy makers can implement some of the most popular topic recognition methods, Latent Dirichlet Allocation, Deep Distributed Representation method, text summarization approaches, Word Based Sentence Ranking method and TextRank for sentence extraction method, to sum up the content of large volume of documents to understand the gist of the overload of information. We have applied popular NLP methods to corporate press releases during the early period and advanced period of Covid-19 pandemic which has resulted in a global unprecedented health and socio-economic crisis, when policymaking and regulations have become especially important to standardize corporate practices for employee and social welfare in the face of similar future unseen crises. The steps undertaken in this study can be replicated to yield insights from relevant documents in any other social decision-making context., Comment: 7 Tables
Published: 2025

24. A search for extremely-high-energy neutrinos and first constraints on the ultra-high-energy cosmic-ray proton fraction with IceCube

Author: IceCube Collaboration, Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S. N., Babu, R., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brisson-Tsavoussis, Z., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Coleman, P., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Esmail, W., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamacher, L., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hmaid, R., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kobayashi, Y., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kowalski, M., Kozynets, T., Krieger, N., Krishnamoorthi, J., Krishnan, T., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Lauber, F., Lazar, J. P., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Mand, A., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Parrish, V., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Pyras, L., Raab, C., Rack-Helleis, J., Rad, N., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Schwirn, S., Sclafani, S., Seckel, D., Seen, L., Seikh, M., Seo, M., Seunarine, S., Myhr, P. A. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, F., Yu, S., Yuan, T., Zegarelli, A., Zhang, S., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present a search for the diffuse extremely-high-energy neutrino flux using $12.6$ years of IceCube data. The non-observation of neutrinos with energies well above $10 \, \mathrm{PeV}$ constrains the all-flavor neutrino flux at $10^{18} \, \mathrm{eV}$ to a level of $E^2 \Phi_{\nu_e + \nu_\mu + \nu_\tau} \simeq 10^{-8} \, \mathrm{GeV} \, \mathrm{cm}^{-2} \, \mathrm{s}^{-1} \, \mathrm{sr}^{-1}$, the most stringent limit to date. Using this data, we constrain the proton fraction of ultra-high-energy cosmic rays (UHECRs) above $\simeq 30 \, \mathrm{EeV}$ to be $\lesssim 70\,$% (at $90\,$% CL) if the cosmological evolution of the sources is comparable to or stronger than the star formation rate. This result complements direct air-shower measurements by being insensitive to uncertainties associated with hadronic interaction models. It is the first such result to disfavor the ``proton-only" hypothesis for UHECRs using neutrino data.
Published: 2025

25. K-dwarf Radius Inflation and a 10-Gyr Spin-down Clock Unveiled through Asteroseismology of HD~219134 from the Keck Planet Finder

Author: Li, Yaguang, Huber, Daniel, Ong, J. M. Joel, van Saders, Jennifer, Costa, R. R., Larsen, Jens Reersted, Basu, Sarbani, Bedding, Timothy R., Dai, Fei, Chontos, Ashley, Carmichael, Theron W., Hey, Daniel, Kjeldsen, Hans, Hon, Marc, Campante, Tiago L., Monteiro, Mário J. P. F. G., Lundkvist, Mia Sloth, Saunders, Nicholas, Isaacson, Howard, Howard, Andrew W., Gibson, Steven R., Halverson, Samuel, Rider, Kodi, Roy, Arpita, Baker, Ashley D., Edelstein, Jerry, Smith, Chris, Fulton, Benjamin J., and Walawender, Josh
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics
Abstract: We present the first asteroseismic analysis of the K3\,V planet host HD~219134, based on four consecutive nights of radial velocities collected with the Keck Planet Finder. We applied Gold deconvolution to the power spectrum to disentangle modes from sidelobes in the spectral window, and extracted 25 mode frequencies with spherical degrees $0\leq\ell\leq3$. We derive the fundamental properties using five different evolutionary-modeling pipelines and report a mass of 0.763 $\pm$ 0.020 (stat) $\pm$ 0.007 (sys) M$_\odot$, a radius of 0.748 $\pm$ 0.007 (stat) $\pm$ 0.002 (sys) R$_\odot$, and an age of 10.151 $\pm$ 1.520 (stat) $\pm$ 0.810 (sys) Gyr. Compared to the interferometric radius 0.783 $\pm$ 0.005~R$_\odot$, the asteroseismic radius is 4\% smaller at the 4-$\sigma$ level -- a discrepancy not easily explained by known interferometric systematics, modeling assumptions on atmospheric boundary conditions and mixing lengths, magnetic fields, or tidal heating. HD~219134 is the first main-sequence star cooler than 5000~K with an asteroseismic age estimate and will serve as a critical calibration point for stellar spin-down relations. We show that existing calibrated prescriptions for angular momentum loss, incorporating weakened magnetic braking with asteroseismically constrained stellar parameters, accurately reproduce the observed rotation period. Additionally, we revised the masses and radii of the super-Earths in the system, which support their having Earth-like compositions. Finally, we confirm that the oscillation amplitude in radial velocity scales as $(L/M)^{1.5}$ in K dwarfs, in contrast to the $(L/M)^{0.7}$ relation observed in G dwarfs. These findings provide significant insights into the structure and angular momentum loss of K-type stars., Comment: 23 pages, 11 figures. submitted to AAS Journals. Comments welcome
Published: 2025

26. Large deviations of geodesic midpoint fluctuations in last-passage percolation with general i.i.d. weights

Author: Alberts, Tom, Basu, Riddhipratim, Groathouse, Sean, and Shen, Xiao
Subjects: Mathematics - Probability, Mathematical Physics, 60K35, 60K37
Abstract: The study of transversal fluctuations of the optimal path is a crucial aspect of the Kardar-Parisi-Zhang (KPZ) universality class. In this work, we establish the large deviation limit for the midpoint transversal fluctuations in a general last-passage percolation (LPP) model with mild assumption on the i.i.d. weights. The rate function is expressed in terms of the right tail large deviation rate function of the last-passage value and the shape function. When the weights are chosen to be i.i.d. exponential random variables, our result verifies a conjecture communicated to us by Liu [Liu'22], showing the asymptotic probability of the geodesic from $(0,0)$ to $(n,n)$ following the corner path $(0,0) \to (n,0) \to (n,n)$ is $({4}/{e^2})^{n+o(n)}$., Comment: 16 pages, 3 figures
Published: 2025

27. Magnetic disk winds in protoplanetary disks: Description of the model and impact on global disk evolution

Author: Kadam, Kundan, Vorobyov, Eduard, Woitke, Peter, Basu, Shantanu, and van Terwisga, Sierk
Subjects: Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract: Canonically, a protoplanetary disk is thought to undergo (gravito-)viscous evolution, wherein the angular momentum of the accreting material is transported outwards. However, several lines of reasoning suggest that the turbulent viscosity in a typical protoplanetary disk is insufficient to drive the observed accretion rates. An emerging paradigm suggests that radially extended magnetic disk winds may play a crucial role in the disk evolution. We propose a global model of magnetic wind-driven accretion for evolution of protoplanetary disks, based on the insights gained from local shearing box simulations. Here we develop this model and constrain its parameters with the help of theoretical expectations and comparison with observations. The magnetic wind is characterized with the associated loss of angular momentum and mass, which depend on the local disk conditions and stellar properties. We incorporate the disk winds self-consistently in the code FEOSAD and study formation and long-term evolution of protoplanetary disks. We include disk self-gravity and an adaptive turbulent alpha, while the co-evolution of dust is also considered. Synthetic observations are obtained via radiation thermo-chemical code ProDiMo. The models with inclusion of disk winds satisfy general expectations from both theory and observations. The disk wind parameters can be guided by observational constraints and the synthetic observations resulting from such a model compare favorably with the selected ALMA survey data of Class II disks. The proposed magnetic disk wind model is a significant step forward in the direction of representing a more complete disk evolution, wherein the disk experiences concurrent torques from viscous, gravitational, and magnetic wind processes., Comment: 24 pages, 8 figures, 5 tables, Accepted
Published: 2025

28. Variability of the UV luminosity function with SPICE

Author: Basu, Arghyadeep, Bhagwat, Aniket, Ciardi, Benedetta, and Costa, Tiago
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We investigate the variability of the UV luminosity function (UVLF) at $z > 5$ using the SPICE suite of cosmological, radiation-hydrodynamic simulations, which include three distinct supernova (SN) feedback models: bursty-sn, smooth-sn, and hyper-sn. The bursty-sn model, driven by intense and episodic SN explosions, produces the highest fluctuations in the star formation rate (SFR). Conversely, the smooth-sn model, characterized by gentler SN feedback, results in minimal SFR variability. The hyper-sn model, featuring a more realistic prescription that incorporates hypernova (HN) explosions, exhibits intermediate variability, closely aligning with the smooth-sn trend at lower redshifts. These fluctuations in SFR significantly affect the $\rm{M_{UV} - M_{halo}}$ relation, a proxy for UVLF variability. Among the models, bursty-sn produces the highest UVLF variability, with a maximum value of 2.5. In contrast, the smooth-sn and hyper-sn models show substantially lower variability, with maximum values of 1.3 and 1.5, respectively. However, in all cases, UVLF variability strongly correlates with host halo mass, with lower-mass halos showing greater variability due to more effective SN feedback in their shallower gravitational wells. The bursty-sn model, though, results in higher amplitudes. Variability decreases in lower mass haloes with decreasing redshift for all feedback models. This study underscores the critical role of SN feedback in shaping the UVLF, and highlights the mass and redshift dependence of its variability, suggesting that UVLF variability may alleviate the bright galaxy tension observed by JWST at high redshifts., Comment: 14 pages, 13 figures, Version submitted in MNRAS Main Journal
Published: 2025

29. Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo

Author: Basu, Sattwik, Dutta, Debottam, Wei, Yu-Lin, and Choudhury, Romit Roy
Subjects: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: This paper considers the problem of estimating chirp parameters from a noisy mixture of chirps. While a rich body of work exists in this area, challenges remain when extending these techniques to chirps of higher order polynomials. We formulate this as a non-convex optimization problem and propose a modified Langevin Monte Carlo (LMC) sampler that exploits the average curvature of the objective function to reliably find the minimizer. Results show that our Curvature-guided LMC (CG-LMC) algorithm is robust and succeeds even in low SNR regimes, making it viable for practical applications.
Published: 2025

30. Magnetic field evolution of X-ray emitting radio-quiet pulsars

Author: Atta, Debasis, Singh, Vinay, and Basu, D. N.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics, Nuclear Theory
Abstract: The intense magnetic fields present in neutron stars are closely linked to their observed temperature and spectral characteristics, timing properties, including spin period and its derivatives. Therefore, a comprehensive theoretical analysis of magnetic field evolution is essential for understanding how the strength of the magnetic field change over time. The decay rate of magnetic field in isolated, non-accreting neutron stars can be assessed by evaluating the second derivative of the spin frequency. Another method to estimate this rate involves monitoring an increase in thermal emission beyond what is expected from standard cooling processes, assuming no additional heating mechanisms are present. Our findings indicate that for X-ray emitting isolated neutron stars, the evolution rate of spin period derivative aligns with the dissipation rate of magnetic energy from the dipolar field, provided that a substantial portion of the released energy is emitted as X-rays. The time scale of magnetic field decay is found to be much shorter than typical age of radio pulsars., Comment: 6 pages including 1 figure and 1 table
Published: 2025

31. WaterWise: Co-optimizing Carbon- and Water-Footprint Toward Environmentally Sustainable Cloud Computing

Author: Jiang, Yankai, Roy, Rohan Basu, Kanakagiri, Raghavendra, and Tiwari, Devesh
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: The carbon and water footprint of large-scale computing systems poses serious environmental sustainability risks. In this study, we discover that, unfortunately, carbon and water sustainability are at odds with each other - and, optimizing one alone hurts the other. Toward that goal, we introduce, WaterWise, a novel job scheduler for parallel workloads that intelligently co-optimizes carbon and water footprint to improve the sustainability of geographically distributed data centers.
Published: 2025

32. Study of SARS-CoV-2 Spike Protein by Surface Enhanced Raman Spectroscopy and Transmission Electron Microscopy

Author: Ghalawat, Monika, Meena, Virendra Kumar, Prasad, Sharda, Poddar, Pankaj, and Basu, Atanu
Subjects: Physics - Biological Physics, Condensed Matter - Materials Science
Abstract: The spike protein (SP) of SARS-CoV-2 is the major molecular target for making diagnostic tests, vaccines, and therapeutic development. We used a combination of transmission electron microscopy (TEM) and surface enhanced Raman microscopy (SERS) to study its structure. Using SERS on an aluminum substrate, we were able to detect a characteristic spectrum of SP mostly due to vibration of three aromatic amino acids producing Raman shifts at 466 cm-1, 524 cm-1, 773 cm-1, 831 cm-1, 1048 cm-1, 1308 cm-1, 1457 cm-1, and 1610 cm-1. Transmission Electron Microscopy (TEM) of the SP showed periodic 2D-lattice orientation. The findings from this study have translational values for developing surface-enhanced Raman spectroscopy (SERS) based detectors for screening and testing SARS-CoV-2 signatures in diagnostic settings and contamination tracking., Comment: 16 pages, 7 figures, 1 table including supporting information
Published: 2025

33. Time-Integrated Southern-Sky Neutrino Source Searches with 10 Years of IceCube Starting-Track Events at Energies Down to 1 TeV

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S. N., Babu, R., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brisson-Tsavoussis, Z., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Coleman, P., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Esmail, W., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamacher, L., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hmaid, R., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kobayashi, Y., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kowalski, M., Kozynets, T., Krieger, N., Krishnamoorthi, J., Krishnan, T., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Lauber, F., Lazar, J. P., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Mand, A., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Parrish, V., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Pyras, L., Raab, C., Rack-Helleis, J., Rad, N., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Schwirn, S., Sclafani, S., Seckel, D., Seen, L., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, F., Yu, S., Yuan, T., Zegarelli, A., Zhang, S., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: In the IceCube Neutrino Observatory, a signal of astrophysical neutrinos is obscured by backgrounds from atmospheric neutrinos and muons produced in cosmic-ray interactions. IceCube event selections used to isolate the astrophysical neutrino signal often focus on t/he morphology of the light patterns recorded by the detector. The analyses presented here use the new IceCube Enhanced Starting Track Event Selection (ESTES), which identifies events likely generated by muon neutrino interactions within the detector geometry, focusing on neutrino energies of 1-500 TeV with a median angular resolution of 1.4{\deg}. Selecting for starting track events filters out not only the atmospheric-muon background, but also the atmospheric-neutrino background in the southern sky. This improves IceCube's muon neutrino sensitivity to southern-sky neutrino sources, especially for Galactic sources that are not expected to produce a substantial flux of neutrinos above 100 TeV. In this work, the ESTES sample was applied for the first time to searches for astrophysical sources of neutrinos, including a search for diffuse neutrino emission from the Galactic plane. No significant excesses were identified from any of the analyses; however, constraining limits are set on the hadronic emission from TeV gamma-ray Galactic plane objects and models of the diffuse Galactic plane neutrino flux., Comment: 23 pages, 8 figures, 4 tables. Submitted to ApJ
Published: 2025

34. R2D2: Remembering, Reflecting and Dynamic Decision Making for Web Agents

Author: Huang, Tenghao, Basu, Kinjal, Abdelaziz, Ibrahim, Kapanipathi, Pavan, May, Jonathan, and Chen, Muhao
Subjects: Computer Science - Artificial Intelligence
Abstract: The proliferation of web agents necessitates advanced navigation and interaction strategies within complex web environments. Current models often struggle with efficient navigation and action execution due to limited visibility and understanding of web structures. Our proposed R2D2 framework addresses these challenges by integrating two paradigms: Remember and Reflect. The Remember paradigm utilizes a replay buffer that aids agents in reconstructing the web environment dynamically, thus enabling the formulation of a detailed ``map'' of previously visited pages. This helps in reducing navigational errors and optimizing the decision-making process during web interactions. Conversely, the Reflect paradigm allows agents to learn from past mistakes by providing a mechanism for error analysis and strategy refinement, enhancing overall task performance. We evaluate R2D2 using the WEBARENA benchmark, demonstrating significant improvements over existing methods, including a 50% reduction in navigation errors and a threefold increase in task completion rates. Our findings suggest that a combination of memory-enhanced navigation and reflective learning promisingly advances the capabilities of web agents, potentially benefiting various applications such as automated customer service and personal digital assistants.
Published: 2025

35. Physics-informed deep learning for infectious disease forecasting

Author: Qian, Ying, Marty, Éric, Basu, Avranil, O'Dea, Eamon B., Wang, Xianqiao, Fox, Spencer, Rohani, Pejman, Drake, John M., and Li, He
Subjects: Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract: Accurate forecasting of contagious illnesses has become increasingly important to public health policymaking, and better prediction could prevent the loss of millions of lives. To better prepare for future pandemics, it is essential to improve forecasting methods and capabilities. In this work, we propose a new infectious disease forecasting model based on physics-informed neural networks (PINNs), an emerging area of scientific machine learning. The proposed PINN model incorporates dynamical systems representations of disease transmission into the loss function, thereby assimilating epidemiological theory and data using neural networks (NNs). Our approach is designed to prevent model overfitting, which often occurs when training deep learning models with observation data alone. In addition, we employ an additional sub-network to account for mobility, vaccination, and other covariates that influence the transmission rate, a key parameter in the compartment model. To demonstrate the capability of the proposed model, we examine the performance of the model using state-level COVID-19 data in California. Our simulation results show that predictions of PINN model on the number of cases, deaths, and hospitalizations are consistent with existing benchmarks. In particular, the PINN model outperforms the basic NN model and naive baseline forecast. We also show that the performance of the PINN model is comparable to a sophisticated Gaussian infection state space with time dependence (GISST) forecasting model that integrates the compartment model with a data observation model and a regression model for inferring parameters in the compartment model. Nonetheless, the PINN model offers a simpler structure and is easier to implement. Our results show that the proposed forecaster could potentially serve as a new computational tool to enhance the current capacity of infectious disease forecasting.
Published: 2025

36. Complex Structure around a Circumstellar Disk Caused by Interchange Instability

Author: Machida, Masahiro N. and Basu, Shantanu
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: We perform a three-dimensional nonideal magnetohydrodynamic simulation of a strongly magnetized cloud core and investigate the complex structure caused by the interchange instability. This is the first simulation that does not use a central sink cell and calculates the long term ($> 10^4$ yr) evolution even as the disk and outflow formation occur. The magnetic field dissipates inside the disk, and magnetic flux accumulates around the edge of the disk, leading to the occurrence of interchange instability. During the main accretion phase, the interchange instability occurs recurrently, disturbing the circumstellar region and forming ring, arc, and cavity structures. These are consistent with recent high-resolution observations of circumstellar regions around young protostars. The structures extend to $>1,000$ au and persist for at least 30,000 yr after protostar formation, demonstrating the dynamic removal process of magnetic flux during star formation. We find that the disk continues to grow even as interchange instability occurs, by accretion through channels between the outgoing cavities. The outflow is initially weak, but becomes strong after $\sim 10^3$ yr., Comment: Accepted for publication in ApJL
Published: 2025

37. Search for neutrino doublets and triplets using 11.4 years of IceCube data

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S. N., Babu, R., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brisson-Tsavoussis, Z., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Coleman, P., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Esmail, W., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamacher, L., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hmaid, R., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kobayashi, Y., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kowalski, M., Kozynets, T., Krieger, N., Krishnamoorthi, J., Krishnan, T., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Lauber, F., Lazar, J. P., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Mand, A., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Parrish, V., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Pyras, L., Raab, C., Rack-Helleis, J., Rad, N., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Schwirn, S., Sclafani, S., Seckel, D., Seen, L., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, F., Yu, S., Yuan, T., Zegarelli, A., Zhang, S., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report a search for high-energy astrophysical neutrino multiplets, detections of multiple neutrino clusters in the same direction within 30 days, based on an analysis of 11.4 years of IceCube data. A new search method optimized for transient neutrino emission with a monthly time scale is employed, providing a higher sensitivity to neutrino fluxes. This result is sensitive to neutrino transient emission, reaching per-flavor flux of approximately $10^{-10}\ {\rm erg}\ {\rm cm}^{-2}\ {\rm sec}^{-1}$ from the Northern sky in the energy range $E\gtrsim 50$~TeV. The number of doublets and triplets identified in this search is compatible with the atmospheric background hypothesis, which leads us to set limits on the nature of neutrino transient sources with emission timescales of one month.
Published: 2025

38. Formally Verified Neural Lyapunov Function for Incremental Input-to-State Stability of Unknown Systems

Author: Basu, Ahan, Dey, Bhabani Shankar, and Jagtap, Pushpak
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: This work presents an approach to synthesize a Lyapunov-like function to ensure incrementally input-to-state stability ($\delta$-ISS) property for an unknown discrete-time system. To deal with challenges posed by unknown system dynamics, we parameterize the Lyapunov-like function as a neural network, which we train using the data samples collected from the unknown system along with appropriately designed loss functions. We propose a validity condition to test the obtained function and incorporate it into the training framework to ensure provable correctness at the end of the training. Finally, the usefulness of the proposed technique is proved using two case studies: a scalar non-linear dynamical system and a permanent magnet DC motor.
Published: 2025

39. Controlled probing of Anderson localization and non-Hermitian skin effect via topolectrical circuits

Author: Halder, Dipendu and Basu, Saurabh
Subjects: Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Applied Physics, Quantum Physics
Abstract: The Anderson localization (AL) and the non-Hermitian skin effect (NHSE) are two distinct wavefunction-localization phenomena arising out of disorder and non-reciprocity, respectively. An integration of both in a single framework will provide a platform to study the interplay between the two. In this connection, we consider the one-dimensional Aubry-Andr\'e (AA) model, which has garnered significant attention among the disordered models due to its self-dual properties. Hence, we investigate a non-reciprocal AA model with complex phase modulation and implement it in suitably designed topolectrical circuits featuring an interface, segregating two non-equivalent circuit networks. In the circuit, the voltage profile localizes at the interface due to the NHSE, while the AL limits localization phenomena within the vicinity of the excitation node. This competing phenomenon leads to a controllable, node-dependent localization or even partial delocalization of the output voltage. Our results not only provide a practical platform for experimentally studying and controlling the wave localization phenomenon but also highlight the potential of circuit architectures in designing highly sensitive sensors and information transfer in communication devices., Comment: 11 pages, (5+2) figures
Published: 2025

40. First result from tetrafluoroethane (C$_2$H$_2$F$_4$) superheated emulsion detector for dark matter search at JUSL

Author: Kumar, V., Ali, S., Das, M., Biswas, N., Das, S., Sahoo, S., Chaddha, N., Basu, J., and Jha, V. N.
Subjects: Physics - Instrumentation and Detectors, Astrophysics - Astrophysics of Galaxies
Abstract: The superheated emulsion detector consisting of the droplets of tetra-fluoroethane (C2HC$_2$H$_2$F$_4$2F4) has been fabricated at the laboratory and installed at the 555m deep underground laboratory, JUSL during July to Dec 2022. The 500ml detector ran for an effective period of 48.6 days at a threshold of 5.87 keV with an exposure of 2.47 kg-days. The acoustic signals produced due to the bubble nucleation were collected by the acoustic sensor and FPGAbased data acquisition system. The data shows a minimum sensitivity of SI-nucleon for carbon at WIMP mass of 22.81 GeV/c$^2$ and SD (p) for fluorine at 30.67 GeV/c$^2$. The threshold of WIMP mass is 5.16 GeV/c$^2$ for F and 4.44 GeV/c$^2$ for C at the operating threshold of 5.87 keV. The first result of the dark matter direct search experiment named InDEx with tetra-fluoro-ethane active liquid from JUSL underground laboratory is reported in this article.
Published: 2025

41. Search for continuous gravitational waves from known pulsars in the first part of the fourth LIGO-Virgo-KAGRA observing run

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. 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L., Pearlman, A. B., Romero, G. E., Shannon, R. M., Shaw, B., Stairs, I. H., Stappers, B. W., Tan, C. M., Theureau, G., Thompson, M., Weltevrede, P., and Zubieta, E.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables
Published: 2025

42. Loss of PV Interneurons in the BLA May Contribute to Altered Network and Behavioral States in Chronically Epileptic Mice.

Author: Colmers, Phillip, Antonoudiou, Pantelis, Basu, Trina, Coleman, Emanuel, He, Yingchu, Scapa, Garrett, Fuller, Patrick, and Maguire, Jamie
Subjects: anxiety, basolateral amygdala, depression, epilepsy, interneurons, local field potentials, network states, oscillations, psychiatric comorbidities, Animals, Interneurons, Basolateral Nuclear Complex, Parvalbumins, Kainic Acid, Male, Disease Models, Animal, Mice, Transgenic, Mice, Inbred C57BL, Mice, Epilepsy, Chronic Disease, Behavior, Animal, Epilepsy, Temporal Lobe
Abstract: Psychiatric disorders, including anxiety and depression, are highly comorbid in people with epilepsy. However, the mechanisms mediating the shared pathophysiology are currently unknown. There is considerable evidence implicating the basolateral amygdala (BLA) in the network communication of anxiety and fear, a process demonstrated to involve parvalbumin-positive (PV) interneurons. The loss of PV interneurons has been well described in the hippocampus of chronically epileptic mice and in postmortem human tissue of patients with temporal lobe epilepsy (TLE). We hypothesize that a loss of PV interneurons in the BLA may contribute to comorbid mood disorders in epilepsy. To test this hypothesis, we employed a ventral intrahippocampal kainic acid model of TLE in mice, which exhibits profound behavioral deficits associated with chronic epilepsy. We demonstrate a loss of PV interneurons and dysfunction of the remaining PV interneurons in the BLA of chronically epileptic mice. Furthermore, we demonstrate altered principal neuron function and impaired coordination of BLA network and behavioral states in chronically epileptic mice. To determine whether the loss of PV interneurons contributes to these altered network and behavioral states, we partially ablated PV interneurons in the BLA by stereotaxically injecting AAV-Flex-DTA into the BLA of PV-Cre mice. Loss of PV interneurons in the BLA is sufficient to alter behavioral states, such as increasing avoidance behaviors and impairing fear learning. These data suggest that compromised inhibition in the BLA in chronically epileptic mice may contribute to behavioral deficits, suggesting a novel mechanism contributing to comorbid anxiety and epilepsy.
Published: 2025

43. History and Habitability of the LP 890-9 Planetary System

Author: Barnes, Rory, do Amaral, Laura NR, Birky, Jessica, Carone, Ludmila, Driscoll, Peter, Livesey, Joseph R, Graham, David, Becker, Juliette, Cui, Kaiming, Schlecker, Martin, Garcia, Rodolfo, Gialluca, Megan, Adams, Arthur, Ahmed, Redyan, Bonney, Paul, Broussard, Wynter, Chawla, Chetan, Damasso, Mario, Danchi, William C, Deitrick, Russell, Ducrot, Elsa, Fromont, Emeline F, Gaches, Brandt AL, Gupta, Sakshi, Hill, Michelle L, Jackman, James AG, Janin, Estelle M, Karawacki, Mikołaj, Koren, Matheus Daniel, La Greca, Roberto, Leung, Michaela, Miranda-Rosete, Arturo, Olohoy, Michael Kent A, Ngo, Cecelia, Paul, Daria, Sahu, Chandan Kumar, Sarkar, Debajyoti Basu, Shadab, Mohammad Afzal, Schwieterman, Edward W, Sedler, Melissa, Texeira, Katie, Vazan, Allona, Vega, Karen N Delgado, Vijayakumar, Rohit, and Wojack, Jonathan T
Subjects: Space Sciences, Physical Sciences, Astronomical Sciences, Astronomical sciences, Space sciences
Abstract: Abstract: We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the host star reaches the main sequence in 1 Gyr and that planet c lies close to the inner boundary of the habitable zone. We find the magma ocean stage can last up to 50 Myr, remove eight Earth oceans of water, and leave up to 2000 bars of oxygen in the atmosphere. However, if the planet forms with a hydrogen envelope as small as 0.1 Earth masses, no water will be lost during the star's pre-main-sequence phase from thermal escape processes. We find that the planets are unlikely to be in a 3:1 mean motion resonance and that both planets tidally circularize within 0.5 Gyr when tidal dissipation is held constant. However, if tidal dissipation is a function of mantle temperature and rheology, then we find that planet c's orbit may require more than 7 Gyr to circularize, during which time tidal heating may reach hundreds of terawatts. We thus conclude that the habitability of planet c depends most strongly on the initial volatile content and internal properties, but no data yet preclude the viability of an active biosphere on the planet.
Published: 2025

44. Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret

Author: Jorge, Emilio, Dimitrakakis, Christos, and Basu, Debabrota
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: Common assumptions, like linear or RKHS models, and Gaussian or log-concave posteriors over the models, do not explain practical success of RL across a wider range of distributions and models. Thus, we study how to design RL algorithms with sublinear regret for isoperimetric distributions, specifically the ones satisfying the Log-Sobolev Inequality (LSI). LSI distributions include the standard setups of RL theory, and others, such as many non-log-concave and perturbed distributions. First, we show that the Posterior Sampling-based RL (PSRL) algorithm yields sublinear regret if the data distributions satisfy LSI and some mild additional assumptions. Also, when we cannot compute or sample from an exact posterior, we propose a Langevin sampling-based algorithm design: LaPSRL. We show that LaPSRL achieves order-optimal regret and subquadratic complexity per episode. Finally, we deploy LaPSRL with a Langevin sampler -- SARAH-LD, and test it for different bandit and MDP environments. Experimental results validate the generality of LaPSRL across environments and its competitive performance with respect to the baselines.
Published: 2024

45. Flat Bands and Compact Localised States: A Carrollian roadmap

Author: Ara, Nisa, Banerjee, Aritra, Basu, Rudranil, and Krishnan, Bhagya
Subjects: High Energy Physics - Theory, Condensed Matter - Strongly Correlated Electrons, Quantum Physics
Abstract: We show how Carrollian symmetries become important in the construction of one-dimensional fermionic systems with all flat-band spectra from first principles. The key ingredient of this construction is the identification of Compact Localised States (CLSs), which appear naturally by demanding $\textit{supertranslation}$ invariance of the system. We use CLS basis states, with inherent $\textit{ultra-local}$ correlations, to write down an interacting theory which shows a non-trivial phase structure and an emergent Carroll conformal symmetry at the gapless points. We analyze this theory in detail for both zero and finite chemical potential.
Published: 2024

46. On Robust Cross Domain Alignment

Author: Chakrabarty, Anish, Basu, Arkaprabha, and Das, Swagatam
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning
Abstract: The Gromov-Wasserstein (GW) distance is an effective measure of alignment between distributions supported on distinct ambient spaces. Calculating essentially the mutual departure from isometry, it has found vast usage in domain translation and network analysis. It has long been shown to be vulnerable to contamination in the underlying measures. All efforts to introduce robustness in GW have been inspired by similar techniques in optimal transport (OT), which predominantly advocate partial mass transport or unbalancing. In contrast, the cross-domain alignment problem being fundamentally different from OT, demands specific solutions to tackle diverse applications and contamination regimes. Deriving from robust statistics, we discuss three contextually novel techniques to robustify GW and its variants. For each method, we explore metric properties and robustness guarantees along with their co-dependencies and individual relations with the GW distance. For a comprehensive view, we empirically validate their superior resilience to contamination under real machine learning tasks against state-of-the-art methods.
Published: 2024

47. Emergent short-range repulsion for attractively coupled active particles

Author: Sarkar, Ritwick and Basu, Urna
Subjects: Condensed Matter - Statistical Mechanics
Abstract: We show that heterogeneity in self-propulsion speed leads to the emergence of effective short-range repulsion among active particles coupled via strong attractive potentials. Taking the example of two harmonically coupled active Brownian particles, we analytically compute the stationary distribution of the distance between them in the strong coupling regime, i.e., where the coupling strength is much larger than the rotational diffusivity of the particles. The effective repulsion in this regime is manifest in the emergence of a minimum distance between the particles, proportional to the difference in their self-propulsion speeds. Physically, this distance of the closest approach is associated to the orientations of the particles being parallel to each other. We show that the physical scenario remains qualitatively similar for any long-range coupling potential, which is attractive everywhere. Moreover, we show that, for a collection of $N$ particles interacting via pairwise attractive potentials, a short-range repulsion emerges for each pair of particles with different self-propulsion speeds. Finally, we show that our results are robust and hold irrespective of the specific active dynamics of the particles.
Published: 2024

48. Crosstalk-induced Side Channel Threats in Multi-Tenant NISQ Computers

Author: Choudhury, Navnil, Mude, Chaithanya Naik, Das, Sanjay, Tikkireddi, Preetham Chandra, Tannu, Swamit, and Basu, Kanad
Subjects: Computer Science - Emerging Technologies
Abstract: As quantum computing rapidly advances, its near-term applications are becoming increasingly evident. However, the high cost and under-utilization of quantum resources are prompting a shift from single-user to multi-user access models. In a multi-tenant environment, where multiple users share one quantum computer, protecting user confidentiality becomes crucial. The varied uses of quantum computers increase the risk that sensitive data encoded by one user could be compromised by others, rendering the protection of data integrity and confidentiality essential. In the evolving quantum computing landscape, it is imperative to study these security challenges within the scope of realistic threat model assumptions, wherein an adversarial user can mount practical attacks without relying on any heightened privileges afforded by physical access to a quantum computer or rogue cloud services. In this paper, we demonstrate the potential of crosstalk as an attack vector for the first time on a Noisy Intermediate Scale Quantum (NISQ) machine, that an adversarial user can exploit within a multi-tenant quantum computing model. The proposed side-channel attack is conducted with minimal and realistic adversarial privileges, with the overarching aim of uncovering the quantum algorithm being executed by a victim. Crosstalk signatures are used to estimate the presence of CNOT gates in the victim circuit, and subsequently, this information is encoded and classified by a graph-based learning model to identify the victim quantum algorithm. When evaluated on up to 336 benchmark circuits, our attack framework is found to be able to unveil the victim's quantum algorithm with up to 85.7\% accuracy.
Published: 2024

49. Atomic Learning Objectives Labeling: A High-Resolution Approach for Physics Education

Author: Liu, Naiming, Sonkar, Shashank, Mallick, Debshila Basu, Baraniuk, Richard, and Chen, Zhongzhou
Subjects: Computer Science - Computers and Society
Abstract: This paper introduces a novel approach to create a high-resolution "map" for physics learning: an "atomic" learning objectives (LOs) system designed to capture detailed cognitive processes and concepts required for problem solving in a college-level introductory physics course. Our method leverages Large Language Models (LLMs) for automated labeling of physics questions and introduces a comprehensive set of metrics to evaluate the quality of the labeling outcomes. The atomic LO system, covering nine chapters of an introductory physics course, uses a "subject-verb-object'' structure to represent specific cognitive processes. We apply this system to 131 questions from expert-curated question banks and the OpenStax University Physics textbook. Each question is labeled with 1-8 atomic LOs across three chapters. Through extensive experiments using various prompting strategies and LLMs, we compare automated LOs labeling results against human expert labeling. Our analysis reveals both the strengths and limitations of LLMs, providing insight into LLMs reasoning processes for labeling LOs and identifying areas for improvement in LOs system design. Our work contributes to the field of learning analytics by proposing a more granular approach to mapping learning objectives with questions. Our findings have significant implications for the development of intelligent tutoring systems and personalized learning pathways in STEM education, paving the way for more effective "learning GPS'' systems., Comment: The paper is accepted to LAK'2025
Published: 2024

50. Addressing Positivity Violations in Extending Inference to a Target Population

Author: Lu, Jun and Basu, Sanjib
Subjects: Statistics - Methodology
Abstract: Enhancing the external validity of trial results is essential for their applicability to real-world populations. However, violations of the positivity assumption can limit both the generalizability and transportability of findings. To address positivity violations in estimating the average treatment effect for a target population, we propose a framework that integrates characterizing the underrepresented group and performing sensitivity analysis for inference in the original target population. Our approach helps identify limitations in trial sampling and improves the robustness of trial findings for real-world populations. We apply this approach to extend findings from phase IV trials of treatments for opioid use disorder to a real-world population based on the 2021 Treatment Episode Data Set.
Published: 2024

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