Author: "Ajith, P" - Searchworks@Jio Institute Digital Library Search Results

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5,491 results on '"Ajith, P"'

1. Indigenous biopreparations and biocontrol agents for growth and disease management in black pepper nursery

Author: Rini, C. R. and Ajith, P. M.
Published: 2023
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2. Variability and association analyses in F2 populations of groundnut (Arachis hypogaea L.)

Author: Ajith, P., Rani, R. Kanchana, Kumar, M., Brindavathy, R., and Thiruvarassan, S.
Published: 2023
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3. Fast and efficient Bayesian method to search for strongly lensed gravitational waves

Author: Barsode, A., Goyal, S., and Ajith, P.
Subjects: General Relativity and Quantum Cosmology
Abstract: A small fraction of the gravitational-wave (GW) signals from binary black holes observable by ground-based detectors will be strongly lensed by intervening objects such as galaxies and clusters. Strong lensing will produce nearly identical copies of the GW signals separated in time. These lensed signals must be identified against a background of unlensed pairs GW events, some of which may appear similar by accident. This is usually done using fast, but approximate methods that, for example, check for the overlap between the posterior distributions of a subset of binary parameters, or using slow, but accurate joint Bayesian parameter estimation. In this work, we present a modified version of the posterior overlap method dubbed "PO2.0" that is mathematically equivalent to joint parameter estimation while still remaining fast. We achieve a significant gain in efficiency by incorporating informative priors about the binary and lensing populations, selection effects, and all the inferred parameters of the binary. For binary black hole signals lensed by galaxies, our improved method can detect 65% lensed events at a pair-wise false alarm probability of $\sim 2\times 10^{-6}$. Consequently, we have a 13% probability of detecting a strongly lensed event above $2.25\sigma$ significance during 18 months of observation by the LIGO-Virgo detectors at their current sensitivity. We also show how we can compute the joint posteriors of the lens and source parameters from a pair of lensed events by reweighting the posteriors of individual events in a computationally inexpensive way., Comment: 17 pages, 15 figures
Published: 2024

4. Refine3DNet: Scaling Precision in 3D Object Reconstruction from Multi-View RGB Images using Attention

Author: Balakrishnan, Ajith, S, Sreeja, and Shine, Linu
Subjects: Computer Science - Computer Vision and Pattern Recognition, I.5
Abstract: Generating 3D models from multi-view 2D RGB images has gained significant attention, extending the capabilities of technologies like Virtual Reality, Robotic Vision, and human-machine interaction. In this paper, we introduce a hybrid strategy combining CNNs and transformers, featuring a visual auto-encoder with self-attention mechanisms and a 3D refiner network, trained using a novel Joint Train Separate Optimization (JTSO) algorithm. Encoded features from unordered inputs are transformed into an enhanced feature map by the self-attention layer, decoded into an initial 3D volume, and further refined. Our network generates 3D voxels from single or multiple 2D images from arbitrary viewpoints. Performance evaluations using the ShapeNet datasets show that our approach, combined with JTSO, outperforms state-of-the-art techniques in single and multi-view 3D reconstruction, achieving the highest mean intersection over union (IOU) scores, surpassing other models by 4.2% in single-view reconstruction., Comment: ICVGIP-2024, 8 pages
Published: 2024
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5. Perturbations of Black Holes Surrounded by Anisotropic Matter Field

Author: C, Sagar J, R, Karthik, Hegde, Katheek, Ajith, K. M., Punacha, Shreyas, and Kumara, A. Naveena
Subjects: General Relativity and Quantum Cosmology
Abstract: Our research aims to probe the anisotropic matter field around black holes using black hole perturbation theory. Black holes in the universe are usually surrounded by matter or fields, and it is important to study the perturbation and the characteristic modes of a black hole that coexists with such a matter field. In this study, we focus on a family of black hole solutions to Einstein's equations that extend the Reissner-Nordstr\"{o}m spacetime to include an anisotropic matter field. In addition to mass and charge, this type of black hole possesses additional hair due to the negative radial pressure of the anisotropic matter. We investigate the perturbations of the massless scalar and electromagnetic fields and calculate the quasinormal modes (QNMs). We also study the critical orbits around the black hole and their properties to investigate the connection between the eikonal QNMs, black hole shadow radius, and Lyapunov exponent. Additionally, we analyze the grey-body factors and scattering coefficients using the perturbation results. Our findings indicate that the presence of anisotropic matter fields leads to a splitting in the QNM frequencies compared to the Schwarzschild case. This splitting feature is also reflected in the shadow radius, Lyapunov exponent, and grey-body factors., Comment: 35 pages, 10 figures
Published: 2024

6. LSHBloom: Memory-efficient, Extreme-scale Document Deduplication

Author: Khan, Arham, Underwood, Robert, Siebenschuh, Carlo, Babuji, Yadu, Ajith, Aswathy, Hippe, Kyle, Gokdemir, Ozan, Brace, Alexander, Chard, Kyle, and Foster, Ian
Subjects: Computer Science - Machine Learning
Abstract: Deduplication is a major focus for assembling and curating training datasets for large language models (LLM) -- detecting and eliminating additional instances of the same content -- in large collections of technical documents. Unrestrained, duplicates in the training dataset increase training costs and lead to undesirable properties such as memorization in trained models or cheating on evaluation. Contemporary approaches to document-level deduplication are often extremely expensive in both runtime and memory. We propose LSHBloom, an extension to MinhashLSH, which replaces the expensive LSHIndex with lightweight Bloom filters. LSHBloom demonstrates the same deduplication performance as MinhashLSH with only a marginal increase in false positives (as low as 1e-5 in our experiments); demonstrates competitive runtime (270\% faster than MinhashLSH on peS2o); and, crucially, uses just 0.6\% of the disk space required by MinhashLSH to deduplicate peS2o. We demonstrate that this space advantage scales with increased dataset size -- at the extreme scale of several billion documents, LSHBloom promises a 250\% speedup and a 54$\times$ space advantage over traditional MinHashLSH scaling deduplication of text datasets to many billions of documents.
Published: 2024

7. Studying the X-ray absorption characteristics of Centaurus X-3 using nearly 14 years of MAXI/GSC data

Author: Balu, Ajith, Roy, Kinjal, Manikantan, Hemanth, Tamang, Abhisek, and Paul, Biswajit
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Centaurus X-3 is a persistent high-mass X-ray binary with the long-term light curve from the source exhibiting orbit-to-orbit intensity variations with no apparent superorbital periodicity. We used $\sim$13.5 years of MAXI/GSC data to study the long-term behaviour of X-ray absorption caused by the stellar wind from the companion star and any absorbing structures present in the binary. We used orbital-phase-resolved spectroscopy to study the variation in the photoelectric absorption along the line of sight of the source for both the intensity-averaged data and intensity-resolved data after dividing all the data binned with orbital period into three intensity levels. We find an asymmetric variation in the photoelectric absorption along the line of sight across an orbit of the source. The orbital-phase-resolved spectra show a clear increase in photoelectric absorption after $\phi_\text{orb}\sim$ 0.5, which deviates from a spherically symmetric stellar wind model. The flux of Cen X-3 shows significant variation between consecutive orbits. An intensity-resolved spectral analysis of the source was performed, followed by an intensity-resolved and orbital-phase-resolved spectral analysis, which showed that at the medium and high intensity levels, the orbital-phase-resolved photoelectric absorption is slightly asymmetric with respect to mid-phase ($\phi_\text{orb}=$ 0.5). The asymmetry is very pronounced at the lowest intensity level and cannot be explained by a spherically symmetric wind from the companion star. The differences in the orbital phase-dependence of absorption for different intensity levels suggest that the presence of an accretion wake, photoionization wake, or tidal stream is more prominent at a lower intensity level for Centaurus X-3 than at a higher intensity level., Comment: Accepted for publication in Astronomy and Astrophysics
Published: 2024

8. SoK: On Finding Common Ground in Loss Landscapes Using Deep Model Merging Techniques

Author: Khan, Arham, Nief, Todd, Hudson, Nathaniel, Sakarvadia, Mansi, Grzenda, Daniel, Ajith, Aswathy, Pettyjohn, Jordan, Chard, Kyle, and Foster, Ian
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Understanding neural networks is crucial to creating reliable and trustworthy deep learning models. Most contemporary research in interpretability analyzes just one model at a time via causal intervention or activation analysis. Yet despite successes, these methods leave significant gaps in our understanding of the training behaviors of neural networks, how their inner representations emerge, and how we can predictably associate model components with task-specific behaviors. Seeking new insights from work in related fields, here we survey literature in the field of model merging, a field that aims to combine the abilities of various neural networks by merging their parameters and identifying task-specific model components in the process. We analyze the model merging literature through the lens of loss landscape geometry, an approach that enables us to connect observations from empirical studies on interpretability, security, model merging, and loss landscape analysis to phenomena that govern neural network training and the emergence of their inner representations. To systematize knowledge in this area, we present a novel taxonomy of model merging techniques organized by their core algorithmic principles. Additionally, we distill repeated empirical observations from the literature in these fields into characterizations of four major aspects of loss landscape geometry: mode convexity, determinism, directedness, and connectivity. We argue that by improving our understanding of the principles underlying model merging and loss landscape geometry, this work contributes to the goal of ensuring secure and trustworthy machine learning in practice.
Published: 2024

9. A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R. ., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Hardison, A. R., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hart, J., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, R., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Henderson-Sapir, O., Hendry, M., Heng, I. S., Hennes, E., Henshaw, C., Hertog, T., Heurs, M., Hewitt, A. L., Heyns, J., Higginbotham, S., Hild, S., Hill, S., Himemoto, Y., Hirata, N., Hirose, C., Ho, W. C. G., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Holmes, Z. J., Holz, D. E., Honet, L., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsavounidis, E., Katzman, W., Kaushik, R., Kawabe, K., Kawamoto, R., Kazemi, A., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadela, R., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khursheed, M., Khusid, N. M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, Y. -M., Kimball, C., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Klimenko, S., Knee, A. M., Knust, N., Kobayashi, K., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kou, X., Koushik, A., Kouvatsos, N., Kovalam, M., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kruska, K., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuntimaddi, N., Kuroyanagi, S., Kurth, N. J., Kuwahara, S., Kwak, K., Kwan, K., Kwok, J., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Laity, A. H., Lakkis, M. H., Lalande, E., Lalleman, M., Lalremruati, P. C., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Lawrence, M. N., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Jean, M. Le, Lemaître, A., Lenti, M., Leonardi, M., Lequime, M., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levin, S. E., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Li, Z., Lihos, A., Lin, C-Y., Lin, C. -Y., Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Lin, Y. -C., Linde, F., Linker, S. D., Littenberg, T. 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K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Ruhama, N., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Saha, S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. 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Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchiyama, T., Udall, R. 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A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables
Published: 2024

10. Mitigating Memorization In Language Models

Author: Sakarvadia, Mansi, Ajith, Aswathy, Khan, Arham, Hudson, Nathaniel, Geniesse, Caleb, Chard, Kyle, Yang, Yaoqing, Foster, Ian, and Mahoney, Michael W.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training data can be problematic, for example, when data are private or sensitive. In this work, we investigate methods to mitigate memorization: three regularizer-based, three finetuning-based, and eleven machine unlearning-based methods, with five of the latter being new methods that we introduce. We also introduce TinyMem, a suite of small, computationally-efficient LMs for the rapid development and evaluation of memorization-mitigation methods. We demonstrate that the mitigation methods that we develop using TinyMem can successfully be applied to production-grade LMs, and we determine via experiment that: regularizer-based mitigation methods are slow and ineffective at curbing memorization; fine-tuning-based methods are effective at curbing memorization, but overly expensive, especially for retaining higher accuracies; and unlearning-based methods are faster and more effective, allowing for the precise localization and removal of memorized information from LM weights prior to inference. We show, in particular, that our proposed unlearning method BalancedSubnet outperforms other mitigation methods at removing memorized information while preserving performance on target tasks.
Published: 2024

11. Comments on 'Privacy-Enhanced Federated Learning Against Poisoning Adversaries'

Author: Schneider, Thomas, Suresh, Ajith, and Yalame, Hossein
Subjects: Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract: In August 2021, Liu et al. (IEEE TIFS'21) proposed a privacy-enhanced framework named PEFL to efficiently detect poisoning behaviours in Federated Learning (FL) using homomorphic encryption. In this article, we show that PEFL does not preserve privacy. In particular, we illustrate that PEFL reveals the entire gradient vector of all users in clear to one of the participating entities, thereby violating privacy. Furthermore, we clearly show that an immediate fix for this issue is still insufficient to achieve privacy by pointing out multiple flaws in the proposed system. Note: Although our privacy issues mentioned in Section II have been published in January 2023 (Schneider et. al., IEEE TIFS'23), several subsequent papers continued to reference Liu et al. (IEEE TIFS'21) as a potential solution for private federated learning. While a few works have acknowledged the privacy concerns we raised, several of subsequent works either propagate these errors or adopt the constructions from Liu et al. (IEEE TIFS'21), thereby unintentionally inheriting the same privacy vulnerabilities. We believe this oversight is partly due to the limited visibility of our comments paper at TIFS'23 (Schneider et. al., IEEE TIFS'23). Consequently, to prevent the continued propagation of the flawed algorithms in Liu et al. (IEEE TIFS'21) into future research, we also put this article to an ePrint., Comment: Published at IEEE Transactions on Information Forensics and Security'23
Published: 2024
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12. Worldline Formalism, Eikonal Expansion and the Classical Limit of Scattering Amplitudes

Author: Ajith, Siddarth, Du, Yuchen, Rajagopal, Ravisankar, and Vaman, Diana
Subjects: High Energy Physics - Theory
Abstract: We revisit the fundamentals of two different methods for calculating classical observables: the eikonal method, which is a scattering amplitude-based method, and the worldline quantum field theory (WQFT) method. The latter has been considered an extension of the worldline effective field theory. We show that the eikonal and WQFT methods are equivalent and that calculations can be translated freely between them. Concretely, we focus on 2-into-2 scattering processes mediated by massless force carriers. On the one hand, taking the classical limit of the QFT scattering amplitude leads to the eikonal method. On the other hand, since in the classical limit the scattering particles are almost on-shell throughout the scattering process, the worldline, a first quantized formalism, is the most efficient framework to study the scattering amplitude. This is an alternate but equivalent formalism to the quantum field theoretic (QFT) framework. By taking the classical limit of the scattering amplitude computed in the worldline, we can derive the WQFT rules of Mogull, Plefka and Steinhoff. In WQFT, the Feynman diagrams are reorganized into a new set of diagrams that facilitate the $\hbar$ expansion. Unlike the QFT eikonal method, which works recursively in identifying the eikonal phase, the worldline-based computation allows to target and systematically extract the classical contributions directly through a specific set of WQFT diagrams. In worldline formalism the perturbative expansion of the scattering amplitude is naturally organized in diagrams which factorize (reducible) and diagrams which are new to that order (irreducible), in a one-to-one map with the structure of the amplitude in the eikonal method. This opened up the possibility to investigate and prove the conjectured exponentiation of the eikonal phase in arXiv: 2409.12895., Comment: 75 pages
Published: 2024

13. A theory of generalised coordinates for stochastic differential equations

Author: Da Costa, Lancelot, Da Costa, Nathaël, Heins, Conor, Medrano, Johan, Pavliotis, Grigorios A., Parr, Thomas, Meera, Ajith Anil, and Friston, Karl
Subjects: Mathematics - Probability, Mathematics - Dynamical Systems, Statistics - Methodology
Abstract: Stochastic differential equations are ubiquitous modelling tools in physics and the sciences. In most modelling scenarios, random fluctuations driving dynamics or motion have some non-trivial temporal correlation structure, which renders the SDE non-Markovian; a phenomenon commonly known as ``colored'' noise. Thus, an important objective is to develop effective tools for mathematically and numerically studying (possibly non-Markovian) SDEs. In this report, we formalise a mathematical theory for analysing and numerically studying SDEs based on so-called `generalised coordinates of motion'. Like the theory of rough paths, we analyse SDEs pathwise for any given realisation of the noise, not solely probabilistically. Like the established theory of Markovian realisation, we realise non-Markovian SDEs as a Markov process in an extended space. Unlike the established theory of Markovian realisation however, the Markovian realisations here are accurate on short timescales and may be exact globally in time, when flows and fluctuations are analytic. This theory is exact for SDEs with analytic flows and fluctuations, and is approximate when flows and fluctuations are differentiable. It provides useful analysis tools, which we employ to solve linear SDEs with analytic fluctuations. It may also be useful for studying rougher SDEs, as these may be identified as the limit of smoother ones. This theory supplies effective, computationally straightforward methods for simulation, filtering and control of SDEs; amongst others, we re-derive generalised Bayesian filtering, a state-of-the-art method for time-series analysis. Looking forward, this report suggests that generalised coordinates have far-reaching applications throughout stochastic differential equations., Comment: 38 pages of main, 45 pages including TOC, Appendix and references
Published: 2024

14. Worldline Proof of Eikonal Exponentiation

Author: Du, Yuchen, Ajith, Siddarth, Rajagopal, Ravisankar, and Vaman, Diana
Subjects: High Energy Physics - Theory
Abstract: In this paper, working in the eikonal approximation, we present a proof for the exponentiation of the 2-body eikonal phase to {\it all orders in the eikonal expansion}, for scalar particles interacting electromagnetically or gravitationally. The proof is based on the worldline formalism, which is an alternative, first quantized method to the standard QFT calculation of the scattering amplitude. We show that in the worldline formalism the 2-body scattering amplitude written in impact parameter space naturally factorizes at each loop order. This factorization is responsible for the exponentiation of the eikonal phase, a result which was anticipated in the work of Mogull, Plefka, and Steinhoff [2010.02865 [hep-th]]., Comment: 40 pages
Published: 2024

15. Spinning LQG black hole as a particle accelerator

Author: Suresh, Ullas P., R, Karthik, Ajith, K. M., Hegde, Kartheek, Punacha, Shreyas, and Kumara, A. Naveena
Subjects: General Relativity and Quantum Cosmology
Abstract: We demonstrate that the spinning LQG black hole can act as a cosmic particle accelerator. The LQG solution is singularity-free and can possess spin greater than that of a Kerr black hole. The additional black hole hair, arising from quantum effects, significantly influences the particle dynamics around the black hole. Under suitable physical conditions, the center-of-mass energy can grow arbitrarily high during the collision of two generic particles in the spacetime of an extremal black hole. In the non-extremal case, there exists a finite upper bound on the center-of-mass energy, the maximum value of which depends on the LQG parameter. These results are particularly interesting from an astrophysical perspective, especially in the context of probing Planck-scale physics., Comment: 19 pages, 6 figures
Published: 2024

16. Constraining binary mergers in AGN disks using the non-observation of lensed gravitational waves

Author: Leong, Samson H. W., Janquart, Justin, Sharma, Aditya Kumar, Martens, Paul, Ajith, Parameswaran, and Hannuksela, Otto A.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies, General Relativity and Quantum Cosmology
Abstract: The dense and dynamic environments within active galactic nuclei (AGN) accretion disks may serve as prolific birthplaces for binary black holes (BBHs) and one possible origin for some of the BBHs detected by gravitational-wave (GW) observatories. We show that a considerable fraction of the BBH in AGN disks will be strongly lensed by the central supermassive black hole (SMBH). Thus, the non-observation of lensed GW signals can be used to constrain the fraction of BBH binaries residing in AGN disks. The non-detection of lensing with current ${\cal O}(100)$ detections will be sufficient to start placing constraints on the fraction of BBHs living within accretion disks near the SMBH. In the next-generation detectors era, with ${\cal O}(10^5)$ BBH observations and no lensed events, we will be able to rule out most migration traps as dominant birthplaces of BBH mergers; moreover, we will be able to constrain the minimum size of the accretion disk. On the other hand, should AGNs constitute a major formation channel, lensed events from AGNs will become prominent in the future., Comment: 8 pages, 4 figures
Published: 2024

17. Probing the nature of dark matter using strongly lensed gravitational waves from binary black holes

Author: Jana, Souvik, Kapadia, Shasvath J., Venumadhav, Tejaswi, More, Surhud, and Ajith, Parameswaran
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Next-generation ground-based gravitational-wave (GW) detectors are expected to detect millions of binary black hole mergers during their operation period. A small fraction ($\sim 0.1 - 1\%$) of them will be strongly lensed by intervening galaxies and clusters, producing multiple copies of the GW signals. The expected number of lensed events and the distribution of the time delay between lensed images will depend on the mass distribution of the lenses at different redshifts. Warm dark matter or fuzzy dark matter models predict lower abundances of small mass dark matter halos as compared to the standard cold dark matter. This will result in a reduction in the number of strongly lensed GW events, especially at small time delays. Using the number of lensed events and the lensing time delay distribution, we can put a lower bound on the mass of the warm/fuzzy dark matter particle from a catalog of lensed GW events. The expected bounds from GW strong lensing from next-generation detectors are significantly better than the current constraints., Comment: 8 pages, 7 figures
Published: 2024

18. An 'ultimate' coupled cluster method based entirely on $T_2$

Author: Windom, Zachary W., Perera, Ajith, and Bartlett, Rodney J.
Subjects: Physics - Chemical Physics
Abstract: Electronic structure methods built around double-electron excitations have a rich history in quantum chemistry. However, it seems to be the case that such methods are only suitable in particular situations and are not naturally equipped to simultaneously handle the variety of electron correlations that might be present in chemical systems. To this end, the current work seeks a computationally efficient, low-rank, "ultimate" coupled cluster method based exclusively on $T_2$ and its products which can effectively emulate more "complete" methods that explicitly consider higher-rank, $T_{2m}$ operators. We introduce a hierarchy of methods designed to systematically account for higher, even order cluster operators - like $T_4, T_6, \cdots, T_{2m}$ - by invoking tenets of the factorization theorem of perturbation theory and expectation-value coupled cluster theory. It is shown that each member within this methodological hierarchy is defined such that both the wavefunction and energy are correct through some order in many-body perturbation theory (MBPT), and can be extended up to arbitrarily high orders in $T_2$. The efficacy of such approximations are determined by studying the potential energy surface of several prototypical systems that are chosen to represent both non-dynamic, static, and dynamic correlation regimes. We find that the proposed hierarchy of augmented $T_2$ methods essentially reduce to standard CCD for problems where dynamic electron correlations dominate, but offer improvements in situations where non-dynamic and static correlations become relevant. A notable highlight of this work is that the cheapest methods in this hierarchy - which are correct through fifth-order in MBPT - consistently emulate the behavior of the $\mathcal{O}(N^{10})$ CCDQ method, yet only require a $\mathcal{O}(N^{6})$ algorithm by virtue of factorized intermediates.
Published: 2024

19. LitSearch: A Retrieval Benchmark for Scientific Literature Search

Author: Ajith, Anirudh, Xia, Mengzhou, Chevalier, Alexis, Goyal, Tanya, Chen, Danqi, and Gao, Tianyu
Subjects: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Digital Libraries, Computer Science - Machine Learning
Abstract: Literature search questions, such as "Where can I find research on the evaluation of consistency in generated summaries?" pose significant challenges for modern search engines and retrieval systems. These questions often require a deep understanding of research concepts and the ability to reason across entire articles. In this work, we introduce LitSearch, a retrieval benchmark comprising 597 realistic literature search queries about recent ML and NLP papers. LitSearch is constructed using a combination of (1) questions generated by GPT-4 based on paragraphs containing inline citations from research papers and (2) questions manually written by authors about their recently published papers. All LitSearch questions were manually examined or edited by experts to ensure high quality. We extensively benchmark state-of-the-art retrieval models and also evaluate two LLM-based reranking pipelines. We find a significant performance gap between BM25 and state-of-the-art dense retrievers, with a 24.8% absolute difference in recall@5. The LLM-based reranking strategies further improve the best-performing dense retriever by 4.4%. Additionally, commercial search engines and research tools like Google Search perform poorly on LitSearch, lagging behind the best dense retriever by up to 32 recall points. Taken together, these results show that LitSearch is an informative new testbed for retrieval systems while catering to a real-world use case., Comment: Accepted by EMNLP 2024. Dataset and code are available at https://github.com/princeton-nlp/LitSearch
Published: 2024

20. Modern Computing: Vision and Challenges

Author: Gill, Sukhpal Singh, Wu, Huaming, Patros, Panos, Ottaviani, Carlo, Arora, Priyansh, Pujol, Victor Casamayor, Haunschild, David, Parlikad, Ajith Kumar, Cetinkaya, Oktay, Lutfiyya, Hanan, Stankovski, Vlado, Li, Ruidong, Ding, Yuemin, Qadir, Junaid, Abraham, Ajith, Ghosh, Soumya K., Song, Houbing Herbert, Sakellariou, Rizos, Rana, Omer, Rodrigues, Joel J. P. C., Kanhere, Salil S., Dustdar, Schahram, Uhlig, Steve, Ramamohanarao, Kotagiri, and Buyya, Rajkumar
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress., Comment: Preprint submitted to Telematics and Informatics Reports, Elsevier (2024)
Published: 2024
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21. Strong-lensing cosmography using third-generation gravitational-wave detectors

Author: Jana, Souvik, Kapadia, Shasvath J, Venumadhav, Tejaswi, More, Surhud, and Ajith, Parameswaran
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present a detailed exposition of a statistical method for estimating cosmological parameters from the observation of a large number of strongly lensed binary-black-hole (BBH) mergers observable by next (third) generation (XG) gravitational-wave (GW) detectors. This method, first presented in Jana (2023 Phys. Rev. Lett. 130 261401), compares the observed number of strongly lensed GW events and their time delay distribution (between lensed images) with observed events to infer cosmological parameters. We show that the precision of the estimation of the cosmological parameters does not have a strong dependance on the assumed BBH redshift distribution model. Using the large number of unlensed mergers, XG detectors are expected to measure the BBH redshift distribution with sufficient precision for the cosmological inference. However, a biased inference of the BBH redshift distribution will bias the estimation of cosmological parameters. An incorrect model for the distribution of lens properties can also lead to a biased cosmological inference. However, Bayesian model selection can assist in selecting the right model from a set of available parametric models for the lens distribution. We also present a way to incorporate the effect of contamination in the data due to the limited efficiency of lensing identification methods, so that it will not bias the cosmological inference., Comment: 17 pages, 5 figures
Published: 2024
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22. Constraints on compact dark matter from the non-observation of gravitational-wave strong lensing

Author: Barsode, A., Kapadia, S. J., and Ajith, P.
Subjects: General Relativity and Quantum Cosmology
Abstract: We use the non-observation of strong lensing of gravitational waves (GWs) in the first three observation runs of LIGO-Virgo detectors to constrain the fraction of dark matter in the form of compact objects in the mass range $10^{6}-10^{9}~{\mathrm{M}_\odot}$. Using a Bayesian formalism supplemented by astrophysical simulations of strong lensing of GWs, we constrain the compact dark matter fraction to $\lesssim 0.4-0.6$ with currently available data and show that they may get significantly tighter in the future. We find that multiple lensing -- i.e., GWs getting deflected by multiple compact objects on their way to us -- is possible. By ignoring this, we underestimate the constraints by a few percent., Comment: 8 pages, 10 figures
Published: 2024

23. Chow ring of the stack of plane nodal curves

Author: Cela, Alessio, Kumaran, Ajith Urundolil, and Yan, Xiaohan
Subjects: Mathematics - Algebraic Geometry, Mathematics - Algebraic Topology, 14H10, 14C15, 14D23, 55R80
Abstract: We compute the Chow ring of the moduli stack of planar nodal curves of fixed degree and express it in terms of tautological classes. Along the way, we extend Vial's results on Chow groups of Brauer-Severi varieties to $G$-equivariant settings., Comment: Appendix by Alexis Aumonier. 25 pages, comments welcome!
Published: 2024

24. SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation

Author: Wan, Yuwei, Liu, Yixuan, Ajith, Aswathy, Grazian, Clara, Hoex, Bram, Zhang, Wenjie, Kit, Chunyu, Xie, Tong, and Foster, Ian
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language models (LLMs). SciQAG consists of a QA generator and a QA evaluator, which work together to extract diverse and research-level questions and answers from scientific papers. Utilizing this framework, we construct a large-scale, high-quality, open-ended science QA dataset containing 188,042 QA pairs extracted from 22,743 scientific papers across 24 scientific domains. We also introduce SciQAG-24D, a new benchmark task designed to evaluate the science question-answering ability of LLMs. Extensive experiments demonstrate that fine-tuning LLMs on the SciQAG dataset significantly improves their performance on both open-ended question answering and scientific tasks. To foster research and collaboration, we make the datasets, models, and evaluation codes publicly available, contributing to the advancement of science question answering and developing more interpretable and reasoning-capable AI systems.
Published: 2024

25. Nonradial instabilities in anisotropic neutron stars

Author: Lau, Shu Yan, Ajith, Siddarth, Guedes, Victor, and Yagi, Kent
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: Non-radial oscillation modes of a neutron star possess valuable information about its internal structure and nuclear physics. Starting from the quadrupolar order, such modes under general relativity are known as quasi-normal modes since they dissipate energy through gravitational radiation and their frequencies are complex. The stability of these modes is governed by the sign of the imaginary part of the frequency, which determines whether the mode would decay or grow over time. In this Letter, we develop a fully consistent framework in general relativity to study quasi-normal modes of neutron stars with anisotropic pressure, whose motivation includes strong internal magnetic fields and non-vanishing shear or viscosity. We employ parametrized models for the anisotropy and solve the perturbed Einstein field equations numerically. We find that, unlike the case for isotropic neutron stars, the imaginary parts of some of the pressure ($p$-)modes flip signs as the degree of anisotropy deviates from zero, depicting a transition from stable modes to unstable modes. This finding indicates that some anisotropic neutron star models are unstable, potentially restricting the form of sustained anisotropy., Comment: 8+6 pages, 2+1 figures, re-submitted to PRD
Published: 2024
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26. LWIRPOSE: A novel LWIR Thermal Image Dataset and Benchmark

Author: Upadhyay, Avinash, Dhupar, Bhipanshu, Sharma, Manoj, Shukla, Ankit, and Abraham, Ajith
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Human pose estimation faces hurdles in real-world applications due to factors like lighting changes, occlusions, and cluttered environments. We introduce a unique RGB-Thermal Nearly Paired and Annotated 2D Pose Dataset, comprising over 2,400 high-quality LWIR (thermal) images. Each image is meticulously annotated with 2D human poses, offering a valuable resource for researchers and practitioners. This dataset, captured from seven actors performing diverse everyday activities like sitting, eating, and walking, facilitates pose estimation on occlusion and other challenging scenarios. We benchmark state-of-the-art pose estimation methods on the dataset to showcase its potential, establishing a strong baseline for future research. Our results demonstrate the dataset's effectiveness in promoting advancements in pose estimation for various applications, including surveillance, healthcare, and sports analytics. The dataset and code are available at https://github.com/avinres/LWIRPOSE, Comment: Submitted in ICIP2024
Published: 2024

27. The Lunar Gravitational-wave Antenna: Mission Studies and Science Case

Author: Ajith, Parameswaran, Seoane, Pau Amaro, Sedda, Manuel Arca, Arcodia, Riccardo, Badaracco, Francesca, Banerjee, Biswajit, Belgacem, Enis, Benetti, Giovanni, Benetti, Stefano, Bobrick, Alexey, Bonforte, Alessandro, Bortolas, Elisa, Braito, Valentina, Branchesi, Marica, Burrows, Adam, Cappellaro, Enrico, Della Ceca, Roberto, Chakraborty, Chandrachur, Subrahmanya, Shreevathsa Chalathadka, Coughlin, Michael W., Covino, Stefano, Derdzinski, Andrea, Doshi, Aayushi, Falanga, Maurizio, Foffa, Stefano, Franchini, Alessia, Frigeri, Alessandro, Futaana, Yoshifumi, Gerberding, Oliver, Gill, Kiranjyot, Di Giovanni, Matteo, Giudice, Ines Francesca, Giustini, Margherita, Gläser, Philipp, Harms, Jan, van Heijningen, Joris, Iacovelli, Francesco, Kavanagh, Bradley J., Kawamura, Taichi, Kenath, Arun, Keppler, Elisabeth-Adelheid, Kobayashi, Chiaki, Komatsu, Goro, Korol, Valeriya, Krishnendu, N. V., Kumar, Prayush, Longo, Francesco, Maggiore, Michele, Mancarella, Michele, Maselli, Andrea, Mastrobuono-Battisti, Alessandra, Mazzarini, Francesco, Melandri, Andrea, Melini, Daniele, Menina, Sabrina, Miniutti, Giovanni, Mitra, Deeshani, Morán-Fraile, Javier, Mukherjee, Suvodip, Muttoni, Niccolò, Olivieri, Marco, Onori, Francesca, Papa, Maria Alessandra, Patat, Ferdinando, Perali, Andrea, Piran, Tsvi, Piranomonte, Silvia, Pol, Alberto Roper, Pookkillath, Masroor C., Prasad, R., Prasad, Vaishak, De Rosa, Alessandra, Chowdhury, Sourav Roy, Serafinelli, Roberto, Sesana, Alberto, Severgnini, Paola, Stallone, Angela, Tissino, Jacopo, Tkalčić, Hrvoje, Tomasella, Lina, Toscani, Martina, Vartanyan, David, Vignali, Cristian, Zaccarelli, Lucia, Zeoli, Morgane, and Zuccarello, Luciano
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Lunar Gravitational-wave Antenna (LGWA) is a proposed array of next-generation inertial sensors to monitor the response of the Moon to gravitational waves (GWs). Given the size of the Moon and the expected noise produced by the lunar seismic background, the LGWA would be able to observe GWs from about 1 mHz to 1 Hz. This would make the LGWA the missing link between space-borne detectors like LISA with peak sensitivities around a few millihertz and proposed future terrestrial detectors like Einstein Telescope or Cosmic Explorer. In this article, we provide a first comprehensive analysis of the LGWA science case including its multi-messenger aspects and lunar science with LGWA data. We also describe the scientific analyses of the Moon required to plan the LGWA mission.
Published: 2024

28. Observation of Gravitational Waves from the Coalescence of a $2.5\text{-}4.5~M_\odot$ Compact Object and a Neutron Star

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akçay, S., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentara, I., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Char, P., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chattopadhyay, D., Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. 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A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., Stevenson, S., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong, H., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Observatory. The primary component of the source has a mass less than $5~M_\odot$ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of $55^{+127}_{-47}~\text{Gpc}^{-3}\,\text{yr}^{-1}$ for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star-black hole merger, GW230529_181500-like sources constitute about 60% of the total merger rate inferred for neutron star-black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star-black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap., Comment: 45 pages (10 pages author list, 13 pages main text, 1 page acknowledgements, 13 pages appendices, 8 pages bibliography), 17 figures, 16 tables. Update to match version published in The Astrophysical Journal Letters. Data products available from https://zenodo.org/records/10845779
Published: 2024
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29. Microstructural, optical, dielectric, and magnetic properties of multifunctional Zn1-x FexO nanoparticles

Author: Parveen, Arzoo, Thakur, Priyanka, Sharma, Navdeep, Kumar, Ajith S., Lekha, C. S. Chitra, Kumar, Pawan, and Lal, Madan
Published: 2024
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30. Intensity inhomogeneity correction in brain MRI: a systematic review of techniques, current trends and future challenges

Author: Mishro, Pranaba K., Agrawal, Sanjay, Panda, Rutuparna, Dora, Lingraj, and Abraham, Ajith
Published: 2024
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31. A Framework to Generate Shape Optimised Profiles for a Cambered Airfoil Approaching Ground

Author: Narayanan, Jithin P. and Arumugham-Achari, Ajith Kumar
Published: 2024
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32. Comparative Efficacy of Flow Diverter Devices in the Treatment of Carotid Sidewall Intracranial Aneurysms: a Retrospective, Multicenter Study

Author: Dmytriw, Adam A., Salim, Hamza Adel, Musmar, Basel, Cancelliere, Nicole M., Griessenauer, Christoph J., Regenhardt, Robert W., Jones, Jesse, Tutino, Vincent, Hasan, Zuha, Limbucci, Nicola, Lay, Sovann V., Spears, Julian, Rabinov, James D., Harrigan, Mark R., Siddiqui, Adnan H., Levy, Elad I., Stapleton, Christopher J., Renieri, Leonardo, Cognard, Christophe, Shaikh, Hamza, Kühn, Anna Luisa, Möhlenbruch, Markus A., Tjoumakaris, Stavropoula I., Jabbour, Pascal, Taussky, Philipp, Settecase, Fabio, Heran, Manraj K. S., Nguyen, Anh, Volders, David, Harker, Pablo, Devia, Diego A., Puri, Ajit S., Psychogios, Marios, Puentes, Juan C., Leone, Giuseppe, Buono, Giuseppe, Tarantino, Margherita, Muto, Mario, Briganti, Francesco, Dalal, Shamsher, Gontu, Vamsi, Alcedo Guardia, Rodolfo E., Vicenty-Padilla, Juan C., Brouwer, Patrick, Schmidt, Matthias H., Schirmer, Clemens, Pickett, Gwynedd E., Andersson, Tommy, Söderman, Michael, Marotta, Thomas R., Cuellar-Saenz, Hugo, Thomas, Ajith J., Patel, Aman B., Mendes Pereira, Vitor, and Adeeb, Nimer
Published: 2024
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33. Functional Melamine-Formaldehyde Cross-linked Cellulose Nanofiber Based Aerogels with Excellent Flame Retardancy for Thermal-Acoustic Insulation Applications

Author: Gopakumar, Deepu A., Baby, Aloshy, Mathew, Ajith, Pai, Avinash R, Basheer, Jishana, Seantier, Bastien, and George, Jinu Jacob
Published: 2024
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34. Outcomes with General Anesthesia Compared to Conscious Sedation for Endovascular Treatment of Medium Vessel Occlusions: Results of an International Multicentric Study

Author: Radu, Răzvan Alexandru, Costalat, Vincent, Romoli, Michele, Musmar, Basel, Siegler, James E., Ghozy, Sherief, Khalife, Jane, Salim, Hamza, Shaikh, Hamza, Adeeb, Nimer, Cuellar-Saenz, Hugo H., Thomas, Ajith J., Kadirvel, Ramanathan, Abdalkader, Mohamad, Klein, Piers, Nguyen, Thanh N., Heit, Jeremy J., Regenhardt, Robert W., Bernstock, Joshua D., Patel, Aman B., Rabinov, James D., Stapleton, Christopher J., Cancelliere, Nicole M., Marotta, Thomas R., Mendes Pereira, Vitor, El Naamani, Kareem, Amllay, Abdelaziz, Tjoumakaris, Stavropoula I., Jabbour, Pascal, Meyer, Lukas, Fiehler, Jens, Faizy, Tobias D., Guerreiro, Helena, Dusart, Anne, Bellante, Flavio, Forestier, Géraud, Rouchaud, Aymeric, Mounayer, Charbel, Kühn, Anna Luisa, Puri, Ajit S., Dyzmann, Christian, Kan, Peter T., Colasurdo, Marco, Marnat, Gaultier, Berge, Jérôme, Barreau, Xavier, Sibon, Igor, Nedelcu, Simona, Henninger, Nils, Ota, Takahiro, Dofuku, Shogo, Yeo, Leonard L. L., Tan, Benjamin YQ, Gopinathan, Anil, Martinez-Gutierrez, Juan Carlos, Salazar-Marioni, Sergio, Sheth, Sunil, Renieri, Leonardo, Capirossi, Carolina, Mowla, Ashkan, Chervak, Lina M., Vagal, Achala, Khandelwal, Priyank, Biswas, Arundhati, Clarençon, Frédéric, Elhorany, Mahmoud, Premat, Kevin, Valente, Iacopo, Pedicelli, Alessandro, Alexandre, Andrea M., Filipe, João Pedro, Varela, Ricardo, Quintero-Consuegra, Miguel, Gonzalez, Nestor R., YMD, Markus A., Jesser, Jessica, Weyland, Charlotte, ter Schiphorst, Adrien, Yedavalli, Vivek, Harker, Pablo, Aziz, Yasmin, Gory, Benjamin, Paul Stracke, Christian, Hecker, Constantin, Killer-Oberpfalzer, Monika, Griessenauer, Christoph J., Hsieh, Cheng-Yang, Liebeskind, David S., Tancredi, Illario, Fahed, Robert, Lubicz, Boris, Essibayi, Muhammed Amir, Baker, Amanda, Altschul, David, Scarcia, Luca, Kalsoum, Erwah, Dmytriw, Adam A., and Guenego, Adrien
Published: 2024
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35. Comparative Performance Analysis of Gr and MoS2 Solid Lubricants During High-Temperature Dry Sliding Wear Behavior of Al2618-Based Hybrid MMCs

Author: Divakar, M. H., Basavarajappa, S., and Joshi, Ajith G.
Published: 2024
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36. Active Versus Passive Learning in Large-Group Sessions in Medical School: A Randomized Cross-Over Trial Investigating Effects on Learning and the Feeling of Learning

Author: Boedeker, Peter, Schlingmann, Tobias, Kailin, Joshua, Nair, Ajith, Foldes, Cara, Rowley, David, Salciccioli, Katherine, Maag, Ronald, Moreno, Nancy, and Ismail, Nadia
Published: 2024
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37. Neurological, psychological, psychosocial complications of long-COVID and their management

Author: Narayanan, Sareesh Naduvil, Padiyath, Sreeshma, Chandrababu, Krishnapriya, Raj, Lima, P. S., Baby Chakrapani, Ninan, George Abraham, Sivadasan, Ajith, Jacobs, Alexander Ryan, Li, Yan Wa, and Bhaskar, Anand
Published: 2024
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38. Fish Parasitic Cymothoid, Nerocila phaiopleura Bleeker, 1857 (Crustacea: Isopoda) Infestation in Delicate Round Herring, Spratelloides delicatulus from Lakshadweep Islands

Author: Kodeeswaran, Paramasivam, Vigneshwaran, P., Rekha, M. U., Dhinakaran, A., and Ajith Kumar, T. T.
Published: 2024
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39. Sappan wood extraction for intelligent gelatin packaging films: a review on implementing green packaging solutions

Author: Ajith, Pranav P., Bashir, Omar, Kaur, Kulwinder, Amin, Tawheed, Shams, Rafeeya, and Dash, Kshirod Kumar
Published: 2024
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40. Comparative tracking of Turbinaria conoides and Gelidium elegans for enhanced bioethanol production

Author: Ravichandran, M., Merlin, A. D., Muthulaxmi, V., Sowndariya, M., Ajith Kumar, T. T., Manoharadas, S., Ahmad, N., Wahab, R., Tamimi, J. A. I.-, and Dineshkumar, R.
Published: 2024
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41. Enhancing quasi solid-state dye-sensitized solar cell performance using mixed-polymer gel electrolytes: the influence of low and high molar weight polymers

Author: Bandara, T. M. W. J., Rajakarunarathne, R. D. M. A. C. B., Wickramasinghe, H. M. N., DeSilva, L. Ajith, Chandrika, R. P., and Yusuf, S. N. F.
Published: 2024
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42. Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction

Author: Singh, Ajay, Dhanaraj, Rajesh Kumar, Kumar, Santosh, and Abraham, Ajith
Published: 2024
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43. A New Observation about the Symbiotic Relationship of Clownfish with its Host Anemones: Documentation in Captivity

Author: S, Jeyaprakashsabari, R, Vinoth, A, Kathirvelpandian, TT, Ajith Kumar, and Sarkar, Uttam Kumar
Published: 2024
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44. Acute Respiratory Syndrome Mimicking Shipping Sickness in Theileria buffeli Infected Buffalo Calf

Author: Raina, Varghese, Ajith, Y., Adithya, Sasi, Anandu, S., Athira, N., Athira, K. S., Preena, P., Vinodkumar, K., Manju, K. Mathew, Mahima, C. S., Akshaya, Jose, Alby, B. Bruce, Nafis, Ayshin, Arshana, A., Tini, Joby, Anandu, P. Asok, Devi, Gopinath, Arun, George, and Ajithkumar, S.
Published: 2024
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45. Chi-square mutated quantum-behaved PSO algorithm for combined economic and emission dispatch

Author: Muraleedharan, Swathy, Babu, C. A., and Kumar Sasidharanpillai, Ajith
Published: 2024
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46. Skin lesion classification using transfer learning

Author: Nivedhitha, G., Kalpana, P., Sidthik, A. Sheik, Rani, V. Anusha, Singh, Ajith B., and Rajagopal, R.
Published: 2024
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47. Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models

Author: Rikhari, Himanshu, Baidya Kayal, Esha, Ganguly, Shuvadeep, Sasi, Archana, Sharma, Swetambri, Antony, Ajith, Rangarajan, Krithika, Bakhshi, Sameer, Kandasamy, Devasenathipathy, and Mehndiratta, Amit
Published: 2024
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48. Trend analysis of environmental radioactivity levels around Kaiga Generating Station, India

Author: Jain, Sanyam, Ajith, T. L., Joshi, R. M., Reji, T. K., James, J. P., Vishnu, M. S., Saradhi, I. V., and Kumar, A. Vinod
Published: 2024
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49. Classification of Sub-Watersheds with Respect to Flooding Susceptibility in a Tropical River Basin Using Multi Criteria Approach Based on VIKOR

Author: Nair, Ajith G., Kumar, K. Sunil, and Sabu, Sonu V.
Published: 2024
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50. Unraveling the etiology of shrimp diseases: a review through the perspectives of gut microbial dynamics

Author: Murugan, Raghul, Priya, P. Snega, Boopathi, Seenivasan, Haridevamuthu, B., Kumar, Thipramalai Thankappan Ajith, and Arockiaraj, Jesu
Published: 2024
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