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1. The Influence of Class Size on Academic Attainment and Student Satisfaction.

Author: Edward W. Clark High School, Las Vegas, NV.
Abstract: This investigation determined if students showed a difference in academic attainment or attitude toward school as a result of membership in an average or above average size group. Some 224 male and female students in average or above average size classes in Business Law, Introduction to Business, and Government served as subjects. They were randomly scheduled into classes. Pretest and posttest scores on teacher-made tests were analyzed to measure academic attainment. No significant difference in academic attainment was found for either Business Law or Introduction to Business classes. A significant difference was found for the course on government. No significant differences for satisfaction with learning environment, resulting from differences in class size, were found for any of the three courses. (PS)
Published: 2024

2. STUDENT ATTITUDE SURVEY, CLARK HIGH SCHOOL.

Author: Clark County School District, Las Vegas, NV. and JEFFS, GEORGE A.
Abstract: AN INVESTIGATION WAS MADE TO DISCOVER CLARK HIGH SCHOOL STUDENTS' ATTITUDES TOWARD PEERS, THE PHYSICAL ENVIRONMENT, TEACHERS, ADMINISTRATORS, THE CURRICULUM, AND EDUCATIONAL VALUES, AND TO CONSTRUCT A STUDENT ATTITUDE INSTRUMENT. A REVIEW OF LITERATURE RELATED TO STUDENT ATTITUDES WAS MADE. EIGHT HUNDRED AND THIRTY-SEVEN CLARK HIGH SCHOOL STUDENTS WERE GIVEN THE CLARK HIGH SCHOOL ATTITUDE SCALE (CHSSAS). THE CHSSAS IS A LIKERT-TYPE ATTITUDE SCALE, CONSISTING OF 65 ITEMS ARRANGED IN SIX SUBSCALES. CORRELATIONS BETWEEN THE SUBSCALES ARE PROVIDED. RESPONSES WERE TRANSFERRED FROM STUDENT RESPONSE CARDS TO IBM KEY-PUNCH CARDS, AND THE NUMBER OF RESPONSES WAS OBTAINED. THE NUMBER OF RESPONSES WAS THEN CONVERTED TO PERCENTAGES, AND MEAN RESPONSE-WEIGHTS FOR SEX AND GRADE LEVELS WERE DETERMINED. ANALYSES OF THE ITEM RESPONSES WERE REPORTED IN 70 TABLES, AND A GENERAL ANALYSIS WAS GIVEN IN FOUR TABLES. A SUMMARY OF THE RESULTS IS GIVEN AND A COPY OF THE QUESTIONNAIRE IS INCLUDED. (PS)
Published: 2024

3. KM3NeT Constraint on Lorentz-Violating Superluminal Neutrino Velocity

Author: KM3NeT Collaboration, Adriani, O., Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Argüelles, C., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Berti, E., Bertin, V., Betti, P., Biagi, S., Boettcher, M., Bonanno, D., Bottai, S., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaş, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gagliardini, S., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Janik, O., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Kamp, N., Keegans, J., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lemaître, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Mori, N., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Pacini, L., Paesani, D., González, J. Palacios, Papalashvili, G., Papini, P., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaş, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Petropavlova, M., Piattelli, P., Plavin, A., Poirè, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Robinson, J., Romanov, A., Ros, E., Šaina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scaringella, M., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Myhr, P. A. Sevle, Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Starodubtsev, O., Stavropoulos, D., Štekl, I., Stocco, D., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vannuccini, E., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., Wen, A. Y., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Lorentz invariance is a fundamental symmetry of spacetime and foundational to modern physics. One of its most important consequences is the constancy of the speed of light. This invariance, together with the geometry of spacetime, implies that no particle can move faster than the speed of light. In this article, we present the most stringent neutrino-based test of this prediction, using the highest energy neutrino ever detected to date, KM3-230213A. The arrival of this event, with an energy of $220^{+570}_{-110}\,\text{PeV}$, sets a constraint on $\delta \equiv c_\nu^2-1 < 4\times10^{-22}$.
Published: 2025

4. The ultra-high-energy event KM3-230213A within the global neutrino landscape

Author: KM3NeT Collaboration, Adriani, O., Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Argüelles, C., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Berti, E., Bertin, V., Betti, P., Biagi, S., Boettcher, M., Bonanno, D., Bottai, S., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaš, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Janik, O., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Kamp, N., Keegans, J., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lemaître, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Mori, N., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Pacini, L., Paesani, D., González, J. Palacios, Papalashvili, G., Papini, P., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaš, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Petropavlova, M., Piattelli, P., Plavin, A., Poirè, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Robinson, J., Romanov, A., Ros, E., Šaina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scaringella, M., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Myhr, P. A. Sevle, Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Starodubtsev, O., Stavropoulos, D., Štekl, I., Stocco, D., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vannuccini, E., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., Wen, A. Y., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: On February 13th, 2023, the KM3NeT/ARCA telescope detected a neutrino candidate with an estimated energy in the hundreds of PeVs. In this article, the observation of this ultra-high-energy neutrino is discussed in light of null observations above tens of PeV from the IceCube and Pierre Auger observatories. Performing a joint fit of all experiments under the assumption of an isotropic $E^{-2}$ flux, the best-fit single-flavour flux normalisation is $E^2 \Phi^{\rm 1f}_{\nu + \bar \nu} = 7.5 \times 10^{-10}~{\rm GeV cm^{-2} s^{-1} sr^{-1}}$ in the 90% energy range of the KM3NeT event. Furthermore, the ultra-high-energy data are then fit together with the IceCube measurements at lower energies, either with a single power law or with a broken power law, allowing for the presence of a new component in the spectrum. The joint fit including non-observations by other experiments in the ultra-high-energy region shows a slight preference for a break in the PeV regime if the ``High-Energy Starting Events'' sample is included, and no such preference for the other two IceCube samples investigated. A stronger preference for a break appears if only the KM3NeT data is considered in the ultra-high-energy region, though the flux resulting from such a fit would be inconsistent with null observations from IceCube and Pierre Auger. In all cases, the observed tension between KM3NeT and other datasets is of the order of $2.5\sigma-3\sigma$, and increased statistics are required to resolve this apparent tension and better characterise the neutrino landscape at ultra-high energies., Comment: 11 pages, 4 figures
Published: 2025

5. On the Potential Galactic Origin of the Ultra-High-Energy Event KM3-230213A

Author: Adriani, O., Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Berti, E., Bertin, V., Betti, P., Biagi, S., Boettcher, M., Bonanno, D., Bottai, S., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaş, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Janik, O., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Keegans, J., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lemaître, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Mori, N., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Pacini, L., Paesani, D., González, J. Palacios, Papalashvili, G., Papini, P., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaş, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Petropavlova, M., Piattelli, P., Plavin, A., Poirè, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Robinson, J., Romanov, A., Ros, E., Šaina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scaringella, M., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Myhr, P. A. Sevle, Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Starodubtsev, O., Stavropoulos, D., Štekl, I., Stocco, D., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vannuccini, E., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The KM3NeT observatory detected the most energetic neutrino candidate ever observed, with an energy between 72 PeV and 2.6 EeV at the 90% confidence level. The observed neutrino is likely of cosmic origin. In this article, it is investigated if the neutrino could have been produced within the Milky Way. Considering the low fluxes of the Galactic diffuse emission at these energies, the lack of a nearby potential Galactic particle accelerator in the direction of the event and the difficulty to accelerate particles to such high energies in Galactic systems, we conclude that if the event is indeed cosmic, it is most likely of extragalactic origin.
Published: 2025

6. Study of tau neutrinos and non-unitary neutrino mixing with the first six detection units of KM3NeT/ORCA

Author: KM3NeT Collaboration, Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Bertin, V., Biagi, S., Boettcher, M., Bonanno, D., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaş, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gagliardini, S., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lema^itre, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Paesani, D., González, J. Palacios, Papalashvili, G., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaş, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Piattelli, P., Plavin, A., Poirè, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Romanov, A., Ros, E., Šaina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Sevle, P., Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Stavropoulos, D., Štekl, I., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tudorache, A., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.
Subjects: High Energy Physics - Experiment
Abstract: Oscillations of atmospheric muon and electron neutrinos produce tau neutrinos with energies in the GeV range, which can be observed by the ORCA detector of the KM3NeT neutrino telescope in the Mediterranean Sea. First measurements with ORCA6, an early subarray corresponding to about 5$\%$ of the final detector, are presented. A sample of 5828 neutrino candidates has been selected from the analysed exposure of 433 kton-years. The $\nu_\tau$ normalisation, defined as the ratio between the number of observed and expected tau neutrino events, is measured to be $S_\tau = 0.48^{+0.5}_{-0.33}$. This translates into a $\nu_\tau$ charged-current cross section measurement of $\sigma_{\tau}^{\text{meas}} = (2.5 ^{+2.6}_{-1.8}) \times 10^{-38}$ cm$^{2}$ nucleon$^{-1}$ at the median $\nu_\tau$ energy of 20.3 GeV. The result is consistent with the measurements of other experiments. In addition, the current limit on the non-unitarity parameter affecting the $\tau$-row of the neutrino mixing matrix was improved, with $\alpha_{33}>$ 0.95 at the 95$\%$ confidence level., Comment: 25 pages, 8 figures
Published: 2025

7. Humanity's Last Exam

Author: Phan, Long, Gatti, Alice, Han, Ziwen, Li, Nathaniel, Hu, Josephina, Zhang, Hugh, Zhang, Chen Bo Calvin, Shaaban, Mohamed, Ling, John, Shi, Sean, Choi, Michael, Agrawal, Anish, Chopra, Arnav, Khoja, Adam, Kim, Ryan, Ren, Richard, Hausenloy, Jason, Zhang, Oliver, Mazeika, Mantas, Nguyen, Tung, Anderson, Daron, Shah, Imad Ali, Doroshenko, Mikhail, Stokes, Alun Cennyth, Mahmood, Mobeen, Lee, Jaeho, Pokutnyi, Oleksandr, Iskra, Oleg, Wang, Jessica P., Gerbicz, Robert, Levin, John-Clark, Popov, Serguei, Feng, Fiona, Feng, Steven Y., Zhao, Haoran, Yu, Michael, Gangal, Varun, Zou, Chelsea, Wang, Zihan, Kazakov, Mstyslav, Galgon, Geoff, Schmitt, Johannes, Sanchez, Alvaro, Lee, Yongki, Yeadon, Will, Sauers, Scott, Roth, Marc, Agu, Chidozie, Riis, Søren, Giska, Fabian, Utpala, Saiteja, Cheatom, Antrell, Giboney, Zachary, Goshu, Gashaw M., Crowson, Sarah-Jane, Naiya, Mohinder Maheshbhai, Burns, Noah, Finke, Lennart, Cheng, Zerui, Park, Hyunwoo, Fournier-Facio, Francesco, Zampese, Jennifer, Wydallis, John B., Hoerr, Ryan G., Nandor, Mark, Gehrunger, Tim, Cai, Jiaqi, McCarty, Ben, Nam, Jungbae, Taylor, Edwin, Jin, Jun, Loume, Gautier Abou, Cao, Hangrui, Garretson, Alexis C, Sileo, Damien, Ren, Qiuyu, Cojoc, Doru, Arkhipov, Pavel, Qazi, Usman, Bacho, Aras, Li, Lianghui, Motwani, Sumeet, de Witt, Christian Schroeder, Kopylov, Alexei, Veith, Johannes, Singer, Eric, Rissone, Paolo, Jin, Jaehyeok, Shi, Jack Wei Lun, Willcocks, Chris G., Prabhu, Ameya, Tang, Longke, Zhou, Kevin, Santos, Emily de Oliveira, Maksimov, Andrey Pupasov, Vendrow, Edward, Zenitani, Kengo, Robinson, Joshua, Mikov, Aleksandar, Guillod, Julien, Li, Yuqi, Pageler, Ben, Vendrow, Joshua, Kuchkin, Vladyslav, Marion, Pierre, Efremov, Denis, Lynch, Jayson, Liang, Kaiqu, Gritsevskiy, Andrew, Martinez, Dakotah, Crispino, Nick, Zvonkine, Dimitri, Fraga, Natanael Wildner, Soori, Saeed, Press, Ori, Tang, Henry, Salazar, Julian, Green, Sean R., Brüssel, Lina, Twayana, Moon, Dieuleveut, Aymeric, Rogers, T. Ryan, Zhang, Wenjin, Finocchio, Ross, Li, Bikun, Yang, Jinzhou, Rao, Arun, Loiseau, Gabriel, Kalinin, Mikhail, Lukas, Marco, Manolescu, Ciprian, Stambaugh, Nate, Mishra, Subrata, Kamdoum, Ariel Ghislain Kemogne, Hogg, Tad, Jin, Alvin, Bosio, Carlo, Sun, Gongbo, Coppola, Brian P, Heidinger, Haline, Sayous, Rafael, Ivanov, Stefan, Cavanagh, Joseph M, Shen, Jiawei, Imperial, Joseph Marvin, Schwaller, Philippe, Senthilkuma, Shaipranesh, Bran, Andres M, Algaba, Andres, Verbeken, Brecht, Houte, Kelsey Van den, Van Der Sypt, Lynn, Noever, David, Schut, Lisa, Sucholutsky, Ilia, Zheltonozhskii, Evgenii, Yuan, Qiaochu, Lim, Derek, Stanley, Richard, Sivarajan, Shankar, Yang, Tong, Maar, John, Wykowski, Julian, Oller, Martí, Sandlin, Jennifer, Sahu, Anmol, Ardito, Cesare Giulio, Hu, Yuzheng, Dias, Felipe Meneguitti, Kreiman, Tobias, Rawal, Kaivalya, Vilchis, Tobias Garcia, Zu, Yuexuan, Lackner, Martin, Koppel, James, Nguyen, Jeremy, Antonenko, Daniil S., Chern, Steffi, Zhao, Bingchen, Arsene, Pierrot, Ivanov, Sergey, Poświata, Rafał, Wang, Chenguang, Li, Daofeng, Crisostomi, Donato, Dehghan, Ali, Achilleos, Andrea, Ambay, John Arnold, Myklebust, Benjamin, Sen, Archan, Perrella, David, Kaparov, Nurdin, Inlow, Mark H, Zang, Allen, Ramakrishnan, Kalyan, Orel, Daniil, Poritski, Vladislav, Ben-David, Shalev, Berger, Zachary, Whitfill, Parker, Foster, Michael, Munro, Daniel, Ho, Linh, Hava, Dan Bar, Kuchkin, Aleksey, Lauff, Robert, Holmes, David, Sommerhage, Frank, Zhang, Anji, Moat, Richard, Schneider, Keith, Pyda, Daniel, Kazibwe, Zakayo, Singh, Mukhwinder, Clarke, Don, Kim, Dae Hyun, Fish, Sara, Elser, Veit, Vilchis, Victor Efren Guadarrama, Klose, Immo, Demian, Christoph, Anantheswaran, Ujjwala, Zweiger, Adam, Albani, Guglielmo, Li, Jeffery, Daans, Nicolas, Radionov, Maksim, Rozhoň, Václav, Ginis, Vincent, Ma, Ziqiao, Stump, Christian, Platnick, Jacob, Nevirkovets, Volodymyr, Basler, Luke, Piccardo, Marco, Cohen, Niv, Singh, Virendra, Tkadlec, Josef, Rosu, Paul, Goldfarb, Alan, Padlewski, Piotr, Barzowski, Stanislaw, Montgomery, Kyle, Menezes, Aline, Patel, Arkil, Wang, Zixuan, Tucker-Foltz, Jamie, Stade, Jack, Grabb, Declan, Goertzen, Tom, Kazemi, Fereshteh, Milbauer, Jeremiah, Shukla, Abhishek, Elgnainy, Hossam, Labrador, Yan Carlos Leyva, He, Hao, Zhang, Ling, Givré, Alan, Wolff, Hew, Demir, Gözdenur, Aziz, Muhammad Fayez, Kaddar, Younesse, Ängquist, Ivar, Chen, Yanxu, Thornley, Elliott, Zhang, Robin, Pan, Jiayi, Terpin, Antonio, Muennighoff, Niklas, Schoelkopf, Hailey, Zheng, Eric, Carmi, Avishy, Shah, Jainam, Brown, Ethan D. L., Zhu, Kelin, Bartolo, Max, Wheeler, Richard, Ho, Andrew, Barkan, Shaul, Wang, Jiaqi, Stehberger, Martin, Kretov, Egor, Bradshaw, Peter, Heimonen, JP, Sridhar, Kaustubh, Hossain, Zaki, Akov, Ido, Makarychev, Yury, Tam, Joanna, Hoang, Hieu, Cunningham, David M., Goryachev, Vladimir, Patramanis, Demosthenes, Krause, Michael, Redenti, Andrew, Aldous, David, Lai, Jesyin, Coleman, Shannon, Xu, Jiangnan, Lee, Sangwon, Magoulas, Ilias, Zhao, Sandy, Tang, Ning, Cohen, Michael K., Carroll, Micah, Paradise, Orr, Kirchner, Jan Hendrik, Steinerberger, Stefan, Ovchynnikov, Maksym, Matos, Jason O., Shenoy, Adithya, Wang, Michael, Nie, Yuzhou, Giordano, Paolo, Petersen, Philipp, Sztyber-Betley, Anna, Faraboschi, Paolo, Riblet, Robin, Crozier, Jonathan, Halasyamani, Shiv, Pinto, Antonella, Verma, Shreyas, Joshi, Prashant, Meril, Eli, Yong, Zheng-Xin, Tee, Allison, Andréoletti, Jérémy, Weller, Orion, Singhal, Raghav, Zhang, Gang, Ivanov, Alexander, Khoury, Seri, Gustafsson, Nils, Mostaghimi, Hamid, Thaman, Kunvar, Chen, Qijia, Khánh, Tran Quoc, Loader, Jacob, Cavalleri, Stefano, Szlyk, Hannah, Brown, Zachary, Narayan, Himanshu, Roberts, Jonathan, Alley, William, Sun, Kunyang, Stendall, Ryan, Lamparth, Max, Reuel, Anka, Wang, Ting, Xu, Hanmeng, Hernández-Cámara, Pablo, Martin, Freddie, Preu, Thomas, Korbak, Tomek, Abramovitch, Marcus, Williamson, Dominic, Bosio, Ida, Chen, Ziye, Bálint, Biró, Lo, Eve J. Y., Nunes, Maria Inês S., Jiang, Yibo, Bari, M Saiful, Kassani, Peyman, Wang, Zihao, Ansarinejad, Behzad, Sun, Yewen, Durand, Stephane, Douville, Guillaume, Tordera, Daniel, Balabanian, George, Anderson, Earth, Kvistad, Lynna, Moyano, Alejandro José, Milliron, Hsiaoyun, Sakor, Ahmad, Eron, Murat, McAlister, Isaac C., O., Andrew Favre D., Shah, Shailesh, Zhou, Xiaoxiang, Kamalov, Firuz, Clark, Ronald, Abdoli, Sherwin, Santens, Tim, Wang, Harrison K, Chen, Evan, Tomasiello, Alessandro, De Luca, G. Bruno, Looi, Shi-Zhuo, Le, Vinh-Kha, Kolt, Noam, Mündler, Niels, Semler, Avi, Rodman, Emma, Drori, Jacob, Fossum, Carl J, Gloor, Luk, Jagota, Milind, Pradeep, Ronak, Fan, Honglu, Shah, Tej, Eicher, Jonathan, Chen, Michael, Thaman, Kushal, Merrill, William, Firsching, Moritz, Harris, Carter, Ciobâcă, Stefan, Gross, Jason, Pandey, Rohan, Gusev, Ilya, Jones, Adam, Agnihotri, Shashank, Zhelnov, Pavel, Usawasutsakorn, Siranut, Mofayezi, Mohammadreza, Piperski, Alexander, Carauleanu, Marc, Zhang, David K., Dobarskyi, Kostiantyn, Ler, Dylan, Leventov, Roman, Soroko, Ignat, Jansen, Thorben, Creighton, Scott, Lauer, Pascal, Duersch, Joshua, Taamazyan, Vage, Bezzi, Dario, Morak, Wiktor, Ma, Wenjie, Held, William, Huy, Tran Đuc, Xian, Ruicheng, Zebaze, Armel Randy, Mohamed, Mohanad, Leser, Julian Noah, Yuan, Michelle X, Yacar, Laila, Lengler, Johannes, Olszewska, Katarzyna, Shahrtash, Hossein, Oliveira, Edson, Jackson, Joseph W., Gonzalez, Daniel Espinosa, Zou, Andy, Chidambaram, Muthu, Manik, Timothy, Haffenden, Hector, Stander, Dashiell, Dasouqi, Ali, Shen, Alexander, Duc, Emilien, Golshani, Bita, Stap, David, Uzhou, Mikalai, Zhidkovskaya, Alina Borisovna, Lewark, Lukas, Rodriguez, Miguel Orbegozo, Vincze, Mátyás, Wehr, Dustin, Tang, Colin, Phillips, Shaun, Samuele, Fortuna, Muzhen, Jiang, Ekström, Fredrik, Hammon, Angela, Patel, Oam, Farhidi, Faraz, Medley, George, Mohammadzadeh, Forough, Peñaflor, Madellene, Kassahun, Haile, Friedrich, Alena, Sparrow, Claire, Perez, Rayner Hernandez, Sakal, Taom, Dhamane, Omkar, Mirabadi, Ali Khajegili, Hallman, Eric, Okutsu, Kenchi, Battaglia, Mike, Maghsoudimehrabani, Mohammad, Amit, Alon, Hulbert, Dave, Pereira, Roberto, Weber, Simon, Handoko, Peristyy, Anton, Malina, Stephen, Albanie, Samuel, Cai, Will, Mehkary, Mustafa, Aly, Rami, Reidegeld, Frank, Dick, Anna-Katharina, Friday, Cary, Sidhu, Jasdeep, Shapourian, Hassan, Kim, Wanyoung, Costa, Mariana, Gurdogan, Hubeyb, Weber, Brian, Kumar, Harsh, Jiang, Tong, Agarwal, Arunim, Ceconello, Chiara, Vaz, Warren S., Zhuang, Chao, Park, Haon, Tawfeek, Andrew R., Aggarwal, Daattavya, Kirchhof, Michael, Dai, Linjie, Kim, Evan, Ferret, Johan, Wang, Yuzhou, Yan, Minghao, Burdzy, Krzysztof, Zhang, Lixin, Franca, Antonio, Pham, Diana T., Loh, Kang Yong, Jackson, Abram, Gul, Shreen, Chhablani, Gunjan, Du, Zhehang, Cosma, Adrian, Colino, Jesus, White, Colin, Votava, Jacob, Vinnikov, Vladimir, Delaney, Ethan, Spelda, Petr, Stritecky, Vit, Shahid, Syed M., Mourrat, Jean-Christophe, Vetoshkin, Lavr, Sponselee, Koen, Bacho, Renas, de la Rosa, Florencia, Li, Xiuyu, Malod, Guillaume, Lang, Leon, Laurendeau, Julien, Kazakov, Dmitry, Adesanya, Fatimah, Portier, Julien, Hollom, Lawrence, Souza, Victor, Zhou, Yuchen Anna, Degorre, Julien, Yalın, Yiğit, Obikoya, Gbenga Daniel, Arnaboldi, Luca, Rai, Bigi, Filippo, Boscá, M. C., Shumar, Oleg, Bacho, Kaniuar, Clavier, Pierre, Recchia, Gabriel, Popescu, Mara, Shulga, Nikita, Tanwie, Ngefor Mildred, Peskoff, Denis, Lux, Thomas C. H., Rank, Ben, Ni, Colin, Brooks, Matthew, Yakimchyk, Alesia, Huanxu, Liu, Häggström, Olle, Verkama, Emil, Gundlach, Hans, Brito-Santana, Leonor, Amaro, Brian, Vajipey, Vivek, Grover, Rynaa, Fan, Yiyang, Silva, Gabriel Poesia Reis e, Xin, Linwei, Kratish, Yosi, Łucki, Jakub, Li, Wen-Ding, Gopi, Sivakanth, Caciolai, Andrea, Xu, Justin, Scaria, Kevin Joseph, Vargus, Freddie, Habibi, Farzad, Long, Lian, Rodolà, Emanuele, Robins, Jules, Cheng, Vincent, Fruhauff, Tony, Raynor, Brad, Qi, Hao, Jiang, Xi, Segev, Ben, Fan, Jingxuan, Martinson, Sarah, Wang, Erik Y., Hausknecht, Kaylie, Brenner, Michael P., Mao, Mao, Zhang, Xinyu, Avagian, David, Scipio, Eshawn Jessica, Ragoler, Alon, Tan, Justin, Sims, Blake, Plecnik, Rebeka, Kirtland, Aaron, Bodur, Omer Faruk, Shinde, D. P., Adoul, Zahra, Zekry, Mohamed, Karakoc, Ali, Santos, Tania C. B., Shamseldeen, Samir, Karim, Loukmane, Liakhovitskaia, Anna, Resman, Nate, Farina, Nicholas, Gonzalez, Juan Carlos, Maayan, Gabe, Hoback, Sarah, Pena, Rodrigo De Oliveira, Sherman, Glen, Kelley, Elizabeth, Mariji, Hodjat, Pouriamanesh, Rasoul, Wu, Wentao, Mendoza, Sandra, Alarab, Ismail, Cole, Joshua, Ferreira, Danyelle, Johnson, Bryan, Safdari, Mohammad, Dai, Liangti, Arthornthurasuk, Siriphan, Pronin, Alexey, Fan, Jing, Ramirez-Trinidad, Angel, Cartwright, Ashley, Pottmaier, Daphiny, Taheri, Omid, Outevsky, David, Stepanic, Stanley, Perry, Samuel, Askew, Luke, Rodríguez, Raúl Adrián Huerta, Minissi, Ali M. R., Ali, Sam, Lorena, Ricardo, Iyer, Krishnamurthy, Fasiludeen, Arshad Anil, Salauddin, Sk Md, Islam, Murat, Gonzalez, Juan, Ducey, Josh, Somrak, Maja, Mavroudis, Vasilios, Vergo, Eric, Qin, Juehang, Borbás, Benjámin, Chu, Eric, Lindsey, Jack, Radhakrishnan, Anil, Jallon, Antoine, McInnis, I. M. J., Kumar, Pawan, Goswami, Laxman Prasad, Bugas, Daniel, Heydari, Nasser, Jeanplong, Ferenc, Apronti, Archimedes, Galal, Abdallah, Ze-An, Ng, Singh, Ankit, Xavier, Joan of Arc, Agarwal, Kanu Priya, Berkani, Mohammed, Junior, Benedito Alves de Oliveira, Malishev, Dmitry, Remy, Nicolas, Hartman, Taylor D., Tarver, Tim, Mensah, Stephen, Gimenez, Javier, Montecillo, Roselynn Grace, Campbell, Russell, Sharma, Asankhaya, Meer, Khalida, Alapont, Xavier, Patil, Deepakkumar, Maheshwari, Rajat, Dendane, Abdelkader, Shukla, Priti, Bogdanov, Sergei, Möller, Sören, Siddiqi, Muhammad Rehan, Saxena, Prajvi, Gupta, Himanshu, Enyekwe, Innocent, P V, Ragavendran, EL-Wasif, Zienab, Maksapetyan, Aleksandr, Rossbach, Vivien, Harjadi, Chris, Bahaloohoreh, Mohsen, Bian, Song, Lai, John, Uro, Justine Leon, Bateman, Greg, Sayed, Mohamed, Menshawy, Ahmed, Duclosel, Darling, Jain, Yashaswini, Aaron, Ashley, Tiryakioglu, Murat, Siddh, Sheeshram, Krenek, Keith, Hoover, Alex, McGowan, Joseph, Patwardhan, Tejal, Yue, Summer, Wang, Alexandr, and Hendrycks, Dan
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures
Published: 2025

8. Probing invisible neutrino decay with the first six detection units of KM3NeT/ORCA

Author: Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Bertin, V., Biagi, S., Boettcher, M., Bonanno, D., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaş, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gagliardini, S., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lemaître, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Paesani, D., González, J. Palacios, Papalashvili, G., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaş, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Piattelli, P., Plavin, A., Poiré, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Romanov, A., Ros, E., Săina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Sevle, P., Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Stavropoulos, D., Štekl, I., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tudorache, A., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.
Subjects: High Energy Physics - Experiment
Abstract: In the era of precision measurements of neutrino oscillation parameters, it is necessary for experiments to disentangle discrepancies that may indicate physics beyond the Standard Model in the neutrino sector. KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study the oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on the initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photosensors. A high-purity neutrino sample was extracted during 2020 and 2021, corresponding to an exposure of 433 kton-years. This sample is analysed following a binned log-likelihood approach to search for invisible neutrino decay, in a three-flavour neutrino oscillation scenario, where the third neutrino mass state $\nu_3$ decays into an invisible state, e.g. a sterile neutrino. The resulting best fit of the invisible neutrino decay parameter is $\alpha_3 = 0.92^{+1.08}_{-0.57}\times 10^{-4}~\mathrm{eV^2}$, corresponding to a scenario with $\theta_{23}$ in the second octant and normal neutrino mass ordering. The results are consistent with the Standard Model, within a $2.1\,\sigma$ interval., Comment: 20 pages, 8 figures
Published: 2025

9. Response Process Evidence for Academic Assessments of Students with Significant Cognitive Disabilities

Author: Meagan Karvonen, Russell Swinburne Romine, and Amy K. Clark
Abstract: This paper describes methods and findings from student cognitive labs, teacher cognitive labs, and test administration observations as evidence evaluated in a validity argument for a computer-based alternate assessment for students with significant cognitive disabilities. Validity of score interpretations and uses for alternate assessments based on alternate academic achievement standards (AA-AAAS) for students with the most significant cognitive disabilities require nontraditional sources of evidence about student-item interactions and the influences teachers have on those interactions. Our findings provide evidence that the assessment has been designed so students can show what they know and can do on multiple choice, technology-enhanced, and teacher administered item types and that teachers administer the assessments in a way that allows students to respond as intended. We conclude with a discussion on how the findings inform future test development, limitations, and implications for the use of these research methods for gathering validity evidence for an AA-AAAS.
Published: 2024

10. Evaluation of Departmentalized Instruction in Elementary Schools: Exploring Implementation Experiences. NCEE-2024-005r

Author: National Center for Education Evaluation and Regional Assistance (NCEE) (ED/IES), Mathematica, Alison Wellington, Melissa Clark, Alyson Burnett, Susanne James-Burdumy, Libby Makowsky, Stacey Brockman, Dallas Dotter, Mariesa Herrmann, and Hanley Chiang
Abstract: Assigning upper elementary grade teachers to teach their strongest subjects to multiple classes ("departmentalizing"), rather than teaching all subjects to a single class, ("self-contained instruction") could mean more specialized instructional expertise in the classroom or focus for teacher planning time and professional development. This study examined the experiences of 90 schools that either voluntarily switched to departmentalized instruction for up to two years or continued with self-contained teaching in 4th and 5th grade classes beginning in fall 2019. The findings show that schools that switched were able to implement the key steps needed to departmentalize instruction but struggled with aspects of the approach. Teachers' reported experiences were consistent with some of both the benefits and challenges that prior research had hypothesized. Although the pandemic significantly disrupted instruction and the study activities, school and teacher experiences during this challenging time may inform schools and districts considering adopting departmentalized instruction.
Published: 2024

11. Evaluation of Departmentalized Instruction in Elementary Schools: Exploring Implementation Experiences. Appendix. NCEE-2024-005a

Author: Mathematica, National Center for Education Evaluation and Regional Assistance (NCEE) (ED/IES), Alison Wellington, Melissa Clark, Alyson Burnett, Susanne James-Burdumy, Libby Makowsky, Stacey Brockman, Dallas Dotter, Mariesa Herrmann, and Hanley Chiang
Abstract: This appendix is a companion to the report "Evaluation of Departmentalized Instruction in Elementary Schools: Exploring Implementation Experiences." The appendix provides additional information about the study, which sought to explore schools' implementation of departmentalized instruction, teachers' experiences with the strategy, and how the strategy was related to key outcomes. This additional information includes details on departmentalized instruction (Section A), study design and execution (Section B), key findings presented in the report (Section C), and additional findings that are not included in the report but provide important context (Section D).
Published: 2024

12. Dynamics of single Au nanoparticles on graphene simultaneously in real- and diffraction space by time-series convergent beam electron diffraction

Author: Mustafi, Sara, Cai, Rongsheng, Sullivan-Allsop, Sam, Smith, Matthew, Clark, Nicholas J., Lindley, Matthew, Peng, Ding, Novoselov, Kostya S., Haigh, Sarah J., and Latychevskaia, Tatiana
Subjects: Condensed Matter - Other Condensed Matter, Physics - Computational Physics, Physics - Optics
Abstract: Convergent beam electron diffraction (CBED) on two-dimensional materials allows simultaneous recording of the real-space image (tens of nanometers in size) and diffraction pattern of the same sample in one single-shot intensity measurement. In this study, we employ time-series CBED to visualize single Au nanoparticles deposited on graphene. The real-space image of the probed region, with the amount, size, and positions of single Au nanoparticles, is directly observed in the zero-order CBED disk, while the atomic arrangement of the Au nanoparticles is available from the intensity distributions in the higher-order CBED disks. From the time-series CBED patterns, the movement of a single Au nanoparticle with rotation up to 4{\deg} was recorded. We also observed facet diffraction lines - intense bright lines formed between the CBED disks of the Au nanoparticle, which we explain by diffraction at the Au nanoparticle's facets. This work showcases CBED as a useful technique for studying adsorbates on graphene using Au nanoparticles as a model platform, and paves the way for future studies of different objects deposited on graphene.
Published: 2025

13. NASA Innovative Advanced Concepts Phase I Final Report -- A Lunar Long-Baseline UV/Optical Imaging Interferometer: Artemis-enabled Stellar Imager (AeSI)

Author: Carpenter, Kenneth G., Boyajian, Tabetha, Buzasi, Derek, Clark, Jim, Creech-Eakman, Michelle, Dean, Bruce, Elliott, Ashley, Foster, Julianne, Gong, Qian, Karovska, Margarita, Kim, David, Hulberg, Jon, Leisawitz, David, Maher, Mike, Morse, Jon, Mozurkewich, Dave, Peacock, Sarah, Petro, Noah, Rau, Gioia, Scowen, Paul, Seals, Len, Smith, Walter, Smuda, Max, Sitarski, Breann, Taylor, Buddy, van Belle, Gerard, and Wilkinson, Erik
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract: This report presents the findings of a NIAC Phase I feasibility study for the Artemis-enabled Stellar Imager (AeSI), a proposed high-resolution, UV/Optical interferometer designed for deployment on the lunar surface. Its primary science goal is to image the surfaces and interiors of stars with unprecedented detail, revealing new details about their magnetic processes and dynamic evolution and enabling the creation of a truly predictive solar/stellar dynamo model. This capability will transform our understanding of stellar physics and has broad applicability across astrophysics, from resolving the cores of Active Galactic Nuclei (AGN) to studying supernovae, planetary nebulae, and the late stages of stellar evolution. By leveraging the stable vacuum environment of the Moon and the infrastructure being established for the Artemis Program, AeSI presents a compelling case for a lunar-based interferometer. In this study, the AeSI Team, working with the NASA Goddard Space Flight Center's Integrated Design Center (IDC), has firmly established the feasibility of building and operating a reconfigurable, dispersed aperture telescope (i.e., an interferometer) on the lunar surface. The collaboration produced a credible Baseline design featuring 15 primary mirrors arranged in an elliptical array with a 1 km major axis, with the potential to expand to 30 mirrors and larger array sizes through staged deployments. Additionally, this study identified numerous opportunities for optimization and the necessary trade studies to refine the design further. These will be pursued in follow-up investigations, such as a NIAC Phase II study, to advance the concept toward implementation., Comment: 128 pages, 59 figures
Published: 2025

14. The Simons Observatory: Science Goals and Forecasts for the Enhanced Large Aperture Telescope

Author: Abitbol, M., Abril-Cabezas, I., Adachi, S., Ade, P., Adler, A. E., Agrawal, P., Aguirre, J., Ahmed, Z., Aiola, S., Alford, T., Ali, A., Alonso, D., Alvarez, M. A., An, R., Arnold, K., Ashton, P., Atkins, Z., Austermann, J., Azzoni, S., Baccigalupi, C., Lizancos, A. Baleato, Barron, D., Barry, P., Bartlett, J., Battaglia, N., Battye, R., Baxter, E., Bazarko, A., Beall, J. A., Bean, R., Beck, D., Beckman, S., Begin, J., Beheshti, A., Beringue, B., Bhandarkar, T., Bhimani, S., Bianchini, F., Biermann, E., Biquard, S., Bixler, B., Boada, S., Boettger, D., Bolliet, B., Bond, J. R., Borrill, J., Borrow, J., Braithwaite, C., Brien, T. L. R., Brown, M. L., Bruno, S. M., Bryan, S., Bustos, R., Cai, H., Calabrese, E., Calafut, V., Carl, F. M., Carones, A., Carron, J., Challinor, A., Chanial, P., Chen, N., Cheung, K., Chiang, B., Chinone, Y., Chluba, J., Cho, H. S., Choi, S. K., Chu, M., Clancy, J., Clark, S. E., Clarke, P., Clements, D. L., Connors, J., Contaldi, C., Coppi, G., Corbett, L., Cothard, N. F., Coulton, W., Crowley, K. D., Crowley, K. T., Cukierman, A., D'Ewart, J. M., Dachlythra, K., Datta, R., Day-Weiss, S., de Haan, T., Devlin, M., Di Mascolo, L., Dicker, S., Dober, B., Doux, C., Dow, P., Doyle, S., Duell, C. J., Duff, S. M., Duivenvoorden, A. J., Dunkley, J., Dutcher, D., Dünner, R., Edenton, M., Bouhargani, H. El, Errard, J., Fabbian, G., Fanfani, V., Farren, G. S., Fergusson, J., Ferraro, S., Flauger, R., Foster, A., Freese, K., Frisch, J. C., Frolov, A., Fuller, G., Galitzki, N., Gallardo, P. A., Ghersi, J. T. Galvez, Ganga, K., Gao, J., Garrido, X., Gawiser, E., Gerbino, M., Giardiello, S., Gill, A., Gilles, V., Giri, U., Gleave, E., Gluscevic, V., Goeckner-Wald, N., Golec, J. E., Gordon, S., Gralla, M., Gratton, S., Green, D., Groh, J. C., Groppi, C., Guan, Y., Gupta, N., Guðmundsson, J. E., Hagstotz, S., Hargrave, P., Haridas, S., Harrington, K., Harrison, I., Hasegawa, M., Hasselfield, M., Haynes, V., Hazumi, M., He, A., Healy, E., Henderson, S. W., Hensley, B. S., Hertig, E., Hervías-Caimapo, C., Higuchi, M., Hill, C. A., Hill, J. C., Hilton, G., Hilton, M., Hincks, A. D., Hinshaw, G., Hložek, R., Ho, A. Y. Q., Ho, S., Ho, S. P., Hoang, T. D., Hoh, J., Hornecker, E., Hornsby, A. L., Hoshino, D., Hotinli, S. C., Huang, Z., Huber, Z. B., Hubmayr, J., Huffenberger, K., Hughes, J. P., Lonappan, A. Idicherian, Ikape, M., Irwin, K., Iuliano, J., Jaffe, A. H., Jain, B., Jense, H. T., Jeong, O., Johnson, A., Johnson, B. R., Johnson, M., Jones, M., Jost, B., Kaneko, D., Karpel, E. D., Kasai, Y., Katayama, N., Keating, B., Keller, B., Keskitalo, R., Kim, J., Kisner, T., Kiuchi, K., Klein, J., Knowles, K., Kofman, A. M., Koopman, B. J., Kosowsky, A., Kou, R., Krachmalnicoff, N., Kramer, D., Krishak, A., Krolewski, A., Kusaka, A., Kusiak, A., La Plante, P., La Posta, A., Laguë, A., Lashner, J., Lattanzi, M., Lee, A., Lee, E., Leech, J., Lessler, C., Leung, J. S., Lewis, A., Li, Y., Li, Z., Limon, M., Lin, L., Link, M., Liu, J., Liu, Y., Lonergan, J., Louis, T., Lucas, T., Ludlam, M., Lungu, M., Lyons, M., MacCrann, N., MacInnis, A., Madhavacheril, M., Mak, D., Maldonado, F., Manduca, A., Mangu, A., Mani, H., Maniyar, A. S., Marques, G. A., Mates, J., Matsuda, F., Matsumura, T., Mauskopf, P., May, A., McCallum, N., McCarrick, H., McCarthy, F., McCulloch, M., McMahon, J., Meerburg, P. D., Mehta, Y., Melin, J., Mertens, J., Meyers, J., Middleton, A., Miller, A., Mirmelstein, M., Moodley, K., Moore, J., Morshed, M., Morton, T., Moser, E., Mroczkowski, T., Murata, M., Münchmeyer, M., Naess, S., Nakata, H., Namikawa, T., Nashimoto, M., Nati, F., Natoli, P., Negrello, M., Nerval, S. K., Newburgh, L., Nguyen, D. V., Nicola, A., Niemack, M. D., Nishino, H., Nishinomiya, Y., Orlando, A., Orlando, G., Orlowski-Scherer, J., Pagano, L., Page, L. A., Pandey, S., Papageorgiou, A., Paraskevakos, I., Partridge, B., Patki, R., Peel, M., Sarmiento, K. Perez, Perrotta, F., Phakathi, P., Piccirillo, L., Pierpaoli, E., Pinsonneault-Marotte, T., Pisano, G., Poletti, D., Puddu, R., Puglisi, G., Qu, F. J., Randall, M. J., Ranucci, C., Raum, C., Reeves, R., Reichardt, C. L., Remazeilles, M., Rephaeli, Y., Riechers, D., Robe, J., Robertson, M. F., Robertson, N., Rogers, K., Rojas, F., Romero, A., Rosenberg, E., Rotti, A., Rowe, S., Roy, A., Sadeh, S., Sailer, N., Sakaguri, K., Sakuma, T., Sakurai, Y., Salatino, M., Sanders, G. H., Sasaki, D., Rao, M. Sathyanarayana, Satterthwaite, T. P., Saunders, L., Scalcinati, L., Schaan, E., Schmitt, B., Schmittfull, M., Sehgal, N., Seibert, J., Seino, Y., Seljak, U., Shaikh, S., Shaw, E., Shellard, P., Sherwin, B., Shimon, M., Shroyer, J. E., Sierra, C., Sievers, J., Sifón, C., Sikhosana, P., Silva-Feaver, M., Simon, S. M., Sinclair, A., Smith, K., Sohn, W., Song, X., Sonka, R. F., Spergel, D., Spisak, J., Staggs, S. T., Stein, G., Stevens, J. R., Stompor, R., Storer, E., Sudiwala, R., Sugiyama, J., Surrao, K. M., Suzuki, A., Suzuki, J., Tajima, O., Takakura, S., Takeuchi, A., Tansieri, I., Taylor, A. C., Teply, G., Terasaki, T., Thomas, A., Thomas, D. B., Thornton, R., Trac, H., Tsan, T., Sang, E. Tsang King, Tucker, C., Ullom, J., Vacher, L., Vagnozzi, S., Vale, L., van Engelen, A., Van Lanen, J., van Marrewijk, J., Van Winkle, D. D., Vargas, C., Vavagiakis, E. M., Veenendaal, I., Vergès, C., Vissers, M., Viña, M., Wagoner, K., Walker, S., Walters, L., Wang, Y., Westbrook, B., Williams, J., Williams, P., Winch, H., Wollack, E. J., Wolz, K., Wong, J., Xu, Z., Yamada, K., Young, E., Yu, B., Yu, C., Zannoni, M., Zheng, K., Zhu, N., Zonca, A., and Zubeldia, I.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We describe updated scientific goals for the wide-field, millimeter-wave survey that will be produced by the Simons Observatory (SO). Significant upgrades to the 6-meter SO Large Aperture Telescope (LAT) are expected to be complete by 2028, and will include a doubled mapping speed with 30,000 new detectors and an automated data reduction pipeline. In addition, a new photovoltaic array will supply most of the observatory's power. The LAT survey will cover about 60% of the sky at a regular observing cadence, with five times the angular resolution and ten times the map depth of Planck. The science goals are to: (1) determine the physical conditions in the early universe and constrain the existence of new light particles; (2) measure the integrated distribution of mass, electron pressure, and electron momentum in the late-time universe, and, in combination with optical surveys, determine the neutrino mass and the effects of dark energy via tomographic measurements of the growth of structure at $z < 3$; (3) measure the distribution of electron density and pressure around galaxy groups and clusters, and calibrate the effects of energy input from galaxy formation on the surrounding environment; (4) produce a sample of more than 30,000 galaxy clusters, and more than 100,000 extragalactic millimeter sources, including regularly sampled AGN light-curves, to study these sources and their emission physics; (5) measure the polarized emission from magnetically aligned dust grains in our Galaxy, to study the properties of dust and the role of magnetic fields in star formation; (6) constrain asteroid regoliths, search for Trans-Neptunian Objects, and either detect or eliminate large portions of the phase space in the search for Planet 9; and (7) provide a powerful new window into the transient universe on time scales of minutes to years, concurrent with observations from Rubin of overlapping sky., Comment: 44 pages, 7 figures; abstract slightly abridged. Author contributions to this paper are available at https://simonsobservatory.org/wp-content/uploads/2025/02/Author-contribution-statement-20250228.pdf
Published: 2025

15. Multidimensional Nova Simulations with an Extended Buffer and Lower Initial Mixing Temperatures

Author: Clark, Alexander Smith and Zingale, Michael
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: A classical nova is a thermonuclear runaway initiated on a white dwarf accreting solar-like material from its stellar companion. Once the white dwarf accretes enough mass, the pressure at the base of the accreted layer reaches a critical point, leading to the ignition of the hydrogen fuel at their interface. This paper presents a set of two-dimensional CO classical nova simulations with an extended buffer zone of a fixed low density and temperature between the top of the accreted layer and the upper boundary, allowing us to capture the thermonuclear outburst in the domain. Our domain reduces the role of the upper-outflow boundary condition that has affected previous simulations and allows us to explore the nucleosynthesis evolution in detail. We also study the effects of the initial temperature perturbation and buffer size to explore their sensitivity in our simulations. Finally, we start our simulations with a lower temperature at the base of the accreted layer ($7\times 10^7\,\mathrm{K}$) than previous work, allowing us to capture mixing earlier in the evolution, reducing the effects of the mixing-length-theory assumptions. This allows for a more realistic description of convective transport in our models., Comment: 10 pages. To be submitted at "The Open Journal of Astrophysics"
Published: 2025

16. Chromatic numbers with closed local modular constraints

Author: Herden, Daniel, Meddaugh, Jonathan, Sepanski, Mark R., Clark, William, Kraus, Adam, Matter, Ellie, Rosengartner, Kyle, Stephens, Elyssa, Stephens, John, Minyard, Mitchell, Michael, Kingsley, and Ramirez, Maricela
Subjects: Mathematics - Combinatorics, Primary: 05C78, Secondary: 05C25
Abstract: Generalizing the notion of odd-sum colorings, a $\mathbb{Z}$-labeling of a graph $G$ is called a closed coloring with remainder $k\mod n$ if the closed neighborhood label sum of each vertex is congruent to $k\mod n$. If such colorings exist, we write $\chi_{n,k}(G)$ for the minimum number of colors used for a closed coloring with remainder $k\mod n$ such that no neighboring vertices have the same color. General estimates for $\chi_{n,k}(G)$ are given along with evaluations of $\chi_{n,k}(G)$ for some finite and infinite order graphs., Comment: 27 pages, 8 figures
Published: 2025

17. XIRVIO: Critic-guided Iterative Refinement for Visual-Inertial Odometry with Explainable Adaptive Weighting

Author: Lam, Chit Yuen, Clark, Ronald, and Kocer, Basaran Bahadir
Subjects: Computer Science - Robotics
Abstract: We introduce XIRVIO, a transformer-based Generative Adversarial Network (GAN) framework for monocular visual inertial odometry (VIO). By taking sequences of images and 6-DoF inertial measurements as inputs, XIRVIO's generator predicts pose trajectories through an iterative refinement process which are then evaluated by the critic to select the iteration with the optimised prediction. Additionally, the self-emergent adaptive sensor weighting reveals how XIRVIO attends to each sensory input based on contextual cues in the data, making it a promising approach for achieving explainability in safety-critical VIO applications. Evaluations on the KITTI dataset demonstrate that XIRVIO matches well-known state-of-the-art learning-based methods in terms of both translation and rotation errors., Comment: 7 pages, 6 figures
Published: 2025

18. Full-sky Models of Galactic Microwave Emission and Polarization at Sub-arcminute Scales for the Python Sky Model

Author: Group, The Pan-Experiment Galactic Science, Borrill, Julian, Clark, Susan E., Delabrouille, Jacques, Frolov, Andrei V., Ghosh, Shamik, Hensley, Brandon S., Hicks, Monica D., Krachmalnicoff, Nicoletta, Lau, King, Norton, Myra M., Pryke, Clement, Puglisi, Giuseppe, Remazeilles, Mathieu, Russier, Elisa, Thorne, Benjamin, Yao, Jian, and Zonca, Andrea
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Polarized foreground emission from the Galaxy is one of the biggest challenges facing current and upcoming cosmic microwave background (CMB) polarization experiments. We develop new models of polarized Galactic dust and synchrotron emission at CMB frequencies that draw on the latest observational constraints, that employ the ``polarization fraction tensor'' framework to couple intensity and polarization in a physically motivated way, and that allow for stochastic realizations of small-scale structure at sub-arcminute angular scales currently unconstrained by full-sky data. We implement these models into the publicly available Python Sky Model (PySM) software and additionally provide PySM interfaces to select models of dust and CO emission from the literature. We characterize the behavior of each model by quantitatively comparing it to observational constraints in both maps and power spectra, demonstrating an overall improvement over previous PySM models. Finally, we synthesize models of the various Galactic foreground components into a coherent suite of three plausible microwave skies that span a range of astrophysical complexity allowed by current data., Comment: Submitted to ApJ. 34 pages, 15 figures. A supplement describing author contributions to this paper can be found at https://pysm3.readthedocs.io/en/latest/pysm_methods_author_contributions.html
Published: 2025

19. Optical switching in a layered altermagnet

Author: De Vita, Alessandro, Bigi, Chiara, Romanin, Davide, Watson, Matthew D., Polewczyk, Vincent, Zonno, Marta, Bertran, François, Petersen, My Bang, Motti, Federico, Vinai, Giovanni, Tuniz, Manuel, Cilento, Federico, Cuoco, Mario, Andersen, Brian M., Kreisel, Andreas, D'Onofrio, Luciano Jacopo, Clark, Oliver J., Edmonds, Mark T., Candelora, Christopher, Xu, Muxian, Cheng, Siyu, LaFleur, Alexander, Antonelli, Tommaso, Sangiovanni, Giorgio, Del Re, Lorenzo, Vobornik, Ivana, Fujii, Jun, Granozio, Fabio Miletto, Sambri, Alessia, Di Gennaro, Emiliano, Jacobsen, Jeppe B., Jacobsen, Henrik, Ernstorfer, Ralph, Zeljkovic, Ilija, Hwang, Younghun, Calandra, Matteo, Miwa, Jill A., and Mazzola, Federico
Subjects: Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Materials Science
Abstract: Altermagnetism defies conventional classifications of collinear magnetic phases, standing apart from ferromagnetism and antiferromagnetism with its unique combination of spin-dependent symmetries, net-zero magnetization, and anomalous Hall transport. Although altermagnetic states have been realized experimentally, their integration into functional devices has been hindered by the structural rigidity and poor tunability of existing materials. First, through cobalt intercalation of the superconducting 2H-NbSe$_2$ polymorph, we induce and stabilize a robust altermagnetic phase and using both theory and experiment, we directly observe the lifting of Kramers degeneracy. Then, using ultrafast laser pulses, we demonstrate how the low temperature phase of this system can be quenched, realizing the first example of an optical altermagnetic switch. While shedding light on overlooked aspects of altermagnetism, our findings open pathways to spin-based technologies and lay a foundation for advancing the emerging field of altertronics.
Published: 2025

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

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

21. ClassInvGen: Class Invariant Synthesis using Large Language Models

Author: Sun, Chuyue, Agashe, Viraj, Chakraborty, Saikat, Taneja, Jubi, Barrett, Clark, Dill, David, Qiu, Xiaokang, and Lahiri, Shuvendu K.
Subjects: Computer Science - Programming Languages, Computer Science - Software Engineering
Abstract: Formal program specifications in the form of preconditions, postconditions, and class invariants have several benefits for the construction and maintenance of programs. They not only aid in program understanding due to their unambiguous semantics but can also be enforced dynamically (or even statically when the language supports a formal verifier). However, synthesizing high-quality specifications in an underlying programming language is limited by the expressivity of the specifications or the need to express them in a declarative manner. Prior work has demonstrated the potential of large language models (LLMs) for synthesizing high-quality method pre/postconditions for Python and Java, but does not consider class invariants. In this work, we describe ClassInvGen, a method for co-generating executable class invariants and test inputs to produce high-quality class invariants for a mainstream language such as C++, leveraging LLMs' ability to synthesize pure functions. We show that ClassInvGen outperforms a pure LLM-based technique to generate specifications (from code) as well as prior data-driven invariant inference techniques such as Daikon. We contribute a benchmark of standard C++ data structures along with a harness that can help measure both the correctness and completeness of generated specifications using tests and mutants. We also demonstrate its applicability to real-world code by performing a case study on several classes within a widely used and high-integrity C++ codebase.
Published: 2025

22. Efficient optimization of neural network backflow for ab-initio quantum chemistry

Author: Liu, An-Jun and Clark, Bryan K.
Subjects: Physics - Chemical Physics, Condensed Matter - Disordered Systems and Neural Networks, Physics - Computational Physics, Quantum Physics
Abstract: The ground state of second-quantized quantum chemistry Hamiltonians is key to determining molecular properties. Neural quantum states (NQS) offer flexible and expressive wavefunction ansatze for this task but face two main challenges: highly peaked ground-state wavefunctions hinder efficient sampling, and local energy evaluations scale quartically with system size, incurring significant computational costs. In this work, we develop algorithmic improvements for optimizing these wave-functions which includes compact subspace construction, truncated local energy evaluations, improved stochastic sampling, and physics-informed modifications. We apply these improvements to the neural network backflow (NNBF) ansatz finding that they lead to improved accuracy and scalability. Using these techniques, we find NNBF surpasses traditional methods like CCSD and CCSD(T), outperform existing NQS approaches, and achieve competitive energies compared to state-of-the-art quantum chemistry methods such as HCI, ASCI, and FCIQMC. An ablation study highlights the contribution of each enhancement, showing significant gains in energy accuracy and computational efficiency. We also examine the dependence of NNBF expressiveness on the inverse participation ratio (IPR), observing that more delocalized states are generally harder to approximate.
Published: 2025

23. Anomalous spin-optical helical effect in Ti-based kagome metal

Author: Mazzola, Federico, Brzezicki, Wojciech, Bigi, Chiara, Consiglio, Armando, Onofrio, Luciano Jacopo D', Mercaldo, Maria Teresa, Kłosiński, Adam, Bertran, François, Fèvre, Patrick Le, Clark, Oliver J., Edmonds, Mark T., Tuniz, Manuel, De Vita, Alessandro, Polewczyk, Vincent, Jacobsen, Jeppe B., Jacobsen, Henrik, Miwa, Jill A., Wells, Justin W., Jana, Anupam, Vobornik, Ivana, Fujii, Jun, Mignani, Niccolò, Tarakameh, Narges Samani, Crepaldi, Alberto, Sangiovanni, Giorgio, Kataria, Anshu, Morresi, Tommaso, Sanna, Samuele, Bonfá, Pietro, Ortiz, Brenden R., Pokharel, Ganesh, Wilson, Stephen D., Di Sante, Domenico, Ortix, Carmine, and Cuoco, Mario
Subjects: Condensed Matter - Strongly Correlated Electrons
Abstract: The kagome lattice stands as a rich platform for hosting a wide array of correlated quantum phenomena, ranging from charge density waves and superconductivity to electron nematicity and loop current states. Direct detection of loop currents in kagome systems has remained a formidable challenge due to their intricate spatial arrangements and the weak magnetic field signatures they produce. This has left their existence and underlying mechanisms a topic of intense debate. In this work, we uncover a hallmark reconcilable with loop currents: spin handedness-selective signals that surpass conventional dichroic, spin, and spin-dichroic responses. We observe this phenomenon in the kagome metal CsTi$_3$Bi$_5$ and we call it the anomalous spin-optical helical effect. This effect arises from the coupling of light' s helicity with spin-orbital electron correlations, providing a groundbreaking method to visualize loop currents in quantum materials. Our discovery not only enriches the debate surrounding loop currents but also paves the way for new strategies to exploit the electronic phases of quantum materials via light-matter interaction.
Published: 2025

24. Exploring $\beta$ decay and $\beta$-delayed neutron emission in exotic $^{46,47}$Cl isotopes

Author: Tripathi, Vandana, Longfellow, B., Volya, A., Rubino, E., Benetti, C., Perello, J. F., Tabor, S. L., Liddick, S. N., Bender, P. C., Carpenter, M. P., Carroll, J. J., Chester, A., Chiara, C. J., Childers, K., Clark, B. R., Crider, B. P., Harke, J. T., Jain, R., Luitel, S., Mogannam, M. J., Ogunbeku, T. H., Richard, A. L., Saha, S., Shehu, O. A., Unz, R., Xiao, Y., and Zhu, Yiyi
Subjects: Nuclear Experiment
Abstract: In this paper, $\beta^-$ and $\beta$-delayed neutron decays of $^{46,47}$Cl are reported from an experiment carried out at the National Superconducting Cyclotron Laboratory using the Beta Counting System. The half-lives of both $^{46}$Cl and $^{47}$Cl were extracted. Based on the delayed $\gamma$-ray transitions observed, the level structure of $N = 28$ $^{46}$Ar was determined. Completely different sets of excited states above the first $2^+$ state in $^{46}$Ar were populated in the $^{46}$Cl $\beta0n$ and $^{47}$Cl $\beta1n$ decay channels. Two new $\gamma$-ray transitions in $^{47}$Ar were identified from the very weak $^{47}$Cl $\beta0n$ decay. Furthermore, $^{46}$Cl $\beta1n$ and $^{47}$Cl $\beta2n$ were also observed to yield different population patterns for levels in $^{45}$Ar, including states of different parities. The experimental results allow us to address some of the open questions related to the delayed neutron emission process. For isotopes with large neutron excess and high $Q_{\beta}$ values, delayed neutron emission remains an important decay mode and can be utilized as a powerful spectroscopic tool. Experimental results were compared with shell-model calculations using the FSU and $V_{MU}$ effective interactions.
Published: 2025

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

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

26. Absence of censoring inequalities in random quantum circuits

Author: Belkin, Daniel, Allen, James, and Clark, Bryan K.
Subjects: Quantum Physics, Mathematical Physics
Abstract: Ref. 1 asked whether deleting gates from a random quantum circuit architecture can ever make the architecture a better approximate $t$-design. We show that it can. In particular, we construct a family of architectures such that the approximate $2$-design depth decreases when certain gates are deleted. We also give some intuition for this construction and discuss the relevance of this result to the approximate $t$-design depth of the 1D brickwork. Deleting gates always decreases scrambledness in the short run, but can sometimes cause it to increase in the long run. Finally, we give analogous results for spectral gaps and when deleting edges of interaction graphs., Comment: 10 pages, 7 figures
Published: 2025

27. On the socle of a class of Steinberg algebras

Author: Clark, Lisa Orloff, Canto, Cristóbal Gil, Barquero, Dolores Martín, González, Cándido Martín, and Campos, Iván Ruiz
Subjects: Mathematics - Operator Algebras, Mathematics - Rings and Algebras, 16S99, 22A22, 16D25, 16D70, 18B40, 46L55, 16S88
Abstract: We study minimal left ideals in Steinberg algebras of Hausdorff groupoids. We establish a relationship between minimal left ideals in the algebra and open singletons in the unit space of the groupoid. We apply this to obtain results about the socle of Steinberg algebras under certain hypotheses. This encompasses known results about Leavitt path algebras and improves on Kumjian-Pask algebra results to include higher-rank graphs that are not row-finite., Comment: 13 pages
Published: 2025

28. Latent Factor Models Meets Instructions:Goal-conditioned Latent Factor Discovery without Task Supervision

Author: Xie, Zhouhang, Khot, Tushar, Mishra, Bhavana Dalvi, Surana, Harshit, McAuley, Julian, Clark, Peter, and Majumder, Bodhisattwa Prasad
Subjects: Computer Science - Computation and Language
Abstract: Instruction-following LLMs have recently allowed systems to discover hidden concepts from a collection of unstructured documents based on a natural language description of the purpose of the discovery (i.e., goal). Still, the quality of the discovered concepts remains mixed, as it depends heavily on LLM's reasoning ability and drops when the data is noisy or beyond LLM's knowledge. We present Instruct-LF, a goal-oriented latent factor discovery system that integrates LLM's instruction-following ability with statistical models to handle large, noisy datasets where LLM reasoning alone falls short. Instruct-LF uses LLMs to propose fine-grained, goal-related properties from documents, estimates their presence across the dataset, and applies gradient-based optimization to uncover hidden factors, where each factor is represented by a cluster of co-occurring properties. We evaluate latent factors produced by Instruct-LF on movie recommendation, text-world navigation, and legal document categorization tasks. These interpretable representations improve downstream task performance by 5-52% than the best baselines and were preferred 1.8 times as often as the best alternative, on average, in human evaluation., Comment: NAACL 2025
Published: 2025

29. Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation

Author: Yang, Yue, Patel, Ajay, Deitke, Matt, Gupta, Tanmay, Weihs, Luca, Head, Andrew, Yatskar, Mark, Callison-Burch, Chris, Krishna, Ranjay, Kembhavi, Aniruddha, and Clark, Christopher
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract: Reasoning about images with rich text, such as charts and documents, is a critical application of vision-language models (VLMs). However, VLMs often struggle in these domains due to the scarcity of diverse text-rich vision-language data. To address this challenge, we present CoSyn, a framework that leverages the coding capabilities of text-only large language models (LLMs) to automatically create synthetic text-rich multimodal data. Given input text describing a target domain (e.g., "nutrition fact labels"), CoSyn prompts an LLM to generate code (Python, HTML, LaTeX, etc.) for rendering synthetic images. With the underlying code as textual representations of the synthetic images, CoSyn can generate high-quality instruction-tuning data, again relying on a text-only LLM. Using CoSyn, we constructed a dataset comprising 400K images and 2.7M rows of vision-language instruction-tuning data. Comprehensive experiments on seven benchmarks demonstrate that models trained on our synthetic data achieve state-of-the-art performance among competitive open-source models, including Llama 3.2, and surpass proprietary models such as GPT-4V and Gemini 1.5 Flash. Furthermore, CoSyn can produce synthetic pointing data, enabling VLMs to ground information within input images, showcasing its potential for developing multimodal agents capable of acting in real-world environments., Comment: 20 pages, 19 figures, 9 tables, website: https://yueyang1996.github.io/cosyn/
Published: 2025

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

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

31. An elementary algebraic proof of the fundamental theorem of algebra

Author: Clark, Katelyn S. and Nielsen, Pace P.
Subjects: Mathematics - Commutative Algebra, Mathematics - History and Overview, Mathematics - Number Theory, 12D05, 12F05
Abstract: We give a new proof of the fundamental theorem of algebra. It is entirely elementary, focused on using long division to its fullest extent. Further, the method quickly recovers a more general version of the theorem recently obtained by Joseph Shipman.
Published: 2025

32. Performance Evaluation of Large Language Models in Statistical Programming

Author: Song, Xinyi, Xie, Kexin, Lee, Lina, Chen, Ruizhe, Clark, Jared M., He, Hao, He, Haoran, Min, Jie, Zhang, Xinlei, Zheng, Simin, Zhang, Zhiyang, Deng, Xinwei, and Hong, Yili
Subjects: Statistics - Applications, Computer Science - Artificial Intelligence
Abstract: The programming capabilities of large language models (LLMs) have revolutionized automatic code generation and opened new avenues for automatic statistical analysis. However, the validity and quality of these generated codes need to be systematically evaluated before they can be widely adopted. Despite their growing prominence, a comprehensive evaluation of statistical code generated by LLMs remains scarce in the literature. In this paper, we assess the performance of LLMs, including two versions of ChatGPT and one version of Llama, in the domain of SAS programming for statistical analysis. Our study utilizes a set of statistical analysis tasks encompassing diverse statistical topics and datasets. Each task includes a problem description, dataset information, and human-verified SAS code. We conduct a comprehensive assessment of the quality of SAS code generated by LLMs through human expert evaluation based on correctness, effectiveness, readability, executability, and the accuracy of output results. The analysis of rating scores reveals that while LLMs demonstrate usefulness in generating syntactically correct code, they struggle with tasks requiring deep domain understanding and may produce redundant or incorrect results. This study offers valuable insights into the capabilities and limitations of LLMs in statistical programming, providing guidance for future advancements in AI-assisted coding systems for statistical analysis., Comment: 27 pages, 8 figures
Published: 2025

33. Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability

Author: Zheng, Simin, Clark, Jared M., Salboukh, Fatemeh, Silva, Priscila, da Mata, Karen, Pan, Fenglian, Min, Jie, Lian, Jiayi, King, Caleb B., Fiondella, Lance, Liu, Jian, Deng, Xinwei, and Hong, Yili
Subjects: Statistics - Applications, Computer Science - Artificial Intelligence
Abstract: Artificial intelligence (AI) technology and systems have been advancing rapidly. However, ensuring the reliability of these systems is crucial for fostering public confidence in their use. This necessitates the modeling and analysis of reliability data specific to AI systems. A major challenge in AI reliability research, particularly for those in academia, is the lack of readily available AI reliability data. To address this gap, this paper focuses on conducting a comprehensive review of available AI reliability data and establishing DR-AIR: a data repository for AI reliability. Specifically, we introduce key measurements and data types for assessing AI reliability, along with the methodologies used to collect these data. We also provide a detailed description of the currently available datasets with illustrative examples. Furthermore, we outline the setup of the DR-AIR repository and demonstrate its practical applications. This repository provides easy access to datasets specifically curated for AI reliability research. We believe these efforts will significantly benefit the AI research community by facilitating access to valuable reliability data and promoting collaboration across various academic domains within AI. We conclude our paper with a call to action, encouraging the research community to contribute and share AI reliability data to further advance this critical field of study., Comment: 34 pages, 12 figures
Published: 2025

34. Relational Norms for Human-AI Cooperation

Author: Earp, Brian D., Mann, Sebastian Porsdam, Aboy, Mateo, Awad, Edmond, Betzler, Monika, Botes, Marietjie, Calcott, Rachel, Caraccio, Mina, Chater, Nick, Coeckelbergh, Mark, Constantinescu, Mihaela, Dabbagh, Hossein, Devlin, Kate, Ding, Xiaojun, Dranseika, Vilius, Everett, Jim A. C., Fan, Ruiping, Feroz, Faisal, Francis, Kathryn B., Friedman, Cindy, Friedrich, Orsolya, Gabriel, Iason, Hannikainen, Ivar, Hellmann, Julie, Jahrome, Arasj Khodadade, Janardhanan, Niranjan S., Jurcys, Paul, Kappes, Andreas, Khan, Maryam Ali, Kraft-Todd, Gordon, Dale, Maximilian Kroner, Laham, Simon M., Lange, Benjamin, Leuenberger, Muriel, Lewis, Jonathan, Liu, Peng, Lyreskog, David M., Maas, Matthijs, McMillan, John, Mihailov, Emilian, Minssen, Timo, Monrad, Joshua Teperowski, Muyskens, Kathryn, Myers, Simon, Nyholm, Sven, Owen, Alexa M., Puzio, Anna, Register, Christopher, Reinecke, Madeline G., Safron, Adam, Shevlin, Henry, Shimizu, Hayate, Treit, Peter V., Voinea, Cristina, Yan, Karen, Zahiu, Anda, Zhang, Renwen, Zohny, Hazem, Sinnott-Armstrong, Walter, Singh, Ilina, Savulescu, Julian, and Clark, Margaret S.
Subjects: Computer Science - Artificial Intelligence, Computer Science - Emerging Technologies
Abstract: How we should design and interact with social artificial intelligence depends on the socio-relational role the AI is meant to emulate or occupy. In human society, relationships such as teacher-student, parent-child, neighbors, siblings, or employer-employee are governed by specific norms that prescribe or proscribe cooperative functions including hierarchy, care, transaction, and mating. These norms shape our judgments of what is appropriate for each partner. For example, workplace norms may allow a boss to give orders to an employee, but not vice versa, reflecting hierarchical and transactional expectations. As AI agents and chatbots powered by large language models are increasingly designed to serve roles analogous to human positions - such as assistant, mental health provider, tutor, or romantic partner - it is imperative to examine whether and how human relational norms should extend to human-AI interactions. Our analysis explores how differences between AI systems and humans, such as the absence of conscious experience and immunity to fatigue, may affect an AI's capacity to fulfill relationship-specific functions and adhere to corresponding norms. This analysis, which is a collaborative effort by philosophers, psychologists, relationship scientists, ethicists, legal experts, and AI researchers, carries important implications for AI systems design, user behavior, and regulation. While we accept that AI systems can offer significant benefits such as increased availability and consistency in certain socio-relational roles, they also risk fostering unhealthy dependencies or unrealistic expectations that could spill over into human-human relationships. We propose that understanding and thoughtfully shaping (or implementing) suitable human-AI relational norms will be crucial for ensuring that human-AI interactions are ethical, trustworthy, and favorable to human well-being., Comment: 76 pages, 2 figures
Published: 2025

35. How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning

Author: Landreman, Matt, Choi, Jong Youl, Alves, Caio, Balaprakash, Prasanna, Churchill, R. Michael, Conlin, Rory, and Roberg-Clark, Gareth
Subjects: Physics - Plasma Physics, Computer Science - Machine Learning
Abstract: Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyze this dependence using multiple machine learning methods and a dataset of > 200,000 nonlinear simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimized and randomly generated stellarator equilibria. At fixed gradients, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection, and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find many previously published proxies do correlate well with both the heat flux and stability boundary.
Published: 2025

36. Search for Heavy Neutral Leptons with IceCube DeepCore

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

37. Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model

Author: Kataria, Saurabh, Xiao, Ran, Ruchti, Timothy, Clark, Matthew, Lu, Jiaying, Lee, Randall J., Grunwell, Jocelyn, and Hu, Xiao
Subjects: Computer Science - Machine Learning
Abstract: Non-invasive patient monitoring for tracking and predicting adverse acute health events is an emerging area of research. We pursue in-hospital cardiac arrest (IHCA) prediction using only single-channel finger photoplethysmography (PPG) signals. Our proposed two-stage model Feature Extractor-Aggregator Network (FEAN) leverages powerful representations from pre-trained PPG foundation models (PPG-GPT of size up to 1 Billion) stacked with sequential classification models. We propose two FEAN variants ("1H", "FH") which use the latest one-hour and (max) 24-hour history to make decisions respectively. Our study is the first to present IHCA prediction results in ICU patients using only unimodal (continuous PPG signal) waveform deep representations. With our best model, we obtain an average of 0.79 AUROC over 24~h prediction window before CA event onset with our model peaking performance at 0.82 one hour before CA. We also provide a comprehensive analysis of our model through architectural tuning and PaCMAP visualization of patient health trajectory in latent space.
Published: 2025

38. Low thermal noise mirror coatings utilising titanium dioxide and germanium dioxide mixtures

Author: Fazio, M., Martin, I. W., Hill, P., Yaala, M. Ben, Chicoine, M., Clark, C., Demos, N., Fejer, M. M., Gibson, D., Gras, S., Hough, J., Markosyan, A., McGhee, G., Rowan, S., Smith, J., Schiettekatte, F., Tait, S., Vajente, G., and Reid, S.
Subjects: Physics - Instrumentation and Detectors, Condensed Matter - Materials Science, Physics - Optics
Abstract: Upgrades to ground-based gravitational-wave observatories will require mirror coatings with reduced thermal noise, enabling improved detector sensitivity and extended astrophysical reach. Recent studies have shown that optical coatings utilising amorphous materials that exhibit a larger fraction of corner-sharing between adjacent structural units of metal-centered polyhedra are a promising route for reducing mechanical dissipation and thus thermal noise at room temperature. We report on multilayer optical coatings that are fabricated using germanium dioxide mixed with titanium dioxide (TiO$_2$:GeO$_2$) for the high index layers, and silicon dioxide (SiO$_2$) for the low index material. Single layers of TiO$_2$:GeO$_2$ are characterised to optimise the mixture proportion and based on that highly reflective multilayer stacks were deposited. Exceptional optical absorption at 1064 nm below 1 part-per-million (ppm) is observed in the multilayer stacks after heat treatment. The annealing process also induces the formation of blisters which leads to increased optical scattering. However, there is indication that blisters can be suppressed by decreasing the water partial pressure in the deposition chamber. Direct thermal noise measurements provide experimental verification of a significant 25\% reduction of thermal noise over the mirrors currently employed, which combined with sub-ppm levels of optical absorption show the potential of TiO$_2$:GeO$_2$ to improve the sensitivity of gravitational-wave observatories.
Published: 2025

39. Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations

Author: Handa, Kunal, Tamkin, Alex, McCain, Miles, Huang, Saffron, Durmus, Esin, Heck, Sarah, Mueller, Jared, Hong, Jerry, Ritchie, Stuart, Belonax, Tim, Troy, Kevin K., Amodei, Dario, Kaplan, Jared, Clark, Jack, and Ganguli, Deep
Subjects: Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract: Despite widespread speculation about artificial intelligence's impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for measuring AI usage patterns across the economy. We leverage a recent privacy-preserving system to analyze over four million Claude.ai conversations through the lens of tasks and occupations in the U.S. Department of Labor's O*NET Database. Our analysis reveals that AI usage primarily concentrates in software development and writing tasks, which together account for nearly half of all total usage. However, usage of AI extends more broadly across the economy, with approximately 36% of occupations using AI for at least a quarter of their associated tasks. We also analyze how AI is being used for tasks, finding 57% of usage suggests augmentation of human capabilities (e.g., learning or iterating on an output) while 43% suggests automation (e.g., fulfilling a request with minimal human involvement). While our data and methods face important limitations and only paint a picture of AI usage on a single platform, they provide an automated, granular approach for tracking AI's evolving role in the economy and identifying leading indicators of future impact as these technologies continue to advance.
Published: 2025

40. ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks

Author: Jha, Saurabh, Arora, Rohan, Watanabe, Yuji, Yanagawa, Takumi, Chen, Yinfang, Clark, Jackson, Bhavya, Bhavya, Verma, Mudit, Kumar, Harshit, Kitahara, Hirokuni, Zheutlin, Noah, Takano, Saki, Pathak, Divya, George, Felix, Wu, Xinbo, Turkkan, Bekir O., Vanloo, Gerard, Nidd, Michael, Dai, Ting, Chatterjee, Oishik, Gupta, Pranjal, Samanta, Suranjana, Aggarwal, Pooja, Lee, Rong, Murali, Pavankumar, Ahn, Jae-wook, Kar, Debanjana, Rahane, Ameet, Fonseca, Carlos, Paradkar, Amit, Deng, Yu, Moogi, Pratibha, Mohapatra, Prateeti, Abe, Naoki, Narayanaswami, Chandrasekhar, Xu, Tianyin, Varshney, Lav R., Mahindru, Ruchi, Sailer, Anca, Shwartz, Laura, Sow, Daby, Fuller, Nicholas C. M., and Puri, Ruchir
Subjects: Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Multiagent Systems
Abstract: Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps). The design enables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable metrics. ITBench includes an initial set of 94 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 13.8% of SRE scenarios, 25.2% of CISO scenarios, and 0% of FinOps scenarios. We expect ITBench to be a key enabler of AI-driven IT automation that is correct, safe, and fast.
Published: 2025

41. Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform V2

Author: Harik, Ramy, Kalach, Fadi El, Samaha, Jad, Samaha, Philip, Clark, Devon, Sander, Drew, Burns, Liam, Yousif, Ibrahim, Gadow, Victor, Mahmoud, Ahmed, and Wuest, Thorsten
Subjects: Computer Science - Machine Learning
Abstract: This paper presents two industry-grade datasets captured during an 8-hour continuous operation of the manufacturing assembly line at the Future Factories Lab, University of South Carolina, on 08/13/2024. The datasets adhere to industry standards, covering communication protocols, actuators, control mechanisms, transducers, sensors, and cameras. Data collection utilized both integrated and external sensors throughout the laboratory, including sensors embedded within the actuators and externally installed devices. Additionally, high-performance cameras captured key aspects of the operation. In a prior experiment [1], a 30-hour continuous run was conducted, during which all anomalies were documented. Maintenance procedures were subsequently implemented to reduce potential errors and operational disruptions. The two datasets include: (1) a time-series analog dataset, and (2) a multi-modal time-series dataset containing synchronized system data and images. These datasets aim to support future research in advancing manufacturing processes by providing a platform for testing novel algorithms without the need to recreate physical manufacturing environments. Moreover, the datasets are open-source and designed to facilitate the training of artificial intelligence models, streamlining research by offering comprehensive, ready-to-use resources for various applications and projects.
Published: 2025

42. CALF-SBM: A Covariate-Assisted Latent Factor Stochastic Block Model

Author: Louit, Sydney, Clark, Evan, Gelbard, Alexander, Vivek, Niketna, Yan, Jun, and Zhang, Panpan
Subjects: Statistics - Methodology
Abstract: We propose a novel network generative model extended from the standard stochastic block model by concurrently utilizing observed node-level information and accounting for network-enabled nodal heterogeneity. The proposed model is so so-called covariate-assisted latent factor stochastic block model (CALF-SBM). The inference for the proposed model is done in a fully Bayesian framework. The primary application of CALF-SBM in the present research is focused on community detection, where a model-selection-based approach is employed to estimate the number of communities which is practically assumed unknown. To assess the performance of CALF-SBM, an extensive simulation study is carried out, including comparisons with multiple classical and modern network clustering algorithms. Lastly, the paper presents two real data applications, respectively based on an extremely new network data demonstrating collaborative relationships of otolaryngologists in the United States and a traditional aviation network data containing information about direct flights between airports in the United States and Canada.
Published: 2025

43. AT 2018dyk: tidal disruption event or active galactic nucleus? Follow-up observations of an extreme coronal line emitter with the Dark Energy Spectroscopic Instrument

Author: Clark, Peter, Callow, Joseph, Graur, Or, Greenwell, Claire, Hu, Lei, Aguilar, Jessica, Ahlen, Steven, Bianchi, Davide, Brooks, David, Claybaugh, Todd, Dawson, Kyle, de la Macorra, Axel, Doel, Peter, Gontcho, Satya Gontcho A, Gutierrez, Gaston, Honscheid, Klaus, Juneau, Stephanie, Kehoe, Robert, Kisner, Theodore, Kremin, Anthony, Landriau, Martin, Guillou, Laurent Le, Meisner, Aaron, Miquel, Ramon, Moustakas, John, Pérez-Ràfols, Ignasi, Sanchez, Eusebio, Schubnell, Michael, Sprayberry, David, Tarlé, Gregory, Weaver, Benjamin A., and Zou, Hu
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present fresh insights into the nature of the tidal disruption event (TDE) candidate AT 2018dyk. AT 2018dyk has sparked a debate in the literature around its classification as either a bona-fide TDE or as an active galactic nucleus (AGN) turn-on state change. A new follow-up spectrum taken with the Dark Energy Spectroscopic Instrument, in combination with host-galaxy analysis using archival SDSS-MaNGA data, supports the identification of AT 2018dyk as a TDE. Specifically, we classify this object as a TDE that occurred within a gas-rich environment, which was responsible for both its mid-infrared (MIR) outburst and development of Fe coronal emission lines. Comparison with the known sample of TDE-linked extreme coronal line emitters (TDE-ECLEs) and other TDEs displaying coronal emission lines (CrL-TDEs) reveals similar characteristics and shared properties. For example, the MIR properties of both groups appear to form a continuum with links to the content and density of the material in their local environments. This includes evidence for a MIR colour-luminosity relationship in TDEs occurring within such gas-rich environments, with those with larger MIR outbursts also exhibiting redder peaks., Comment: 44 pages, 16 total figures. Submitted to MNRAS
Published: 2025

44. Malleable Robots

Author: Clark, Angus B., Wang, Xinran, Ranne, Alex, and Rojas, Nicolas
Subjects: Computer Science - Robotics
Abstract: This chapter is about the fundamentals of fabrication, control, and human-robot interaction of a new type of collaborative robotic manipulators, called malleable robots, which are based on adjustable architectures of varying stiffness for achieving high dexterity with lower mobility arms. Collaborative robots, or cobots, commonly integrate six or more degrees of freedom (DOF) in a serial arm in order to allow positioning in constrained spaces and adaptability across tasks. Increasing the dexterity of robotic arms has been indeed traditionally accomplished by increasing the number of degrees of freedom of the system; however, once a robotic task has been established (e.g., a pick-and-place operation), the motion of the end-effector can be normally achieved using less than 6-DOF (i.e., lower mobility). The aim of malleable robots is to close the technological gap that separates current cobots from achieving flexible, accessible manufacturing automation with a reduced number of actuators., Comment: 37 pages, 22 figures, chapter 7 of "Handbook on Soft Robotics"
Published: 2025
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45. VERITAS and multiwavelength observations of the Blazar B3 2247+381 in response to an IceCube neutrino alert

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

46. Action-Free Reasoning for Policy Generalization

Author: Clark, Jaden, Mirchandani, Suvir, Sadigh, Dorsa, and Belkhale, Suneel
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract: End-to-end imitation learning offers a promising approach for training robot policies. However, generalizing to new settings remains a significant challenge. Although large-scale robot demonstration datasets have shown potential for inducing generalization, they are resource-intensive to scale. In contrast, human video data is abundant and diverse, presenting an attractive alternative. Yet, these human-video datasets lack action labels, complicating their use in imitation learning. Existing methods attempt to extract grounded action representations (e.g., hand poses), but resulting policies struggle to bridge the embodiment gap between human and robot actions. We propose an alternative approach: leveraging language-based reasoning from human videos-essential for guiding robot actions-to train generalizable robot policies. Building on recent advances in reasoning-based policy architectures, we introduce Reasoning through Action-free Data (RAD). RAD learns from both robot demonstration data (with reasoning and action labels) and action-free human video data (with only reasoning labels). The robot data teaches the model to map reasoning to low-level actions, while the action-free data enhances reasoning capabilities. Additionally, we will release a new dataset of 3,377 human-hand demonstrations with reasoning annotations compatible with the Bridge V2 benchmark and aimed at facilitating future research on reasoning-driven robot learning. Our experiments show that RAD enables effective transfer across the embodiment gap, allowing robots to perform tasks seen only in action-free data. Furthermore, scaling up action-free reasoning data significantly improves policy performance and generalization to novel tasks. These results highlight the promise of reasoning-driven learning from action-free datasets for advancing generalizable robot control. Project page: https://rad-generalization.github.io, Comment: 13 pages, 10 figures
Published: 2025

47. Efficiently Generating Expressive Quadruped Behaviors via Language-Guided Preference Learning

Author: Clark, Jaden, Hejna, Joey, and Sadigh, Dorsa
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract: Expressive robotic behavior is essential for the widespread acceptance of robots in social environments. Recent advancements in learned legged locomotion controllers have enabled more dynamic and versatile robot behaviors. However, determining the optimal behavior for interactions with different users across varied scenarios remains a challenge. Current methods either rely on natural language input, which is efficient but low-resolution, or learn from human preferences, which, although high-resolution, is sample inefficient. This paper introduces a novel approach that leverages priors generated by pre-trained LLMs alongside the precision of preference learning. Our method, termed Language-Guided Preference Learning (LGPL), uses LLMs to generate initial behavior samples, which are then refined through preference-based feedback to learn behaviors that closely align with human expectations. Our core insight is that LLMs can guide the sampling process for preference learning, leading to a substantial improvement in sample efficiency. We demonstrate that LGPL can quickly learn accurate and expressive behaviors with as few as four queries, outperforming both purely language-parameterized models and traditional preference learning approaches. Website with videos: https://lgpl-gaits.github.io/, Comment: 8 pages 5 figures
Published: 2025

48. Swarm Characteristic Classification using Robust Neural Networks with Optimized Controllable Inputs

Author: Peltier III, Donald W., Kaminer, Isaac, Clark, Abram, and Orescanin, Marko
Subjects: Computer Science - Machine Learning
Abstract: Having the ability to infer characteristics of autonomous agents would profoundly revolutionize defense, security, and civil applications. Our previous work was the first to demonstrate that supervised neural network time series classification (NN TSC) could rapidly predict the tactics of swarming autonomous agents in military contexts, providing intelligence to inform counter-maneuvers. However, most autonomous interactions, especially military engagements, are fraught with uncertainty, raising questions about the practicality of using a pretrained classifier. This article addresses that challenge by leveraging expected operational variations to construct a richer dataset, resulting in a more robust NN with improved inference performance in scenarios characterized by significant uncertainties. Specifically, diverse datasets are created by simulating variations in defender numbers, defender motions, and measurement noise levels. Key findings indicate that robust NNs trained on an enriched dataset exhibit enhanced classification accuracy and offer operational flexibility, such as reducing resources required and offering adherence to trajectory constraints. Furthermore, we present a new framework for optimally deploying a trained NN by the defenders. The framework involves optimizing defender trajectories that elicit adversary responses that maximize the probability of correct NN tactic classification while also satisfying operational constraints imposed on the defenders.
Published: 2025

49. Super-diffusion of Photoexcited Carriers in Topological Insulator Nanoribbons

Author: Silva, Rodrigo Becerra, Huang, Jay, Wang, Bob Minyu, Song, Ziyi, Travaglini, Henry Clark, and Yu, Dong
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Understanding the ultrafast dynamics and transport of photoexcited carriers in topological insulators is crucial for the optical manipulation of spins and may shed light on the nature of topological excitons. Here we investigate bulk-insulating Sb-doped $\mathrm{Bi_2Se_3}$ nanoribbons via ultrafast transient photovoltage microscopy. The probe-pulse-induced photovoltage is substantially suppressed by a pump pulse. Recovery time increases from 50 to 1600 picoseconds as the pump fluence increases. We found that the diffusivity of photoexcited carriers increases significantly at lower carrier concentrations, up to 800 cm$^2$/s at 21 K, two to three orders of magnitude higher than that of band-edge carriers. Remarkably, the photoexcited carriers travel up to 10 $\mu$m for hundreds of picoseconds at this high diffusivity. The diffusivity peaks in intrinsic devices and is reduced at high temperatures. We discuss the possible mechanisms of long-ranged super-diffusion in the frames of hot carriers and exciton condensation., Comment: 20 pages, 6 figures
Published: 2025

50. A Mechanistic Study on Environment Gases in Metal Additive Manufacturing

Author: Ren, Zhongshu, Clark, Samuel J., Gao, Lin, Fezzaa, Kamel, and Sun, Tao
Subjects: Physics - General Physics
Abstract: A variety of protective or reactive environmental gases have recently gained growing attention in laser-based metal additive manufacturing (AM) technologies due to their unique thermophysical properties and the potential improvements they can bring to the build processes. However, much remains unclear regarding the effects of different gas environments on critical phenomena in laser AM, such as rapid cooling, energy coupling, and defect generation. Through simultaneous high-speed synchrotron x-ray imaging and thermal imaging, we identify distinct effects of various environmental gases in laser AM and gained a deeper understanding of the underlying mechanisms. Compared to the commonly used protective gas, argon, it is found that helium has a negligible effect on cooling the part. However, helium can suppress unstable keyholes by decreasing effective energy absorption, thus mitigating keyhole porosity generation and reducing pore size under certain processing conditions. These observations provide guidelines for the strategic use of environmental gases in laser AM to produce parts with improved quality.
Published: 2025

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