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1. Impact of waste bread on the nutrient utilization and performance of buffalo calves

Author: Bhargava, A., Wadhwa, M., and Bakshi, M.P.S.
Published: 2022
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2. Are We Ready for Out-of-Distribution Detection in Digital Pathology?

Author: Oh, Ji-Hun, Falahkheirkhah, Kianoush, and Bhargava, Rohit
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: The detection of semantic and covariate out-of-distribution (OOD) examples is a critical yet overlooked challenge in digital pathology (DP). Recently, substantial insight and methods on OOD detection were presented by the ML community, but how do they fare in DP applications? To this end, we establish a benchmark study, our highlights being: 1) the adoption of proper evaluation protocols, 2) the comparison of diverse detectors in both a single and multi-model setting, and 3) the exploration into advanced ML settings like transfer learning (ImageNet vs. DP pre-training) and choice of architecture (CNNs vs. transformers). Through our comprehensive experiments, we contribute new insights and guidelines, paving the way for future research and discussion.
Published: 2024

3. Disentangling Representations in RNNs through Multi-task Learning

Author: Vafidis, Pantelis, Bhargava, Aman, and Rangel, Antonio
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning
Abstract: Abstract, or disentangled, representations are a promising mathematical framework for efficient and effective generalization in both biological and artificial systems. We investigate abstract representations in the context of multi-task classification over noisy evidence streams -- a canonical decision-making neuroscience paradigm. We derive theoretical bounds that guarantee the emergence of disentangled representations in the latent state of any optimal multi-task classifier, when the number of tasks exceeds the dimensionality of the state space. We experimentally confirm that RNNs trained on multi-task classification learn disentangled representations in the form of continuous attractors, leading to zero-shot out-of-distribution (OOD) generalization. We demonstrate the flexibility of the abstract RNN representations across various decision boundary geometries and in tasks requiring classification confidence estimation. Our framework suggests a general principle for the formation of cognitive maps that organize knowledge to enable flexible generalization in biological and artificial systems alike, and closely relates to representations found in humans and animals during decision-making and spatial reasoning tasks., Comment: 32 pages, 12 figures
Published: 2024

4. Galaxy cluster matter profiles: I. Self-similarity and mass calibration

Author: Singh, A., Mohr, J. J., Davies, C. T., Bocquet, S., Grandis, S., Klein, M., Marshall, J. L., Aguena, M., Allam, S. S., Alves, O., Andrade-Oliveira, F., Bacon, D., Bhargava, S., Brooks, D., Rosell, A. Carnero, Carretero, J., Costanzi, M., da Costa, L. N., Pereira, M. E. S., Desai, S., Diehl, H. T., Doel, P., Everett, S., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gruendl, R. A., Gutierrez, G., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lima, M., Mena-Fernández, J., Menanteau, F., Miquel, R., Myles, J., Pieres, A., Romer, A. K., Samuroff, S., Sanchez, E., Cid, D. Sanchez, Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Tucker, D. L., Vikram, V., Weaverdyck, N., and Wiseman, P.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present a study of the weak lensing matter profiles of 698 South Pole Telescope (SPT) thermal Sunyaev-Zel'dovich effect (tSZE) selected galaxy clusters in the redshift range $0.25
Published: 2024

5. Explicit Commutative ROABPs from Partial Derivatives

Author: Bhargava, Vishwas and Tengse, Anamay
Subjects: Computer Science - Computational Complexity, F.1.3, I.1.1
Abstract: The dimension of partial derivatives (Nisan and Wigderson, 1997) is a popular measure for proving lower bounds in algebraic complexity. It is used to give strong lower bounds on the Waring decomposition of polynomials (called Waring rank). This naturally leads to an interesting open question: does this measure essentially characterize the Waring rank of any polynomial? The well-studied model of Read-once Oblivious ABPs (ROABPs for short) lends itself to an interesting hierarchy of 'sub-models': Any-Order-ROABPs (ARO), Commutative ROABPs, and Diagonal ROABPs. It follows from previous works that for any polynomial, a bound on its Waring rank implies an analogous bound on its Diagonal ROABP complexity (called the duality trick), and a bound on its dimension of partial derivatives implies an analogous bound on its 'ARO complexity': ROABP complexity in any order (Nisan, 1991). Our work strengthens the latter connection by showing that a bound on the dimension of partial derivatives in fact implies a bound on the commutative ROABP complexity. Thus, we improve our understanding of partial derivatives and move a step closer towards answering the above question. Our proof builds on the work of Ramya and Tengse (2022) to show that the commutative-ROABP-width of any homogeneous polynomial is at most the dimension of its partial derivatives. The technique itself is a generalization of the proof of the duality trick due to Saxena (2008).
Published: 2024

6. Probing outbursts of the transient neutron star low mass X-ray binary Aql X-1 with NICER: a study of spectral evolution

Author: Putha, Karthik Gananath, Bhargava, Yash, and Bhattacharyya, Sudip
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: X-ray observations of neutron star (NS) low mass X-ray binaries (LMXBs) are useful to probe physical processes close to the NS and to constrain source parameters. Aql X-1 is a transient NS LMXB which frequently undergoes outbursts provides an excellent opportunity to study source properties and accretion mechanism in strong gravity regime over a wide range of accretion rates. In this work, we systematically investigate the spectral evolution of Aql X-1 using NICER observations during the source outbursts in 2019 and 2020. The NICER observations cover the complete transition of the source from its canonical hard state to soft state and back. The spectra extracted from most observations can be explained by a partially Comptonised accretion disc. We find that the system can be described by an accretion disk with an inner temperature of $\approx0.7$ keV and a Comptonising medium of thermal electrons at $\approx2$ keV, while the photon index is strongly degenerate with the covering fraction of the medium. We also find evidence of Fe K$\alpha$ fluorescence emission in the spectra indicating reprocessing of the Comptonised photons. We observe an absorption column density higher than the Galactic column density for most of the observations indicating a significant local absorption. But for some of the observations in 2020 outburst, the local absorption is negligible., Comment: 10 pages,5 figures,5 tables,accepted for publication in MNRAS
Published: 2024

7. An Extended AW-Rascle Model with Source Terms and Its Numerical Solution

Author: Maiti, Nandan and Chilukuri, Bhargava Rama
Subjects: Mathematics - Analysis of PDEs
Abstract: Nonlinear hyperbolic partial differential equations govern continuum traffic flow models. Higher-order traffic flow models consisting of continuum equations and velocity dynamics were introduced to address the limitations of the Lighthill, Whitham, and Richards (LWR) model. However, these models are ineffective in incorporating road heterogeneity. This paper integrates an extended AW-Rascle higher-order model with the source terms in the continuum equation to predict the traffic states in heterogeneous road conditions. The system of the equations was solved numerically with the central dispersion (CD) method incorporated into the standard McCormack scheme. Smoothing is applied to take care of the numerical oscillation of the higher-order model. Different combinations of initial conditions with source terms showed that the proposed model with the numerical methods could produce a stable solution and eliminate oscillation of the McCormack scheme.
Published: 2024

8. Effective-LDAM: An Effective Loss Function To Mitigate Data Imbalance for Robust Chest X-Ray Disease Classification

Author: S, Sree Rama Vamsidhar, Satya, Bhargava, and Gorthi, Rama Krishna
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Deep Learning (DL) approaches have gained prominence in medical imaging for disease diagnosis. Chest X-ray (CXR) classification has emerged as an effective method for detecting various diseases. Among these methodologies, Chest X-ray (CXR) classification has proven to be an effective approach for detecting and analyzing various diseases. However, the reliable performance of DL classification algorithms is dependent upon access to large and balanced datasets, which pose challenges in medical imaging due to the impracticality of acquiring sufficient data for every disease category. To tackle this problem, we propose an algorithmic-centric approach called Effective-Label Distribution Aware Margin (E-LDAM), which modifies the margin of the widely adopted Label Distribution Aware Margin (LDAM) loss function using an effective number of samples in each class. Experimental evaluations on the COVIDx CXR dataset focus on Normal, Pneumonia, and COVID-19 classification. The experimental results demonstrate the effectiveness of the proposed E-LDAM approach, achieving a remarkable recall score of 97.81% for the minority class (COVID-19) in CXR image prediction. Furthermore, the overall accuracy of the three-class classification task attains an impressive level of 95.26%.
Published: 2024

9. Rapid Mid-Infrared Spectral-Timing with JWST. I. The prototypical black hole X-ray Binary GRS 1915+105 during a MIR-bright and X-ray-obscured state

Author: Gandhi, P., Borowski, E. S., Byrom, J., Hynes, R. I., Maccarone, T. J., Shaw, A. W., Adegoke, O. K., Altamirano, D., Baglio, M. C., Bhargava, Y., Britt, C. T., Buckley, D. A. H., Buisson, D. J. K., Casella, P., Segura, N. Castro, Charles, P. A., Corral-Santana, J. M., Dhillon, V. S., Fender, R., Gúrpide, A., Heinke, C. O., Igl, A. B., Knigge, C., Markoff, S., Mastroserio, G., McCollough, M. L., Middleton, M., Miller, J. M., Miller-Jones, J. C. A., Motta, S. E., Paice, J. A., Pawar, D. D., Plotkin, R. M., Pradhan, P., Ressler, M. E., Russell, D. M., Russell, T. D., Santos-Sanz, P., Shahbaz, T., Sivakoff, G. R., Steeghs, D., Tetarenko, A. J., Tomsick, J. A., Vincentelli, F. M., George, M., Gurwell, M., and Rao, R.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We present mid-infrared (MIR) spectral-timing measurements of the prototypical Galactic microquasar GRS 1915+105. The source was observed with the Mid-Infrared Instrument (MIRI) onboard JWST in June 2023 at a MIR luminosity L(MIR)~10^{36} erg/s exceeding past IR levels by about a factor of 10. By contrast, the X-ray flux is much fainter than the historical average, in the source's now-persistent 'obscured' state. The MIRI low-resolution spectrum shows a plethora of emission lines, the strongest of which are consistent with recombination in the hydrogen Pfund (Pf) series and higher. Low amplitude (~1%) but highly significant peak-to-peak photometric variability is found on timescales of ~1,000 s. The brightest Pf(6-5) emission line lags the continuum. Though difficult to constrain accurately, this lag is commensurate with light-travel timescales across the outer accretion disc or with expected recombination timescales inferred from emission line diagnostics. Using the emission line as a bolometric indicator suggests a moderate (~5-30% Eddington) intrinsic accretion rate. Multiwavelength monitoring shows that JWST caught the source close in-time to unprecedentedly bright MIR and radio long-term flaring. Assuming a thermal bremsstrahlung origin for the MIRI continuum suggests an unsustainably high mass-loss rate during this time unless the wind remains bound, though other possible origins cannot be ruled out. PAH features previously detected with Spitzer are now less clear in the MIRI data, arguing for possible destruction of dust in the interim. These results provide a preview of new parameter space for exploring MIR spectral-timing in XRBs and other variable cosmic sources on rapid timescales., Comment: Dedicated to the memory of our colleague, Tomaso Belloni. Submitted 2024 June 21; Comments welcome
Published: 2024

10. The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators

Author: Huang, Tzu-Heng, Cao, Catherine, Bhargava, Vaishnavi, and Sala, Frederic
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Large pretrained models can be used as annotators, helping replace or augment crowdworkers and enabling distilling generalist models into smaller specialist models. Unfortunately, this comes at a cost: employing top-of-the-line models often requires paying thousands of dollars for API calls, while the resulting datasets are static and challenging to audit. To address these challenges, we propose a simple alternative: rather than directly querying labels from pretrained models, we task models to generate programs that can produce labels. These programs can be stored and applied locally, re-used and extended, and cost orders of magnitude less. Our system, Alchemist, obtains comparable to or better performance than large language model-based annotation in a range of tasks for a fraction of the cost: on average, improvements amount to a 12.9% enhancement while the total labeling costs across all datasets are reduced by a factor of approximately 500x.
Published: 2024

11. An IXPE-Led X-ray Spectro-Polarimetric Campaign on the Soft State of Cygnus X-1: X-ray Polarimetric Evidence for Strong Gravitational Lensing

Author: Steiner, James F., Nathan, Edward, Hu, Kun, Krawczynski, Henric, Dovciak, Michal, Veledina, Alexandra, Muleri, Fabio, Svoboda, Jiri, Alabarta, Kevin, Parra, Maxime, Bhargava, Yash, Matt, Giorgio, Poutanen, Juri, Petrucci, Pierre-Olivier, Tennant, Allyn F., Baglio, M. Cristina, Baldini, Luca, Barnier, Samuel, Bhattacharyya, Sudip, Bianchi, Stefano, Brigitte, Maimouna, Cabezas, Mauricio, Cangemi, Floriane, Capitanio, Fiamma, Casey, Jacob, Cavero, Nicole Rodriguez, Castellano, Simone, Cavazzuti, Elisabetta, Chun, Sohee, Churazov, Eugene, Costa, Enrico, Di Lalla, Niccolo, Di Marco, Alessandro, Egron, Elise, Ewing, Melissa, Fabiani, Sergio, Garcia, Javier A., Green, David A., Grinberg, Victoria, Hadrava, Petr, Ingram, Adam, Kaaret, Philip, Kislat, Fabian, Kitaguchi, Takao, Kravtsov, Vadim, Kubatova, Brankica, La Monaca, Fabio, Latronico, Luca, Loktev, Vladislav, Malacaria, Christian, Marin, Frederic, Marinucci, Andrea, Maryeva, Olga, Mastroserio, Guglielmo, Mizuno, Tsunefumi, Negro, Michela, Omodei, Nicola, Podgorny, Jakub, Rankin, John, Ratheesh, Ajay, Rhodes, Lauren, Russell, David M., Slechta, Miroslav, Soffitta, Paolo, Spooner, Sean, Suleimanov, Valery, Tombesi, Francesco, Trushkin, Sergei A., Weisskopf, Martin C., Zane, Silvia, Zdziarski, Andrzej A., Zhang, Sixuan, Zhang, Wenda, Zhou, Menglei, Agudo, Ivan, Antonelli, Lucio A., Bachetti, Matteo, Baumgartner, Wayne H., Bellazzini, Ronaldo, Bongiorno, Stephen D., Bonino, Raffaella, Brez, Alessandro, Bucciantini, Niccolo, Chen, Chien-Ting, Ciprini, Stefano, De Rosa, Alessandra, Del Monte, Ettore, Di Gesu, Laura, Donnarumma, Immacolata, Doroshenko, Victor, Ehlert, Steven R., Enoto, Teruaki, Evangelista, Yuri, Ferrazzoli, Riccardo, Gunji, Shuichi, Hayashida, Kiyoshi, Heyl, Jeremy, Iwakiri, Wataru, Jorstad, Svetlana G., Karas, Vladimir, Kolodziejczak, Jeffery J., Liodakis, Ioannis, Maldera, Simone, Manfreda, Alberto, Marscher, Alan P., Marshall, Herman L., Massaro, Francesco, Mitsuishi, Ikuyuki, Ng, Chi-Yung, O'Dell, Stephen L., Oppedisano, Chiara, Papitto, Alessandro, Pavlov, George G., Peirson, Abel L., Perri, Matteo, Pesce-Rollins, Melissa, Pilia, Maura, Possenti, Andrea, Puccetti, Simonetta, Ramsey, Brian D., Roberts, Oliver J., Romani, Roger W., Sgro, Carmelo, Slane, Patrick, Spandre, Gloria, Swartz, Douglas A., Tamagawa, Toru, Tavecchio, Fabrizio, Taverna, Roberto, Tawara, Yuzuru, Thomas, Nicholas E., Trois, Alessio, Tsygankov, Sergey S., Turolla, Roberto, Vink, Jacco, Wu, Kinwah, and Xie, Fei
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the first X-ray spectropolarimetric results for Cygnus X-1 in its soft state from a campaign of five IXPE observations conducted during 2023 May-June. Companion multiwavelength data during the campaign are likewise shown. The 2-8 keV X-rays exhibit a net polarization degree PD=1.99%+/-0.13% (68% confidence). The polarization signal is found to increase with energy across IXPE's 2-8 keV bandpass. The polarized X-rays exhibit an energy-independent polarization angle of PA=-25.7+/-1.8 deg. East of North (68% confidence). This is consistent with being aligned to Cyg X-1's AU-scale compact radio jet and its pc-scale radio lobes. In comparison to earlier hard-state observations, the soft state exhibits a factor of 2 lower polarization degree, but a similar trend with energy and a similar (also energy-independent) position angle. When scaling by the natural unit of the disk temperature, we find the appearance of a consistent trendline in the polarization degree between soft and hard states. Our favored polarimetric model indicates Cyg X-1's spin is likely high (a* above ~0.96). The substantial X-ray polarization in Cyg X-1's soft state is most readily explained as resulting from a large portion of X-rays emitted from the disk returning and reflecting off the disk surface, generating a high polarization degree and a polarization direction parallel to the black hole spin axis and radio jet. In IXPE's bandpass, the polarization signal is dominated by the returning reflection emission. This constitutes polarimetric evidence for strong gravitational lensing of X-rays close to the black hole., Comment: 20 pages, accepted for publication in ApJL
Published: 2024

12. Off-Policy Evaluation from Logged Human Feedback

Author: Bhargava, Aniruddha, Jain, Lalit, Kveton, Branislav, Liu, Ge, and Mukherjee, Subhojyoti
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: Learning from human feedback has been central to recent advances in artificial intelligence and machine learning. Since the collection of human feedback is costly, a natural question to ask is if the new feedback always needs to collected. Or could we evaluate a new model with the human feedback on responses of another model? This motivates us to study off-policy evaluation from logged human feedback. We formalize the problem, propose both model-based and model-free estimators for policy values, and show how to optimize them. We analyze unbiasedness of our estimators and evaluate them empirically. Our estimators can predict the absolute values of evaluated policies, rank them, and be optimized.
Published: 2024

13. X-ray and Radio campaign of the Z-source GX 340+0: discovery of X-ray polarization and its implications

Author: Bhargava, Yash, Ng, Mason, Zhang, Liang, Balasubramanian, Arvind, Russell, Thomas D., Kaushik, Aman, Jadoliya, Vishal, Ravi, Swati, Bhattacharyya, Sudip, Pahari, Mayukh, Homan, Jeroen, Marshall, Herman L., Chakrabarty, Deepto, and Carotenuto, Francesco
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the discovery of X-ray polarization from the neutron star low-mass X-ray binary and Z-source, GX~340$+$0, using an Imaging X-ray Polarimetry Explorer (IXPE) observation in March 2024. Along with the IXPE observation, we conducted an extensive X-ray and radio monitoring campaign to ascertain the source properties during and around the IXPE observation. The source was within the horizontal branch throughout the multiwavelength campaign. We measured a significant X-ray polarization in 2--8 keV with polarization degree (PD) = $4.02 \pm 0.35$% and polarization angle (PA) = $37.6 \pm 2.5^\circ$. The energy-dependent polarization indicates that in the 2-2.5 keV energy range, the PA is much lower, $\sim9\pm8^\circ$, while other energy bands are consistent with the PA found over 2.5--8 keV. The simultaneous AstroSat-IXPE spectro-polarimetric observations provide some evidence for independent polarization from various spectral components, hinting at a disparity in the PA from the accretion disk and the Comptonized emission, while suggesting an unpolarized emission from the blackbody component. Radio observations in the 0.7--9 GHz frequency range reveal a non-detection of radio emission in 0.7-1.5 GHz and a significant detection in 5.5--9 GHz, suggesting the presence of a spectral break in 1.5-5.5 GHz. Using ATCA observation we place upper limits on the radio polarization at $<$6% on the linear polarization and $<$4% on the circular polarization at 3$\sigma$ level. We discuss the origin of the X-ray polarization and its implications on the geometry of the spectral components., Comment: Submitted in ApJL, 4 figures, 3 tables
Published: 2024

14. Euclid. I. Overview of the Euclid mission

Author: Euclid Collaboration, Mellier, Y., Abdurro'uf, Barroso, J. A. Acevedo, Achúcarro, A., Adamek, J., Adam, R., Addison, G. E., Aghanim, N., Aguena, M., Ajani, V., Akrami, Y., Al-Bahlawan, A., Alavi, A., Albuquerque, I. S., Alestas, G., Alguero, G., Allaoui, A., Allen, S. W., Allevato, V., Alonso-Tetilla, A. V., Altieri, B., Alvarez-Candal, A., Amara, A., Amendola, L., Amiaux, J., Andika, I. T., Andreon, S., Andrews, A., Angora, G., Angulo, R. E., Annibali, F., Anselmi, A., Anselmi, S., Arcari, S., Archidiacono, M., Aricò, G., Arnaud, M., Arnouts, S., Asgari, M., Asorey, J., Atayde, L., Atek, H., Atrio-Barandela, F., Aubert, M., Aubourg, E., Auphan, T., Auricchio, N., Aussel, B., Aussel, H., Avelino, P. P., Avgoustidis, A., Avila, S., Awan, S., Azzollini, R., Baccigalupi, C., Bachelet, E., Bacon, D., Baes, M., Bagley, M. B., Bahr-Kalus, B., Balaguera-Antolinez, A., Balbinot, E., Balcells, M., Baldi, M., Baldry, I., Balestra, A., Ballardini, M., Ballester, O., Balogh, M., Bañados, E., Barbier, R., Bardelli, S., Barreiro, T., Barriere, J. -C., Barros, B. J., Barthelemy, A., Bartolo, N., Basset, A., Battaglia, P., Battisti, A. J., Baugh, C. M., Baumont, L., Bazzanini, L., Beaulieu, J. -P., Beckmann, V., Belikov, A. N., Bel, J., Bellagamba, F., Bella, M., Bellini, E., Benabed, K., Bender, R., Benevento, G., Bennett, C. L., Benson, K., Bergamini, P., Bermejo-Climent, J. R., Bernardeau, F., Bertacca, D., Berthe, M., Berthier, J., Bethermin, M., Beutler, F., Bevillon, C., Bhargava, S., Bhatawdekar, R., Bisigello, L., Biviano, A., Blake, R. P., Blanchard, A., Blazek, J., Blot, L., Bosco, A., Bodendorf, C., Boenke, T., Böhringer, H., Bolzonella, M., Bonchi, A., Bonici, M., Bonino, D., Bonino, L., Bonvin, C., Bon, W., Booth, J. T., Borgani, S., Borlaff, A. S., Borsato, E., Bose, B., Botticella, M. T., Boucaud, A., Bouche, F., Boucher, J. S., Boutigny, D., Bouvard, T., Bouy, H., Bowler, R. A. A., Bozza, V., Bozzo, E., Branchini, E., Brau-Nogue, S., Brekke, P., Bremer, M. N., Brescia, M., Breton, M. -A., Brinchmann, J., Brinckmann, T., Brockley-Blatt, C., Brodwin, M., Brouard, L., Brown, M. L., Bruton, S., Bucko, J., Buddelmeijer, H., Buenadicha, G., Buitrago, F., Burger, P., Burigana, C., Busillo, V., Busonero, D., Cabanac, R., Cabayol-Garcia, L., Cagliari, M. S., Caillat, A., Caillat, L., Calabrese, M., Calabro, A., Calderone, G., Calura, F., Quevedo, B. Camacho, Camera, S., Campos, L., Canas-Herrera, G., Candini, G. P., Cantiello, M., Capobianco, V., Cappellaro, E., Cappelluti, N., Cappi, A., Caputi, K. I., Cara, C., Carbone, C., Cardone, V. F., Carella, E., Carlberg, R. G., Carle, M., Carminati, L., Caro, F., Carrasco, J. M., Carretero, J., Carrilho, P., Duque, J. Carron, Carry, B., Carvalho, A., Carvalho, C. S., Casas, R., Casas, S., Casenove, P., Casey, C. M., Cassata, P., Castander, F. J., Castelao, D., Castellano, M., Castiblanco, L., Castignani, G., Castro, T., Cavet, C., Cavuoti, S., Chabaud, P. -Y., Chambers, K. C., Charles, Y., Charlot, S., Chartab, N., Chary, R., Chaumeil, F., Cho, H., Chon, G., Ciancetta, E., Ciliegi, P., Cimatti, A., Cimino, M., Cioni, M. -R. L., Claydon, R., Cleland, C., Clément, B., Clements, D. L., Clerc, N., Clesse, S., Codis, S., Cogato, F., Colbert, J., Cole, R. E., Coles, P., Collett, T. E., Collins, R. S., Colodro-Conde, C., Colombo, C., Combes, F., Conforti, V., Congedo, G., Conseil, S., Conselice, C. J., Contarini, S., Contini, T., Conversi, L., Cooray, A. R., Copin, Y., Corasaniti, P. -S., Corcho-Caballero, P., Corcione, L., Cordes, O., Corpace, O., Correnti, M., Costanzi, M., Costille, A., Courbin, F., Mifsud, L. Courcoult, Courtois, H. M., Cousinou, M. -C., Covone, G., Cowell, T., Cragg, C., Cresci, G., Cristiani, S., Crocce, M., Cropper, M., Crouzet, P. E, Csizi, B., Cuby, J. -G., Cucchetti, E., Cucciati, O., Cuillandre, J. -C., Cunha, P. A. C., Cuozzo, V., Daddi, E., D'Addona, M., Dafonte, C., Dagoneau, N., Dalessandro, E., Dalton, G. B., D'Amico, G., Dannerbauer, H., Danto, P., Das, I., Da Silva, A., da Silva, R., Daste, G., Davies, J. E., Davini, S., de Boer, T., Decarli, R., De Caro, B., Degaudenzi, H., Degni, G., de Jong, J. T. A., de la Bella, L. F., de la Torre, S., Delhaise, F., Delley, D., Delucchi, G., De Lucia, G., Denniston, J., De Paolis, F., De Petris, M., Derosa, A., Desai, S., Desjacques, V., Despali, G., Desprez, G., De Vicente-Albendea, J., Deville, Y., Dias, J. D. F., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Diego, J. M., Di Ferdinando, D., Di Giorgio, A. M., Dimauro, P., Dinis, J., Dolag, K., Dolding, C., Dole, H., Sánchez, H. Domínguez, Doré, O., Dournac, F., Douspis, M., Dreihahn, H., Droge, B., Dryer, B., Dubath, F., Duc, P. -A., Ducret, F., Duffy, C., Dufresne, F., Duncan, C. A. J., Dupac, X., Duret, V., Durrer, R., Durret, F., Dusini, S., Ealet, A., Eggemeier, A., Eisenhardt, P. R. M., Elbaz, D., Elkhashab, M. Y., Ellien, A., Endicott, J., Enia, A., Erben, T., Vigo, J. A. Escartin, Escoffier, S., Sanz, I. Escudero, Essert, J., Ettori, S., Ezziati, M., Fabbian, G., Fabricius, M., Fang, Y., Farina, A., Farina, M., Farinelli, R., Farrens, S., Faustini, F., Feltre, A., Ferguson, A. M. N., Ferrando, P., Ferrari, A. G., Ferré-Mateu, A., Ferreira, P. G., Ferreras, I., Ferrero, I., Ferriol, S., Ferruit, P., Filleul, D., Finelli, F., Finkelstein, S. L., Finoguenov, A., Fiorini, B., Flentge, F., Focardi, P., Fonseca, J., Fontana, A., Fontanot, F., Fornari, F., Fosalba, P., Fossati, M., Fotopoulou, S., Fouchez, D., Fourmanoit, N., Frailis, M., Fraix-Burnet, D., Franceschi, E., Franco, A., Franzetti, P., Freihoefer, J., Frittoli, G., Frugier, P. -A., Frusciante, N., Fumagalli, A., Fumagalli, M., Fumana, M., Fu, Y., Gabarra, L., Galeotta, S., Galluccio, L., Ganga, K., Gao, H., García-Bellido, J., Garcia, K., Gardner, J. P., Garilli, B., Gaspar-Venancio, L. -M., Gasparetto, T., Gautard, V., Gavazzi, R., Gaztanaga, E., Genolet, L., Santos, R. Genova, Gentile, F., George, K., Ghaffari, Z., Giacomini, F., Gianotti, F., Gibb, G. P. S., Gillard, W., Gillis, B., Ginolfi, M., Giocoli, C., Girardi, M., Giri, S. K., Goh, L. W. K., Gómez-Alvarez, P., Gonzalez, A. H., Gonzalez, E. J., Gonzalez, J. C., Beauchamps, S. Gouyou, Gozaliasl, G., Gracia-Carpio, J., Grandis, S., Granett, B. R., Granvik, M., Grazian, A., Gregorio, A., Grenet, C., Grillo, C., Grupp, F., Gruppioni, C., Gruppuso, A., Guerbuez, C., Guerrini, S., Guidi, M., Guillard, P., Gutierrez, C. M., Guttridge, P., Guzzo, L., Gwyn, S., Haapala, J., Haase, J., Haddow, C. R., Hailey, M., Hall, A., Hall, D., Hamaus, N., Haridasu, B. S., Harnois-Déraps, J., Harper, C., Hartley, W. G., Hasinger, G., Hassani, F., Hatch, N. A., Haugan, S. V. H., Häußler, B., Heavens, A., Heisenberg, L., Helmi, A., Helou, G., Hemmati, S., Henares, K., Herent, O., Hernández-Monteagudo, C., Heuberger, T., Hewett, P. C., Heydenreich, S., Hildebrandt, H., Hirschmann, M., Hjorth, J., Hoar, J., Hoekstra, H., Holland, A. D., Holliman, M. S., Holmes, W., Hook, I., Horeau, B., Hormuth, F., Hornstrup, A., Hosseini, S., Hu, D., Hudelot, P., Hudson, M. J., Huertas-Company, M., Huff, E. M., Hughes, A. C. N., Humphrey, A., Hunt, L. K., Huynh, D. D., Ibata, R., Ichikawa, K., Iglesias-Groth, S., Ilbert, O., Ilić, S., Ingoglia, L., Iodice, E., Israel, H., Israelsson, U. E., Izzo, L., Jablonka, P., Jackson, N., Jacobson, J., Jafariyazani, M., Jahnke, K., Jansen, H., Jarvis, M. J., Jasche, J., Jauzac, M., Jeffrey, N., Jhabvala, M., Jimenez-Teja, Y., Muñoz, A. Jimenez, Joachimi, B., Johansson, P. H., Joudaki, S., Jullo, E., Kajava, J. J. E., Kang, Y., Kannawadi, A., Kansal, V., Karagiannis, D., Kärcher, M., Kashlinsky, A., Kazandjian, M. V., Keck, F., Keihänen, E., Kerins, E., Kermiche, S., Khalil, A., Kiessling, A., Kiiveri, K., Kilbinger, M., Kim, J., King, R., Kirkpatrick, C. C., Kitching, T., Kluge, M., Knabenhans, M., Knapen, J. H., Knebe, A., Kneib, J. -P., Kohley, R., Koopmans, L. V. E., Koskinen, H., Koulouridis, E., Kou, R., Kovács, A., Kova{č}ić, I., Kowalczyk, A., Koyama, K., Kraljic, K., Krause, O., Kruk, S., Kubik, B., Kuchner, U., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lacasa, F., Lacey, C. G., La Franca, F., Lagarde, N., Lahav, O., Laigle, C., La Marca, A., La Marle, O., Lamine, B., Lam, M. C., Lançon, A., Landt, H., Langer, M., Lapi, A., Larcheveque, C., Larsen, S. S., Lattanzi, M., Laudisio, F., Laugier, D., Laureijs, R., Lavaux, G., Lawrenson, A., Lazanu, A., Lazeyras, T., Boulc'h, Q. Le, Brun, A. M. C. Le, Brun, V. Le, Leclercq, F., Lee, S., Graet, J. Le, Legrand, L., Leirvik, K. N., Jeune, M. Le, Lembo, M., Mignant, D. Le, Lepinzan, M. D., Lepori, F., Lesci, G. F., Lesgourgues, J., Leuzzi, L., Levi, M. E., Liaudat, T. I., Libet, G., Liebing, P., Ligori, S., Lilje, P. B., Lin, C. -C., Linde, D., Linder, E., Lindholm, V., Linke, L., Li, S. -S., Liu, S. J., Lloro, I., Lobo, F. S. N., Lodieu, N., Lombardi, M., Lombriser, L., Lonare, P., Longo, G., López-Caniego, M., Lopez, X. Lopez, Alvarez, J. Lorenzo, Loureiro, A., Loveday, J., Lusso, E., Macias-Perez, J., Maciaszek, T., Magliocchetti, M., Magnard, F., Magnier, E. A., Magro, A., Mahler, G., Mainetti, G., Maino, D., Maiorano, E., Malavasi, N., Mamon, G. A., Mancini, C., Mandelbaum, R., Manera, M., Manjón-García, A., Mannucci, F., Mansutti, O., Outeiro, M. Manteiga, Maoli, R., Maraston, C., Marcin, S., Marcos-Arenal, P., Margalef-Bentabol, B., Marggraf, O., Marinucci, D., Marinucci, M., Markovic, K., Marleau, F. R., Marpaud, J., Martignac, J., Martín-Fleitas, J., Martin-Moruno, P., Martin, E. L., Martinelli, M., Martinet, N., Martin, H., Martins, C. J. A. P., Marulli, F., Massari, D., Massey, R., Masters, D. C., Matarrese, S., Matsuoka, Y., Matthew, S., Maughan, B. J., Mauri, N., Maurin, L., Maurogordato, S., McCarthy, K., McConnachie, A. W., McCracken, H. J., McDonald, I., McEwen, J. D., McPartland, C. J. R., Medinaceli, E., Mehta, V., Mei, S., Melchior, M., Melin, J. -B., Ménard, B., Mendes, J., Mendez-Abreu, J., Meneghetti, M., Mercurio, A., Merlin, E., Metcalf, R. B., Meylan, G., Migliaccio, M., Mignoli, M., Miller, L., Miluzio, M., Milvang-Jensen, B., Mimoso, J. P., Miquel, R., Miyatake, H., Mobasher, B., Mohr, J. J., Monaco, P., Monguió, M., Montoro, A., Mora, A., Dizgah, A. Moradinezhad, Moresco, M., Moretti, C., Morgante, G., Morisset, N., Moriya, T. J., Morris, P. W., Mortlock, D. J., Moscardini, L., Mota, D. F., Moustakas, L. A., Moutard, T., Müller, T., Munari, E., Murphree, G., Murray, C., Murray, N., Musi, P., Nadathur, S., Nagam, B. C., Nagao, T., Naidoo, K., Nakajima, R., Nally, C., Natoli, P., Navarro-Alsina, A., Girones, D. Navarro, Neissner, C., Nersesian, A., Nesseris, S., Nguyen-Kim, H. N., Nicastro, L., Nichol, R. C., Nielbock, M., Niemi, S. -M., Nieto, S., Nilsson, K., Noller, J., Norberg, P., Nourizonoz, A., Ntelis, P., Nucita, A. A., Nugent, P., Nunes, N. J., Nutma, T., Ocampo, I., Odier, J., Oesch, P. A., Oguri, M., Oliveira, D. Magalhaes, Onoue, M., Oosterbroek, T., Oppizzi, F., Ordenovic, C., Osato, K., Pacaud, F., Pace, F., Padilla, C., Paech, K., Pagano, L., Page, M. J., Palazzi, E., Paltani, S., Pamuk, S., Pandolfi, S., Paoletti, D., Paolillo, M., Papaderos, P., Pardede, K., Parimbelli, G., Parmar, A., Partmann, C., Pasian, F., Passalacqua, F., Paterson, K., Patrizii, L., Pattison, C., Paulino-Afonso, A., Paviot, R., Peacock, J. A., Pearce, F. R., Pedersen, K., Peel, A., Peletier, R. F., Ibanez, M. Pellejero, Pello, R., Penny, M. T., Percival, W. J., Perez-Garrido, A., Perotto, L., Pettorino, V., Pezzotta, A., Pezzuto, S., Philippon, A., Piersanti, O., Pietroni, M., Piga, L., Pilo, L., Pires, S., Pisani, A., Pizzella, A., Pizzuti, L., Plana, C., Polenta, G., Pollack, J. E., Poncet, M., Pöntinen, M., Pool, P., Popa, L. A., Popa, V., Popp, J., Porciani, C., Porth, L., Potter, D., Poulain, M., Pourtsidou, A., Pozzetti, L., Prandoni, I., Pratt, G. W., Prezelus, S., Prieto, E., Pugno, A., Quai, S., Quilley, L., Racca, G. D., Raccanelli, A., Rácz, G., Radinović, S., Radovich, M., Ragagnin, A., Ragnit, U., Raison, F., Ramos-Chernenko, N., Ranc, C., Raylet, N., Rebolo, R., Refregier, A., Reimberg, P., Reiprich, T. H., Renk, F., Renzi, A., Retre, J., Revaz, Y., Reylé, C., Reynolds, L., Rhodes, J., Ricci, F., Ricci, M., Riccio, G., Ricken, S. O., Rissanen, S., Risso, I., Rix, H. -W., Robin, A. C., Rocca-Volmerange, B., Rocci, P. -F., Rodenhuis, M., Rodighiero, G., Monroy, M. Rodriguez, Rollins, R. P., Romanello, M., Roman, J., Romelli, E., Romero-Gomez, M., Roncarelli, M., Rosati, P., Rosset, C., Rossetti, E., Roster, W., Rottgering, H. J. A., Rozas-Fernández, A., Ruane, K., Rubino-Martin, J. A., Rudolph, A., Ruppin, F., Rusholme, B., Sacquegna, S., Sáez-Casares, I., Saga, S., Saglia, R., Sahlén, M., Saifollahi, T., Sakr, Z., Salvalaggio, J., Salvaterra, R., Salvati, L., Salvato, M., Salvignol, J. -C., Sánchez, A. G., Sanchez, E., Sanders, D. B., Sapone, D., Saponara, M., Sarpa, E., Sarron, F., Sartori, S., Sassolas, B., Sauniere, L., Sauvage, M., Sawicki, M., Scaramella, R., Scarlata, C., Scharré, L., Schaye, J., Schewtschenko, J. A., Schindler, J. -T., Schinnerer, E., Schirmer, M., Schmidt, F., Schmidt, M., Schneider, A., Schneider, M., Schneider, P., Schöneberg, N., Schrabback, T., Schultheis, M., Schulz, S., Schwartz, J., Sciotti, D., Scodeggio, M., Scognamiglio, D., Scott, D., Scottez, V., Secroun, A., Sefusatti, E., Seidel, G., Seiffert, M., Sellentin, E., Selwood, M., Semboloni, E., Sereno, M., Serjeant, S., Serrano, S., Shankar, F., Sharples, R. M., Short, A., Shulevski, A., Shuntov, M., Sias, M., Sikkema, G., Silvestri, A., Simon, P., Sirignano, C., Sirri, G., Skottfelt, J., Slezak, E., Sluse, D., Smith, G. P., Smith, L. C., Smith, R. E., Smit, S. J. A., Soldano, F., Solheim, B. G. B., Sorce, J. G., Sorrenti, F., Soubrie, E., Spinoglio, L., Mancini, A. Spurio, Stadel, J., Stagnaro, L., Stanco, L., Stanford, S. A., Starck, J. -L., Stassi, P., Steinwagner, J., Stern, D., Stone, C., Strada, P., Strafella, F., Stramaccioni, D., Surace, C., Sureau, F., Suyu, S. H., Swindells, I., Szafraniec, M., Szapudi, I., Taamoli, S., Talia, M., Tallada-Crespí, P., Tanidis, K., Tao, C., Tarrío, P., Tavagnacco, D., Taylor, A. N., Taylor, J. E., Taylor, P. L., Teixeira, E. M., Tenti, M., Idiago, P. Teodoro, Teplitz, H. I., Tereno, I., Tessore, N., Testa, V., Testera, G., Tewes, M., Teyssier, R., Theret, N., Thizy, C., Thomas, P. D., Toba, Y., Toft, S., Toledo-Moreo, R., Tolstoy, E., Tommasi, E., Torbaniuk, O., Torradeflot, F., Tortora, C., Tosi, S., Tosti, S., Trifoglio, M., Troja, A., Trombetti, T., Tronconi, A., Tsedrik, M., Tsyganov, A., Tucci, M., Tutusaus, I., Uhlemann, C., Ulivi, L., Urbano, M., Vacher, L., Vaillon, L., Valdes, I., Valentijn, E. A., Valenziano, L., Valieri, C., Valiviita, J., Broeck, M. Van den, Vassallo, T., Vavrek, R., Venemans, B., Venhola, A., Ventura, S., Kleijn, G. Verdoes, Vergani, D., Verma, A., Vernizzi, F., Veropalumbo, A., Verza, G., Vescovi, C., Vibert, D., Viel, M., Vielzeuf, P., Viglione, C., Viitanen, A., Villaescusa-Navarro, F., Vinciguerra, S., Visticot, F., Voggel, K., von Wietersheim-Kramsta, M., Vriend, W. J., Wachter, S., Walmsley, M., Walth, G., Walton, D. M., Walton, N. A., Wander, M., Wang, L., Wang, Y., Weaver, J. R., Weller, J., Whalen, D. J., Wiesmann, M., Wilde, J., Williams, O. R., Winther, H. -A., Wittje, A., Wong, J. H. W., Wright, A. H., Yankelevich, V., Yeung, H. W., Youles, S., Yung, L. Y. A., Zacchei, A., Zalesky, L., Zamorani, G., Vitorelli, A. Zamorano, Marc, M. Zanoni, Zennaro, M., Zerbi, F. M., Zinchenko, I. A., Zoubian, J., Zucca, E., and Zumalacarregui, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Paper submitted as part of the A&A special issue`Euclid on Sky'
Published: 2024

15. Leader-Follower Identification with Vehicle-Following Calibration for Non-Lane-Based Traffic

Author: Kulkarni, Mihir Mandar, Chaudhari, Ankit Anil, Srinivasan, Karthik K., Chilukuri, Bhargava Rama, Treiber, Martin, and Okhrin, Ostap
Subjects: Physics - Physics and Society
Abstract: Most car-following models were originally developed for lane-based traffic. Over the past two decades, efforts have been made to calibrate car-following models for non-lane-based traffic. However, traffic conditions with varying vehicle dimensions, intermittent following, and multiple leaders often occur and make subjective Leader-Follower (LF) pair identification challenging. In this study, we analyze Vehicle Following (VF) behavior in traffic with a lack of lane discipline using high-resolution microscopic trajectory data collected in Chennai, India. The paper's main contributions are threefold. Firstly, three criteria are used to identify LF pairs from the driver's perspective, taking into account the intermittent following, lack of lane discipline due to consideration of lateral separation, and the presence of in-between vehicles. Second, the psycho-physical concept of the regime in the Wiedemann-99 model is leveraged to determine the traffic-dependent "influence zone" for LF identification. Third, a joint and consistent framework is proposed for identifying LF pairs and estimating VF parameters. The proposed methodology outperforms other heuristic-based LF identification methods from the literature in terms of quantitative and qualitative performance measures. The proposed approach can enable robust and more realistic LF identification and VF parameter calibration with practical applications such as LOS analysis, capacity, and travel time estimation.
Published: 2024

16. X-ray observations of the Zwicky 3146 galaxy cluster reveal a 3.5 keV excess

Author: Bhargava, Sunayana, Giles, Paul, Romer, Kathy, Jeltema, Tesla, Hollowood, Devon, and Hilton, Matt
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: In this note, we present spectral fits of the well-documented sloshing cool-core cluster Zwicky 3146 ($z=0.291$), to test the existence of the highly speculated 3.5 keV line. We report excesses at $>3\sigma$ significance at $E=3.575$ keV, yielding a flux $F = 8.73_{-2.22}^{+2.17}$ $\times 10^{-6}$ photons cm$^{-2}$ s$^{-1}$, in \textit{XMM-Newton}, and $E=3.55$ keV, with a flux $F = 10.0_{-2.96}^{+3.05}$ $\times 10^{-6}$ photons cm$^{-2}$ s$^{-1}$ in \textit{Chandra}. We explore the possibility that the 3.5 keV excess is correlated to the presence of cold gas within the cluster, based on optical and sub-mm literature analyses. Following the launch of the X-ray Imaging and Spectroscopy Mission (XRISM), high resolution spectroscopy ($\leq 7$ eV) will reveal in unprecedented detail, the origin of this unidentified feature, for which Zwicky 3146 should be considered a viable target, due to the strength of the feature in two independent X-ray telescopes, opening a new window into plasma or charge exchange studies in galaxy clusters., Comment: 3 pages, 1 figure. Accepted for publication in the Research Notes of the AAS
Published: 2024
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17. Enhancing Arterial Blood Flow Simulations through Physics-Informed Neural Networks

Author: Bhargava, Shivam and Chamakuri, Nagaiah
Subjects: Mathematics - Numerical Analysis, Physics - Fluid Dynamics
Abstract: This study introduces a computational approach leveraging Physics-Informed Neural Networks (PINNs) for the efficient computation of arterial blood flows, particularly focusing on solving the incompressible Navier-Stokes equations by using the domain decomposition technique. Unlike conventional computational fluid dynamics methods, PINNs offer advantages by eliminating the need for discretized meshes and enabling the direct solution of partial differential equations (PDEs). In this paper, we propose the weighted Extended Physics-Informed Neural Networks (WXPINNs) and weighted Conservative Physics-Informed Neural Networks (WCPINNs), tailored for detailed hemodynamic simulations based on generalized space-time domain decomposition techniques. The inclusion of multiple neural networks enhances the representation capacity of the weighted PINN methods. Furthermore, the weighted PINNs can be efficiently trained in parallel computing frameworks by employing separate neural networks for each sub-domain. We show that PINNs simulation results circumvent backflow instabilities, underscoring a notable advantage of employing PINNs over traditional numerical methods to solve such complex blood flow models. They naturally address such challenges within their formulations. The presented numerical results demonstrate that the proposed weighted PINNs outperform traditional PINNs settings, where sub-PINNs are applied to each subdomain separately. This study contributes to the integration of deep learning methodologies with fluid mechanics, paving the way for accurate and efficient high-fidelity simulations in biomedical applications, particularly in modeling arterial blood flow.
Published: 2024

18. Hydrodynamical simulations favor a pure deflagration origin of the near-Chandrasekhar mass supernova remnant 3C 397

Author: Mehta, Vrutant, Sullivan, Jack, Fisher, Robert, Ohshiro, Yuken, Yamaguchi, Hiroya, Bhargava, Khanak, and Neopane, Sudarshan
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: Suzaku X-ray observations of the Type Ia supernova remnant (SNR) 3C 397 discovered exceptionally high mass ratios of Mn/Fe, Ni/Fe, and Cr/Fe, consistent with a near $M_{\rm Ch}$ progenitor white dwarf (WD). The Suzaku observations have established 3C 397 as our best candidate for a near-$M_{\rm Ch}$ SNR Ia, and opened the way to address additional outstanding questions about the origin and explosion mechanism of these transients. In particular, subsequent XMM-Newton observations revealed an unusually clumpy distribution of iron group elemental (IGE) abundances within the ejecta of 3C 397. In this paper, we undertake a suite of two dimensional hydrodynamical models, varying both the explosion mechanism -- either deflagration-to-detonation (DDT), or pure deflagration -- WD progenitors, and WD progenitor metallicity, and analyze their detailed nucleosynthetic abundances and associated clumping. We find that pure deflagrations naturally give rise to clumpy distributions of neutronized species concentrated towards the outer limb of the remnant, and confirm DDTs have smoothly structured ejecta with a central concentration of neutronization. Our findings indicate that 3C 397 was most likely a pure deflagration of a high central density WD. We discuss a range of implications of these findings for the broader SN Ia progenitor problem., Comment: 13 pages, 6 figures, Stable mean nucleosynthetic yields' datasets are available at https://doi.org/10.5281/zenodo.10927265 . Comments are welcome
Published: 2024

19. On Fractional Kinetic Equations Involving Srivastava Polynomial

Author: Sharma, Komal Prasad, Bhargava, Alok, and Saini, Omprakash
Subjects: Mathematical Physics, 26A33, 33E12, 33E20, 44A99
Abstract: Kinetic equations hold a very important place in physics and further their fractional generalization enhances the scope of their applicability and significance in describing the continuity of motion in materials. After the development of generalized form of fractional kinetic equations, many researchers proffered several new forms of these equations and found their solutions by different techniques. In this work, we have proposed some novel generalised fractional kinetic equations involving the Srivastava polynomial and, by applying the Laplace transform approach, their solutions are calculated. Further, to study the behaviour of these, numerical and graphical interpretation of the solutions are also provided., Comment: 10 pages
Published: 2024

20. The XMM Cluster Survey: Automating the estimation of hydrostatic mass for large samples of galaxy clusters I -- Methodology, Validation, & Application to the SDSSRM-XCS sample

Author: Turner, D. J., Giles, P. A., Romer, A. K., Pilling, J., Lingard, T. K., Wilkinson, R., Hilton, M., Upsdell, E. W., Al-Serkal, R., Cheng, T., Eappen, R., Rooney, P. J., Bhargava, S., Collins, C. A., Mayers, J., Miller, C., Nichol, R. C., Sahén, M., and Viana, P. T. P.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: We describe features of the X-ray: Generate and Analyse (XGA) open-source software package that have been developed to facilitate automated hydrostatic mass ($M_{\rm hydro}$) measurements from XMM X-ray observations of clusters of galaxies. This includes describing how XGA measures global, and radial, X-ray properties of galaxy clusters. We then demonstrate the reliability of XGA by comparing simple X-ray properties, namely the X-ray temperature and gas mass, with published values presented by the XMM Cluster Survey (XCS), the Ultimate XMM eXtragaLactic survey project (XXL), and the Local Cluster Substructure Survey (LoCuSS). XGA measured values for temperature are, on average, within 1% of the values reported in the literature for each sample. XGA gas masses for XXL clusters are shown to be ${\sim}$10% lower than previous measurements (though the difference is only significant at the $\sim$1.8$\sigma$ level), LoCuSS $R_{2500}$ and $R_{500}$ gas mass re-measurements are 3% and 7% lower respectively (representing a 1.5$\sigma$ and 3.5$\sigma$ difference). Like-for-like comparisons of hydrostatic mass are made to LoCuSS results, which show that our measurements are $10{\pm}3%$ ($19{\pm}7%$) higher for $R_{2500}$ ($R_{500}$). The comparison between $R_{500}$ masses shows significant scatter. Finally, we present new $M_{\rm hydro}$ measurements for 104 clusters from the SDSS DR8 redMaPPer XCS sample (SDSSRM-XCS). Our SDSSRM-XCS hydrostatic mass measurements are in good agreement with multiple literature estimates, and represent one of the largest samples of consistently measured hydrostatic masses. We have demonstrated that XGA is a powerful tool for X-ray analysis of clusters; it will render complex-to-measure X-ray properties accessible to non-specialists., Comment: 24 pages (18 + 6 appendices), 15 figures, submitted to MNRAS; see https://github.com/DavidT3/XCS-Mass-Paper-I-Analysis for the code and samples
Published: 2024

21. Linear stability of cylindrical, multicomponent vesicles

Author: Venkatesh, Anirudh, Bhargava, Aman, and Narsimhan, Vivek
Subjects: Condensed Matter - Soft Condensed Matter, Mathematical Physics, Physics - Fluid Dynamics
Abstract: Vesicles are important surrogate structures made up of multiple phospholipids and cholesterol distributed in the form of a lipid bilayer. Tubular vesicles can undergo pearling i.e., formation of beads on the liquid thread akin to the Rayleigh-Plateau instability. Previous studies have inspected the effects of surface tension on the pearling instabilities of single-component vesicles. In this study, we perform a linear stability analysis on a multicomponent cylindrical vesicle. We solve the Stokes equations along with the Cahn-Hilliard equations to develop the linearized dynamic equations governing the vesicle shape and surface concentration fields. This helps us show that multicomponent vesicles can undergo pearling, buckling, and wrinkling even in the absence of surface tension, which is a significantly different result from studies on single-component vesicles. This behaviour arises due to the competition between the free energies of phase separation, line tension, and bending for this multi-phospholipid system. We determine the conditions under which axisymmetric and non-axisymmetric modes are dominant, and supplement our results with an energy analysis that shows the sources for these instabilities. We further show that these trends qualitatively match recent experiments.
Published: 2024

22. A study guide for the $\ell^2$ decoupling theorem for the paraboloid

Author: Bhargava, Ataleshvara, Chan, Tiklung, Lim, Zi Li, and Pang, Yixuan
Subjects: Mathematics - Classical Analysis and ODEs, 42B15, 42-02
Abstract: This article serves as a study guide for the $\ell^2$ decoupling theorem for the paraboloid originally proved by Bourgain and Demeter. Given its popularity and importance, many expositions about the $\ell^2$ decoupling theorem already exist. Our study guide is intended to complement and combine these existing resources in order to provide a more gentle introduction to the subject., Comment: 77 pages, 3 figures. Study guide written at UPenn Study Guide Writing Workshop 2023 https://sites.google.com/view/studyguideworkshop2023/home
Published: 2024

23. The SRG/eROSITA All-Sky Survey: Dark Energy Survey Year 3 Weak Gravitational Lensing by eRASS1 selected Galaxy Clusters

Author: Grandis, S., Ghirardini, V., Bocquet, S., Garrel, C., Mohr, J. J., Liu, A., Kluge, M., Kimmig, L., Reiprich, T. H., Alarcon, A., Amon, A., Artis, E., Bahar, Y. E., Balzer, F., Bechtol, K., Becker, M. R., Bernstein, G., Bulbul, E., Campos, A., Rosell, A. Carnero, Kind, M. Carrasco, Cawthon, R., Chang, C., Chen, R., Chiu, I., Choi, A., Clerc, N., Comparat, J., Cordero, J., Davis, C., Derose, J., Diehl, H. T., Dodelson, S., Doux, C., Drlica-Wagner, A., Eckert, K., Elvin-Poole, J., Everett, S., Ferte, A., Gatt, M., Giannini, G., Giles, P., Gruen, D., Gruendl, R. A., Harrison, I., Hartley, W. G., Herner, K., Huf, E. M., Kleinebreil, F., Kuropatkin, N., Leget, P. F., Maccrann, N., Mccullough, J., Merloni, A., Myles, J., Nandra, K., Navarro-Alsina, A., Okabe, N., Pacaud, F., Pandey, S., Prat, J., Predehl, P., Ramos, M., Raveri, M., Rollins, R. P., Roodman, A., Ross, A. J., Rykoff, E. S., Sanchez, C., Sanders, J., Schrabback, T., Secco, L. F., Seppi, R., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M., Tutusaus, I., Varga, T. N., Wu, H., Yanny, B., Yin, B., Zhang, X., Zhang, Y., Alves, O., Bhargava, S., Brooks, D., Burke, D. L., Carretero, J., Costanzi, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Desai, S., Doel, P., Ferrero, I., Flaugher, B., Friedel, D., Frieman, J., García-Bellido, J., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Jeffrey, N., Lahav, O., Lee, S., Marshall, J. L., Menanteau, F., Ogando, R. L. C., Pieres, A., Malagón, A. A. Plazas, Romer, A. K., Sanchez, E., Schubnell, M., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Weaverdyck, N., and Weller, J.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Number counts of galaxy clusters across redshift are a powerful cosmological probe, if a precise and accurate reconstruction of the underlying mass distribution is performed -- a challenge called mass calibration. With the advent of wide and deep photometric surveys, weak gravitational lensing by clusters has become the method of choice to perform this measurement. We measure and validate the weak gravitational lensing (WL) signature in the shape of galaxies observed in the first 3 years of the DES Y3 caused by galaxy clusters selected in the first all-sky survey performed by SRG/eROSITA. These data are then used to determine the scaling between X-ray photon count rate of the clusters and their halo mass and redshift. We empirically determine the degree of cluster member contamination in our background source sample. The individual cluster shear profiles are then analysed with a Bayesian population model that self-consistently accounts for the lens sample selection and contamination, and includes marginalization over a host of instrumental and astrophysical systematics. To quantify the accuracy of the mass extraction of that model, we perform mass measurements on mock cluster catalogs with realistic synthetic shear profiles. This allows us to establish that hydro-dynamical modelling uncertainties at low lens redshifts ($z<0.6$) are the dominant systematic limitation. At high lens redshift the uncertainties of the sources' photometric redshift calibration dominate. With regard to the X-ray count rate to halo mass relation, we constrain all its parameters. This work sets the stage for a joint analysis with the number counts of eRASS1 clusters to constrain a host of cosmological parameters. We demonstrate that WL mass calibration of galaxy clusters can be performed successfully with source galaxies whose calibration was performed primarily for cosmic shear experiments., Comment: 27 pages, 18 figures, 2 appendices, submitted to A\&A
Published: 2024
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24. SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking

Author: Kulkarni, Atharva, Tseng, Bo-Hsiang, Moniz, Joel Ruben Antony, Piraviperumal, Dhivya, Yu, Hong, and Bhargava, Shruti
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt, requiring access to labeled training data. Procuring such training data for a wide range of domains and applications is time-consuming, expensive, and, at times, infeasible. While zero-shot learning requires no training data, it significantly lags behind the few-shot setup. Thus, `\textit{Can we efficiently generate synthetic data for any dialogue schema to enable few-shot prompting?}' Addressing this question, we propose \method, a data generation framework tailored for DST, utilizing LLMs. Our approach only requires the dialogue schema and a few hand-crafted dialogue templates to synthesize natural, coherent, and free-flowing dialogues with DST annotations. Few-shot learning using data from {\method} results in $4-5%$ improvement in Joint Goal Accuracy over the zero-shot baseline on MultiWOZ 2.1 and 2.4. Remarkably, our few-shot learning approach recovers nearly $98%$ of the performance compared to the few-shot setup using human-annotated training data. Our synthetic data and code can be accessed at https://github.com/apple/ml-synthdst, Comment: 9 pages. 4 figures, EACL 2024 main conference
Published: 2024

25. Can Large Language Models Understand Context?

Author: Zhu, Yilun, Moniz, Joel Ruben Antony, Bhargava, Shruti, Lu, Jiarui, Piraviperumal, Dhivya, Li, Site, Zhang, Yuan, Yu, Hong, and Tseng, Bo-Hsiang
Subjects: Computer Science - Computation and Language
Abstract: Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the evaluation of LLMs encompasses various domains within the realm of Natural Language Processing, limited attention has been paid to probing their linguistic capability of understanding contextual features. This paper introduces a context understanding benchmark by adapting existing datasets to suit the evaluation of generative models. This benchmark comprises of four distinct tasks and nine datasets, all featuring prompts designed to assess the models' ability to understand context. First, we evaluate the performance of LLMs under the in-context learning pretraining scenario. Experimental results indicate that pre-trained dense models struggle with understanding more nuanced contextual features when compared to state-of-the-art fine-tuned models. Second, as LLM compression holds growing significance in both research and real-world applications, we assess the context understanding of quantized models under in-context-learning settings. We find that 3-bit post-training quantization leads to varying degrees of performance reduction on our benchmark. We conduct an extensive analysis of these scenarios to substantiate our experimental results., Comment: Findings of EACL 2024
Published: 2024

26. Early Postoperative Prosthesis for Below-Knee Amputee: An Innovative Technique and Design (Siachen Hospital EPOP)

Author: Bhargava, Suveer and Bhargava, Neeta
Published: 2024
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27. Study of Potassium in Synovial Fluid as an Aid in Determining the Time Since Death

Author: Sharma, Anushree, Bohra, Bhavesh, and Bhargava, A.K.
Published: 2019
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28. AstroSat and NICER timing view of the Z-type Neutron Star X-ray binary GX 340+0

Author: Pahari, Mayukh, Suman, Shree, Bhargava, Yash, Weston, Alexander, Zhang, Liang, Bhattacharyya, Sudip, Misra, Ranjeev, and McHardy, Ian
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The timing properties of the Z-type low-mass X-ray binaries provide insights into the emission components involved in producing the unique Z-shaped track in the hardness-intensity diagrams of these sources. In this work, we investigate the AstroSat and NICER observations of the GX 340+0 covering the complete 'Z'-track from the horizontal branch (HB) to the extended flaring branch (EFB). For the first time, we present the Z-track as seen in soft X-rays using the AstroSat/SXT and NICER (the soft colour is defined as a ratio of 3-6 keV to 0.5-3 keV). The shape of the track is distinctly different in soft X-rays, strongly suggesting the presence of additional components active in soft X-rays. The detailed timing analysis revealed significant quasi-periodic oscillation throughout the HB and the normal branch (NB) using LAXPC and the first NICER detection of 33.1 +/- 1.1 Hz horizontal branch oscillation (HBO) in 3-6 keV. The oscillations at the HB/NB vertex are observed to have higher frequencies (41-52 Hz) than the HB oscillations (16-31 Hz) and NB oscillations (6.2-8 Hz) but significantly lower rms (~1.6%). The HB oscillation is also limited to the energy range of 3-20 keV, indicating an association of HBO origin with the non-thermal component. It is also supported by earlier studies that found the strongest X-ray polarisation during HB., Comment: 15 pages, 12 figures, 4 tables, accepted for publication in the MNRAS
Published: 2024

29. Conformal Prediction Sets Improve Human Decision Making

Author: Cresswell, Jesse C., Sui, Yi, Kumar, Bhargava, and Vouitsis, Noël
Subjects: Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, Statistics - Machine Learning
Abstract: In response to everyday queries, humans explicitly signal uncertainty and offer alternative answers when they are unsure. Machine learning models that output calibrated prediction sets through conformal prediction mimic this human behaviour; larger sets signal greater uncertainty while providing alternatives. In this work, we study the usefulness of conformal prediction sets as an aid for human decision making by conducting a pre-registered randomized controlled trial with conformal prediction sets provided to human subjects. With statistical significance, we find that when humans are given conformal prediction sets their accuracy on tasks improves compared to fixed-size prediction sets with the same coverage guarantee. The results show that quantifying model uncertainty with conformal prediction is helpful for human-in-the-loop decision making and human-AI teams., Comment: Published at ICML 2024. Code available at https://github.com/layer6ai-labs/hitl-conformal-prediction
Published: 2024

30. SPT Clusters with DES and HST Weak Lensing. II. Cosmological Constraints from the Abundance of Massive Halos

Author: Bocquet, S., Grandis, S., Bleem, L. E., Klein, M., Mohr, J. J., Schrabback, T., Abbott, T. M. C., Ade, P. A. R., Aguena, M., Alarcon, A., Allam, S., Allen, S. W., Alves, O., Amon, A., Anderson, A. J., Annis, J., Ansarinejad, B., Austermann, J. E., Avila, S., Bacon, D., Bayliss, M., Beall, J. A., Bechtol, K., Becker, M. R., Bender, A. N., Benson, B. A., Bernstein, G. M., Bhargava, S., Bianchini, F., Brodwin, M., Brooks, D., Bryant, L., Campos, A., Canning, R. E. A., Carlstrom, J. E., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Cawthon, R., Chang, C. L., Chang, C., Chaubal, P., Chen, R., Chiang, H. C., Choi, A., Chou, T-L., Citron, R., Moran, C. Corbett, Cordero, J., Costanzi, M., Crawford, T. M., Crites, A. T., da Costa, L. N., Pereira, M. E. S., Davis, C., Davis, T. M., DeRose, J., Desai, S., de Haan, T., Diehl, H. T., Dobbs, M. A., Dodelson, S., Doux, C., Drlica-Wagner, A., Eckert, K., Elvin-Poole, J., Everett, S., Everett, W., Ferrero, I., Ferté, A., Flores, A. M., Frieman, J., Gallicchio, J., García-Bellido, J., Gatti, M., George, E. M., Giannini, G., Gladders, M. D., Gruen, D., Gruendl, R. A., Gupta, N., Gutierrez, G., Halverson, N. W., Harrison, I., Hartley, W. G., Herner, K., Hinton, S. R., Holder, G. P., Hollowood, D. L., Holzapfel, W. L., Honscheid, K., Hrubes, J. D., Huang, N., Hubmayr, J., Huff, E. M., Huterer, D., Irwin, K. D., James, D. J., Jarvis, M., Khullar, G., Kim, K., Knox, L., Kraft, R., Krause, E., Kuehn, K., Kuropatkin, N., Kéruzoré, F., Lahav, O., Lee, A. T., Leget, P. -F., Li, D., Lin, H., Lowitz, A., MacCrann, N., Mahler, G., Mantz, A., Marshall, J. L., McCullough, J., McDonald, M., McMahon, J. J., Mena-Fernández, J., Menanteau, F., Meyer, S. S., Miquel, R., Montgomery, J., Myles, J., Natoli, T., Navarro-Alsina, A., Nibarger, J. P., Noble, G. I., Novosad, V., Ogando, R. L. C., Omori, Y., Padin, S., Pandey, S., Paschos, P., Patil, S., Pieres, A., Malagón, A. A. Plazas, Porredon, A., Prat, J., Pryke, C., Raveri, M., Reichardt, C. L., Roberson, J., Rollins, R. P., Romero, C., Roodman, A., Ruhl, J. E., Rykoff, E. S., Saliwanchik, B. R., Salvati, L., Sánchez, C., Sanchez, E., Cid, D. Sanchez, Saro, A., Schaffer, K. K., Secco, L. F., Sevilla-Noarbe, I., Sharon, K., Sheldon, E., Shin, T., Sievers, C., Smecher, G., Smith, M., Somboonpanyakul, T., Sommer, M., Stalder, B., Stark, A. A., Stephen, J., Strazzullo, V., Suchyta, E., Tarle, G., To, C., Troxel, M. A., Tucker, C., Tutusaus, I., Varga, T. N., Veach, T., Vieira, J. D., Vikhlinin, A., von der Linden, A., Wang, G., Weaverdyck, N., Weller, J., Whitehorn, N., Wu, W. L. K., Yanny, B., Yefremenko, V., Yin, B., Young, M., Zebrowski, J. A., Zhang, Y., Zohren, H., and Zuntz, J.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel'dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range $0.25-1.78$ over a total sky area of 5,200 deg$^2$. We use DES Year 3 weak-lensing data for 688 clusters with redshifts $z<0.95$ and HST weak-lensing data for 39 clusters with $0.6
Published: 2024

31. Gemini: A Family of Highly Capable Multimodal Models

Author: Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.
Published: 2023

32. Soft X-ray and FUV observations of Nova Her 2021 (V1674~Her) with AstroSat

Author: Bhargava, Yash, Dewangan, Gulab Chand, Anupama, G. C., Kamath, U. S., Sonith, L. S., Singh, Kulinder Pal, Drake, J. J., Beardmore, A., Luna, G. J. M., Orio, M., and Page, K. L.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Nova Her 2021 or V1674 Her was one of the fastest novae to be observed so far. We report here the results from our timing and spectral studies of the source observed at multiple epochs with AstroSat. We report the detection of a periodicity in the source in soft X-rays at a period of 501.4--501.5 s which was detected with high significance after the peak of the super-soft phase, but was not detected in the far ultraviolet (FUV) band of AstroSat. The shape of the phase-folded X-ray light curves has varied significantly as the nova evolved. The phase-resolved spectral studies reveal the likely presence of various absorption features in the soft X-ray band of 0.5--2 keV, and suggest that the optical depth of these absorption features may be marginally dependent on the pulse phase. Strong emission lines from Si, N and O are detected in the FUV, and their strength declined continuously as the nova evolved and went through a bright X-ray state., Comment: 11 pages, 11 figures, Accepted for publication in MNRAS
Published: 2023

33. ALOHA: from Attention to Likes -- a unified mOdel for understanding HumAn responses to diverse visual content

Author: Li, Peizhao, He, Junfeng, Li, Gang, Bhargava, Rachit, Shen, Shaolei, Valliappan, Nachiappan, Liang, Youwei, Gu, Hongxiang, Ramachandran, Venky, Farhadi, Golnaz, Li, Yang, Kohlhoff, Kai J, and Navalpakkam, Vidhya
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Progress in human behavior modeling involves understanding both implicit, early-stage perceptual behavior such as human attention and explicit, later-stage behavior such as subjective preferences/likes. Yet, most prior research has focused on modeling implicit and explicit human behavior in isolation; and often limited to a specific type of visual content. Can we build a unified model of human attention and preference behavior that works reliably across diverse types of visual content? Such a model would enable predicting subjective feedback such as satisfaction or aesthetic quality, along with the underlying human attention or interaction heatmaps and viewing order, enabling designers and content-creation models to optimize their creation for human-centric improvements. In this paper, we propose ALOHA -- a unified model for understanding human responses from attention to likes, across diverse visual content. ALOHA leverages a multimodal transformer % featuring distinct prediction heads for each facet, and predicts different human responses such as attention heatmaps, scanpath or viewing order, as well as subjective rating/preference. We train ALOHA on diverse public datasets spanning natural images, webpages and graphic designs, and achieve SOTA performance on multiple benchmarks across different image domains and various behavior modeling tasks. Potential applications include providing instant feedback on the effectiveness of UIs/designs/images, and serving as a reward model to further optimize visual-content creation.
Published: 2023

34. A Comparative Analysis of Text-to-Image Generative AI Models in Scientific Contexts: A Case Study on Nuclear Power

Author: Joynt, Veda, Cooper, Jacob, Bhargava, Naman, Vu, Katie, Kwon, O Hwang, Allen, Todd R., Verma, Aditi, and Radaideh, Majdi I.
Subjects: Computer Science - Computers and Society
Abstract: In this work, we propose and assess the potential of generative artificial intelligence (AI) to generate public engagement around potential clean energy sources. Such an application could increase energy literacy -- an awareness of low-carbon energy sources among the public therefore leading to increased participation in decision-making about the future of energy systems. We explore the use of generative AI to communicate technical information about low-carbon energy sources to the general public, specifically in the realm of nuclear energy. We explored 20 AI-powered text-to-image generators and compared their individual performances on general and scientific nuclear-related prompts. Of these models, DALL-E, DreamStudio, and Craiyon demonstrated promising performance in generating relevant images from general-level text related to nuclear topics. However, these models fall short in three crucial ways: (1) they fail to accurately represent technical details of energy systems; (2) they reproduce existing biases surrounding gender and work in the energy sector; and (3) they fail to accurately represent indigenous landscapes -- which have historically been sites of resource extraction and waste deposition for energy industries. This work is performed to motivate the development of specialized generative tools and their captions to improve energy literacy and effectively engage the public with low-carbon energy sources., Comment: 26 pages, 11 figures, 9 tables, submitted to review
Published: 2023

35. Evaluating Pretrained models for Deployable Lifelong Learning

Author: Lekkala, Kiran, Bhargava, Eshan, Ge, Yunhao, and Itti, Laurent
Subjects: Computer Science - Machine Learning
Abstract: We create a novel benchmark for evaluating a Deployable Lifelong Learning system for Visual Reinforcement Learning (RL) that is pretrained on a curated dataset, and propose a novel Scalable Lifelong Learning system capable of retaining knowledge from the previously learnt RL tasks. Our benchmark measures the efficacy of a deployable Lifelong Learning system that is evaluated on scalability, performance and resource utilization. Our proposed system, once pretrained on the dataset, can be deployed to perform continual learning on unseen tasks. Our proposed method consists of a Few Shot Class Incremental Learning (FSCIL) based task-mapper and an encoder/backbone trained entirely using the pretrain dataset. The policy parameters corresponding to the recognized task are then loaded to perform the task. We show that this system can be scaled to incorporate a large number of tasks due to the small memory footprint and fewer computational resources. We perform experiments on our DeLL (Deployment for Lifelong Learning) benchmark on the Atari games to determine the efficacy of the system., Comment: In submission to CoLLA 2024. Also published in the Proceedings of WACV 2024 Workshop on Pretraining
Published: 2023

36. MARRS: Multimodal Reference Resolution System

Author: Ates, Halim Cagri, Bhargava, Shruti, Li, Site, Lu, Jiarui, Maddula, Siddhardha, Moniz, Joel Ruben Antony, Nalamalapu, Anil Kumar, Nguyen, Roman Hoang, Ozyildirim, Melis, Patel, Alkesh, Piraviperumal, Dhivya, Renkens, Vincent, Samal, Ankit, Tran, Thy, Tseng, Bo-Hsiang, Yu, Hong, Zhang, Yuan, and Zou, Rong
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Successfully handling context is essential for any dialog understanding task. This context maybe be conversational (relying on previous user queries or system responses), visual (relying on what the user sees, for example, on their screen), or background (based on signals such as a ringing alarm or playing music). In this work, we present an overview of MARRS, or Multimodal Reference Resolution System, an on-device framework within a Natural Language Understanding system, responsible for handling conversational, visual and background context. In particular, we present different machine learning models to enable handing contextual queries; specifically, one to enable reference resolution, and one to handle context via query rewriting. We also describe how these models complement each other to form a unified, coherent, lightweight system that can understand context while preserving user privacy., Comment: Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023)
Published: 2023
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37. Significant Variability in the Identification and Reporting of Band Neutrophils by Participants Enrolled in the College of American Pathologists Proficiency: Time for a Change

Author: Vergara-Lluri, Maria, Kovach, Alexandra E., Nakashima, Megan O., Bradley, Kyle T., Mahe, Etienne, Tsao, Lawrence, Savage, Natasha M., Salansky, Stephanie A., Long, Thomas, Perkins, Sherrie L., Hsi, Eric D., Pozdnyakova, Olga, and Bhargava, Parul
Subjects: Neutrophils -- Health aspects, Sepsis -- Diagnosis -- Care and treatment, Health
Abstract: * Context.--Increased band neutrophils in blood smear differential counts ('bandemia') are entrenched in medicine as a flag for sepsis. However, laboratory hematology experts have long advocated for discontinuation of reporting bands separately from segmented neutrophils because of poor sensitivity and specificity, poor interobserver agreement, and availability of alternative biomarkers for sepsis. Objective.--To describe band neutrophil reporting practices and reproducibility of band classification among laboratories participating in the College of American Pathologists (CAP) proficiency testing (PT) program. Design.--A survey questionnaire was distributed to hematology PT participants. A subsequent morphologic challenge included 12 preselected cell identifications of segmented neutrophils, bands, and metamyelocytes, and a 100-cell manual differential count of a digitally scanned blood smear. Results.--Among laboratories that reported manual differentials, most respondents reported bands (4554 of 5268; 86.4%). Only 3222 of 4412 respondents (73.0%) provided band reference ranges. Though participants classified 'easy' band neutrophils well (78.0%-98.3%), categorization of cell identifications for 'moderate' and 'difficult' bands was poor (3.1%-39.0% of laboratories), with classification instead as segmented neutrophils. This pattern was seen regardless of laboratory demographic characteristics. Marked variability in band counts was observed on the 100cell differential count for both CAP PT participants and CAP Hematology and Clinical Microscopy Committee (HCMC) members (coefficients of variation, 55.8% and 32.9%, respectively). Variability was significantly improved when segmented and band neutrophils were grouped together (coefficients of variation, 6.2% and 5.0%, respectively). Conclusions.--Most CAP PT-participating laboratories report band counts, many without reference ranges. The survey confirms significant interlaboratory variability of band enumeration when bands are separately identified from segmented neutrophils. This study reaffirms the CAP Hematology and Clinical Microscopy Committee's strong recommendation to group segmented and band neutrophils together in manual differential counts. (Arch Pathol Lab Med. 2024;148:666-676; doi: 10.5858/arpa.2023-0015-CP), Band neutrophil counting has long been entrenched in the medical literature as a laboratory indicator of sepsis, with the belief that 'bandemia' is useful in predicting the presence of bacterial [...]
Published: 2024
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38. In-silico Based Genome-Wide Identification and Analysis of Glutathione S-Transferase Gene Family in Beet (Beta vulgaris subsp. vulgaris)

Author: Tiwari, Shivani, Vaish, Swati, Singh, Nootan, Basantani, Mahesh, and Bhargava, Atul
Published: 2024
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39. The effect of gadolinium-doped ceria interlayer on the oxygen reduction reaction in a LSCF cathode-ScSZ electrolyte supported IT-SOFCs

Author: Nisar, Jamila, Giddey, Sarbjit, Kaur, Gurpreet, Kulkarni, Aniruddha P., Biswas, Saheli, Jones, Lathe A., and Bhargava, Suresh K.
Published: 2024
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40. Fire Performance of Corroded Reinforced Concrete Columns

Author: Chandra, Shashank, Sharma, Umesh Kumar, Green, Mark, Gales, John, and Bhargava, Pradeep
Published: 2024
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41. On-tree fruit detection system using Darknet-19 based SSD network

Author: Agarwal, Diwakar and Bhargava, Anuja
Published: 2024
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42. Politics, industrialization and technical education in colonial India: A case study of Imperial Institute of Sugar Technology, Kanpur

Author: Bhargava, Prakrati
Published: 2024
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43. Characterization of Diesel Degrading Indigenous Bacterial Strains, Acinetobacter pittii and Pseudomonas aeruginosa, Isolated from Oil Contaminated Soils

Author: Dohare, Sonam, Rawat, Hemant Kumar, Bhargava, Yogesh, and Kango, Naveen
Published: 2024
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44. Risk Factors Associated with Mortality in Patients with Mucormycosis Post Severe Acute Respiratory Syndrome Coronavirus-2 (Sars-Cov-2) Infection

Author: Goel, Ashiya, Arora, Nikhil, Kumar, Pratik, and Bhargava, Aditya
Published: 2024
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45. Hybrid total alloplastic temporomandibular joint replacement prosthesis: a pilot study to evaluate feasibility, functional performance and impact on post-operative quality of life

Author: Bhargava, Darpan
Published: 2024
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46. Exploring the performance of 2-D square lattice photonic crystal channel drop filters with varying horizontal cavity dimensions

Author: Singh, Rajpal, Sharma, M. D., and Bhargava, Anami
Published: 2024
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47. CRISPR/Cas9 Mediated Editing of the white (wh) locus Affects Body Size and Reproduction of the Oriental Fruit Fly, Bactocera dorsalis (Hendel)

Author: Bhargava, Chikmagalur Nagaraja, Ashok, Karuppannasamy, Asokan, Ramasamy, Prasad Babu, Karakatti, Parvathy, Madhusoodanan Sujatha, Yogi, Dhawane, Shashikala, Thalooru, Chiranth, Rampura Kidinethra, Ashok, Ulligundam, and Harsha, Chowdenalli Gangadharaiah
Published: 2024
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48. The Paradigms and Paradoxes of Sigma Metrics in the Analytical Phase of the Medical Diagnostic Laboratory

Author: Bhargava, Seema
Published: 2024
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49. Deregulated DNA ADP-ribosylation impairs telomere replication

Author: Wondisford, Anne R., Lee, Junyeop, Lu, Robert, Schuller, Marion, Groslambert, Josephine, Bhargava, Ragini, Schamus-Haynes, Sandra, Cespedes, Leyneir C., Opresko, Patricia L., Pickett, Hilda A., Min, Jaewon, Ahel, Ivan, and O’Sullivan, Roderick J.
Published: 2024
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50. The active papillary muscle sign in 18F-FDG PET/CT cardiac sarcoidosis exams and its relationship with myocardial suppression

Author: Özütemiz, Can, Koksel, Yasemin, Froelich, Jerry W., Rubin, Nathan, Bhargava, Maneesh, Roukoz, Henri, Cogswell, Rebecca, Markowitz, Jeremy, Perlman, David M., and Steinberger, Daniel
Published: 2024
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