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1. Hierarchical Preference Optimization: Learning to achieve goals via feasible subgoals prediction

Author: Singh, Utsav, Chakraborty, Souradip, Suttle, Wesley A., Sadler, Brian M., Sahu, Anit Kumar, Shah, Mubarak, Namboodiri, Vinay P., and Bedi, Amrit Singh
Subjects: Computer Science - Machine Learning
Abstract: This work introduces Hierarchical Preference Optimization (HPO), a novel approach to hierarchical reinforcement learning (HRL) that addresses non-stationarity and infeasible subgoal generation issues when solving complex robotic control tasks. HPO leverages maximum entropy reinforcement learning combined with token-level Direct Preference Optimization (DPO), eliminating the need for pre-trained reference policies that are typically unavailable in challenging robotic scenarios. Mathematically, we formulate HRL as a bi-level optimization problem and transform it into a primitive-regularized DPO formulation, ensuring feasible subgoal generation and avoiding degenerate solutions. Extensive experiments on challenging robotic navigation and manipulation tasks demonstrate impressive performance of HPO, where it shows an improvement of up to 35% over the baselines. Furthermore, ablation studies validate our design choices, and quantitative analyses confirm the ability of HPO to mitigate non-stationarity and infeasible subgoal generation issues in HRL.
Published: 2024

2. Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking

Author: Chib, Pranav Singh and Singh, Pravendra
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Robotics
Abstract: Pedestrian trajectory prediction is crucial for several applications such as robotics and self-driving vehicles. Significant progress has been made in the past decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past movements and predict future trajectories. However, these datasets and methods typically assume that the observed trajectory sequence is complete, ignoring real-world issues such as sensor failure, occlusion, and limited fields of view that can result in missing values in observed trajectories. To address this challenge, we present TrajImpute, a pedestrian trajectory prediction dataset that simulates missing coordinates in the observed trajectory, enhancing real-world applicability. TrajImpute maintains a uniform distribution of missing data within the observed trajectories. In this work, we comprehensively examine several imputation methods to reconstruct the missing coordinates and benchmark them for imputing pedestrian trajectories. Furthermore, we provide a thorough analysis of recent trajectory prediction methods and evaluate the performance of these models on the imputed trajectories. Our experimental evaluation of the imputation and trajectory prediction methods offers several valuable insights. Our dataset provides a foundational resource for future research on imputation-aware pedestrian trajectory prediction, potentially accelerating the deployment of these methods in real-world applications. Publicly accessible links to the datasets and code files are available at https://github.com/Pranav-chib/TrajImpute., Comment: Accepted at NeurIPS 2024
Published: 2024

3. KALAM: toolKit for Automating high-Level synthesis of Analog computing systeMs

Author: Nandi, Ankita, Gandhi, Krishil, Singh, Mahendra Pratap, Chakrabartty, Shantanu, and Thakur, Chetan Singh
Subjects: Electrical Engineering and Systems Science - Systems and Control, Computer Science - Hardware Architecture, Computer Science - Emerging Technologies, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract: Diverse computing paradigms have emerged to meet the growing needs for intelligent energy-efficient systems. The Margin Propagation (MP) framework, being one such initiative in the analog computing domain, stands out due to its scalability across biasing conditions, temperatures, and diminishing process technology nodes. However, the lack of digital-like automation tools for designing analog systems (including that of MP analog) hinders their adoption for designing large systems. The inherent scalability and modularity of MP systems present a unique opportunity in this regard. This paper introduces KALAM (toolKit for Automating high-Level synthesis of Analog computing systeMs), which leverages factor graphs as the foundational paradigm for synthesizing MP-based analog computing systems. Factor graphs are the basis of various signal processing tasks and, when coupled with MP, can be used to design scalable and energy-efficient analog signal processors. Using Python scripting language, the KALAM automation flow translates an input factor graph to its equivalent SPICE-compatible circuit netlist that can be used to validate the intended functionality. KALAM also allows the integration of design optimization strategies such as precision tuning, variable elimination, and mathematical simplification. We demonstrate KALAM's versatility for tasks such as Bayesian inference, Low-Density Parity Check (LDPC) decoding, and Artificial Neural Networks (ANN). Simulation results of the netlists align closely with software implementations, affirming the efficacy of our proposed automation tool., Comment: 5 Pages, 4 figures
Published: 2024

4. DA-VIL: Adaptive Dual-Arm Manipulation with Reinforcement Learning and Variable Impedance Control

Author: Karim, Md Faizal, Bollimuntha, Shreya, Hashmi, Mohammed Saad, Das, Autrio, Singh, Gaurav, Sridhar, Srinath, Singh, Arun Kumar, Govindan, Nagamanikandan, and Krishna, K Madhava
Subjects: Computer Science - Robotics
Abstract: Dual-arm manipulation is an area of growing interest in the robotics community. Enabling robots to perform tasks that require the coordinated use of two arms, is essential for complex manipulation tasks such as handling large objects, assembling components, and performing human-like interactions. However, achieving effective dual-arm manipulation is challenging due to the need for precise coordination, dynamic adaptability, and the ability to manage interaction forces between the arms and the objects being manipulated. We propose a novel pipeline that combines the advantages of policy learning based on environment feedback and gradient-based optimization to learn controller gains required for the control outputs. This allows the robotic system to dynamically modulate its impedance in response to task demands, ensuring stability and dexterity in dual-arm operations. We evaluate our pipeline on a trajectory-tracking task involving a variety of large, complex objects with different masses and geometries. The performance is then compared to three other established methods for controlling dual-arm robots, demonstrating superior results.
Published: 2024

5. Observational constraints on Gong-Zhang parametrizations in $f(Q)$ gravity

Author: Garg, Romanshu, Singh, G. P., and Singh, Ashutosh
Subjects: General Relativity and Quantum Cosmology
Abstract: The expansion dynamics of the universe in $f(Q)$ gravity is examined with the Gong-Zhang kind of dark energy equation of state parameter. By considering the $f(Q)$ gravity form $f(Q)=Q+ \alpha Q^{n}$, we obtain the Hubble parameters in terms of redshift $z$. Using Bayesian statistical methods based on the $\chi^{2}$ minimization methodology, we constrain the model parameters by using the supernovae Pantheon sample with cosmic chronometer sample. The evolution of equation of state parameter, energy density, pressure with the statefinder diagnostic are thoroughly investigated to examine the universe evolution in model. We have also obtained the universe's current age for these models. The considered models may exhibit either the quintom evolution or the future decelerating universe evolution., Comment: 20 pages, 16 figures
Published: 2024

6. Search for gravitational waves emitted from SN 2023ixf

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., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. 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., 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., Obergaulinger, M., 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. B., Liu, A., Liu, G. C., Liu, Jian, Villarreal, F. Llamas, Llobera-Querol, J., Lo, R. K. L., Locquet, J. -P., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Lorenzo-Medina, A., Loriette, V., Lormand, M., Losurdo, G., Lott IV, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lowry, M. J., Lu, N., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., Macedo, A., MacInnis, M., Maciy, R. R., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Macri, D., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magare, S. S., Magazzù, C., Magee, R. M., Maggio, E., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Makelele, E., Malaquias-Reis, J. A., Mali, U., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markosyan, A. S., Markowitz, A., Maros, E., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V., Martini, A., Martinovic, K., Martins, J. C., Martynov, D. V., Marx, E. J., Massaro, L., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Matcovich, T., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McEachin, S., McElhenny, C., McGhee, G. I., McGinn, J., McGowan, K. B. M., McIver, J., McLeod, A., McRae, T., Meacher, D., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mera, F., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Mérou, J. R., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Miller, A. 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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. L., Sayah, S., Scacco, V., Schaetzl, D., Scheel, M., Schiebelbein, A., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schulte, B. W., Schutz, B. F., Schwartz, E., Scialpi, M., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Serra, M., Servignat, G., Sevrin, A., Shaffer, T., Shah, U. S., Shaikh, M. A., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shaw, M. R., Shawhan, P., Shcheblanov, N. S., Sheridan, E., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singh, S., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., 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-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. 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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
Abstract: We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table
Published: 2024

7. Movie Gen: A Cast of Media Foundation Models

Author: Polyak, Adam, Zohar, Amit, Brown, Andrew, Tjandra, Andros, Sinha, Animesh, Lee, Ann, Vyas, Apoorv, Shi, Bowen, Ma, Chih-Yao, Chuang, Ching-Yao, Yan, David, Choudhary, Dhruv, Wang, Dingkang, Sethi, Geet, Pang, Guan, Ma, Haoyu, Misra, Ishan, Hou, Ji, Wang, Jialiang, Jagadeesh, Kiran, Li, Kunpeng, Zhang, Luxin, Singh, Mannat, Williamson, Mary, Le, Matt, Yu, Matthew, Singh, Mitesh Kumar, Zhang, Peizhao, Vajda, Peter, Duval, Quentin, Girdhar, Rohit, Sumbaly, Roshan, Rambhatla, Sai Saketh, Tsai, Sam, Azadi, Samaneh, Datta, Samyak, Chen, Sanyuan, Bell, Sean, Ramaswamy, Sharadh, Sheynin, Shelly, Bhattacharya, Siddharth, Motwani, Simran, Xu, Tao, Li, Tianhe, Hou, Tingbo, Hsu, Wei-Ning, Yin, Xi, Dai, Xiaoliang, Taigman, Yaniv, Luo, Yaqiao, Liu, Yen-Cheng, Wu, Yi-Chiao, Zhao, Yue, Kirstain, Yuval, He, Zecheng, He, Zijian, Pumarola, Albert, Thabet, Ali, Sanakoyeu, Artsiom, Mallya, Arun, Guo, Baishan, Araya, Boris, Kerr, Breena, Wood, Carleigh, Liu, Ce, Peng, Cen, Vengertsev, Dimitry, Schonfeld, Edgar, Blanchard, Elliot, Juefei-Xu, Felix, Nord, Fraylie, Liang, Jeff, Hoffman, John, Kohler, Jonas, Fire, Kaolin, Sivakumar, Karthik, Chen, Lawrence, Yu, Licheng, Gao, Luya, Georgopoulos, Markos, Moritz, Rashel, Sampson, Sara K., Li, Shikai, Parmeggiani, Simone, Fine, Steve, Fowler, Tara, Petrovic, Vladan, and Du, Yuming
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user's image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models. All videos from this paper are available at https://go.fb.me/MovieGenResearchVideos.
Published: 2024

8. Evolution of the universe with quintessence model in Rastall gravity

Author: Singh, J. K., Singh, Akanksha, Shaily, Ghosh, Sushant G., and Maharaj, Sunil D.
Subjects: General Relativity and Quantum Cosmology
Abstract: We investigate the universe's evolution within the framework of Rastall gravity, which is an extension of the standard $\Lambda$CDM model. Utilizing a linear parametrization of the Equation of State (EoS) in a Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) background, we constrain the model parameters through analysis of cosmic chronometers (CC), Pantheon, Gold, Gamma Ray Burst (GRB), and Baryon Acoustic Oscillations (BAO) datasets, as well as their joint analysis, under $1\sigma$ and $2\sigma$ confidence levels, considering the Rastall parameter $\lambda$. The constrained parameters are then used to compare our model with the standard $\Lambda$CDM model. Our findings include a detailed examination of the model's physical interpretations and demonstrate the potential for an accelerating universe expansion in later times, aligning with the observed behavior of dark energy., Comment: 21 pages, 19 figures, accepted for publication in Physica Scripta
Published: 2024

9. Spatial-Temporal Bearing Fault Detection Using Graph Attention Networks and LSTM

Author: Singh, Moirangthem Tiken, Prasad, Rabinder Kumar, Michael, Gurumayum Robert, Singh, N. Hemarjit, and Kaphungkui, N. K.
Subjects: Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, I.2, J.6
Abstract: Purpose: This paper aims to enhance bearing fault diagnosis in industrial machinery by introducing a novel method that combines Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) networks. This approach captures both spatial and temporal dependencies within sensor data, improving the accuracy of bearing fault detection under various conditions. Methodology: The proposed method converts time series sensor data into graph representations. GAT captures spatial relationships between components, while LSTM models temporal patterns. The model is validated using the Case Western Reserve University (CWRU) Bearing Dataset, which includes data under different horsepower levels and both normal and faulty conditions. Its performance is compared with methods such as K-Nearest Neighbors (KNN), Local Outlier Factor (LOF), Isolation Forest (IForest) and GNN-based method for bearing fault detection (GNNBFD). Findings: The model achieved outstanding results, with precision, recall, and F1-scores reaching 100\% across various testing conditions. It not only identifies faults accurately but also generalizes effectively across different operational scenarios, outperforming traditional methods. Originality: This research presents a unique combination of GAT and LSTM for fault detection, overcoming the limitations of traditional time series methods by capturing complex spatial-temporal dependencies. Its superior performance demonstrates significant potential for predictive maintenance in industrial applications.
Published: 2024

10. Energy-Efficient Cryogenic Ternary Content Addressable Memory using Ferroelectric SQUID

Author: Alam, Shamiul, Thomann, Simon, Parihar, Shivendra Singh, Chauhan, Yogesh Singh, Ni, Kai, Amrouch, Hussam, and Aziz, Ahmedullah
Subjects: Computer Science - Emerging Technologies, Physics - Applied Physics
Abstract: Ternary content addressable memories (TCAMs) are useful for certain computing tasks since they allow us to compare a search query with a whole dataset stored in the memory array. They can also unlock unique advantages for cryogenic applications like quantum computing, high-performance computing, and space exploration by improving speed and energy efficiency through parallel searching. This paper explores the design and implementation of a cryogenic ternary content addressable memory based on ferroelectric superconducting quantum interference devices (FeSQUIDs). The use of FeSQUID for designing the TCAM provides several unique advantages. First, we can get binary decisions (zero or non-zero voltage) for matching and mismatching conditions without using any peripheral circuitry. Moreover, the proposed TCAM needs ultra-low energy (1.36 aJ and 26.5 aJ average energy consumption for 1-bit binary and ternary search, respectively), thanks to the use of energy-efficient SQUIDs. Finally, we show the efficiency of FeSQUID through the brain-inspired application of Hyperdimensional Computing (HDC). Here, the FeSQUID-based TCAM implements the associative memory to support the highly parallel search needed in the inference step. We estimate an energy consumption of 89.4 fJ per vector comparison using a vector size of 10,000 bits. We also compare the FeSQUID-based TCAM array with the 5nm FinFET-based cryogenic SRAM-based TCAM array and observe that the proposed FeSQUID-based TCAM array consumes over one order of magnitude lower energy while performing the same task., Comment: 6 figures
Published: 2024

11. Measurement of the double-differential cross section of muon-neutrino charged-current interactions with low hadronic energy in the NOvA Near Detector

Author: Acero, M. A., Acharya, B., Adamson, P., Aliaga, L., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Balashov, N., Baldi, P., Bambah, B. A., Bannister, E., Barros, A., Bashar, S., Bat, A., Bays, K., Bernstein, R., Bezerra, T. J. C., Bhatnagar, V., Bhattarai, D., Bhuyan, B., Bian, J., Booth, A. C., Bowles, R., Brahma, B., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Carroll, T. J., Catano-Mur, E., Cesar, J. P., Chatla, A., Chirco, R., Choudhary, B. C., Christensen, A., Cicala, M. F., Coan, T. E., Cooleybeck, A., Cortes-Parra, C., Coveyou, D., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dobbs, K., Dolce, M., Doyle, D., Tonguino, D. Dueñas, Dukes, E. C., Dye, A., Ehrlich, R., Ewart, E., Filip, P., Frank, M. J., Gallagher, H. R., Gao, F., Giri, A., Gomes, R. A., Goodman, M. C., Groh, M., Group, R., Habig, A., Hakl, F., Hartnell, J., Hatcher, R., He, M., Heller, K., Hewes, V, Himmel, A., Horoho, T., Ivaneev, Y., Ivanova, A., Jargowsky, B., Jarosz, J., Johnson, C., Judah, M., Kakorin, I., Kaplan, D. M., Kalitkina, A., Kirezli-Ozdemir, B., Kleykamp, J., Klimov, O., Koerner, L. W., Kolupaeva, L., Kralik, R., Kumar, A., Kus, V., Lackey, T., Lang, K., Lesmeister, J., Lister, A., Liu, J., Lock, J. A., Lokajicek, M., MacMahon, M., Magill, S., Mann, W. A., Manoharan, M. T., Plata, M. Manrique, Marshak, M. L., Martinez-Casales, M., Matveev, V., Mehta, B., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Morozova, A., Mu, W., Mualem, L., Muether, M., Mulder, K., Myers, D., Naples, D., Nath, A., Nelleri, S., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Norrick, A., Nosek, T., Oh, H., Olshevskiy, A., Olson, T., Ozkaynak, M., Pal, A., Paley, J., Panda, L., Patterson, R. B., Pawloski, G., Petti, R., Porter, J. C. C., Prais, L. R., Rabelhofer, M., Rafique, A., Raj, V., Rajaoalisoa, M., Ramson, B., Rebel, B., Roy, P., Samoylov, O., Sanchez, M. C., Falero, S. Sanchez, Shanahan, P., Sharma, P., Sheshukov, A., Shivam, Shmakov, A., Shorrock, W., Shukla, S., Singha, D. K., Singh, I., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Sutton, K., Swain, S., Sweeney, C., Sztuc, A., Oregui, B. Tapia, Tas, P., Thakore, T., Thomas, J., Tiras, E., Torun, Y., Tran, D., Trokan-Tenorio, J., Urheim, J., Vahle, P., Vallari, Z., Villamil, J. D., Vockerodt, K. J., Wallbank, M., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wieber, T., Wolcott, J., Wrobel, M., Wu, S., Wu, W., Xiao, Y., Yaeggy, B., Yahaya, A., Yankelevich, A., Yonehara, K., Yu, Y., Zadorozhnyy, S., Zalesak, J., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: The NOvA collaboration reports cross-section measurements for $\nu_{\mu}$ charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV for pions) in the NOvA Near Detector. The results are presented as a double-differential cross section as a function of the direct observables of the final-state muon kinematics. Results are also presented as a single-differential cross section as a function of the derived square of the four-momentum transfer, $Q^{2}$, and as a function of the derived neutrino energy. The data correspond to an accumulated 8.09$\times10^{20}$ protons-on-target (POT) in the neutrino mode of the NuMI beam, with a narrow band of neutrino energies peaked at 1.8 GeV. The analysis provides a sample of neutrino-nucleus interactions with an enhanced fraction of quasi-elastic and two-particle-two-hole (2p2h) interactions. This enhancement allows quantitative comparisons with various nuclear models. We find strong disagreement between data and theory-based models in various regions of the muon kinematic phase space, especially in the forward muon direction., Comment: 20 pages, 12 figures
Published: 2024

12. High sensitivity pressure and temperature quantum sensing in organic crystals

Author: Singh, Harpreet, DSouza, Noella, Garrett, Joseph, Singh, Angad, Blankenship, Brian, Druga, Emanuel, Montis, Riccardo, Tan, Liang, and Ajoy, Ashok
Subjects: Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract: The inherent sensitivity of quantum sensors to their physical environment can make them good reporters of parameters such as temperature, pressure, strain, and electric fields. Here, we present a molecular platform for pressure (P) and temperature (T) sensing using para-terphenyl crystals doped with pentacene. We leverage the optically detected magnetic resonance (ODMR) of the photoexcited triplet electron in the pentacene molecule, that serves as a sensitive probe for lattice changes in the host para-terphenyl due to pressure or temperature variations. We observe maximal ODMR frequency variations of df/dP=1.8 MHz/bar and df/dT=247 kHz/K, which are over 1,200 times and three times greater, respectively, than those seen in nitrogen-vacancy centers in diamond. This results in a >85-fold improvement in pressure sensitivity over best previously reported. The larger variation reflects the weaker nature of the para-terphenyl lattice, with first-principles DFT calculations indicating that even picometer-level shifts in the molecular orbitals due to P, T changes are measurable. The platform offers additional advantages including high levels of sensor doping, narrow ODMR linewidths and high contrasts, and ease of deployment, leveraging the ability for large single crystals at low cost. Overall, this work paves the way for low-cost, optically-interrogated pressure and temperature sensors and lays the foundation for even more versatile sensors enabled by synthetic tunability in designer molecular systems., Comment: 7 pages, 4 figures, 1 table
Published: 2024

13. Advanced Gesture Recognition in Autism: Integrating YOLOv7, Video Augmentation and VideoMAE for Video Analysis

Author: Singh, Amit Kumar, Shrivastava, Trapti, and Singh, Vrijendra
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Deep learning and advancements in contactless sensors have significantly enhanced our ability to understand complex human activities in healthcare settings. In particular, deep learning models utilizing computer vision have been developed to enable detailed analysis of human gesture recognition, especially repetitive gestures which are commonly observed behaviors in children with autism. This research work aims to identify repetitive behaviors indicative of autism by analyzing videos captured in natural settings as children engage in daily activities. The focus is on accurately categorizing real-time repetitive gestures such as spinning, head banging, and arm flapping. To this end, we utilize the publicly accessible Self-Stimulatory Behavior Dataset (SSBD) to classify these stereotypical movements. A key component of the proposed methodology is the use of \textbf{VideoMAE}, a model designed to improve both spatial and temporal analysis of video data through a masking and reconstruction mechanism. This model significantly outperformed traditional methods, achieving an accuracy of 97.7\%, a 14.7\% improvement over the previous state-of-the-art.
Published: 2024

14. Exploring Non-Isotropic Lorentz Invariance Violation Through Sidereal Effect at DUNE

Author: Mishra, Shashank, Shukla, Saurabh, Singh, Lakhwinder, and Singh, Venktesh
Subjects: High Energy Physics - Phenomenology
Abstract: Lorentz Invariance Violation (LIV) presents an intriguing opportunity to investigate fundamental symmetries, with neutrinos serving as a particularly effective probe for this phenomenon. Long-baseline neutrino experiments, such as the Deep Underground Neutrino Experiment (DUNE), excel at exploring non-isotropic LIV, especially through the observation of sidereal effects. This study comprehensively examines the full parameter space of non-isotropic, non-diagonal LIV parameters with sidereal dependence, focusing on two distinct flux scenarios: a low-energy flux and a tau-optimized flux. Through this analysis, we derive more stringent constraints on LIV parameters. Our results indicate that DUNE may achieve enhanced sensitivity for some LIV parameters, exceeding all previously established limits and marking a significant advancement in the investigation of LIV.
Published: 2024

15. 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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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

16. Heterogeneous Graph Auto-Encoder for CreditCard Fraud Detection

Author: Singh, Moirangthem Tiken, Prasad, Rabinder Kumar, Michael, Gurumayum Robert, Kaphungkui, N K, and Singh, N. Hemarjit
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: The digital revolution has significantly impacted financial transactions, leading to a notable increase in credit card usage. However, this convenience comes with a trade-off: a substantial rise in fraudulent activities. Traditional machine learning methods for fraud detection often struggle to capture the inherent interconnectedness within financial data. This paper proposes a novel approach for credit card fraud detection that leverages Graph Neural Networks (GNNs) with attention mechanisms applied to heterogeneous graph representations of financial data. Unlike homogeneous graphs, heterogeneous graphs capture intricate relationships between various entities in the financial ecosystem, such as cardholders, merchants, and transactions, providing a richer and more comprehensive data representation for fraud analysis. To address the inherent class imbalance in fraud data, where genuine transactions significantly outnumber fraudulent ones, the proposed approach integrates an autoencoder. This autoencoder, trained on genuine transactions, learns a latent representation and flags deviations during reconstruction as potential fraud. This research investigates two key questions: (1) How effectively can a GNN with an attention mechanism detect and prevent credit card fraud when applied to a heterogeneous graph? (2) How does the efficacy of the autoencoder with attention approach compare to traditional methods? The results are promising, demonstrating that the proposed model outperforms benchmark algorithms such as Graph Sage and FI-GRL, achieving a superior AUC-PR of 0.89 and an F1-score of 0.81. This research significantly advances fraud detection systems and the overall security of financial transactions by leveraging GNNs with attention mechanisms and addressing class imbalance through an autoencoder.
Published: 2024

17. Measurement of d2sigma/d|q|dEavail in charged current neutrino-nucleus interactions at <Ev> = 1.86 GeV using the NOvA Near Detector

Author: Acero, M. A., Acharya, B., Adamson, P., Aliaga, L., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Balashov, N., Baldi, P., Bambah, B. A., Bannister, E., Barros, A., Bashar, S., Bat, A., Bays, K., Bernstein, R., Bezerra, T. J. C., Bhatnagar, V., Bhattarai, D., Bhuyan, B., Bian, J., Booth, A. C., Bowles, R., Brahma, B., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Carroll, T. J., Catano-Mur, E., Cesar, J. P., Chatla, A., Chirco, R., Choudhary, B. C., Christensen, A., Cicala, M. F., Coan, T. E., Cooleybeck, A., Cortes-Parra, C., Coveyou, D., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dobbs, K., Dolce, M., Doyle, D., Tonguino, D. Duenas, Dukes, E. C., Dye, A., Ehrlich, R., Ewart, E., Filip, P., Frank, M. J., Gallagher, H. R., Gao, F., Giri, A., Gomes, R. A., Goodman, M. C., Groh, M., Group, R., Habig, A., Hakl, F., Hartnell, J., Hatcher, R., He, M., Heller, K., Hewes, V, Himmel, A., Horoho, T., Ivaneev, Y., Ivanova, A., Jargowsky, B., Jarosz, J., Johnson, C., Judah, M., Kakorin, I., Kaplan, D. M., Kalitkina, A., Kirezli-Ozdemir, B., Kleykamp, J., Klimov, O., Koerner, L. W., Kolupaeva, L., Kralik, R., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Lesmeister, J., Lister, A., Liu, J., Lock, J. A., Lokajicek, M., MacMahon, M., Magill, S., Mann, W. A., Manoharan, M. T., Plata, M. Manrique, Marshak, M. L., Martinez-Casales, M., Matveev, V., Mehta, B., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S., Mishra, S. R., Mohanta, R., Moren, A., Morozova, A., Mu, W., Mualem, L., Muether, M., Mulder, K., Myers, D., Naples, D., Nath, A., Nelleri, S., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Norrick, A., Nosek, T., Oh, H., Olshevskiy, A., Olson, T., Ozkaynak, M., Pal, A., Paley, J., Panda, L., Patterson, R. B., Pawloski, G., Petti, R., Plunkett, R. K., Prais, L. R., Rabelhofer, M., Rafique, A., Raj, V., Rajaoalisoa, M., Ramson, B., Rebel, B., Roy, P., Samoylov, O., Sanchez, M. C., Falero, S. Sanchez, Shanahan, P., Sharma, P., Sheshukov, A., Shivam, Shmakov, A., Shorrock, W., Shukla, S., Singha, D. K., Singh, I., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Sutton, K., Swain, S., Sweeney, C., Sztuc, A., Oregui, B. Tapia, Tas, P., Thakore, T., Thomas, J., Tiras, E., Torun, Y., Tran, D., Trokan-Tenorio, J., Urheim, J., Vahle, P., Vallari, Z., Villamil, J. D., Vockerodt, K. J., Wallbank, M., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wieber, T., Wolcott, J., Wrobel, M., Wu, S., Wu, W., Xiao, Y., Yaeggy, B., Yahaya, A., Yankelevich, A., Yonehara, K., Yu, Y., Zadorozhnyy, S., Zalesak, J., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: Double- and single-differential cross sections for inclusive charged-current neutrino-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transfer and 0 to 2 GeV in available energy, at a mean muon-neutrino energy of 1.86 GeV. The measurements are based on an estimated 995,760 muon-neutrino CC interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole reactions is identified by the cross-section excess relative to predictions for neutrino-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2- hole processes are rated by chi-square comparisons of the predicted-versus-measured muon-neutrino CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based Val`encia and SuSAv2 2p2h models., Comment: 20 pages, 14 figures
Published: 2024

18. Octant Ambiguity in the Presence of Non-isotropic Lorentz Invariance Violation

Author: Shukla, Saurabh, Mishra, Shashank, Singh, Lakhwinder, and Singh, Venktesh
Subjects: High Energy Physics - Phenomenology
Abstract: Global analyses of neutrino data suggest that the mixing angle $\theta_{23}$ is likely to be nonmaximal with two closely matched solutions emerging: one representing a smaller angle ($\theta_{23}$ < $\pi/4$) and the other a larger angle ($\theta_{23}$ > $\pi/4$). This ambiguity, known as the octant ambiguity of $\theta_{23}$, presents a significant challenge in neutrino research and is a primary objective of future long-baseline experiments. In this study, for the first time, we explore how non-isotropic Lorentz violation affects measurements of mixing angle $\theta_{23}$, with a particular emphasis on sidereal effects in the Deep Underground Neutrino Experiment. Our findings reveal that ability of DUNE to resolve the octant ambiguity is significantly compromised in the presence of the $c^{xy}_{e \tau}$ parameter. Furthermore, we demonstrate that LIV exacerbates the degeneracy between the Dirac CP-phase $\delta_{cp}$ and $\theta_{23}$.
Published: 2024

19. KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators

Author: Singh, Ashutosh, Singh, Ashish, Imbiriba, Tales, Erdogmus, Deniz, and Borsoi, Ricardo
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Approaches based on Koopman operators have shown great promise in forecasting time series data generated by complex nonlinear dynamical systems (NLDS). Although such approaches are able to capture the latent state representation of a NLDS, they still face difficulty in long term forecasting when applied to real world data. Specifically many real-world NLDS exhibit time-varying behavior, leading to nonstationarity that is hard to capture with such models. Furthermore they lack a systematic data-driven approach to perform data assimilation, that is, exploiting noisy measurements on the fly in the forecasting task. To alleviate the above issues, we propose a Koopman operator-based approach (named KODA - Koopman Operator with Data Assimilation) that integrates forecasting and data assimilation in NLDS. In particular we use a Fourier domain filter to disentangle the data into a physical component whose dynamics can be accurately represented by a Koopman operator, and residual dynamics that represents the local or time varying behavior that are captured by a flexible and learnable recursive model. We carefully design an architecture and training criterion that ensures this decomposition lead to stable and long-term forecasts. Moreover, we introduce a course correction strategy to perform data assimilation with new measurements at inference time. The proposed approach is completely data-driven and can be learned end-to-end. Through extensive experimental comparisons we show that KODA outperforms existing state of the art methods on multiple time series benchmarks such as electricity, temperature, weather, lorenz 63 and duffing oscillator demonstrating its superior performance and efficacy along the three tasks a) forecasting, b) data assimilation and c) state prediction.
Published: 2024

20. Simultaneous Multiparty Quantum Teleportation Supervised by a Controller

Author: Singh, Nikhita and Singh, Ravi S.
Subjects: Quantum Physics
Abstract: Quantum network harbours a technology of multiparty transmission and computation of quantum information. We here design a quantum circuit comprising of Hadamard and controlled-Not gates for preparation of a cluster state of symmetric and antisymmetric Bell pairs involving seventeen qubits which is later on utilized as a quantum channel. This seventeen qubits quantum channel is employed to strategize a simultaneous multiparty (quattro directional) quantum teleportation protocol in which four senders (Alice, Bob, Charlie and David) transmit their arbitrary unknown two-qubit states to respective four receivers (Fancy1, Fancy2, Fancy3 and Fancy4) under the supervision of a controller Elle. After successful accomplishment of the protocol, we assess and compare our scheme with contemporary protocols based on quantum (classical) resource consumption, transmitted qubits, operation complexity and efficiency. We found that intrinsic efficiency of our protocol pegs at 21.65 percent., Comment: 15 Pages; 02 Figures; Appendices: A and B; Tables:1, 2 and 3
Published: 2024

21. Symmetric-Cyclic Bidirectional Quantum Teleportation of Bell-like State via Entanglement-Swapping

Author: Singh, Nikhita and Singh, Ravi S
Subjects: Quantum Physics
Abstract: Quantum teleportation is a primitive foundational protocol for exchanging quantum information in a quantum network as well as infrastructural operational strategy in the measurement-based quantum computation. Designing an efficient scheme for quantum teleportation is a vibrant field of intensive research. We propose a scheme wherein Bell-like states are being exchanged simultaneously in cyclic sequence, i.e., symmetric-cyclic bi-directional quantum teleportation, amongst three communicating parties forming a quantum network, Alice, Bob and Charlie via entanglement-swapping with the aid of a cluster of three maximally entangled GHZ-states as the quantum channel. Moreover, based upon communication- and operation- complexity, we compare our protocol with other equivalent protocols and found that the intrinsic efficiency of our protocol is maximum pegging at 33.33%., Comment: 15 pages; Figs: 1 and 2; Tables: 1 and 2; Appendix: A
Published: 2024

22. The hypothetical track-length fitting algorithm for energy measurement in liquid argon TPCs

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Alex, N. S., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Choi, G., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., Gago, A. M., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Groetschla, F. T., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hart, A. L., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hellmuth, P., Henry, S., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Jung, K. Y., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., King, B., Kirby, B., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kvasnicka, J., Labree, T., Lackey, T., Lalău, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laudrain, A., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Silverio, D. Leon, Lepin, L. M., Li, J. -Y, Li, S. W., Li, Y., Liao, H., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lokajicek, M., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., Lord, T., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. C. N., Mefodiev, A., Mehta, B., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Mineev, O., Minotti, A., Miralles, L., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, Z., Moreno, D., Moreno-Palacios, O., Morescalchi, L., Moretti, D., Moretti, R., Morris, C., Mossey, C., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mukhamejanov, Y., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Nandakumar, R., Naples, D., Narita, S., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, A., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olshevskiy, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panda, P., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papaleo, R., Papanestis, A., Papoulias, D., Paramesvaran, S., Paris, A., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Perez, A. Pena, Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pfaff, M., Pia, V., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Plows, K., Pollack, C., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paixão, L. G. Porto, Potekhin, M., Potenza, R., Pozzato, M., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Queen, J., Raaf, J. L., Radeka, V., Rademacker, J., Radics, B., Raffaelli, F., Rafique, A., Raguzin, E., Rahaman, U., Rai, M., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Ray, R., Razafinime, H., Razakamiandra, R. F., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renner, E., Renshaw, A., Rescia, S., Resnati, F., Restrepo, Diego, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Ross-Lonergan, M., Roy, N., Roy, P., Rubbia, C., Ruggeri, A., Ferreira, G. Ruiz, Russell, B., Ruterbories, D., Rybnikov, A., Sacerdoti, S., Saha, S., Sahoo, S. K., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Bravo, A. Sánchez, Sánchez-Castillo, A., Sanchez-Lucas, P., Sandberg, V., Sanders, D. A., Sanfilippo, S., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Segade, A., Segreto, E., Selyunin, A., Senadheera, D., Senise, C. R., Sensenig, J., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Poudel, S. Sharma, Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Sheshukov, A., Shi, J., Shi, W., Shin, S., Shivakoti, S., Shoemaker, I., Shooltz, D., Shrock, R., Siddi, B., Siden, M., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, E., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spinella, F., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thomas, S., Thompson, A., Thorn, C., Timm, S. C., Tiras, E., Tishchenko, V., Tiwari, S., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turnberg, S., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Van de Water, R. G., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Vasina, S., Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Vizarreta, R., Hernandez, A. P. Vizcaya, Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Westerdale, S., Wetstein, M., Whalen, K., White, A., Whitehead, L. H., Whittington, D., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.
Published: 2024

23. VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals

Author: Singh, Alakhsimar, Verma, Nischay, Goyal, Kanav, Singh, Amritpal, Kumar, Puneet, and Li, Xiaobai
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: This paper presents VisioPhysioENet, a novel multimodal system that leverages visual cues and physiological signals to detect learner engagement. It employs a two-level approach for visual feature extraction using the Dlib library for facial landmark extraction and the OpenCV library for further estimations. This is complemented by extracting physiological signals using the plane-orthogonal-to-skin method to assess cardiovascular activity. These features are integrated using advanced machine learning classifiers, enhancing the detection of various engagement levels. We rigorously evaluate VisioPhysioENet on the DAiSEE dataset, where it achieves an accuracy of 63.09%, demonstrating a superior ability to discern various levels of engagement compared to existing methodologies. The proposed system's code can be accessed at https://github.com/MIntelligence-Group/VisioPhysioENet., Comment: 5 Pages, 2 figures
Published: 2024

24. Avengers Assemble: Amalgamation of Non-Semantic Features for Depression Detection

Author: Phukan, Orchid Chetia, Behera, Swarup Ranjan, Singh, Shubham, Singh, Muskaan, Rajan, Vandana, Buduru, Arun Balaji, Sharma, Rajesh, and Prasanna, S. R. Mahadeva
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound, 68T45, I.2.7
Abstract: In this study, we address the challenge of depression detection from speech, focusing on the potential of non-semantic features (NSFs) to capture subtle markers of depression. While prior research has leveraged various features for this task, NSFs-extracted from pre-trained models (PTMs) designed for non-semantic tasks such as paralinguistic speech processing (TRILLsson), speaker recognition (x-vector), and emotion recognition (emoHuBERT)-have shown significant promise. However, the potential of combining these diverse features has not been fully explored. In this work, we demonstrate that the amalgamation of NSFs results in complementary behavior, leading to enhanced depression detection performance. Furthermore, to our end, we introduce a simple novel framework, FuSeR, designed to effectively combine these features. Our results show that FuSeR outperforms models utilizing individual NSFs as well as baseline fusion techniques and obtains state-of-the-art (SOTA) performance in E-DAIC benchmark with RMSE of 5.51 and MAE of 4.48, establishing it as a robust approach for depression detection., Comment: Submitted to ICASSP 2025
Published: 2024

25. Measurement of elliptic flow of J$/\psi$ in $\sqrt{s_{_{NN}}}=200$ GeV Au$+$Au collisions at forward rapidity

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Aidala, C., Ajitanand, N. N., Akiba, Y., Alfred, M., Antsupov, S., Aoki, K., Apadula, N., Asano, H., Ayuso, C., Azmoun, B., Babintsev, V., Bai, M., Bandara, N. S., Bannier, B., Bannikov, E., Barish, K. N., Bathe, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Blankenship, B., Blau, D. S., Boer, M., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Bumazhnov, V., Butler, C., Campbell, S., Roman, V. Canoa, Chen, C. -H., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Chujo, T., Citron, Z., Connors, M., Corliss, R., Csanád, M., Csörgő, T., Liu, L. D., Danley, T. W., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dion, A., Diss, P. B., Doomra, V., Do, J. H., Drees, A., Drees, K. A., Dumancic, M., Durham, J. M., Durum, A., Elder, T., Enokizono, A., Esha, R., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukuda, Y., Gallus, P., Gal, C., Garg, P., Ge, H., Giordano, F., Glenn, A., Goto, Y., Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Hemmick, T. K., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ito, Y., Ivanishchev, D., Jacak, B., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Jorjadze, V., Jouan, D., Jumper, D. S., Kanda, S., Kang, J. H., Kapukchyan, D., Karthas, S., Kawall, D., Kazantsev, A. V., Key, J. A., Khachatryan, V., Khanzadeev, A., Kimelman, B., Kim, C., Kim, D. J., Kim, E. -J., Kim, G. W., Kim, M., Kim, M. H., Kincses, D., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Komkov, B., Kotov, D., Kovacs, L., Kudo, S., Kurita, K., Kurosawa, M., Kwon, Y., Lajoie, J. G., Lallow, E. O., Lebedev, A., Lee, S., Lee, S. H., Leitch, M. J., Leung, Y. H., Lewis, N. A., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Lökös, S., Loomis, D. A., Lynch, D., Majoros, T., Makdisi, Y. I., Makek, M., Malaev, M., Manion, A., Manko, V. I., Mannel, E., Masuda, H., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Meles, A., Mendoza, M., Mignerey, A. C., Mihalik, D. E., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morrison, D. P., Morrow, S. I., Moukhanova, T. V., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagai, K., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakano, K., Nattrass, C., Netrakanti, P. K., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novotny, R., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, J. S., Park, S., Patel, M., Pate, S. F., Peng, J. -C., Peng, W., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Pun, A., Purschke, M. L., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Rolnick, S. D., Rosati, M., Rowan, Z., Rubin, J. G., Runchey, J., Sahlmueller, B., Saito, N., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, K., Sato, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seidl, R., Seleznev, A., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stepanov, M., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Syed, S., Sziklai, J., Takeda, A., Taketani, A., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarnai, G., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Vazquez-Carson, S., Velkovska, J., Virius, M., Vrba, V., Wang, X. R., Wang, Z., Watanabe, Y., Watanabe, Y. S., Wei, F., White, A. S., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yanovich, A., Yin, P., Yoon, I., Yoo, J. H., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., Zhou, S., and Zou, L.
Subjects: Nuclear Experiment
Abstract: We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2024

26. Measurements at forward rapidity of elliptic flow of charged hadrons and open-heavy-flavor muons in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Aidala, C., Ajitanand, N. N., Akiba, Y., Alfred, M., Antsupov, S., Aoki, K., Apadula, N., Asano, H., Ayuso, C., Azmoun, B., Babintsev, V., Bai, M., Bandara, N. S., Bannier, B., Bannikov, E., Barish, K. N., Bathe, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Blankenship, B., Blau, D. S., Boer, M., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Bumazhnov, V., Butler, C., Campbell, S., Roman, V. Canoa, Chen, C. -H., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Chujo, T., Citron, Z., Connors, M., Corliss, R., Csanád, M., Csörgő, T., Liu, L. D., Danley, T. W., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dion, A., Diss, P. B., Doomra, V., Do, J. H., Drees, A., Drees, K. A., Dumancic, M., Durham, J. M., Durum, A., Elder, T., Enokizono, A., Esha, R., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukuda, Y., Gallus, P., Gal, C., Garg, P., Ge, H., Giordano, F., Glenn, A., Goto, Y., Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Hemmick, T. K., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ito, Y., Ivanishchev, D., Jacak, B., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Jorjadze, V., Jouan, D., Jumper, D. S., Kanda, S., Kang, J. H., Kapukchyan, D., Karthas, S., Kawall, D., Kazantsev, A. V., Key, J. A., Khachatryan, V., Khanzadeev, A., Kimelman, B., Kim, C., Kim, D. J., Kim, E. -J., Kim, G. W., Kim, M., Kim, M. H., Kincses, D., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Komkov, B., Kotov, D., Kovacs, L., Kudo, S., Kurita, K., Kurosawa, M., Kwon, Y., Lajoie, J. G., Lallow, E. O., Lebedev, A., Lee, S., Lee, S. H., Leitch, M. J., Leung, Y. H., Lewis, N. A., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Lökös, S., Loomis, D. A., Lynch, D., Majoros, T., Makdisi, Y. I., Makek, M., Malaev, M., Manion, A., Manko, V. I., Mannel, E., Masuda, H., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Meles, A., Mendoza, M., Mignerey, A. C., Mihalik, D. E., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morrison, D. P., Morrow, S. I., Moukhanova, T. V., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagai, K., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakano, K., Nattrass, C., Netrakanti, P. K., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novotny, R., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, J. S., Park, S., Patel, M., Pate, S. F., Peng, J. -C., Peng, W., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Pun, A., Purschke, M. L., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Rolnick, S. D., Rosati, M., Rowan, Z., Rubin, J. G., Runchey, J., Sahlmueller, B., Saito, N., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, K., Sato, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seidl, R., Seleznev, A., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stepanov, M., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Syed, S., Sziklai, J., Takeda, A., Taketani, A., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarnai, G., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Vazquez-Carson, S., Velkovska, J., Virius, M., Vrba, V., Wang, X. R., Wang, Z., Watanabe, Y., Watanabe, Y. S., Wei, F., White, A. S., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yanovich, A., Yin, P., Yoon, I., Yoo, J. H., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., Zhou, S., and Zou, L.
Subjects: Nuclear Experiment
Abstract: We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1
Published: 2024

27. Time-Reversal Symmetry Breaking in Re-Based Kagome Lattice Superconductor

Author: Mandal, Manasi, Kataria, A., Meena, P. K., Kushwaha, R. K., Singh, D., Biswas, P. K., Stewart, R., Hillier, A. D., and Singh, R. P.
Subjects: Condensed Matter - Superconductivity
Abstract: We investigated the Re-based kagome superconductor Re$_2$Zr through various measurements, including resistivity, magnetization, specific heat, and muon spin rotation and relaxation spectroscopy. These results suggest that Re$_2$Zr is a moderately coupled potential two-gap superconductor. Zero-field muon relaxation data indicate the possible presence of a time-reversal symmetry-breaking state in the superconducting ground state. Our investigation identifies Re$_{2}$Zr as a new unconventional superconductor with a potential complex order parameter that warrants considerable experimental and theoretical interest., Comment: 9 pages, 6 figures
Published: 2024

28. Quantum light generation with ultra-high spatial resolution in 2D semiconductors via ultra-low energy electron irradiation

Author: Dash, Ajit Kumar, Yadav, Sharad Kumar, Roux, Sebastien, Singh, Manavendra Pratap, Watanabe, Kenji, Taniguchi, Takashi, Naik, Akshay, Robert, Cedric, Marie, Xavier, and Singh, Akshay
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract: Single photon emitters (SPEs) are building blocks of quantum technologies. Defect engineering of 2D materials is ideal to fabricate SPEs, wherein spatially deterministic and quality-preserving fabrication methods are critical for integration into quantum devices and cavities. Existing methods use combination of strain and electron irradiation, or ion irradiation, which make fabrication complex, and limited by surrounding lattice damage. Here, we utilise only ultra-low energy electron beam irradiation (5 keV) to create dilute defect density in hBN-encapsulated monolayer MoS2, with ultra-high spatial resolution (< 50 nm, extendable to 10 nm). Cryogenic photoluminescence spectra exhibit sharp defect peaks, following power-law for finite density of single defects, and characteristic Zeeman splitting for MoS2 defect complexes. The sharp peaks have low spectral jitter (< 200 {\mu}eV), and are tuneable with gate-voltage and electron beam energy. Use of low-momentum electron irradiation, ease of processing, and high spatial resolution, will disrupt deterministic creation of high-quality SPEs., Comment: 31 pages, 16 figures, 1 table. Supplementary Material included
Published: 2024

29. Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers

Author: Singh, Namita, Wang'ombe, Jacqueline, Okanga, Nereah, Zelenska, Tetyana, Repishti, Jona, K, Jayasankar G, Mishra, Sanjeev, Manokaran, Rajsekar, Singh, Vineet, Rafiq, Mohammed Irfan, Gandhi, Rikin, and Nambi, Akshay
Subjects: Computer Science - Emerging Technologies, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract: Small and medium-sized agricultural holders face challenges like limited access to localized, timely information, impacting productivity and sustainability. Traditional extension services, which rely on in-person agents, struggle with scalability and timely delivery, especially in remote areas. We introduce FarmerChat, a generative AI-powered chatbot designed to address these issues. Leveraging Generative AI, FarmerChat offers personalized, reliable, and contextually relevant advice, overcoming limitations of previous chatbots in deterministic dialogue flows, language support, and unstructured data processing. Deployed in four countries, FarmerChat has engaged over 15,000 farmers and answered over 300,000 queries. This paper highlights how FarmerChat's innovative use of GenAI enhances agricultural service scalability and effectiveness. Our evaluation, combining quantitative analysis and qualitative insights, highlights FarmerChat's effectiveness in improving farming practices, enhancing trust, response quality, and user engagement., Comment: 35 pages
Published: 2024

30. Asymmetric Bidirectional Quantum Teleportation: Arbitrary bi-modal Information State

Author: Pathak, Ankita, Chauhan, Madan Singh, and Singh, Ravi S.
Subjects: Quantum Physics
Abstract: Optical coherent states are experimentally realizable continuous variable quantum states of which preparation by lasers, as well as its manipulation and monitoring by linear optical gadgets are well established. We propose a strategy to send an arbitrary superposition of four-component bimodal entangled coherent states from a sender to a receiver who, simultaneously, tries to transmit an unknown Schrodinger Cat coherent state to sender via employing a cluster consisting of three superposition of two component bimodal entangled coherent states as the quantum channel and utilizing linear optical gadgets. Heralded detection of photons in laboratories of sender and receiver followed by classical communications of even and odd number of photons and local unitary operations, impeccably, accomplishes simultaneous faithful asymmetric bidirectional quantum teleportation with one eighth of probability of success. It is seen that not all detection events implement the protocol and, therefore, one has to locally apply displacement operator, a necessary evil. We analyze near faithful partial asymmetric bidirectional quantum teleportation and associated probability of success therein. We demonstrated that, for an intense coherent optical field, fidelity approach unity., Comment: 30 pages;Figs: 1-5; Appendices: A and B; Tables(1-6); To be submitted for possible publication
Published: 2024

31. Multiplicity dependent $J/\psi$ and $\psi(2S)$ production at forward and backward rapidity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Aidala, C., Akiba, Y., Alfred, M., Andrieux, V., Antsupov, S., Apadula, N., Asano, H., Azmoun, B., Babintsev, V., Bandara, N. S., Bannikov, E., Barish, K. N., Bathe, S., Bazilevsky, A., Beaumier, M., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Blankenship, B., Blau, D. S., Bok, J. S., Borisov, V., Brooks, M. L., Bryslawskyj, J., Bumazhnov, V., Campbell, S., Cervantes, R., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Citron, Z., Connors, M., Corliss, R., Cronin, N., Csanád, M., Csörgő, T., Danley, T. W., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dion, A., Dixit, D., Doomra, V., Do, J. H., Drees, A., Drees, K. A., Durham, J. M., Durum, A., En'yo, H., Enokizono, A., Esha, R., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukuda, Y., Gallus, P., Gal, C., Garg, P., Ge, H., Giordano, F., Goto, Y., Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Guragain, H., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Hasegawa, S., Haseler, T. O. S., Hemmick, T. K., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Imai, K., Inaba, M., Iordanova, A., Isenhower, D., Ivanishchev, D., Jacak, B., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Jouan, D., Jumper, D. S., Kang, J. H., Kapukchyan, D., Karthas, S., Kawall, D., Kazantsev, A. V., Khachatryan, V., Khanzadeev, A., Kim, C., Kim, E. -J., Kim, M., Kincses, D., Kistenev, E., Klatsky, J., Kline, P., Koblesky, T., Kotov, D., Kovacs, L., Kudo, S., Kurita, K., Kwon, Y., Lajoie, J. G., Lebedev, A., Lee, S., Leitch, M. J., Leung, Y. H., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Lökös, S., Loomis, D. A., Lovasz, K., Lynch, D., Majoros, T., Makdisi, Y. I., Makek, M., Manko, V. I., Mannel, E., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Mendoza, M., Mignerey, A. C., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Montuenga, P., Moon, T., Morrison, D. P., Mulilo, B., Murakami, T., Murata, J., Nagai, K., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakano, K., Nattrass, C., Niida, T., Nouicer, R., Novitzky, N., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ottino, G. J., Ozawa, K., Pantuev, V., Papavassiliou, V., Park, J. S., Park, S., Patel, M., Pate, S. F., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pisani, R. P., Potekhin, M., Purschke, M. L., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Rolnick, S. D., Rosati, M., Rowan, Z., Safonov, A. S., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seidl, R., Seleznev, A., Sen, A., Seto, R., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shioya, T., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Sziklai, J., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Tarnai, G., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Towell, C. L., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Velkovska, J., Virius, M., Vrba, V., Vukman, N., Wang, X. R., Watanabe, Y. S., Woody, C. L., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yamamoto, H., Yanovich, A., Yoon, I., Yoo, J. H., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., and Zou, L.
Subjects: High Energy Physics - Experiment
Abstract: The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2024

32. SN 2021foa: Deriving a continuity between SN IIn and SN Ibn

Author: Gangopadhyay, Anjasha, Dukiya, Naveen, Moriya, Takashi J, Tanaka, Masaomi, Maeda, Keiichi, Howell, D. Andrew, Singh, Mridweeka, Singh, Avinash, Sollerman, Jesper, Kawabata, Koji S, Brennan, Sean J, Pellegrino, Craig, Dastidar, Raya, Nakaoka, Tatsuya, Kawabata, Miho, Misra, Kuntal, Schulze, Steve, Chandra, Poonam, Taguchi, Kenta, Sahu, Devendra K, McCully, Curtis, Bostroem, K. Azalee, Gonzalez, Estefania Padilla, Newsome, Megan, Hiramatsu, Daichi, Takei, Yuki, Yamanaka, Masayuki, Tajitsu, Akito, and Isogai, Keisuke
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the long-term photometric and spectroscopic analysis of a transitioning SN~IIn/Ibn from $-$10.8 d to 150.7 d post $V$-band maximum. SN~2021foa shows prominent He {\sc i} lines comparable in strength to the H$\alpha$ line around peak, placing SN~2021foa between the SN~IIn and SN~Ibn populations. The spectral comparison shows that it resembles the SN~IIn population at pre-maximum, becomes intermediate between SNe~IIn/Ibn and at post-maximum matches with SN~IIn 1996al. The photometric evolution shows a precursor at $-$50 d and a light curve shoulder around 17d. The peak luminosity and color evolution of SN 2021foa are consistent with most SNe~IIn and Ibn in our comparison sample. SN~2021foa shows the unique case of a SN~IIn where the narrow P-Cygni in H$\alpha$ appear at later stages. The H$\alpha$ profile consists of a narrow (500 -- 1200 km s$^{-1}$) component, intermediate width (3000 -- 8000 km s$^{-1}$) and broad component in absorption. Temporal evolution of the H$\alpha$ profile favours a disk-like CSM geometry. Hydrodynamical modelling of the lightcurve well reproduces a two-component CSM structure with different densities ($\rho$ $\propto$ r$^{-2}$ -- $\rho$ $\propto$ r$^{-5}$), mass-loss rates (10$^{-3}$ -- 10$^{-1}$ M$_{\odot}$ yr$^{-1}$) assuming a wind velocity of 1000 km s$^{-1}$ and having a CSM mass of 0.18 M$_{\odot}$. The overall evolution indicates that SN~2021foa most likely originated from a LBV star transitioning to a WR star with the mass-loss rate increasing in the period from 5 to 0.5 years before the explosion or it could be due to a binary interaction., Comment: Submitted to MNRAS; 20 pages, 16 figures, 4 tables
Published: 2024

33. AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning

Author: Khosravi, Mahsa, Carroll, Matthew, Tan, Kai Liang, Van der Laan, Liza, Raigne, Joscif, Mueller, Daren S., Singh, Arti, Balu, Aditya, Ganapathysubramanian, Baskar, Singh, Asheesh Kumar, and Sarkar, Soumik
Subjects: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Agricultural production requires careful management of inputs such as fungicides, insecticides, and herbicides to ensure a successful crop that is high-yielding, profitable, and of superior seed quality. Current state-of-the-art field crop management relies on coarse-scale crop management strategies, where entire fields are sprayed with pest and disease-controlling chemicals, leading to increased cost and sub-optimal soil and crop management. To overcome these challenges and optimize crop production, we utilize machine learning tools within a virtual field environment to generate localized management plans for farmers to manage biotic threats while maximizing profits. Specifically, we present AgGym, a modular, crop and stress agnostic simulation framework to model the spread of biotic stresses in a field and estimate yield losses with and without chemical treatments. Our validation with real data shows that AgGym can be customized with limited data to simulate yield outcomes under various biotic stress conditions. We further demonstrate that deep reinforcement learning (RL) policies can be trained using AgGym for designing ultra-precise biotic stress mitigation strategies with potential to increase yield recovery with less chemicals and lower cost. Our proposed framework enables personalized decision support that can transform biotic stress management from being schedule based and reactive to opportunistic and prescriptive. We also release the AgGym software implementation as a community resource and invite experts to contribute to this open-sourced publicly available modular environment framework. The source code can be accessed at: https://github.com/SCSLabISU/AgGym.
Published: 2024

34. Scholastic Achievement among Adolescents: Role of Unique Aspects of Home and School Conditions

Author: Yashpal Singh and Davinder Singh Johal
Abstract: Students generally consider scholastic achievement to be the grade they receive after a period of specific instruction. Researchers evaluate a person's scholastic achievement based on the knowledge and skills they have developed in the academic domain, as well as the test results they have obtained. Scholastic achievement is one of the critical parameters to measure affluence; therefore, researchers are keen to investigate the various dimensions that are going to affect it directly or indirectly. The present study focuses on analyzing the distinct dimensions of the home and school environment that show a direct association with adolescents' scholastic achievement. The study uses a correlational research design. This study has selected a sample of 154 individuals from the Delhi region of India. The results obtained through correlation and regression analysis clearly indicate that control and rejection in school settings, along with a permissive home environment, significantly predict the scholastic achievement of female adolescents. The results also revealed that a permissive, protective, and punishing home environment, as well as restrictions imposed in a school setting, are significant predictors of male adolescents' scholastic achievement. From this, it can be concluded that the home environment and school environment dimensions play a pivotal role in the attainment of scholastic achievement among adolescents.
Published: 2024

35. Contributions of Positive Psychology to Higher Education across Asia: A Scoping Review and Unifying Thematic Framework

Author: Deborah A. Hall, Jesika Juliana, Mageswary Manickam, Anil Singh Toor Sunil Singh, Sylvia Tan Sze Wei, Phuong Anh Vuong, Feifei Wu, and Amira Firdaus
Abstract: Positive psychology offers a scientific window onto understanding and enhancing the welfare and growth of university communities, and as well as improving academic performance. This holistic approach is on the rise, yet most research is conducted in Western countries. This situation prevails despite the fact that two-thirds of the world's population live in Asia. This review collated and synthesised published work on applications of positive psychology in higher education conducted in Asia, to describe the current status, explore conceptual perspectives and identify knowledge gaps. A total of 147 articles (157 experimental studies), published since 2000, were included. These were descriptive explorations (12.1%), quantifying associations between positive psychology constructs (62.4%), interventions (19.7%), and psychometric evaluations (5.7%). Key topics were academic leadership, organisational commitment, student engagement and foreign language learning. The thematic framework centered on 'Optimal Functioning', with 'Personal Resources One Can Draw On' and 'How One Interacts With The World' as direct influencing factors, and 'Environment' as an indirect factor. Across the Asian region, positive psychology's major contribution is to identify what types of personal resources are associated with optimal functioning in higher education, but there is little high-quality evidence for intervention benefits, nor a deep understanding of how those resources can be effectively deployed to achieve well-being. As part of the third-wave positive psychology movement, scholars in Asia can play a greater leading role in re-evaluating traditional Western concepts to account for the socio-cultural context in which students and staff are embedded.
Published: 2024
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36. Effectiveness of Buzz Session Teaching and Perception among Medical Students: An Academic Interventional Study

Author: Vibha Gangwar, Pooja Ramakant, Vimala Venkatesh, Saumyendra Vikram Singh, and Amita Singh
Abstract: This interventional study was designed to evaluate the effectiveness of buzz session teaching in large groups and assess undergraduate medical students' perceptions of the buzz session teaching method. The study involved 100 first-year medical students divided into two groups, i.e., "group I" as "buzz first" and "group II" as "didactic first" comprising 50 students each. The topic "Physiology of the Cerebellum" was taught to "group I" through a buzz session and to "group II" through a didactic lecture. After a week, group I received a didactic lecture on the topic "Anterolateral Pathway in the Spinal Cord," whereas "group II" was taught the same by a buzz session. The students of both groups underwent a multiple choice question exam related to the taught topic immediately and again after 15 days of the teaching session. All students were provided feedback on a five-point Likert scale for the buzz session. According to students' perceptions, buzz sessions boosted communication skills and confidence levels by 94.8% and 96.3%, respectively. Of the students, 93.7% felt that the buzz session helped them retain more information and 94.1% thought they made the classroom environment more lively. More buzz sessions were desired by 94.8% of the participants. There was no difference in the marks gained for the acquired topics using buzz sessions and didactic lectures as teaching methods (P > 0.05). The students scored more marks in the tests taken after the buzz session than after the didactic lecture at 15 days of instruction (P < 0.05). The study concluded that students enjoyed the buzz session teaching method. The buzz session increased short-term retention. NEW & NOTEWORTHY: In this current interventional study, we assessed the effectiveness of a buzz session as a novel teaching tool for large-group physiology instruction in first-year undergraduate medical students. Furthermore, we assessed student responses to see how the buzz session was perceived. Experimental evidence indicates that the buzz sessions led to greater retention after 15 days than the didactic lecture approach for physiology teaching in a preclinical context.
Published: 2024
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37. Encrypted malware detection methodology without decryption using deep learning-based approaches

Author: Singh, Abhay Pratap, Singh, Mahendra, Bhatia, Karamjit, and Pathak, Heman
Published: 2024

38. Exercise and Breast Cancer: Exploring Dopamine, Insulin and Estrogen Pathways

Author: Chaudhary, Zaim, Singh, Jaideep, Singh, Kavi Daniel, Fanaei, Mahsa, Gadiparthy, Megan, Ma, Michelle Yingen, and Nallagatla, Tanvi
Abstract: Breast Cancer remains a significant health issue, with roughly around 42,000 maternal deaths occurring each year. The relationship between exercise and breast cancer has been widely studied with substantial evidence suggesting that higher exercise can lower breast cancer proliferation. The meta analysis aimed to investigate the relationship between exercise activating the hormones and receptors of dopamine, insulin, and estrogen and breast cancer across different populations. We conducted a comprehensive search of electronic databases and included studies that reported the association between exercise and the hormones and their impact on breast cancer. Through this analysis, we were able to generate novel mechanisms for the hormones and were able to link exercise and breast cancer. Our findings suggest that an increase in exercise increased the levels of dopamine while lowering insulin and estrogen levels. These effects have a strong relationship with lowering breast cancer proliferation. Previous studies focused on generating already researched pathways to a higher degree. Our research incorporates previously researched pathways with the extension of introducing a new pathway that has not been researched thoroughly. Our research employs a meta-analysis of research papers and scientific data, which allows for a more comprehensive and rigorous examination of the research question and can help identify patterns and trends across studies.
Published: 2024

39. Controlling electronic properties of hexagonal manganites through aliovalent doping and thermoatmospheric history

Author: Småbråten, Didrik R., Danmo, Frida H., Gaukås, Nikolai H., Singh, Sathya P., Kanas, Nikola, Meier, Dennis, Wiik, Kjell, Einarsrud, Mari-Ann, and Selbach, Sverre M.
Subjects: Condensed Matter - Materials Science, Condensed Matter - Strongly Correlated Electrons, J.2.9
Abstract: The family of hexagonal manganites is intensively studied for its multiferroicity, magnetoelectric coupling, improper ferroelectricity, functional domain walls, and topology-related scaling behaviors. It is established that these physical properties are co-determined by the cation sublattices and that aliovalent doping can readily be leveraged to modify them. The doping, however, also impacts the anion defect chemistry and semiconducting properties, which makes the system highly sensitive to the synthesis and processing conditions. Here, we study the electronic properties of YMnO3 as function of aliovalent cation doping and thermoatmospheric history, combining density functional theory calculations with thermopower and thermogravimetric measurements. We show that the charge carrier concentration and transport properties can be controlled via both aliovalent cation dopants and anion defects, enabling reversible switching between n-type and p-type conductivity. This tunability is of importance for envisaged applications of hexagonal manganites in, e.g. next-generation capacitors and domain-wall nanoelectronics, or as catalysts or electrodes in fuel cells or electrolyzers. Furthermore, our approach is transferrable to other transition metal oxides, providing general guidelines for controlling their semiconducting properties., Comment: 6 figures
Published: 2024

40. TurtleBench: A Visual Programming Benchmark in Turtle Geometry

Author: Rismanchian, Sina, Razeghi, Yasaman, Singh, Sameer, and Doroudi, Shayan
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Humans have the ability to reason about geometric patterns in images and scenes from a young age. However, developing large multimodal models (LMMs) capable of similar reasoning remains a challenge, highlighting the need for robust evaluation methods to assess these capabilities. We introduce TurtleBench, a benchmark designed to evaluate LMMs' capacity to interpret geometric patterns -- given visual examples, textual instructions, or both -- and generate precise code outputs. Inspired by turtle geometry, a notion used to teach children foundational coding and geometric concepts, TurtleBench features tasks with patterned shapes that have underlying algorithmic logic. Our evaluation reveals that leading LMMs struggle significantly with these tasks, with GPT-4o achieving only 19\% accuracy on the simplest tasks and few-shot prompting only marginally improves their performance ($<2\%$). TurtleBench highlights the gap between human and AI performance in intuitive and visual geometrical understanding, setting the stage for future research in this area. TurtleBench stands as one of the few benchmarks to evaluate the integration of visual understanding and code generation capabilities in LMMs, setting the stage for future research. Code and Dataset for this paper is provided here: https://github.com/sinaris76/TurtleBench
Published: 2024

41. Interpretable Language Modeling via Induction-head Ngram Models

Author: Kim, Eunji, Mantena, Sriya, Yang, Weiwei, Singh, Chandan, Yoon, Sungroh, and Gao, Jianfeng
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Recent large language models (LLMs) have excelled across a wide range of tasks, but their use in high-stakes and compute-limited settings has intensified the demand for interpretability and efficiency. We address this need by proposing Induction-head ngram models (Induction-Gram), a method that builds an efficient, interpretable LM by bolstering modern ngram models with a hand-engineered "induction head". This induction head uses a custom neural similarity metric to efficiently search the model's input context for potential next-word completions. This process enables Induction-Gram to provide ngram-level grounding for each generated token. Moreover, experiments show that this simple method significantly improves next-word prediction over baseline interpretable models (up to 26%p) and can be used to speed up LLM inference for large models through speculative decoding. We further study Induction-Gram in a natural-language neuroscience setting, where the goal is to predict the next fMRI response in a sequence. It again provides a significant improvement over interpretable models (20% relative increase in the correlation of predicted fMRI responses), potentially enabling deeper scientific investigation of language selectivity in the brain. The code is available at https://github.com/ejkim47/induction-gram.
Published: 2024

42. ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs

Author: Yang, Yuchen, Ugare, Shubham, Zhao, Yifan, Singh, Gagandeep, and Misailovic, Sasa
Subjects: Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition
Abstract: Mixed precision quantization has become an important technique for enabling the execution of deep neural networks (DNNs) on limited resource computing platforms. Traditional quantization methods have primarily concentrated on maintaining neural network accuracy, either ignoring the impact of quantization on the robustness of the network, or using only empirical techniques for improving robustness. In contrast, techniques for robustness certification, which can provide strong guarantees about the robustness of DNNs have not been used during quantization due to their high computation cost. This paper introduces ARQ, an innovative mixed-precision quantization method that not only preserves the clean accuracy of the smoothed classifiers but also maintains their certified robustness. ARQ uses reinforcement learning to find accurate and robust DNN quantization, while efficiently leveraging randomized smoothing, a popular class of statistical DNN verification algorithms, to guide the search process. We compare ARQ with multiple state-of-the-art quantization techniques on several DNN architectures commonly used in quantization studies: ResNet-20 on CIFAR-10, ResNet-50 on ImageNet, and MobileNetV2 on ImageNet. We demonstrate that ARQ consistently performs better than these baselines across all the benchmarks and the input perturbation levels. In many cases, the performance of ARQ quantized networks can reach that of the original DNN with floating-point weights, but with only 1.5% instructions.
Published: 2024

43. Benchmark Data Repositories for Better Benchmarking

Author: Longjohn, Rachel, Kelly, Markelle, Singh, Sameer, and Smyth, Padhraic
Subjects: Computer Science - Machine Learning, Computer Science - Digital Libraries
Abstract: In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets. While a growing body of work establishes guidelines for -- and levies criticisms at -- data and benchmarking practices in machine learning, comparatively less attention has been paid to the data repositories where these datasets are stored, documented, and shared. In this paper, we analyze the landscape of these $\textit{benchmark data repositories}$ and the role they can play in improving benchmarking. This role includes addressing issues with both datasets themselves (e.g., representational harms, construct validity) and the manner in which evaluation is carried out using such datasets (e.g., overemphasis on a few datasets and metrics, lack of reproducibility). To this end, we identify and discuss a set of considerations surrounding the design and use of benchmark data repositories, with a focus on improving benchmarking practices in machine learning., Comment: Accepted to NeurIPS Datasets and Benchmarks 2024
Published: 2024

44. A Comprehensive Review of Current Robot- Based Pollinators in Greenhouse Farming

Author: Singh, Rajmeet, Seneviratne, lakmal, and Hussain, Irfan
Subjects: Computer Science - Robotics
Abstract: The decline of bee and wind-based pollination systems in greenhouses due to controlled environments and limited access has boost the importance of finding alternative pollination methods. Robotic based pollination systems have emerged as a promising solution, ensuring adequate crop yield even in challenging pollination scenarios. This paper presents a comprehensive review of the current robotic-based pollinators employed in greenhouses. The review categorizes pollinator technologies into major categories such as air-jet, water-jet, linear actuator, ultrasonic wave, and air-liquid spray, each suitable for specific crop pollination requirements. However, these technologies are often tailored to particular crops, limiting their versatility. The advancement of science and technology has led to the integration of automated pollination technology, encompassing information technology, automatic perception, detection, control, and operation. This integration not only reduces labor costs but also fosters the ongoing progress of modern agriculture by refining technology, enhancing automation, and promoting intelligence in agricultural practices. Finally, the challenges encountered in design of pollinator are addressed, and a forward-looking perspective is taken towards future developments, aiming to contribute to the sustainable advancement of this technology., Comment: 20 pages, 21 figures
Published: 2024

45. SceneComplete: Open-World 3D Scene Completion in Complex Real World Environments for Robot Manipulation

Author: Agarwal, Aditya, Singh, Gaurav, Sen, Bipasha, Lozano-Pérez, Tomás, and Kaelbling, Leslie Pack
Subjects: Computer Science - Robotics
Abstract: Careful robot manipulation in every-day cluttered environments requires an accurate understanding of the 3D scene, in order to grasp and place objects stably and reliably and to avoid mistakenly colliding with other objects. In general, we must construct such a 3D interpretation of a complex scene based on limited input, such as a single RGB-D image. We describe SceneComplete, a system for constructing a complete, segmented, 3D model of a scene from a single view. It provides a novel pipeline for composing general-purpose pretrained perception modules (vision-language, segmentation, image-inpainting, image-to-3D, and pose-estimation) to obtain high-accuracy results. We demonstrate its accuracy and effectiveness with respect to ground-truth models in a large benchmark dataset and show that its accurate whole-object reconstruction enables robust grasp proposal generation, including for a dexterous hand.
Published: 2024

46. Sample-Efficient Agnostic Boosting

Author: Ghai, Udaya and Singh, Karan
Subjects: Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract: The theory of boosting provides a computational framework for aggregating approximate weak learning algorithms, which perform marginally better than a random predictor, into an accurate strong learner. In the realizable case, the success of the boosting approach is underscored by a remarkable fact that the resultant sample complexity matches that of a computationally demanding alternative, namely Empirical Risk Minimization (ERM). This in particular implies that the realizable boosting methodology has the potential to offer computational relief without compromising on sample efficiency. Despite recent progress, in agnostic boosting, where assumptions on the conditional distribution of labels given feature descriptions are absent, ERM outstrips the agnostic boosting methodology in being quadratically more sample efficient than all known agnostic boosting algorithms. In this paper, we make progress on closing this gap, and give a substantially more sample efficient agnostic boosting algorithm than those known, without compromising on the computational (or oracle) complexity. A key feature of our algorithm is that it leverages the ability to reuse samples across multiple rounds of boosting, while guaranteeing a generalization error strictly better than those obtained by blackbox applications of uniform convergence arguments. We also apply our approach to other previously studied learning problems, including boosting for reinforcement learning, and demonstrate improved results., Comment: NeurIPS 2024 camera ready version
Published: 2024

47. Outcomes from a Workshop on a National Center for Quantum Education

Author: Barnes, Edwin, Bennett, Michael B., Boltasseva, Alexandra, Borish, Victoria, Brown, Bennett, Carr, Lincoln D., Ceballos, Russell R., Dukes, Faith, Easton, Emily W., Economou, Sophia E., Edwards, E. E., Finkelstein, Noah D., Fracchiolla, C., Franklin, Diana, Freericks, J. K., Goss, Valerie, Hannum, Mark, Holincheck, Nancy, Kelly, Angela M., Lanes, Olivia, Lewandowski, H. J., Matsler, Karen Jo, Mercurio, Emily, Montaño, Inès, Murdock, Maajida, Peltz, Kiera, Perron, Justin K., Richardson, Christopher J. K., Rosenberg, Jessica L., Ross, Richard S., Ryu, Minjung, Samuel, Raymond E., Schrode, Nicole, Schwamberger, Susan, Searles, Thomas A., Singh, Chandralekha, Tingle, Alexandra, and Zwickl, Benjamin M.
Subjects: Physics - Physics Education
Abstract: In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.
Published: 2024

48. Quantum Sensing of Broadband Spin Dynamics and Magnon Transport in Antiferromagnets

Author: Melendez, Alex Lee, Das, Shekhar, Rodriguez, Francisco Ayala, Kao, I-Hsuan, Liu, Wenhao, Williams, Archibald J., Lv, Bing, Goldberger, Joshua, Chatterjee, Shubhayu, Singh, Simranjeet, and Hammel, P. Chris
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Optical detection of magnetic resonance using quantum spin sensors (QSS) provides a spatially local and sensitive technique to probe spin dynamics in magnets. However, its utility as a probe of antiferromagnetic resonance (AFMR), wherein the characteristic resonant frequencies substantially exceed the QSS probing frequency, remains an open question. Here, using the nitrogen-vacancy center in diamond as a QSS, we report the first experimental demonstration of optically detected AFMR in layered van der Waals antiferromagnets up to frequencies of 24 GHz, significantly higher than the sensor's spin resonance frequency of 2.87 GHz. To achieve this, we leverage the enhancement of the QSS spin relaxation rate due to low-frequency magnetic field fluctuations that arise from collective late-time dynamics of finite-wavevector magnons excited by the driven uniform AFMR mode. Using optically detected AFMR, we first characterize the temperature and magnetic field dependence on the AFMR modes, which shed light on the intrinsic exchange fields and magnetic anisotropies. Second, we exploit the highly localized sensitivity of the QSS to demonstrate efficient magnon transport over tens of micrometers. Finally, we find that optical detection efficiency in fact increases with increasing frequency, enabling broadband detection of magnetization dynamics. Our work showcases the dual capabilities of QSS as detectors of both high frequency magnetization dynamics and magnon transport, paving the way for understanding and controlling magnetism in Neel states in antiferromagnets.
Published: 2024

49. The Emergence of Laplace Universality in Correlated Processes

Author: Singh, R. K. and Burov, Stanislav
Subjects: Condensed Matter - Statistical Mechanics, Condensed Matter - Soft Condensed Matter
Abstract: In transport processes across materials like glasses, living cells, and porous media, the probability density function of displacements exhibits exponential decay rather than Gaussian behavior. We show that this universal behavior of rare events, termed Laplace tails, emerges even when correlations and memory affect the dynamics. Using a renormalization-based approach, we demonstrate that correlations and memory do not suppress these tails but rather enhance their visibility, even at short timescales. The developed analytical framework refines the concept of correlations for rare events and enables the computation of effective parameters that govern Laplace tails in correlated processes. These findings suggest that correlations can serve as a tunable parameter to control the behavior of rare events in transport., Comment: 2 figures
Published: 2024

50. Exploring Topological Transitivity in Families of Functions

Author: Singh, Anil and Lal, Banarsi
Subjects: Mathematics - Complex Variables
Abstract: We have established various criteria for the topological transitivity of families of continuous (holomorphic) functions. Furthermore, by leveraging the properties of expanding families of meromorphic functions, we offer an alternative proof of Montel's three point theorem.
Published: 2024

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