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1. Synthesis of a silica matrix with ZnO nanoparticles for the fabrication of a recyclable photodegradation system to eliminate methylene blue dye

Author: Naik Harshala Sandip, Sah Parvindar Manejar, Dhangade Manali, Lakkakula Jaya, Raut Rajesh Warluji, Roy Arpita, Alghamdi Saad, Qusty Naeem, Alhindi Zain, Kabrah Ahmed, and Rani Anju
Subjects: silica matrix, zno nanoparticles, photocatalytic activity, recoverable matrix, Chemistry, QD1-999
Abstract: In this study, a silica matrix was utilized as a substrate for zinc oxide nanoparticles (ZnO NPs) to enhance their photocatalytic activity for the degradation of methylene blue (MB) dye. The recovery of the prepared material was also investigated. To compare the performance of the prepared material with ZnO NPs and bare silica, various analyses were conducted. ZnO NPs were synthesized via a coprecipitation method and characterized using Fourier-transform infrared spectra and X-ray diffraction (XRD). The XRD results revealed highly crystalline ZnO NPs with an average crystallite size of less than 100 nm. The presence of ZnO on the silica matrix was confirmed using scanning electron microscopy (SEM) and EDX analysis. The prepared ZnO NPs showed enhanced photocatalytic activity for the degradation of MB dye, and reasonable material recovery was also observed. The silica-coated ZnO NPs degraded MB dye by 97% in just 40 min and retained their photocatalytic activity for up to 20 cycles. In comparison, bare silica exhibited effective photodegradation but lost its photodegradation capacity after five cycles. ZnO NPs without silica coating took 5 h to degrade MB dye. The significant accomplishment in this study is the development of novel materials with high recoverability, simple preparation, and efficient photocatalytic activity. In the future, ZnO NPs supported on a silica matrix can be utilized for various applications.
Published: 2023
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2. Green synthesis of silver nanoparticles via Taxus wallichiana Zucc. plant-derived Taxol: Novel utilization as anticancer, antioxidation, anti-inflammation, and antiurolithic potential

Author: Bhusari Sachin, Sah Parvindar M., Lakkakula Jaya, Roy Arpita, Raut Rajesh, Chondekar Ramesh, Alghamdi Saad, Almehmadi Mazen, Allahyani Mamdouh, Alsaiari Ahad Amer, Aljuaid Abdulelah, and Al-Abdullah Nabeela
Subjects: silver nanoparticle, taxus wallichiana, applications, anti-inflammation, anticancer, Chemistry, QD1-999
Abstract: Throughout history, the utilization of plant products as medicinal remedies has been widespread, with numerous modern drugs finding their origins in the plant kingdom. Taxol, derived from Taxus species, stands out as an exceptional and highly potent anticancer medication. In this study, we present a rapid one-pot synthesis method for silver nanoparticles (AgNPs) using the leaves of Taxus wallichiana Zucca in the presence of sunlight. The synthesized AgNPs were comprehensively characterized using X-ray diffraction, transmission electron microscopy, and HPLC Q-TOF. The AgNPs were further investigated for their antioxidative, anticancer, anti-inflammatory, and antiurolithi properties. The anticancer activity was assessed through a sulforhodamine B assay conducted on the MDA-MB-231 human breast carcinoma cell line and SiHa human cervical cancer cell line. The findings of this study reveal the impressive antioxidative, anticancer, anti-inflammatory, and antiurolithi characteristics exhibited by AgNPs synthesized from leaf extracts. This research highlights an environmentally friendly and cost-effective approach to producing AgNPs by utilizing plant extracts as reducing agents, underscoring the immense potential of natural resources in advancing nanotechnology and its applications.
Published: 2023
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3. Composing Diffusion Policies for Few-shot Learning of Movement Trajectories

Author: Patil, Omkar, Sah, Anant, and Gopalan, Nakul
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract: Humans can perform various combinations of physical skills without having to relearn skills from scratch every single time. For example, we can swing a bat when walking without having to re-learn such a policy from scratch by composing the individual skills of walking and bat swinging. Enabling robots to combine or compose skills is essential so they can learn novel skills and tasks faster with fewer real world samples. To this end, we propose a novel compositional approach called DSE- Diffusion Score Equilibrium that enables few-shot learning for novel skills by utilizing a combination of base policy priors. Our method is based on probabilistically composing diffusion policies to better model the few-shot demonstration data-distribution than any individual policy. Our goal here is to learn robot motions few-shot and not necessarily goal oriented trajectories. Unfortunately we lack a general purpose metric to evaluate the error between a skill or motion and the provided demonstrations. Hence, we propose a probabilistic measure - Maximum Mean Discrepancy on the Forward Kinematics Kernel (MMD-FK), that is task and action space agnostic. By using our few-shot learning approach DSE, we show that we are able to achieve a reduction of over 30% in MMD-FK across skills and number of demonstrations. Moreover, we show the utility of our approach through real world experiments by teaching novel trajectories to a robot in 5 demonstrations., Comment: 6(+1) pages, 6 figures
Published: 2024

4. 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. L., Miller, S., Millhouse, M., Milotti, E., Milotti, V., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. -A., Mishra, A. K., Mishra, A., Mishra, C., Mishra, T., Mitchell, A. L., Mitchell, J. G., Mitra, S., Mitrofanov, V. P., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Moraru, D., More, A., More, S., Moreno, G., Morgan, C., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mourier, P., Mours, B., Mow-Lowry, C. M., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Samanwaya, Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Mundi, J., Mungioli, C. L., Oberg, W. R. Munn, Murakami, Y., Murakoshi, M., Murray, P. G., Muusse, S., Nabari, D., Nadji, S. L., Nagar, A., Nagarajan, N., Nagler, K. 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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

5. Does the oscillatory behavior of the Momentum Spectrum depend on the basis in the Post-Transient Stage?

Author: Sah, Deepak and Singh, Manoranjan P.
Subjects: Quantum Physics, High Energy Physics - Phenomenology, High Energy Physics - Theory, Physics - Plasma Physics
Abstract: Pair creation by a spatially homogeneous, time-dependent electric field is studied within the framework of scalar quantum electrodynamics. We employ the standard Bogoliubov transformation approach to compute the single-particle distribution function in an adiabatic basis. We analyzed the distribution function of created particles in two different adiabatic bases (related by a unitary transformation). A novel dynamical scaling is observed while analyzing the oscillatory momentum spectrum of the pairs created by the Sauter pulsed field at intermediate times, calculated using the two adiabatic bases. In these bases, the same oscillatory momentum spectra are observed but at different times. However, when we scale the time by the point marking the end of the transient stage of dynamical evolution for each case of central momentum, the respective momentum spectra overlap. Furthermore, we study the time evolution of the momentum spectrum in the multi-photon regime and find that the spectra show a multi-modal profile structure at finite times for both choices of basis., Comment: arXiv admin note: text overlap with arXiv:1909.12284 by other authors
Published: 2024

6. Token Pruning using a Lightweight Background Aware Vision Transformer

Author: Sah, Sudhakar, Kumar, Ravish, Rohmetra, Honnesh, and Saboori, Ehsan
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: High runtime memory and high latency puts significant constraint on Vision Transformer training and inference, especially on edge devices. Token pruning reduces the number of input tokens to the ViT based on importance criteria of each token. We present a Background Aware Vision Transformer (BAViT) model, a pre-processing block to object detection models like DETR/YOLOS aimed to reduce runtime memory and increase throughput by using a novel approach to identify background tokens in the image. The background tokens can be pruned completely or partially before feeding to a ViT based object detector. We use the semantic information provided by segmentation map and/or bounding box annotation to train a few layers of ViT to classify tokens to either foreground or background. Using 2 layers and 10 layers of BAViT, background and foreground tokens can be separated with 75% and 88% accuracy on VOC dataset and 71% and 80% accuracy on COCO dataset respectively. We show a 2 layer BAViT-small model as pre-processor to YOLOS can increase the throughput by 30% - 40% with a mAP drop of 3% without any sparse fine-tuning and 2% with sparse fine-tuning. Our approach is specifically targeted for Edge AI use cases., Comment: 7 pages, 2 tables, 4 figures, FITML workshop@NeuRIPS 2024
Published: 2024

7. 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. 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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. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables
Published: 2024

8. ActNAS : Generating Efficient YOLO Models using Activation NAS

Author: Sah, Sudhakar, Kumar, Ravish, Ganji, Darshan C., and Saboori, Ehsan
Subjects: Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract: Activation functions introduce non-linearity into Neural Networks, enabling them to learn complex patterns. Different activation functions vary in speed and accuracy, ranging from faster but less accurate options like ReLU to slower but more accurate functions like SiLU or SELU. Typically, same activation function is used throughout an entire model architecture. In this paper, we conduct a comprehensive study on the effects of using mixed activation functions in YOLO-based models, evaluating their impact on latency, memory usage, and accuracy across CPU, NPU, and GPU edge devices. We also propose a novel approach that leverages Neural Architecture Search (NAS) to design YOLO models with optimized mixed activation functions.The best model generated through this method demonstrates a slight improvement in mean Average Precision (mAP) compared to baseline model (SiLU), while it is 22.28% faster and consumes 64.15% less memory on the reference NPU device., Comment: 7 pages, 4 figures, FITML workshop, NeuRIPS 2024
Published: 2024

9. CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments

Author: Azghan, Reza Rahimi, Glodosky, Nicholas C., Sah, Ramesh Kumar, Cuttler, Carrie, McLaughlin, Ryan, Cleveland, Michael J., and Ghasemzadeh, Hassan
Subjects: Computer Science - Machine Learning
Abstract: Wearable sensor systems have demonstrated a great potential for real-time, objective monitoring of physiological health to support behavioral interventions. However, obtaining accurate labels in free-living environments remains difficult due to limited human supervision and the reliance on self-labeling by patients, making data collection and supervised learning particularly challenging. To address this issue, we introduce CUDLE (Cannabis Use Detection with Label Efficiency), a novel framework that leverages self-supervised learning with real-world wearable sensor data to tackle a pressing healthcare challenge: the automatic detection of cannabis consumption in free-living environments. CUDLE identifies cannabis consumption moments using sensor-derived data through a contrastive learning framework. It first learns robust representations via a self-supervised pretext task with data augmentation. These representations are then fine-tuned in a downstream task with a shallow classifier, enabling CUDLE to outperform traditional supervised methods, especially with limited labeled data. To evaluate our approach, we conducted a clinical study with 20 cannabis users, collecting over 500 hours of wearable sensor data alongside user-reported cannabis use moments through EMA (Ecological Momentary Assessment) methods. Our extensive analysis using the collected data shows that CUDLE achieves a higher accuracy of 73.4%, compared to 71.1% for the supervised approach, with the performance gap widening as the number of labels decreases. Notably, CUDLE not only surpasses the supervised model while using 75% less labels, but also reaches peak performance with far fewer subjects., Comment: 8 pages, 5 figures, 1 table
Published: 2024

10. MCUBench: A Benchmark of Tiny Object Detectors on MCUs

Author: Sah, Sudhakar, Ganji, Darshan C., Grimaldi, Matteo, Kumar, Ravish, Hoffman, Alexander, Rohmetra, Honnesh, and Saboori, Ehsan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We introduce MCUBench, a benchmark featuring over 100 YOLO-based object detection models evaluated on the VOC dataset across seven different MCUs. This benchmark provides detailed data on average precision, latency, RAM, and Flash usage for various input resolutions and YOLO-based one-stage detectors. By conducting a controlled comparison with a fixed training pipeline, we collect comprehensive performance metrics. Our Pareto-optimal analysis shows that integrating modern detection heads and training techniques allows various YOLO architectures, including legacy models like YOLOv3, to achieve a highly efficient tradeoff between mean Average Precision (mAP) and latency. MCUBench serves as a valuable tool for benchmarking the MCU performance of contemporary object detectors and aids in model selection based on specific constraints., Comment: Code and data are available at https://github.com/Deeplite/deeplite-torch-zoo
Published: 2024

11. Generating Analytic Specifications for Data Visualization from Natural Language Queries using Large Language Models

Author: Sah, Subham, Mitra, Rishab, Narechania, Arpit, Endert, Alex, Stasko, John, and Dou, Wenwen
Subjects: Computer Science - Human-Computer Interaction
Abstract: Recently, large language models (LLMs) have shown great promise in translating natural language (NL) queries into visualizations, but their "black-box" nature often limits explainability and debuggability. In response, we present a comprehensive text prompt that, given a tabular dataset and an NL query about the dataset, generates an analytic specification including (detected) data attributes, (inferred) analytic tasks, and (recommended) visualizations. This specification captures key aspects of the query translation process, affording both explainability and debuggability. For instance, it provides mappings from the detected entities to the corresponding phrases in the input query, as well as the specific visual design principles that determined the visualization recommendations. Moreover, unlike prior LLM-based approaches, our prompt supports conversational interaction and ambiguity detection capabilities. In this paper, we detail the iterative process of curating our prompt, present a preliminary performance evaluation using GPT-4, and discuss the strengths and limitations of LLMs at various stages of query translation. The prompt is open-source and integrated into NL4DV, a popular Python-based natural language toolkit for visualization, which can be accessed at https://nl4dv.github.io., Comment: 6 pages, 3 figures. To appear in NLVIZ workshop 2024 (IEEE VIS 2024)
Published: 2024

12. Optical control of multiple resistance levels in graphene for memristic applications

Author: Mann, Harsimran Kaur, Mondal, Mainak, Sah, Vivek, Watanabe, Kenji, Taniguchi, Takashi, Singh, Akshay, and Bid, Aveek
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract: Neuromorphic computing has emphasized the need for memristors with non-volatile, multiple conductance levels. This paper demonstrates the potential of hexagonal boron nitride (hBN)/graphene heterostructures to act as memristors with multiple resistance states that can be optically tuned using visible light. The number of resistance levels in graphene can be controlled by modulating doping levels, achieved by varying the electric field strength or adjusting the duration of optical illumination. Our measurements show that this photodoping of graphene results from the optical excitation of charge carriers from the nitrogen-vacancy levels of hBN to its conduction band, with these carriers then being transferred to graphene by the gate-induced electric field. We develop a quantitative model to describe our observations. Additionally, utilizing our device architecture, we propose a memristive crossbar array for vector-matrix multiplications., Comment: 22 pages, Comments welcome
Published: 2024

13. Discovering the Cosmic Evolution of Supermassive Black Holes using Nano-Hertz Gravitational Waves and Galaxy Surveys

Author: Sah, Mohit Raj and Mukherjee, Suvodip
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, General Relativity and Quantum Cosmology
Abstract: The formation and evolution of supermassive black holes (SMBHs) in the Universe remains an open question in cosmology. We show for the first time that the evolution of SMBHs with redshift leads to a unique signature on the angular cross-correlation power spectrum between the multi-frequency nano-hertz (nHz) stochastic gravitational wave (SGWB) and the galaxy density in the Universe. By using galaxy catalogs from the upcoming Rubin LSST Observatory in synergy with the nHz SGWB signal accessible from the Square Kilometer Array, we can measure this signal with a signal-to-noise ratio above five, thereby opening a new observational window to the cosmic evolution of SMBHs across redshift. This discovery space that can be opened by the cross-correlation of the nHz SGWB will not be possible by any other currently known techniques., Comment: 10 pages, 6 figures
Published: 2024

14. Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run

Author: Raman, Gayathri, Ronchini, Samuele, Delaunay, James, Tohuvavohu, Aaron, Kennea, Jamie A., Parsotan, Tyler, Ambrosi, Elena, Bernardini, Maria Grazia, Campana, Sergio, Cusumano, Giancarlo, D'Ai, Antonino, D'Avanzo, Paolo, D'Elia, Valerio, De Pasquale, Massimiliano, Dichiara, Simone, Evans, Phil, Hartmann, Dieter, Kuin, Paul, Melandri, Andrea, O'Brien, Paul, Osborne, Julian P., Page, Kim, Palmer, David M., Sbarufatti, Boris, Tagliaferri, Gianpiero, Troja, Eleonora, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., 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., Anand, S., 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., 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., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., 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., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., 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., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., 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., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., 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., Castaldi, G., 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, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, R. -J., Chanial, P., Chao, S., Chapman-Bird, C., 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, K. H., Chen, X., Chen, Yi-Ru, Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corbitt, T. 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P., Spera, M., Spinicelli, P., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Sullivan, A. G., Sullivan, K. D., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., 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, Shubhanshu, Tiwari, Srishti, 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., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., 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., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, D., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, L. -C., Yang, Y., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables
Published: 2024

15. Effect of Strain on the Band Gap of Monolayer MoS$_2$

Author: Sah, Raj K., Tang, Hong, Shahi, Chandra, Ruzsinszky, Adrienn, and Perdew, John P.
Subjects: Condensed Matter - Materials Science
Abstract: Monolayer $\mathrm{MoS_2}$ under strain has many interesting properties and possible applications in technology. A recent experimental study examined the effect of strain on the bandgap of monolayer $\mathrm{MoS_2}$ on a mildly curved graphite surface, reporting that under biaxial strain with a Poisson's ratio of 0.44, the bandgap decreases at a rate of 400 meV/% strain. In this work, we performed density functional theory (DFT) calculations for a free-standing $\mathrm{MoS_2}$ monolayer, using the generalized gradient approximation (GGA) PBE, the hybrid functional HSE06, and many-body perturbation theory with the GW approximation using PBE wavefunctions (G0W0@PBE). We found that under biaxial strain with the experimental Poisson's ratio, the bandgap decreases at rates of 63 meV/% strain (PBE), 73 meV/% strain (HSE06), and 43 meV/% strain (G0W0@PBE), which are significantly smaller than the experimental rate. We also found that PBE predicts a similarly smaller rate (90 meV/% strain) for a different Poisson's ratio of 0.25. Spin-orbit correction (SOC) has little effect on the gap or its strain dependence. Additionally, we observed a semiconductor-to-metal transition under an equal tensile biaxial strain of 10% and a transition from a direct to an indirect bandgap, consistent with previous theoretical work.
Published: 2024

16. Accessibility and Utilization of Sexual and Reproductive Health Services among People with Disabilities in Nepal

Author: Singh, Devendra Raj, K.C, Samita, Sunuwar, Dev Ram, Shrestha, Sarina, Sah, Rajeeb Kumar, Ghimire, Sushmita, Khadka, Dan Bahadur, Sah, Lalita Kumari, Shah, Jennifer Mathias, and Karki, Kshitij
Published: 2024
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17. Sustaining Critical Approaches to Translanguaging in Education: A Contextual Framework

Author: Anna Mendoza, Laura Hamman-Ortiz, Zhongfeng Tian, Shakina Rajendram, Kevin W. H. Tai, Wing Yee Jenifer Ho, and Pramod K. Sah
Abstract: Translanguaging remains a timely and important topic in bi/multilingual education. The most recent turn in translanguaging scholarship involves attention to translanguaging "in context" in response to critiques of translanguaging as a universally empowering educational practice. In this paper, seven early career translanguaging scholars propose a framework for researching translanguaging "in context," drawing on the Douglas Fir Group's (2016) transdisciplinary framework for language acquisition. Examining translanguaging in context entails paying attention to who in a classroom wields power, as a result of their greater proficiency in societally valued languages, their more "standard" ways of speaking these languages, their greater familiarity with academic literacies valued at school, and/or their more "legitimate" forms of translanguaging. In our framework for researching translanguaging in context, we propose three principles. The first principle is obvious: (1) not to do so apolitically. The other two principles describe a synergy between ethnographic research and teacher-researcher collaborative research: (2) ethnographic research can assess macro-level language ideologies and enacted language hegemonies at the micro- and meso levels, and (3) teacher-researcher collaborations must create and sustain inclusive, equitable classroom social orders and alternative academic norms "different" from the ones documented to occur in context if left by chance.
Published: 2024
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18. On the Spielman-Teng Conjecture

Author: Sah, Ashwin, Sahasrabudhe, Julian, and Sawhney, Mehtaab
Subjects: Mathematics - Probability, Mathematics - Combinatorics
Abstract: Let $M$ be an $n\times n$ matrix with iid subgaussian entries with mean $0$ and variance $1$ and let $\sigma_n(M)$ denote the least singular value of $M$. We prove that \[\mathbb{P}\big( \sigma_{n}(M) \leq \varepsilon n^{-1/2} \big) = (1+o(1)) \varepsilon + e^{-\Omega(n)}\] for all $0 \leq \varepsilon \ll 1$. This resolves, up to a $1+o(1)$ factor, a seminal conjecture of Spielman and Teng., Comment: 27 pages
Published: 2024

19. Adaptive Koopman Embedding for Robust Control of Complex Nonlinear Dynamical Systems

Author: Singh, Rajpal, Sah, Chandan Kumar, and Keshavan, Jishnu
Subjects: Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract: The discovery of linear embedding is the key to the synthesis of linear control techniques for nonlinear systems. In recent years, while Koopman operator theory has become a prominent approach for learning these linear embeddings through data-driven methods, these algorithms often exhibit limitations in generalizability beyond the distribution captured by training data and are not robust to changes in the nominal system dynamics induced by intrinsic or environmental factors. To overcome these limitations, this study presents an adaptive Koopman architecture capable of responding to the changes in system dynamics online. The proposed framework initially employs an autoencoder-based neural network that utilizes input-output information from the nominal system to learn the corresponding Koopman embedding offline. Subsequently, we augment this nominal Koopman architecture with a feed-forward neural network that learns to modify the nominal dynamics in response to any deviation between the predicted and observed lifted states, leading to improved generalization and robustness to a wide range of uncertainties and disturbances compared to contemporary methods. Extensive tracking control simulations, which are undertaken by integrating the proposed scheme within a Model Predictive Control framework, are used to highlight its robustness against measurement noise, disturbances, and parametric variations in system dynamics., Comment: Corrected the title
Published: 2024

20. Longitudinal Momentum Spectra of pair created in a pulsed field at finite times: Are Oscillations 'Real'

Author: Sah, Deepak and Singh, Manoranjan P.
Subjects: High Energy Physics - Phenomenology, Quantum Physics
Abstract: We discuss the mechanism of production of electron-positron pairs from the vacuum in a time-varying, spatially uniform pulsed electric field. We analytically compute the probability of $(e^+ e^-) $pair production in momentum space using the exact solution of the one-particle time-dependent Dirac equation and compare the result with quantum kinetic theory (QKT). Both approaches allow us to study the particle momentum spectrum at any instant in time and can potentially unveil valuable information regarding quantum non-equilibrium physics. We analyze both approaches' momentum spectra of the created particles at any instant. We observe a multi-profile structure in the LMS. This multi-profile structure clearly illustrates the quantum interference effects associated with pair production. It is worth noting that both approaches exhibit quantum interference patterns at finite times, manifested as oscillations observed in the LMS. We highlight that this quantum signature is a universal behavior seen in the momentum spectra at finite times, where the electric field is nearly zero., Comment: Contribution to the 5th Conference on Extremely High-Intensity Laser Physics (ExHILP 2023) at the Center for Relativistic Laser Science (CoReLS), Institute for Basic Science (IBS), Gwangju, Korea. For the complete version, see arXiv:2405.02947v1
Published: 2024

21. Depth-resolved profile of the interfacial ferromagnetism in $CaMnO_{3}/CaRuO_{3}$ superlattices

Author: Paudel, J. R., Tehrani, A. Mansouri, Terilli, M., Kareev, M., Grassi, J., Sah, R. K., Wu, L., Strocov, V. N., Klewe, C., Shafer, P., Chakhalian, J., Spaldin, N. A., and Gray, A. X.
Subjects: Condensed Matter - Materials Science, Condensed Matter - Strongly Correlated Electrons
Abstract: Emergent magnetic phenomena at interfaces represent a frontier in materials science, pivotal for advancing technologies in spintronics and magnetic storage. In this letter, we utilize a suite of advanced X-ray spectroscopic and scattering techniques to investigate emergent interfacial ferromagnetism in oxide superlattices comprised of antiferromagnetic CaMnO3 and paramagnetic CaRuO3. Our findings challenge prior theoretical models by demonstrating that the ferromagnetism extends beyond the interfacial layer into multiple unit cells of CaMnO3 and exhibits an asymmetric profile. Complementary density functional calculations reveal that the interfacial ferromagnetism is driven by the double exchange mechanism, facilitated by charge transfer from Ru to Mn ions. Additionally, defect chemistry, particularly the presence of oxygen vacancies, likely plays a crucial role in modifying the magnetic moments at the interface, leading to the observed asymmetry between the top and bottom CaMnO3 interfacial magnetic layers. Our findings underscore the potential of manipulating interfacial ferromagnetism through point defect engineering.
Published: 2024

22. Can Perplexity Predict Fine-Tuning Performance? An Investigation of Tokenization Effects on Sequential Language Models for Nepali

Author: Luitel, Nishant, Bekoju, Nirajan, Sah, Anand Kumar, and Shakya, Subarna
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Recent language models use subwording mechanisms to handle Out-of-Vocabulary(OOV) words seen during test time and, their generation capacity is generally measured using perplexity, an intrinsic metric. It is known that increasing the subword granularity results in a decrease of perplexity value. However, the study of how subwording affects the understanding capacity of language models has been very few and only limited to a handful of languages. To reduce this gap we used 6 different tokenization schemes to pretrain relatively small language models in Nepali and used the representations learned to finetune on several downstream tasks. Although byte-level BPE algorithm has been used in recent models like GPT, RoBERTa we show that on average they are sub-optimal in comparison to algorithms such as SentencePiece in finetuning performances for Nepali. Additionally, similar recent studies have focused on the Bert-based language model. We, however, pretrain and finetune sequential transformer-based language models., Comment: 11 pages
Published: 2024

23. Contextual Spelling Correction with Language Model for Low-resource Setting

Author: Luitel, Nishant, Bekoju, Nirajan, Sah, Anand Kumar, and Shakya, Subarna
Subjects: Computer Science - Computation and Language
Abstract: The task of Spell Correction(SC) in low-resource languages presents a significant challenge due to the availability of only a limited corpus of data and no annotated spelling correction datasets. To tackle these challenges a small-scale word-based transformer LM is trained to provide the SC model with contextual understanding. Further, the probabilistic error rules are extracted from the corpus in an unsupervised way to model the tendency of error happening(error model). Then the combination of LM and error model is used to develop the SC model through the well-known noisy channel framework. The effectiveness of this approach is demonstrated through experiments on the Nepali language where there is access to just an unprocessed corpus of textual data., Comment: 8 pages
Published: 2024
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24. Imprints of Supermassive Black Hole Evolution on the Spectral and Spatial Anisotropy of Nano-Hertz Stochastic Gravitational-Wave Background

Author: Sah, Mohit Raj, Mukherjee, Suvodip, Saeedzadeh, Vida, Babul, Arif, Tremmel, Michael, and Quinn, Thomas R.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: The formation and evolution of supermassive black holes (SMBHs) remains an open question in the field of modern cosmology. The detection of nanohertz (n-Hz) gravitational waves via pulsar timing arrays (PTAs) in the form of individual events and the stochastic gravitational wave background (SGWB) offers a promising avenue for studying SMBH evolution across cosmic time, with SGWB signal being the immediately detectable signal with the currently accessible telescope sensitivities. By connecting the galaxy properties in the large scale (Gpc scale) cosmological simulation such as \texttt{MICECAT} with the small scale ($\sim$ Mpc scale) galaxy simulations from \texttt{ROMULUS}, we show that different scenarios of galaxy-SMBH evolution with redshift leads to a frequency-dependent spatial anisotropy in the SGWB signal. The presence of slow evolution of the SMBHs in the Universe leads to a pronounced blue anisotropic spectrum of the SGWB. In contrast, if SMBHs grow faster in the Universe in lighter galaxies, the frequency-dependent spatial anisotropy exhibits a more flattened anisotropic spectrum. This additional aspect of the SGWB signal on top of the monopole SGWB signal, can give insight on how the SMBHs form in the high redshift Universe and its interplay with the galaxy formation from future measurements., Comment: 17 pages, 11 figures
Published: 2024
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25. Comparing Meta-GGAs, +U Corrections, and Hybrid Functionals for Polaronic Point Defects in Layered MnO$_2$, NiO$_2$, and KCoO$_2$

Author: Sah, Raj K., Zdilla, Michael J., Borguet, Eric, and Perdew, John P.
Subjects: Physics - Computational Physics
Abstract: Defects in a material can significantly tune properties and enhance utility. Hybrid functionals like HSE06 are often used to describe solids with such defects. However, geometry optimization (including accounting for effects such as Jahn-Teller distortion) using hybrid functionals is challenging for the large supercells needed for defect study. The proposed r$^2$SCAN+rVV10+U+$\mathrm{U_d}$ method, which is computationally much cheaper and faster than hybrid functionals, can successfully describe defects in materials with the proper choice of U (for the d orbitals of the host atom) and $\mathrm{U_d}$ (for those of the defect atom), as shown here for small polaron defects in layered transition-metal oxides. For a range of U and $\mathrm{U_d}$ around literature values (from solid-state reaction energies) for a given transition-metal ion and its oxidation state, we find that this approach predicts localized polaronic states in band gaps, as hybrid functionals do. The layered materials birnessite ($\mathrm{K_nMnO_2}, n=0.03 $) and $\mathrm{K_nNiO_2},n=0.03$, with one K atom intercalated between layers in a supercell, are found to have one localized occupied $\mathrm{e_g}$ polaronic state on the transition metal ion reduced by the insertion of the K atom, when the geometry is calculated as above using published U values. The expected Jahn-Teller distortion is not observed when U=$\mathrm{U_d}$=0. Layered cobalt oxide with additional potassium ions intercalated ($\mathrm{K_nCoO_2},n=1.03$) is different, due to a dramatic difference in electronic configuration of the defected Co(II) ion: A single extra K atom in the supercell leads to four localized electrons in the band gap, using standard U values, and even for U=$\mathrm{U_d}$=0.
Published: 2024

26. AipanVR: A Virtual Reality Experience for Preserving Uttarakhand's Traditional Art Form

Author: Chaudhary, Nishant, Raj, Mihir, Bhattacharjee, Richik, Srivastava, Anmol, Sah, Rakesh, and Badoni, Pankaj
Subjects: Computer Science - Human-Computer Interaction
Abstract: This paper presents a demonstration of the developed prototype showcasing a way to preserve the Intangible Cultural Heritage of Uttarakhand, India. Aipan is a traditional art form practiced in the Kumaon region in the state of Uttarakhand. It is typically used to decorate floors and walls at places of worship or entrances of homes and is considered auspicious to begin any work or event. This art is associated with a great degree of social, cultural as well as religious significance and is passed from generation to generation. However, in the present era of modernization and technological advancements, this art form now stands on the verge of depletion. This study presents a humble attempt to preserve this vanishing art form through the use of Virtual Reality (VR). Ethnographic studies were conducted in Almora, Nainital, and Haldwani regions of Uttarakhand to trace the origins as well as to gain a deeper understanding of this art form. A total of ten (N =10) Aipan designers were interviewed. Several interesting insights are revealed through these studies that show the potential to be incorporated as a VR experience., Comment: Demonstrated at ISMAR 2020
Published: 2024

27. QGen: On the Ability to Generalize in Quantization Aware Training

Author: AskariHemmat, MohammadHossein, Jeddi, Ahmadreza, Hemmat, Reyhane Askari, Lazarevich, Ivan, Hoffman, Alexander, Sah, Sudhakar, Saboori, Ehsan, Savaria, Yvon, and David, Jean-Pierre
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract: Quantization lowers memory usage, computational requirements, and latency by utilizing fewer bits to represent model weights and activations. In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance. In particular, first, we develop a theoretical model for quantization in neural networks and demonstrate how quantization functions as a form of regularization. Second, motivated by recent work connecting the sharpness of the loss landscape and generalization, we derive an approximate bound for the generalization of quantized models conditioned on the amount of quantization noise. We then validate our hypothesis by experimenting with over 2000 models trained on CIFAR-10, CIFAR-100, and ImageNet datasets on convolutional and transformer-based models.
Published: 2024

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

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akçay, S., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentara, I., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Char, P., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chattopadhyay, D., Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. 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C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Observatory. The primary component of the source has a mass less than $5~M_\odot$ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of $55^{+127}_{-47}~\text{Gpc}^{-3}\,\text{yr}^{-1}$ for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star-black hole merger, GW230529_181500-like sources constitute about 60% of the total merger rate inferred for neutron star-black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star-black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap., Comment: 45 pages (10 pages author list, 13 pages main text, 1 page acknowledgements, 13 pages appendices, 8 pages bibliography), 17 figures, 16 tables. Update to match version published in The Astrophysical Journal Letters. Data products available from https://zenodo.org/records/10845779
Published: 2024
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29. Ultralight vector dark matter search using data from the KAGRA O3GK run

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., 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., Anand, S., 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., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., 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., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., 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. 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Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract: Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for $U(1)_{B-L}$ gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the $U(1)_{B-L}$ gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM., Comment: 20 pages, 5 figures
Published: 2024

30. Prediction of central deflection and slenderness limit for lateral stability of simply supported concrete beam using machine learning techniques

Author: Mustafa, Rashid, Ahmad, Md Talib, Kumar, Akash, Kumar, Sonu, Sah, Navin Kumar, and Kumar, Abhishek
Published: 2024
Full Text: View/download PDF

31. Assessing seismic gap in adjacent RC buildings post Gorkha earthquake in Nepal

Author: Upadhayay, Aviral, Maskey, Prem Nath, Manandhar, Rajiv, and Sah, Binay Kumar
Published: 2024
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32. Unveiling genetic diversity and population structure for nutraceutical and grain ionome profile in traditional rice cultivars

Author: Anadhu, S., Visakh, R. L., Sah, R. P., Soni, K. B., Alex, Swapna, Manju, R. V., and Beena, R.
Published: 2024
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33. Unraveling the genetic diversity in selected rice cultivars released in the last 60 years using gene-based yield-related markers

Author: Mohanty, S. P., Khan, A., Patra, S., Behera, S., Nayak, A. K., Upadhyaya, S., Moharana, D., T. P., Muhammed Azharudheen, Anilkumar, C., Kar, M. K., Gowda, G. Basana, Marndi, B. C., and Sah, R. P.
Published: 2024
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34. A CFD Model of COREX Gas-Based DRI Furnace for Optimum Gas Consumption

Author: Blahatia, Kunal, Veeramani, Vignesh, Rajendran, Vijayakumar, Pasupalak, Mrunmaya, and Sah, Rameshwar
Published: 2024
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35. Parameters affecting the success rate of microscopic testicular sperm extraction in male patients with a solitary testis and non-obstructive azoospermia

Author: Hasirci, Eray, Ceyhan, Erman, Gultekin, Mehmet Hamza, Kayra, Mehmet Vehbi, Kizilkan, Yalcin, Yildirim, Omer, Altan, Mesut, Ure, Iyimser, Cicek, Tufan, Sah, Cem, Incekas, Caner, Gul, Umit, and Turunc, Tahsin
Published: 2024
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36. Advancing COVID-19 poverty estimation with satellite imagery-based deep learning techniques: a systematic review

Author: Mishra, Shruti, Satapathy, Sandeep Kumar, Cho, Sung-Bae, Mohanty, Sachi Nandan, Sah, Sweeti, and Sharma, Shweta
Published: 2024
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37. Feasible Beneficiation Studies to Enrich Grade and Recovery by Adopting Grinding Followed by Gravity and Magnetic Separation

Author: Mitra, D. Vijaya, Jason, Kevin, Parmeswaran, Karthik, Devi, T. Uma, and Sah, Rameswar
Published: 2024
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38. Enhancing corrosion resistance of plasma electrolytic oxidation coatings on AM50 Mg alloy by inhibitor containing Ba(NO3)2 solutions

Author: Ma, Jirui, Lu, Xiaopeng, Sah, Santosh Prasad, Chen, Qianqian, Zhang, You, and Wang, Fuhui
Published: 2024
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39. Patient Experience of Hepatocellular Carcinoma and Their Treatment Goals: An International Qualitative Study and Patient Journey Map

Author: Wörns, Marcus-Alexander, Burns, Danielle, Paskow, Michael, Makin, Harriet, Miller, Jordan, Turner, Lucy M., and Sah, Janvi
Published: 2024
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40. Chemical Characteristics and Public Health Risk Assessment of PM2.5-Bound Elements in Sheohar, India

Author: Sah, Dinesh
Published: 2024
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41. Identification of Locations in Mecca using Image Pre-Processing, Neural Networks and Deep Learning

Author: Taha, Mohammed Abdulghani, Sah, Melike, and Direkoglu, Cem
Published: 2024
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42. Quasipolynomial bounds on the inverse theorem for the Gowers $U^{s+1}[N]$-norm

Author: Leng, James, Sah, Ashwin, and Sawhney, Mehtaab
Subjects: Mathematics - Combinatorics, Mathematics - Dynamical Systems, Mathematics - Number Theory
Abstract: We prove quasipolynomial bounds on the inverse theorem for the Gowers $U^{s+1}[N]$-norm. The proof is modeled after work of Green, Tao, and Ziegler and uses as a crucial input recent work of the first author regarding the equidistribution of nilsequences. In a companion paper, this result will be used to improve the bounds on Szemer\'{e}di's theorem., Comment: 100 pages
Published: 2024

43. Improved Bounds for Szemer\'{e}di's Theorem

Author: Leng, James, Sah, Ashwin, and Sawhney, Mehtaab
Subjects: Mathematics - Combinatorics, Mathematics - Number Theory
Abstract: Let $r_k(N)$ denote the size of the largest subset of $[N] = \{1,\ldots,N\}$ with no $k$-term arithmetic progression. We show that for $k\ge 5$, there exists $c_k>0$ such that \[r_k(N)\ll N\exp(-(\log\log N)^{c_k}).\] Our proof is a consequence of recent quasipolynomial bounds on the inverse theorem for the Gowers $U^k$-norm as well as the density increment strategy of Heath-Brown and Szemer\'{e}di as reformulated by Green and Tao., Comment: 13 pages
Published: 2024

44. Decay-protected superconducting qubit with fast control enabled by integrated on-chip filters

Author: Sah, Aashish, Kundu, Suman, Suominen, Heikki, Chen, Qiming, and Möttönen, Mikko
Subjects: Quantum Physics
Abstract: Achieving fast gates and long coherence times for superconducting qubits presents challenges, typically requiring either a stronger coupling of the drive line or an excessively strong microwave signal to the qubit. To address this, we introduce on-chip filters of the qubit drive exhibiting a stopband at the qubit frequency, thus enabling long coherence times and strong coupling at the subharmonic frequency, facilitating fast single-qubit gates, and reduced thermal load. The filters exhibit an extrinsic relaxation time of a few seconds while enabling sub-10-ns gates with subharmonic control. Here we show up to 200-fold improvement in the measured relaxation time at the stopband. Furthermore, we implement subharmonic driving of Rabi oscillations with a $\pi$ pulse duration of 12 ns. Our demonstration of on-chip filters and efficient subharmonic driving in a two-dimensional quantum processor paves the way for a scalable qubit architecture with reduced thermal load and noise from the control line., Comment: Main document (15 pages, 5 figures), Supplementary material (10 pages, 7 figures, 1 table)
Published: 2024
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45. A central limit theorem for the matching number of a sparse random graph

Author: Glasgow, Margalit, Kwan, Matthew, Sah, Ashwin, and Sawhney, Mehtaab
Subjects: Mathematics - Combinatorics, Mathematics - Probability
Abstract: In 1981, Karp and Sipser proved a law of large numbers for the matching number of a sparse Erd\H{o}s-R\'enyi random graph, in an influential paper pioneering the so-called differential equation method for analysis of random graph processes. Strengthening this classical result, and answering a question of Aronson, Frieze and Pittel, we prove a central limit theorem in the same setting: the fluctuations in the matching number of a sparse random graph are asymptotically Gaussian. Our new contribution is to prove this central limit theorem in the subcritical and critical regimes, according to a celebrated algorithmic phase transition first observed by Karp and Sipser. Indeed, in the supercritical regime, a central limit theorem has recently been proved in the PhD thesis of Krea\v{c}i\'c, using a stochastic generalisation of the differential equation method (comparing the so-called Karp-Sipser process to a system of stochastic differential equations). Our proof builds on these methods, and introduces new techniques to handle certain degeneracies present in the subcritical and critical cases. Curiously, our new techniques lead to a non-constructive result: we are able to characterise the fluctuations of the matching number around its mean, despite these fluctuations being much smaller than the error terms in our best estimates of the mean. We also prove a central limit theorem for the rank of the adjacency matrix of a sparse random graph.
Published: 2024

46. Many-excitation removal of a transmon qubit using a single-junction quantum-circuit refrigerator and a two-tone microwave drive

Author: Teixeira, Wallace, Mörstedt, Timm, Viitanen, Arto, Kivijärvi, Heidi, Gunyhó, András, Tiiri, Maaria, Kundu, Suman, Sah, Aashish, Vadimov, Vasilii, and Möttönen, Mikko
Subjects: Quantum Physics
Abstract: Achieving fast and precise initialization of qubits is a critical requirement for the successful operation of quantum computers. The combination of engineered environments with all-microwave techniques has recently emerged as a promising approach for the reset of superconducting quantum devices. In this work, we experimentally demonstrate the utilization of a single-junction quantum-circuit refrigerator (QCR) for an expeditious removal of several excitations from a transmon qubit. The QCR is indirectly coupled to the transmon through a resonator in the dispersive regime, constituting a carefully engineered environmental spectrum for the transmon. Using single-shot readout, we observe excitation stabilization times down to roughly $500$ ns, a $20$-fold speedup with QCR and a simultaneous two-tone drive addressing the $e$-$f$ and $f0$-$g1$ transitions of the system. Our results are obtained at a $48$-mK fridge temperature and without postselection, fully capturing the advantage of the protocol for the short-time dynamics and the drive-induced detrimental asymptotic behavior in the presence of relatively hot other baths of the transmon. We validate our results with a detailed Liouvillian model truncated up to the three-excitation subspace, from which we estimate the performance of the protocol in optimized scenarios, such as cold transmon baths and fine-tuned driving frequencies. These results pave the way for optimized reset of quantum-electric devices using engineered environments and for dissipation-engineered state preparation., Comment: 14 pages, 5 figures
Published: 2024
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47. Minimum-Cost Sensor Channel Selection For Wearable Computing

Author: Sah, Ramesh Kumar and Ghasemzadeh, Hassan
Subjects: Computer Science - Networking and Internet Architecture
Abstract: Sensor systems are constrained by design and finding top sensor channel(s) for a given computational task is an important but hard problem. We define an optimization framework and mathematically formulate the minimum-cost channel selection problem. We then propose two novel algorithms of varying scope and complexity to solve the optimization problem. Branch and bound channel selection finds a globally optimal channel subset and the greedy channel selection finds the best intermediate subset based on the value of a score function. Proposed channel selection algorithms are conditioned with performance as well as the cost of the channel subset. We evaluate both algorithms on two publicly available time series datasets of human activity recognition and mental task detection. Branch and bound channel selection achieved a cost saving of up to 94.8% and the greedy search reduced the cost by 89.6% while maintaining performance thresholds.
Published: 2024

48. The Impact of Elicitation and Contrasting Narratives on Engagement, Recall and Attitude Change with News Articles Containing Data Visualization

Author: Rogha, Milad, Sah, Subham, Karduni, Alireza, Markant, Douglas, and Dou, Wenwen
Subjects: Computer Science - Human-Computer Interaction
Abstract: News articles containing data visualizations play an important role in informing the public on issues ranging from public health to politics. Recent research on the persuasive appeal of data visualizations suggests that prior attitudes can be notoriously difficult to change. Inspired by an NYT article, we designed two experiments to evaluate the impact of elicitation and contrasting narratives on attitude change, recall, and engagement. We hypothesized that eliciting prior beliefs leads to more elaborative thinking that ultimately results in higher attitude change, better recall, and engagement. Our findings revealed that visual elicitation leads to higher engagement in terms of feelings of surprise. While there is an overall attitude change across all experiment conditions, we did not observe a significant effect of belief elicitation on attitude change. With regard to recall error, while participants in the draw trend elicitation exhibited significantly lower recall error than participants in the categorize trend condition, we found no significant difference in recall error when comparing elicitation conditions to no elicitation. In a follow-up study, we added contrasting narratives with the purpose of making the main visualization (communicating data on the focal issue) appear strikingly different. Compared to the results of study 1, we found that contrasting narratives improved engagement in terms of surprise and interest but interestingly resulted in higher recall error and no significant change in attitude. We discuss the effects of elicitation and contrasting narratives in the context of topic involvement and the strengths of temporal trends encoded in the data visualization.
Published: 2024
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49. Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems

Author: Sah, Chandan Kumar, Xiaoli, Lian, and Islam, Muhammad Mirajul
Subjects: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Software Engineering
Abstract: The rise of generative artificial intelligence, particularly Large Language Models (LLMs), has intensified the imperative to scrutinize fairness alongside accuracy. Recent studies have begun to investigate fairness evaluations for LLMs within domains such as recommendations. Given that personalization is an intrinsic aspect of recommendation systems, its incorporation into fairness assessments is paramount. Yet, the degree to which current fairness evaluation frameworks account for personalization remains unclear. Our comprehensive literature review aims to fill this gap by examining how existing frameworks handle fairness evaluations of LLMs, with a focus on the integration of personalization factors. Despite an exhaustive collection and analysis of relevant works, we discovered that most evaluations overlook personalization, a critical facet of recommendation systems, thereby inadvertently perpetuating unfair practices. Our findings shed light on this oversight and underscore the urgent need for more nuanced fairness evaluations that acknowledge personalization. Such improvements are vital for fostering equitable development within the AI community., Comment: 10 pages
Published: 2024
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50. Conserved and divergent features of trophoblast stem cells.

Author: Sah, Nirvay and Soncin, Francesca
Subjects: epigenetics, epigenomics, placenta, trophoblast stem cells, Mice, Pregnancy, Animals, Female, Cattle, Humans, Placenta, Epigenesis, Genetic, Histones, Trophoblasts, Stem Cells, Cell Differentiation, Mammals
Abstract: Trophoblast stem cells (TSCs) are a proliferative multipotent population derived from the trophectoderm of the blastocyst, which will give rise to all the functional cell types of the trophoblast compartment of the placenta. The isolation and culture of TSCs in vitro represent a robust model to study mechanisms of trophoblast differentiation into mature cells both in successful and diseased pregnancy. Despite the highly conserved functions of the placenta, there is extreme variability in placental morphology, fetal-maternal interface, and development among eutherian mammals. This review aims to summarize the establishment and maintenance of TSCs in mammals such as primates, including human, rodents, and nontraditional animal models with a primary emphasis on epigenetic regulation of their origin while defining gaps in the current literature and areas of further development. FGF signaling is critical for mouse TSCs but dispensable for derivation of TSCs in other species. Human, simian, and bovine TSCs have much more complicated requirements of signaling pathways including activation of WNT and inhibition of TGFβ cascades. Epigenetic features such as DNA and histone methylation as well as histone acetylation are dynamic during development and are expressed in cell- and gestational age-specific pattern in placental trophoblasts. While TSCs from different species seem to recapitulate some select epigenomic features, there is a limitation in the comprehensive understanding of TSCs and how well TSCs retain placental epigenetic marks. Therefore, future studies should be directed at investigating epigenomic features of global and placental-specific gene expression in primary trophoblasts and TSCs.
Published: 2024

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