200,626 results on '"Miller, P"'
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2. A Massive Black Hole 0.8 kpc from the Host Nucleus Revealed by the Offset Tidal Disruption Event AT2024tvd
- Author
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Yao, Yuhan, Chornock, Ryan, Ward, Charlotte, Hammerstein, Erica, Sfaradi, Itai, Margutti, Raffaella, Kelley, Luke Zoltan, Lu, Wenbin, Liu, Chang, Wise, Jacob, Sollerman, Jesper, Alexander, Kate D., Bellm, Eric C., Drake, Andrew J., Fremling, Christoffer, Gilfanov, Marat, Graham, Matthew J., Groom, Steven L., Hinds, K. R., Kulkarni, S. R., Miller, Adam A., Miller-Jones, James C. A., Nicholl, Matt, Perley, Daniel A., Purdum, Josiah, Ravi, Vikram, Rich, R. Michael, Rehemtulla, Nabeel, Riddle, Reed, Smith, Roger, Stein, Robert, Sunyaev, Rashid, van Velzen, Sjoert, and Wold, Avery
- Subjects
Astrophysics - Astrophysics of Galaxies ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
Tidal disruption events (TDEs) that are spatially offset from the nucleus of their host galaxies offer a new probe of massive black hole (MBH) wanderers, binaries, triples, and recoiling MBHs. Here we present AT2024tvd, the first off-nuclear TDE identified through optical sky surveys. High-resolution imaging with the Hubble Space Telescope shows that AT2024tvd is $0.914\pm 0.010$ arcsec offset from the apparent center of its host galaxy, corresponding to a projected distance of $0.808\pm 0.009$ kpc at $z=0.045$. AT2024tvd exhibits typical properties of nuclear TDEs, including a persistent hot UV/optical component that peaks at $L_{ bb}\sim 6\times 10^{43}\,erg\,s^{-1}$, broad hydrogen lines in its optical spectra, and delayed brightening of luminous ($L_{ X,peak}\sim 3\times 10^{43}\,erg\,s^{-1}$), highly variable soft X-ray emission. The MBH mass of AT2024tvd is $10^{6\pm1}\,M_\odot$, at least 10 times lower than its host galaxy's central black hole mass ($\gtrsim 10^8\,M_\odot$). The MBH in AT2024tvd has two possible origins: a wandering MBH from the lower-mass galaxy in a minor merger during the dynamical friction phase or a recoiling MBH ejected by triple interactions. Combining AT2024tvd with two previously known off-nuclear TDEs discovered in X-rays (3XMM\,J2150 and EP240222a), which likely involve intermediate-mass black holes in satellite galaxies, we find that the parent galaxies of all three events are very massive ($\sim 10^{10.9}\,M_\odot$). This result aligns with expectations from cosmological simulations that the number of offset MBHs scales linearly with the host halo mass., Comment: 26 pages, 16 figures, 4 tables. Submitted to journal on 24 Feb 2025. Comments welcome
- Published
- 2025
3. Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning
- Author
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DUNE Collaboration, Abud, A. Abed, Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Alemanno, F., Alex, N. S., Allison, K., Alrashed, M., Alton, A., Alvarez, R., Alves, T., Aman, A., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anjarazafy, F., Antic, D., Antoniassi, M., Antonova, M., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Gómez, D. Ávila, Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Baigarashev, D., Balasubramanian, S., Balboni, A., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Basu, D., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, B., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolini, E., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bezawada, Y., Bezerra, A. T., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bogart, B., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Booth, A., Boran, F., Merlo, R. Borges, Bostan, N., Botogoske, G., Bottino, B., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M. B., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chalifour, M., Chappell, A., Chatterjee, A., Chauhan, B., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen, Z., Cherdack, D., Chhibra, S. S., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Choi, G., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conrad, J. M., Convery, M., Conway, K., Copello, S., Cova, P., Cox, C., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., De Jong, P., Sanchez, P. Del Amo, De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Di Silvestre, V., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Dolan, S., Dolce, M., Dolek, F., Dolinski, M. J., Domenici, D., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Emark, W., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Felix, J., Feng, Y., Fernandez-Martinez, E., da Silva, M. Ferreira, Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fucci, M., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., Gago, A. M, Galizzi, F., Gallagher, H., Galli, M., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghosh, A., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonzalez-Diaz, D., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Haegel, L., Hagaman, L., Hahn, A., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Handley, M., Happacher, F., Harris, D. A., Hart, A. L., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hawkins, S., Hays, J., He, M., Heavey, A., Heeger, K. M., Heindel, A., Heise, J., Hellmuth, P., Henderson, L., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Zink, J. Hoefken, Hoff, J., Holin, A., Holvey, T., Hong, C., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Hosokawa, R., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Huang, X., Hulcher, Z., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Oliveira, M. Ismerio, Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Jung, K. Y., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kalikulov, O., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasetti, S. P., Kashur, L., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Keloth, R., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., Kim, S., King, B., King, M., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kordosky, M., Kosc, T., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kroupova, T., Kubota, S., Kubu, M., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kus, V., Kutter, T., Kvasnicka, J., Labree, T., Lackey, T., Lalău, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Larkin, J., Lasorak, P., Last, D., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Miotto, G. Lehmann, Lehnert, R., Leitner, M., Lemoine, H., Silverio, D. Leon, Lepin, L. M., Li, J. -Y, Li, S. W., Li, Y., Liao, H., Lima, R., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Luo, X., Luppi, E., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Malige, A., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Manzanillas-Velez, L., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, Martinez-Casales, M., López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Mastbaum, A., Masud, M., Matichard, F., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mayhew, F., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., McNulty, C., Meazza, L., Meddage, V. C. N., Mehmood, M., Mehta, B., Mehta, P., Mei, F., Melas, P., Mellet, L., Mena, O., Mendez, H., Méndez, D. P., Mendonca, A. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Minotti, A., Miralles, L., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mladenov, D., Mocioiu, I., Mogan, A., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, M., Moore, Z., Moreno, D., Moreno-Granados, G., Moreno-Palacios, O., Morescalchi, L., Moretti, R., Morris, C., Mossey, C., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mukhamejanov, Y., Mukhamejanova, A., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Naples, D., Narita, S., Nava, J., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nielsen, A., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, A., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panareo, M., Panda, P., Pandey, V., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papadopoulou, A., Papaleo, R., Papoulias, D., Paramesvaran, S., Parke, S., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Camargo, G. Patiño, Paton, J. L., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paul, J., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Peake, A., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pfaff, M., Pia, V., Pickering, L., Pierini, L., Pietropaolo, F., Pimentel, V. 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V., Ritchie-Yates, A., Ritter, S., Rivera, D., Robert, A., Roberts, A., Robles, E., Rocha, J. L. Rocabado, Roda, M., Rodrigues, M. J. O., Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Roy, N., Roy, P., Rubbia, C., Rudik, D., Ruggeri, A., Ferreira, G. Ruiz, Rushiya, K., Russell, B., Sacerdoti, S., Saduyev, N., Sahoo, S. K., Sahu, N., Sakhiyev, S., Sala, P., Salmoria, G., Samanta, S., Samios, N., Sanchez, M. C., Bravo, A. Sánchez, Sánchez-Castillo, A., Sanchez-Lucas, P., Sanders, D. A., Sanfilippo, S., Santoro, D., Saoulidou, N., Sapienza, P., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Schwartz, S., Segade, A., Segreto, E., Senise, C. R., Sensenig, J., Seppela, D., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Poudel, S. Sharma, Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Shi, J., Shi, W., Shin, S., Shivakoti, S., Shmakov, A., Shoemaker, I., Shooltz, D., Shrock, R., Siden, M., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Nunes, M. Soares, Sobel, H., Soderberg, M., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Soto-Oton, J., Sousa, A., Soustruznik, K., Correia, D. Souza, Spinella, F., Spitz, J., Spooner, N. J. C., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stewart, J., Stillwell, B., Stock, J., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Sweeney, C., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thomas, S., Thompson, A., Thorn, C., Thorpe, C., Timm, S. C., Tiras, E., Tishchenko, V., Tiwari, S., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Muñoz, D. Torres, Torti, M., Tortola, M., Torun, Y., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valdiviesso, G. A., Vale, V., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Auccalla, T. Vargas, Oliva, D. Vargas, Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Vences, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Vizarreta, R., Hernandez, A. P. Vizcaya, Vlachos, S., Vorobyev, G., Vuong, Q., Waldron, A. V., Wallach, M., Walsh, J., Walton, T., Wan, L., Wang, B., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Westerdale, S., Wetstein, M., Whalen, K., White, A., Whitehead, L. 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- Subjects
High Energy Physics - Experiment - Abstract
The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures
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- 2025
4. Towards Best Practices for Open Datasets for LLM Training
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Baack, Stefan, Biderman, Stella, Odrozek, Kasia, Skowron, Aviya, Bdeir, Ayah, Bommarito, Jillian, Ding, Jennifer, Gahntz, Maximilian, Keller, Paul, Langlais, Pierre-Carl, Lindahl, Greg, Majstorovic, Sebastian, Marda, Nik, Penedo, Guilherme, Van Segbroeck, Maarten, Wang, Jennifer, von Werra, Leandro, Baker, Mitchell, Belião, Julie, Chmielinski, Kasia, Fadaee, Marzieh, Gutermuth, Lisa, Kydlíček, Hynek, Leppert, Greg, Lewis-Jong, EM, Larsen, Solana, Longpre, Shayne, Lungati, Angela Oduor, Miller, Cullen, Miller, Victor, Ryabinin, Max, Siminyu, Kathleen, Strait, Andrew, Surman, Mark, Tumadóttir, Anna, Weber, Maurice, Weiss, Rebecca, White, Lee, and Wolf, Thomas
- Subjects
Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
Many AI companies are training their large language models (LLMs) on data without the permission of the copyright owners. The permissibility of doing so varies by jurisdiction: in countries like the EU and Japan, this is allowed under certain restrictions, while in the United States, the legal landscape is more ambiguous. Regardless of the legal status, concerns from creative producers have led to several high-profile copyright lawsuits, and the threat of litigation is commonly cited as a reason for the recent trend towards minimizing the information shared about training datasets by both corporate and public interest actors. This trend in limiting data information causes harm by hindering transparency, accountability, and innovation in the broader ecosystem by denying researchers, auditors, and impacted individuals access to the information needed to understand AI models. While this could be mitigated by training language models on open access and public domain data, at the time of writing, there are no such models (trained at a meaningful scale) due to the substantial technical and sociological challenges in assembling the necessary corpus. These challenges include incomplete and unreliable metadata, the cost and complexity of digitizing physical records, and the diverse set of legal and technical skills required to ensure relevance and responsibility in a quickly changing landscape. Building towards a future where AI systems can be trained on openly licensed data that is responsibly curated and governed requires collaboration across legal, technical, and policy domains, along with investments in metadata standards, digitization, and fostering a culture of openness.
- Published
- 2025
5. Search for continuous gravitational waves from known pulsars in the first part of the fourth LIGO-Virgo-KAGRA observing run
- Author
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The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. 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Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. 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F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. 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Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Jin, H., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. 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C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. 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L., Pearlman, A. B., Romero, G. E., Shannon, R. M., Shaw, B., Stairs, I. H., Stappers, B. W., Tan, C. M., Theureau, G., Thompson, M., Weltevrede, P., and Zubieta, E.
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables
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- 2025
6. Charter School Expansion, Catholic School Enrollment, & the Equity Implications of School Choice. EdWorkingPaper No. 24-1027
- Author
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Annenberg Institute for School Reform at Brown University, Shaun M. Dougherty, Andrew Miller, and Yerin Yoon
- Abstract
Catholic schools have seen more than a 30% decline in enrollment over the past 20 years. While some of the decline in enrollment may have been spurred by secular trends or the Church abuse scandal, the increase in schools of choice, principally public charter schools, may explain at least some of this decline. In this paper we estimate the effect of the opening of charter schools in proximity to Catholic schools across the entire U.S. We find that the opening of a nearby charter school has a negative impact on Catholic school enrollment and increases the likelihood that the school will close. We also find that charter openings induce greater racial isolation. Findings are especially pronounced in K8 schools, rather than high schools.
- Published
- 2024
7. Vitamin B12 Levels Association with Functional and Structural Biomarkers of Central Nervous System Injury in Older Adults
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Beaudry‐Richard, Alexandra, Abdelhak, Ahmed, Saloner, Rowan, Sacco, Simone, Montes, Shivany C, Oertel, Frederike C, Cordano, Christian, Jabassini, Nour, Ananth, Kirtana, Gomez, Apraham, Keihani, Azeen, Chapman, Makenna, Javvadi, Sree, Saha, Shikha, Staffaroni, Adam, Songster, Christopher, Warren, Martin, Boscardin, John W, Kramer, Joel, Miller, Bruce, Miller, Joshua W, Green, Ralph, and Green, Ari J
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Biomedical and Clinical Sciences ,Clinical Sciences ,Nutrition ,Neurodegenerative ,Brain Disorders ,Aging ,Clinical Research ,Neurosciences ,2.1 Biological and endogenous factors ,Neurological ,Neurology & Neurosurgery ,Clinical sciences - Abstract
ObjectiveVitamin B12 (B12) plays a critical role in fatty- and amino-acid metabolism and nucleotide synthesis. While the association between B12 deficiency and neurological dysfunction is well-known, the exact threshold for adequacy remains undefined in terms of functional impairment and evidence of injury. The objective was to assess whether B12 levels within the current normal range in a cohort of healthy older adults may be associated with measurable evidence of neurological injury or dysfunction.MethodsWe enrolled 231 healthy elderly volunteers (median age 71.2 years old) with a median B12 blood concentration of 414.8 pmol/L (as measured by automated chemiluminescence assay). We performed multifocal visual evoked potential testing, processing speed testing, and magnetic resonance imaging to assess neurological status. Moreover, we measured serum biomarkers of neuroaxonal injury, astrocyte involvement, and amyloid pathology.ResultsLow (log-transformed) B12, especially decreased holo-transcobalamin, was associated with visual evoked potential latency delay (estimate = -0.04; p = 0.023), processing speed impairment (in an age-dependent manner; standardized β = -2.39; p = 0.006), and larger volumes of white matter hyperintensities on MRI (β = -0.21; p = 0.039). Remarkably, high levels of holo-haptocorrin (biologically inactive fraction of B12) correlated with serum levels of Tau, a biomarker of neurodegeneration (β = 0.22, p = 0.015).InterpretationHealthy older subjects exhibit neurological changes at both ends of the measurable "normal" B12 spectrum. These findings challenge our current understanding of optimal serum B12 levels and suggest revisiting how we establish appropriate nutritional recommendations. ANN NEUROL 2025.
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- 2025
8. An Enigmatic Wild Passerine Mortality Event in the Eastern United States.
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Greening, Sabrina, Ellis, Julie, Lewis, Nicole, Needle, David, Tato, Cristina, Knowles, Susan, Shearn-Bochsler, Valerie, Miller, Jaimie, Grear, Daniel, Lorch, Jeffrey, Blehert, David, Burrell, Caitlin, Murphy, Lisa, Miller, Erica, Ogbunugafor, C, Ayala, Andrea, Thomas, W, Sevigny, Joseph, Gordon, Lawrence, Baillargeon, Tessa, Mwakibete, Lusajo, Kirchgessner, Megan, Casey, Christine, Barton, Ethan, Yabsley, Michael, Anis, Eman, Gagne, Roderick, Klein, Patrice, Driscoll, Cindy, Sykes, Chelsea, Poppenga, Robert, and Nemeth, Nicole
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conjunctivitis ,diagnostic evaluation ,mass mortality ,passerines ,songbird ,wildlife investigation - Abstract
The ability to rapidly respond to wildlife health events is essential. However, such events are often unpredictable, especially with anthropogenic disturbances and climate-related environmental changes driving unforeseen threats. Many events also are short-lived and go undocumented, making it difficult to draw on lessons learned from past investigations. We report on the response to a mortality event observed predominantly in wild passerines in the eastern United States. The event began in May 2021 when wildlife rehabilitators and private citizens reported large numbers of sick and dead juvenile birds, mostly presenting as single cases with neurologic signs and/or ocular and periocular lesions. Early efforts by rehabilitators, veterinarians, state and federal wildlife agencies, and universities helped gather public reports and fuel rapid responses by government agencies. Collective efforts included live bird and carcass collections; submission to diagnostic laboratories and evaluation; information sharing; and coordinated messaging to stakeholders and interested parties. Extensive diagnostic evaluations failed to identify a causative pathogen or other etiology, although congruent results across laboratories have helped drive further investigation into alternative causes, such as nutritional deficiencies. This report highlights the strengths of a multi-agency, interdisciplinary investigation while exposing the need for an operational framework with approaches and resources dedicated to wildlife health.
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- 2025
9. Phase estimation with partially randomized time evolution
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Günther, Jakob, Witteveen, Freek, Schmidhuber, Alexander, Miller, Marek, Christandl, Matthias, and Harrow, Aram
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Quantum Physics - Abstract
Quantum phase estimation combined with Hamiltonian simulation is the most promising algorithmic framework to computing ground state energies on quantum computers. Its main computational overhead derives from the Hamiltonian simulation subroutine. In this paper we use randomization to speed up product formulas, one of the standard approaches to Hamiltonian simulation. We propose new partially randomized Hamiltonian simulation methods in which some terms are kept deterministically and others are randomly sampled. We perform a detailed resource estimate for single-ancilla phase estimation using partially randomized product formulas for benchmark systems in quantum chemistry and obtain orders-of-magnitude improvements compared to other simulations based on product formulas. When applied to the hydrogen chain, we have numerical evidence that our methods exhibit asymptotic scaling with the system size that is competitive with the best known qubitization approaches., Comment: 42 pages, 19 figures
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- 2025
10. Improved Two-source Extractors against Quantum Side Information
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Miller, Jakob, Sandfuchs, Martin, and Ferradini, Carla
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Quantum Physics ,Computer Science - Cryptography and Security - Abstract
Two-source extractors aim to extract randomness from two independent sources of weak randomness. It has been shown that any two-source extractor which is secure against classical side information remains secure against quantum side information. Unfortunately, this generic reduction comes with a significant penalty to the performance of the extractor. In this paper, we show that the two-source extractor from Dodis et al. performs equally well against quantum side information as in the classical realm, surpassing previously known results about this extractor. Additionally, we derive a new quantum XOR-Lemma which allows us to re-derive the generic reduction but also allows for improvements for a large class of extractors., Comment: Semester thesis, 18+6 pages, comments are welcome
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- 2025
11. Using the XMM-Newton Small Window Mode to investigate systematic uncertainties in the particle background of X-ray CCD detectors
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Schellenberger, Gerrit, Kraft, Ralph, Nulsen, Paul, Miller, Eric D., Bautz, Marshall W., Grant, Catherine E., Wilkins, Dan, Allen, Steven, Molendi, Silvano, Burrows, David N., Falcone, Abraham D., Fioretti, Valentina, Foster, Richard F., Hall, David, Hubbard, Michael W. J., Perinati, Emanuele, Poliszczuk, Artem, Rau, Arne, Sarkar, Arnab, and Schneider, Benjamin
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Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
The level and uncertainty of the particle induced background in CCD detectors plays a crucial role for future X-ray instruments, such as the Wide Field Imager (WFI) onboard Athena. To mitigate the background systematic uncertainties, which will limit the Athena science goals, we aim to understand the relationship between the energetic charged particles interacting in the detector and satellite, and the instrumental science background to an unprecedented level. These particles produce easily identified "cosmic-ray tracks" along with less easily identified signals produced by secondary particles, e.g., X-rays generated by particle interactions with the instrument and indistinguishable from genuine sky X-rays. We utilize the Small Window Mode of the PN camera onboard XMM-Newton to understand the time, spatial and energy dependence of the various background components, particularly the particle induced background. While the distribution of particle events follows expected detector readout patterns, we find a particle track length distribution inconsistent with the simple, isotropic model. We also find that the detector mode-specific readout results in a shifted Cu fluorescent line. We illustrate that on long timescales the variability of the particle background correlates well with the solar cycle. This 20-year lightcurve, can be reproduced by a particle detector onboard Chandra, the HRC anti-coincidence shield. We conclude that the self-anti-coincidence method of removing X-ray-like events near detected particle tracks in the same frame can be optimized with the inclusion of additional information, such as the energy of the X-ray. The results presented here are relevant for any future pixelated X-ray imaging detector, and could allow the WFI to probe to truly faint X-ray surface brightness., Comment: 35 pages, 16 figures. Accepted for publication in JATIS
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- 2025
12. Measurement of the Branching Fraction of $\Lambda_c^+ \to p K_S^0 \pi^0$ at Belle
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Belle, The, Collaborations, Belle II, Adachi, I., Aggarwal, L., Ahmed, H., Ahn, J. K., Aihara, H., Akopov, N., Alhakami, M., Aloisio, A., Althubiti, N., Angelsmark, M., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Barrett, M., Bartl, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhardwaj, V., Bhuyan, B., Bianchi, F., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dossett, D., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Epifanov, D., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Graziani, E., Greenwald, D., Gruberová, Z., Guan, Y., Gudkova, K., Haide, I., Halder, S., Han, Y., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobi, D., Jacobs, W. W., Jang, E. -J., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kandra, J., Kang, K. H., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kim, Y. J., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, D., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lalwani, K., Lam, T., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Li, C., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Lin, S., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Mehta, R., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, H., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Onuki, Y., Otani, F., Pakhlov, P., Pakhlova, G., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitz, J., Schneider, S., Schnell, G., Schnepf, M., Schoenning, K., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Song, W., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wakai, M., Wallner, S., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yuan, J., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhu, L., Zhukova, V. I., and Žlebčík, R.
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High Energy Physics - Experiment - Abstract
We report a precise measurement of the ratio of branching fractions $\mathcal{B}(\Lambda_c^+\to p K_S^0 \pi^0)/\mathcal{B}(\Lambda_c^+\to p K^- \pi^+)$ using 980 fb$^{-1}$ of $e^+e^-$ data from the Belle experiment. We obtain a value of $\mathcal{B}(\Lambda_c^+\to p K_S^0 \pi^0)/\mathcal{B}(\Lambda_c^+\to p K^- \pi^+)=0.339\pm 0.002\pm 0.009$, where the first and second uncertainties are statistical and systematic, respectively. This Belle result is consistent with the previous measurement from the CLEO experiment but has a fivefold improvement in precision. By combining our result with the world average $\mathcal{B}(\Lambda_c^+\to p K^- \pi^+)$, we obtain the absolute branching fraction $\mathcal{B}(\Lambda_c^+\to p K_S^0 \pi^0)=(2.12\pm 0.01\pm 0.05 \pm 0.10)\%$, where the uncertainties are statistical, systematic, and the uncertainty in the absolute branching fraction scale $\mathcal{B}(\Lambda_c^+\to p K^- \pi^+)$, respectively. This measurement can shed light on hadronic decay mechanisms in charmed baryon decays., Comment: 20 pages, 7 figures
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- 2025
13. All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
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Joshi, Chaitanya K., Fu, Xiang, Liao, Yi-Lun, Gharakhanyan, Vahe, Miller, Benjamin Kurt, Sriram, Anuroop, and Ulissi, Zachary W.
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Diffusion models are the standard toolkit for generative modelling of 3D atomic systems. However, for different types of atomic systems - such as molecules and materials - the generative processes are usually highly specific to the target system despite the underlying physics being the same. We introduce the All-atom Diffusion Transformer (ADiT), a unified latent diffusion framework for jointly generating both periodic materials and non-periodic molecular systems using the same model: (1) An autoencoder maps a unified, all-atom representations of molecules and materials to a shared latent embedding space; and (2) A diffusion model is trained to generate new latent embeddings that the autoencoder can decode to sample new molecules or materials. Experiments on QM9 and MP20 datasets demonstrate that jointly trained ADiT generates realistic and valid molecules as well as materials, exceeding state-of-the-art results from molecule and crystal-specific models. ADiT uses standard Transformers for both the autoencoder and diffusion model, resulting in significant speedups during training and inference compared to equivariant diffusion models. Scaling ADiT up to half a billion parameters predictably improves performance, representing a step towards broadly generalizable foundation models for generative chemistry. Open source code: https://github.com/facebookresearch/all-atom-diffusion-transformer
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- 2025
14. Acoustic phonon phase gates with number-resolving phonon detection
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Qiao, Hong, Wang, Zhaoyou, Andersson, Gustav, Anferov, Alexander, Conner, Christopher R., Joshi, Yash J., Li, Shiheng, Miller, Jacob M., Wu, Xuntao, Yan, Haoxiong, Jiang, Liang, and Cleland, Andrew N.
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Quantum Physics ,Physics - Optics - Abstract
Linear optical quantum computing (LOQC) provides a compelling approach to quantum information processing, with a short list of physical requirements; however, experimental implementations have faced significant challenges. Itinerant phonons in quantum acoustics, combined with superconducting qubits, offer a compelling alternative to the quantum optics approach. Here we demonstrate key advances in the ability to manipulate and measure acoustic phonon quantum states: First, we demonstrate deterministic phase control of itinerant one- and two-phonon qubit states, measured using an acoustic Mach-Zehnder interferometer. We implement phonon phase control using the frequency-dependent scattering of phonon states from a superconducting transmon qubit. The acoustic interferometer used to measure the resulting phonon phase achieves a noise-floor-limited Hong-Ou-Mandel (HOM) interference visibility of 98.1%, representing a significant improvement over our previous demonstration. Additionally, we propose and implement a multi-phonon detection scheme that enables coherent conversion between itinerant one- and two-phonon Fock states and transmon qutrit states, transforming for example the Hong-Ou-Mandel two-phonon entangled output state $|02\rangle - |20\rangle$ into the entangled state of two transmons. The tight integration of quantum acoustics with superconducting circuits native to our implementation promises further advances, including deterministic phonon quantum gates with direct applications to quantum computing.
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- 2025
15. Searching for continuous gravitational waves from highly deformed compact objects with DECIGO
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Miller, Andrew L. and De Lillo, Federico
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General Relativity and Quantum Cosmology ,Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
Searches for continuous gravitational waves from isolated compact objects and those in binary systems aim to detect non-axisymmetric, deformed neutron stars at particular locations in the Galaxy or all-sky. However, a large fraction of known pulsars have rotational frequencies that lie outside the audio frequency band, rendering current detectors insensitive to these pulsars. In this work, we show that DECIGO, a future space-based deci-hertz gravitational-wave interferometer, will be sensitive to severely deformed compact objects, e.g. hybrid stars, neutron stars, or magnetars. We estimate the number of possible compact objects that could be detected with such high deformations, both via their individual continuous gravitational-wave emission and the stochastic gravitational-wave background created by a superposition of gravitational waves from the $\sim 10^8$ compact objects in the Galaxy. Furthermore, we show that the existence of such compact objects could be probed across a wide parameter space at a fraction of the computational cost of current searches for isolated compact objects and those in binary systems. For known pulsars, we will be able to both beat the spin-down limit and probe the Brans-Dicke modified theory of gravity parameter $\zeta<1$ for approximately 85% of known pulsars with $f_{\rm gw}<10$ Hz, the latter of which is currently only possible for $O(10)$ pulsars. DECIGO will thus open a new window to probe highly deformed compact objects and over half of the known pulsars, both of which are currently inaccessible to ground-based detectors., Comment: 12 pages, 9 figures, comments are welcome!
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- 2025
16. Gravitational wave probes of particle dark matter: a review
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Miller, Andrew L.
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Astrophysics - High Energy Astrophysical Phenomena ,General Relativity and Quantum Cosmology - Abstract
Various theories of dark matter predict distinctive astrophysical signatures in gravitational-wave sources that could be observed by ground- and space-based laser interferometers. Different candidates-including axions, dark photons, macroscopic dark matter, WIMPs, and dark-matter spikes-may appear in interferometer data via their coupling to gravity or the Standard Model, altering the measured gravitational-wave strain in distinct ways. Despite their differences, these candidates share two key features: (1) they can be probed through their effects on gravitational waves from inspiraling compact objects, isolated black holes, and neutron stars, or via direct interactions with detectors, and (2) their signatures likely persist far longer than the seconds-long mergers detected today, necessitating new data analysis methods beyond matched filtering. This review outlines these dark matter candidates, their observational signatures, and approaches for their detection., Comment: 42 pages + references, 27 figures, comments are welcome! Invited review article to be submitted to IJMP D; added new summary plot Fig. 1
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- 2025
17. The Ejection of Transient Jets in Swift J1727.8-1613 Revealed by Time-Dependent Visibility Modelling
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Wood, Callan M., Miller-Jones, James C. A., Bahramian, Arash, Tingay, Steven J., Liu, He-Xin, Altamirano, Diego, Fender, Rob, Körding, Elmar, Maitra, Dipankar, Markoff, Sera, Russell, David M., Russell, Thomas D., Sarazin, Craig L., Sivakoff, Gregory R., Soria, Roberto, Tetarenko, Alexandra J., and Tudose, Valeriu
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
High angular resolution radio observations of relativistic jets are necessary to understand the causal connection between accretion and jet ejection in low mass X-ray binaries. Images from these observations can be difficult to reconstruct due to the rapid intra-observational motion and variability of transient jets. We have developed a time-dependent visibility model fitting and self-calibration procedure and applied it to a single four-hour VLBA observation of the low-mass X-ray binary Swift J1727.8-1613 during the bright flaring period of its 2023 outburst. This allowed us to detect and model a slightly resolved self-absorbed compact core, as well as three downstream transient jet knots. We were able to precisely measure the proper motion and flux density variability of these three jet knots, as well as (for the first time) their intra-observational expansion. Using simultaneous multi-frequency data, we were also able to measure the spectral index of the furthest downstream jet knot, and the core, as well as the frequency-dependent core shift between 2.3 and 8.3 GHz. Using these measurements, we inferred the ejection dates of the three jet knots, including one to within $\pm40$ minutes, which is one of the most precise ever measured. The ejection of the transient jet knots coincided with a bright X-ray flare and a drastic change in the X-ray spectral and timing properties as seen by HXMT, which is the clearest association ever seen between the launching of transient relativistic jets in an X-ray binary and a sudden change in the X-ray properties of the accretion inflow., Comment: 10 pages, 5 figures, submitted to ApJL
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- 2025
18. Semiclassical Measures on Hyperbolic Manifolds
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Kim, Elena and Miller, Nicholas
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Mathematics - Analysis of PDEs ,Mathematics - Dynamical Systems ,Mathematics - Geometric Topology ,Mathematics - Spectral Theory - Abstract
We examine semiclassical measures for Laplace eigenfunctions on compact hyperbolic $(n+1)$-manifolds. We prove their support must contain the cosphere bundle of a compact immersed totally geodesic submanifold. Our proof adapts the argument of Dyatlov and Jin to higher dimensions and classifies the closures of horocyclic orbits using Ratner theory. An important step in the proof is a generalization of the higher-dimensional fractal uncertainty principle of Cohen to Fourier integral operators, which may be of independent interest., Comment: 59 pages, 1 figure
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- 2025
19. A confirmed deficit of hot and cold dust emission in the most luminous Little Red Dots
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Setton, David J., Greene, Jenny E., Spilker, Justin S., Williams, Christina C., Labbe, Ivo, Ma, Yilun, Wang, Bingjie, Whitaker, Katherine E., Leja, Joel, de Graaff, Anna, Alberts, Stacey, Bezanson, Rachel, Boogaard, Leindert A., Brammer, Gabriel, Cutler, Sam E., Cleri, Nikko J., Cooper, Olivia R., Dayal, Pratika, Fujimoto, Seiji, Furtak, Lukas J., Goulding, Andy D., Hirschmann, Michaela, Kokorev, Vasily, Maseda, Michael V., McConachie, Ian, Matthee, Jorryt, Miller, Tim B., Naidu, Rohan P., Oesch, Pascal A., Pan, Richard, Price, Sedona H., Suess, Katherine A., Weaver, John R., Xiao, Mengyuan, Zhang, Yunchong, and Zitrin, Adi
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Astrophysics - Astrophysics of Galaxies - Abstract
Luminous broad H$\alpha$ emission and red rest-optical SEDs are the hallmark of compact Little Red Dots (LRDs), implying highly attenuated dusty starbursts and/or obscured active galactic nuclei. However, the lack of observed FIR emission has proved difficult to reconcile with the implied attenuated luminosity in these models. Here, we utilize deep new ALMA imaging, new and existing JWST/MIRI imaging, and archival Spitzer/Herschel imaging of two of the rest-optically brightest LRDs ($z=3.1$ and $z=4.47$) to place the strongest constraints on the IR luminosity in LRDs to date. The detections at $\lambda_\mathrm{rest}=1-4 \ \mu$m imply flat slopes in the rest-IR, ruling out a contribution from hot ($T\gtrsim500$ K) dust. Similarly, FIR non-detections rule out any appreciable cold ($T\lesssim75$ K) dust component. Assuming energy balance, these observations are inconsistent with the typical FIR dust emission of dusty starbursts and quasar torii, which usually show a mixture of cold and hot dust. Additionally, our [$\mathrm{C}_{II}$] non-detections rule out typical dusty starbursts. We compute empirical maximum IR SEDs and find that both LRDs must have $\log(L_\mathrm{IR}/L_\odot) \lesssim 12.2$ at the $3\sigma$ level. These limits are in tension with the predictions of rest-optical spectrophotometric fits, be they galaxy only, AGN only, or composite. It is unlikely that LRDs are highly dust-reddened intrinsically blue sources with a dust temperature distribution that conspires to avoid current observing facilities. Rather, we favor an intrinsically redder LRD SED model that alleviates the need for strong dust attenuation., Comment: 16 pages, 5 figures, 3 tables. Submitted to ApJ Letters. Comments welcome!
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- 2025
20. Direct Summation of the Madelung Constant using Axial Multipoles
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Calara, Joven V. and Miller, Jan D.
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Condensed Matter - Materials Science - Abstract
A direct summation method for the Madelung constant calculation is presented where a crystal lattice is constructed from linear arrays of charges or axial multipoles. An array is designed to have vanishing low order electric moments such that its potential at the origin from a distance $r$ decays at least as fast as $r^{-5}$, but preferably as fast as $r^{-13}$. High potential decay rates render the summation absolutely convergent in up to 6 dimensions. Convergence speed increases with higher decay rates. It is also shown that the limit approached by the summation is independent of the growth geometry. Madelung constants for NaCl bulk, surface, and edge lattice points are calculated, as well as on off-lattice points such as interstitial positions and external neighborhoods of surfaces. In addition, bulk CsCl Madelung constant was calculated. In 1D, 2D, and 3D, accuracy of 13 decimal places are attained within 40 nearest neighbor distance from the reference ion.
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- 2025
21. Personalizing the meshed SPL/NAC Brain Atlas for patient-specific scientific computing using SynthMorph
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Huynh, Andy, Zwick, Benjamin, Halle, Michael, Wittek, Adam, and Miller, Karol
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Computer Science - Computational Engineering, Finance, and Science - Abstract
Developing personalized computational models of the human brain remains a challenge for patient-specific clinical applications and neuroscience research. Efficient and accurate biophysical simulations rely on high-quality personalized computational meshes derived from patient's segmented anatomical MRI scans. However, both automatic and manual segmentation are particularly challenging for tissues with limited visibility or low contrast. In this work, we present a new method to create personalized computational meshes of the brain, streamlining the development of computational brain models for clinical applications and neuroscience research. Our method uses SynthMorph, a state-of-the-art anatomy-aware, learning-based medical image registration approach, to morph a comprehensive hexahedral mesh of the open-source SPL/NAC Brain Atlas to patient-specific MRI scans. Each patient-specific mesh includes over 300 labeled anatomical structures, more than any existing manual or automatic methods. Our registration-based method takes approximately 20 minutes, significantly faster than current state-of-the-art mesh generation pipelines, which can take up to two hours. We evaluated several state-of-the-art medical image registration methods, including SynthMorph, to determine the most optimal registration method to morph our meshed anatomical brain atlas to patient MRI scans. Our results demonstrate that SynthMorph achieved high DICE similarity coefficients and low Hausdorff Distance metrics between anatomical structures, while maintaining high mesh element quality. These findings demonstrate that our registration-based method efficiently and accurately produces high-quality, comprehensive personalized brain meshes, representing an important step toward clinical translation.
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- 2025
22. The Simons Observatory: Science Goals and Forecasts for the Enhanced Large Aperture Telescope
- Author
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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.
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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
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- 2025
23. Roadmap on Nonlocality in Photonic Materials and Metamaterials
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Monticone, Francesco, Mortensen, N. Asger, Fernández-Domínguez, Antonio I., Luo, Yu, Tserkezis, Christos, Khurgin, Jacob B., Shahbazyan, Tigran V., Chaves, André J., Peres, Nuno M. R., Wegner, Gino, Busch, Kurt, Hu, Huatian, Della Sala, Fabio, Zhang, Pu, Ciracì, Cristian, Aizpurua, Javier, Babaze, Antton, Borisov, Andrei G., Chen, Xue-Wen, Christensen, Thomas, Yan, Wei, Yang, Yi, Hohenester, Ulrich, Huber, Lorenz, Wubs, Martijn, De Liberato, Simone, Gonçalves, P. A. D., De Abajo, F. Javier García, Hess, Ortwin, Tarasenko, Illya, Cox, Joel D., Jelver, Line, Dias, Eduardo J. C., Sánchez, Miguel Sánchez, Margetis, Dionisios, Gómez-Santos, Guillermo, Stauber, Tobias, Tretyakov, Sergei, Simovski, Constantin, Pakniyat, Samaneh, Gómez-Díaz, J. Sebastián, Bondarev, Igor V., Biehs, Svend-Age, Boltasseva, Alexandra, Shalaev, Vladimir M., Krasavin, Alexey V., Zayats, Anatoly V., Alù, Andrea, Song, Jung-Hwan, Brongersma, Mark L., Levy, Uriel, Long, Olivia Y., Guo, Cheng, Fan, Shanhui, Bozhevolnyi, Sergey I., Overvig, Adam, Prudêncio, Filipa R., Silveirinha, Mário G., Gangaraj, S. Ali Hassani, Argyropoulos, Christos, Huidobro, Paloma A., Galiffi, Emanuele, Yang, Fan, Pendry, John B., and Miller, David A. B.
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Condensed Matter - Mesoscale and Nanoscale Physics ,Physics - Optics - Abstract
Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials--metamaterials and metasurfaces--can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward--toward new scientific discoveries and technological advancements.
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- 2025
24. RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs
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Pan, Tom, Dramko, Evan, Miller, Mitchell D., Phillips Jr., George N., and Kyrillidis, Anastasios
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Quantitative Biology - Quantitative Methods ,Computer Science - Machine Learning ,Mathematics - Optimization and Control ,I.2.1 - Abstract
Determining protein structures at an atomic level remains a significant challenge in structural biology. We introduce $\texttt{RecCrysFormer}$, a hybrid model that exploits the strengths of transformers with the aim of integrating experimental and ML approaches to protein structure determination from crystallographic data. $\texttt{RecCrysFormer}$ leverages Patterson maps and incorporates known standardized partial structures of amino acid residues to directly predict electron density maps, which are essential for constructing detailed atomic models through crystallographic refinement processes. $\texttt{RecCrysFormer}$ benefits from a ``recycling'' training regimen that iteratively incorporates results from crystallographic refinements and previous training runs as additional inputs in the form of template maps. Using a preliminary dataset of synthetic peptide fragments based on Protein Data Bank, $\texttt{RecCrysFormer}$ achieves good accuracy in structural predictions and shows robustness against variations in crystal parameters, such as unit cell dimensions and angles., Comment: 16 pages, 9 figures. To be published in Proceedings of CPAL 2025
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- 2025
25. Performance measurements of the electromagnetic calorimeter and readout electronics system for the DarkQuest experiment
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Apyan, Aram, Cosby, Christopher, Feng, Yongbin, Gelgen, Alp, Gori, Stefania, Harris, Philip, Liu, Xinlong, Liu, Mia, Maksimovic, Petar, Mantilla-Suarez, Cristina, McLaughlin, Ryan, Miller, Catherine, Mitra, Amitav, Paladino, Noah, Das, Arghya Ranjan, Slokenbergs, Valdis, Sperka, David, Tran, Nhan, and Wan, Zijie
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Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
This paper presents performance measurements of a new readout electronics system based on silicon photomultipliers for the PHENIX electromagnetic calorimeter. Installation of the lead-scintillator Shashlik style calorimeter into the SeaQuest/SpinQuest spectrometer has been proposed to broaden the experiment's dark sector search program, an upgrade known as DarkQuest. The calorimeter and electronics system were subjected to testing and calibration at the Fermilab Test Beam Facility. Detailed studies of the energy response and resolution, as well as particle identification capabilities, were performed. The background rate in the actual experimental environment was also examined. The system is found to be well-suited for a dark sector search program on the Fermilab 120 GeV proton beamline., Comment: Prepared for submission to Nuclear Instrumentation and Methods
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- 2025
26. Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. I: Luminosity Function, Volumetric Rate
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Das, Kaustav K., Kasliwal, Mansi M., Fremling, Christoffer, Sollerman, Jesper, Perley, Daniel A., De, Kishalay, Tzanidakis, Anastasios, Sit, Tawny, Adams, Scott, Anand, Shreya, Ahumuda, Tomas, Andreoni, Igor, Brennan, Sean, Brink, Thomas, Bruch, Rachel J., Chen, Ping, Chu, Matthew R., Cook, David O., Covarrubias, Sofia, Dahiwale, Aishwarya, Earley, Nicholas, Ho, Anna Y. Q., Gal-Yam, Avishay, Gangopadhyay, Anjasha, Hammerstein, Erica, Hinds, K-Ryan, Karambelkar, Viraj, Kong, Yihan, Kulkarni, S. R., Laz, Theophile Jegou du, Liu, Chang, Meynardie, William, Miller, Adam A., Nir, Guy, Patra, Kishore C., Pessi, Priscila J., Rich, R. Michael, Rehemtulla, Nabeel, Rose, Sam, Rusholme, Ben, Schulze, Steve, Sharma, Yashvi, Singh, Avinash, Smith, Roger, Stein, Robert, Mandigo-Stoba, Milan Sharma, Strotjohann, Nora L., Qin, Yu-Jing, Wise, Jacob, Wold, Avery, Yan, Lin, Yang, Yi, Yao, Yuhan, and Zimmerman, Erez
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Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Solar and Stellar Astrophysics - Abstract
We present the luminosity function and volumetric rate of a sample of Type IIP supernovae (SNe) from the Zwicky Transient Facility Census of the Local Universe survey (CLU). This is the largest sample of Type IIP SNe from a systematic volume-limited survey to-date. The final sample includes 330 Type IIP SNe and 36 low-luminosity Type II (LLIIP) SNe with $M_{\textrm{r,peak}}>-16$ mag, which triples the literature sample of LLIIP SNe. The fraction of LLIIP SNe is $19^{+3}_{-4}\%$ of the total CLU Type IIP SNe population ($8^{+1}_{-2}\%$ of all core-collapse SNe). This implies that while LLIIP SNe likely represent the fate of core-collapse SNe of $8-12$ \Msun\ progenitors, they alone cannot account for the fate of all massive stars in this mass range. To derive an absolute rate, we estimate the ZTF pipeline efficiency as a function of the apparent magnitude and the local surface brightness. We derive a volumetric rate of $(3.9_{-0.4}^{+0.4}) \times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for Type IIP SNe and $(7.3_{-0.6}^{+0.6}) \times 10^{3}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for LLIIP SNe. Now that the rate of LLIIP SNe is robustly derived, the unresolved discrepancy between core-collapse SN rates and star-formation rates cannot be explained by LLIIP SNe alone., Comment: Submitted to PASP
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- 2025
27. On the stability-instability transition in large Bose-Fermi mixtures
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Cárdenas, Esteban, Miller, Joseph K., Mitrouskas, David, and Pavlović, Nataša
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Mathematical Physics ,Mathematics - Analysis of PDEs - Abstract
We study the low-energy spectrum of large Bose-Fermi mixtures. In the chosen scaling, the fermions induce an effective attraction among the bosons, which competes with their intrinsic repulsive interaction. Our main result demonstrates the convergence of the eigenvalues towards those of an effective Bose Hamiltonian. For short-range potentials, we apply this result to derive a stability-instability transition in the bosonic subsystem, driven by the Bose-Fermi coupling strength $g$. For small $|g|$, the bosons form a stable Bose-Einstein condensate with the energy per particle uniformly bounded from below. For large $|g|$, the energy per particle is no longer uniformly bounded from below, signalling the collapse of the condensate., Comment: 34 pages, 2 figures
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- 2025
28. To Deepfake or Not to Deepfake: Higher Education Stakeholders' Perceptions and Intentions towards Synthetic Media
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Roe, Jasper, Perkins, Mike, Somoray, Klaire, Miller, Dan, and Furze, Leon
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Computer Science - Computers and Society ,Computer Science - Human-Computer Interaction - Abstract
Advances in deepfake technologies, which use generative artificial intelligence (GenAI) to mimic a person's likeness or voice, have led to growing interest in their use in educational contexts. However, little is known about how key stakeholders perceive and intend to use these tools. This study investigated higher education stakeholder perceptions and intentions regarding deepfakes through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using a mixed-methods approach combining survey data (n=174) with qualitative interviews, we found that academic stakeholders demonstrated a relatively low intention to adopt these technologies (M=41.55, SD=34.14) and held complex views about their implementation. Quantitative analysis revealed adoption intentions were primarily driven by hedonic motivation, with a gender-specific interaction in price-value evaluations. Qualitative findings highlighted potential benefits of enhanced student engagement, improved accessibility, and reduced workload in content creation, but concerns regarding the exploitation of academic labour, institutional cost-cutting leading to automation, degradation of relationships in education, and broader societal impacts. Based on these findings, we propose a framework for implementing deepfake technologies in higher education that addresses institutional policies, professional development, and equitable resource allocation to thoughtfully integrate AI while maintaining academic integrity and professional autonomy.
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- 2025
29. SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models
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Miller, Kevin, Mishra, Samarth, Gangrade, Aditya, Saenko, Kate, and Saligrama, Venkatesh
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Zero-shot multi-label recognition (MLR) with Vision-Language Models (VLMs) faces significant challenges without training data, model tuning, or architectural modifications. Existing approaches require prompt tuning or architectural adaptations, limiting zero-shot applicability. Our work proposes a novel solution treating VLMs as black boxes, leveraging scores without training data or ground truth. Using large language model insights on object co-occurrence, we introduce compound prompts grounded in realistic object combinations. Analysis of these prompt scores reveals VLM biases and ``AND''/``OR'' signal ambiguities, notably that maximum compound scores are surprisingly suboptimal compared to second-highest scores. We address these through a debiasing and score-fusion algorithm that corrects image bias and clarifies VLM response behaviors. Our method enhances other zero-shot approaches, consistently improving their results. Experiments show superior mean Average Precision (mAP) compared to methods requiring training data, achieved through refined object ranking for robust zero-shot MLR.
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- 2025
30. The Kelly Criterion And Utility Function Optimisation For Stochastic Binary Games: Submartingale And Supermartingale Regimes
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Miller, Steven D
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Mathematics - Probability - Abstract
A reformulation of the Kelly Criterion is presented. Let $\mathfrak{G}$ be a generic stochastic Bernoulli binary game with outcomes $\mathscr{Z}(I)\in\lbrace -1,1\rbrace$ of N trials for $I=1...N$. The binomial probabilities are $\mathsf{P}(\mathscr{Z}(I)=1)=p$ and ${\mathsf{P}}(\mathscr{Z}(I)=-1)=q$ with $p+q=1$. For a fair game $p=q=\tfrac{1}{2}$ and for a biased game $p>q$. If $\mathscr{W}(0)$ is the initial wealth then at the $I^{th}$ trial one bets a fraction $\mathcal{F}$ so that the bet is $B(I)=\mathcal{F}\mathscr{W}(I-1)$. If one wagers $B(I)$ and wins one recovers the original wager plus $B(I)$ if $\mathscr{Z}(I)=+1$, or a loss of $B(I)$ if $\mathscr{Z}(I)=-1$. The wealth at the $N^{th}$ trial/bet for large $N$ is the random walk $\mathscr{W}(N)=\mathscr{W} (0)+\sum_{I=1}^{N}B(I)\mathscr{Z}(I)=\mathscr{W}(0)\prod_{I=1}^{N}(1+\mathcal{F}\mathscr{Z}(I))$ with expectation $\mathsf{E}[\mathscr{W}(N)]$. Defining a 'utility function' $\mathsf{U}(\mathcal{F},p)=\mathsf{E}[\log(\mathscr{W}(N)/\mathscr{W}(0))^{1/N}]$ then $\mathsf{U}(\mathcal{F},p)$ is optimised by the Kelly fraction $\mathcal{F}=\mathcal{F}_{K}=p-q=2p-1$, which is essentially a critical point of $\mathsf{U}(\mathcal{F},p)$. Also $\mathsf{U}(\mathcal{F}_{K},p)$ can be related to the Shannon entropy. If $[0,1]=[0,\mathcal{F}_{*})\bigcup [\mathcal{F}_{*}]\bigcup (\mathcal{F}_{*},1]$ with $\mathsf{U}(\mathcal{F}_{*},p)=0$ then $\mathsf{U}(\mathcal{F},p)>0, \forall\mathcal{F}\in[0,\mathcal{F}_{*})$ and $\mathscr{W}(N)$ is a submartingale for $p>1/2$; also $\mathsf{U}(\mathcal{F},p)<0,\forall \mathcal{F}\in(\mathcal{F}_{*},1]$, and $\mathscr{W}(\mathcal{F},p)$ is a supermartingale. Estimates are derived for variance and volatility $\mathsf{VAR}(\mathscr{W}(N))$ and $\sigma(\mathscr{W}(N))=\sqrt{\mathsf{VAR}(\mathscr{W}(N)})$. For large $N$ and $\mathcal{F}=\mathcal{F}_{K}$, $\mathsf{E}[\mathscr{W}(N)]$ grows exponentially., Comment: 26 Pages
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- 2025
31. Mapping Parameter Correlations in Spinning Binary Black Hole Mergers
- Author
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Kang, Karen, Miller, Simona J., Chatziioannou, Katerina, and Ferguson, Deborah
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General Relativity and Quantum Cosmology ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
The spins of black holes in binaries measured with gravitational waves provide insights about the formation, evolution, and dynamics of these systems. The imprint of spin in the inspiral, where the black holes are well-separated, is understood through analytic equations for the binary dynamics. During the merger phase, the binary dynamics can only be studied with numerical relativity simulations. Though such simulations provide an exact solution (to within numerical error), the imprint of the full six spin degrees of freedom on the signal is not transparent. In the absence of analytic expressions for the merger, here we propose a waveform-based approach. Leveraging a neural network to efficiently calculate mismatches between waveforms, we identify regions in the parameter space of spins and mass ratio that result in low mismatches and thus similar waveforms. We map these regions with a Gaussian fit, thus identifying correlations between the mass ratio and spins and quantifying their strength. For low-mass, inspiral-dominated systems, we recover the known physical imprint: larger aligned spins are correlated with more equal masses as they have opposite effects on the inspiral length. For high-mass, merger-dominated signals, a qualitatively similar correlation is present, though its shape is altered and strength decreases with increasing total mass. Correlations between in-plane spins and mass ratio follow a similar trend, with their shape and strength altered as the mass increases. Waveform-based correlation mapping can motivate effective spin parameters and reveal the imprint of spins on signals for which no simple analytic descriptions exist., Comment: 19 pages including appendices and bibliography; 18 figures. Submitted to PRD
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- 2025
32. On the admissibility of bounds on the mean of discrete, scalar probability distributions from an iid sample
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Learned-Miller, Erik
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Mathematics - Statistics Theory - Abstract
We address the problem of producing a lower bound for the mean of a discrete probability distribution, with known support over a finite set of real numbers, from an iid sample of that distribution. Up to a constant, this is equivalent to bounding the mean of a multinomial distribution (with known support) from a sample of that distribution. Our main contribution is to characterize the complete set of admissible bound functions for any sample space, and to show that certain previously published bounds are admissible. We prove that the solution to each one of a set of simple-to-state optimization problems yields such an admissible bound. Single examples of such bounds, such as the trinomial bound by Miratrix and Stark [2009] have been previously published, but without an analysis of admissibility, and without a discussion of the full set of alternative admissible bounds. In addition to a variety of results about admissible bounds, we prove the non-existence of optimal bounds for sample spaces with supports of size greater than 1 and samples sizes greater than 1., Comment: 24 pages
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- 2025
33. Moving Past Single Metrics: Exploring Short-Text Clustering Across Multiple Resolutions
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Miller, Justin and Alexander, Tristram
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Statistics - Machine Learning - Abstract
Cluster number is typically a parameter selected at the outset in clustering problems, and while impactful, the choice can often be difficult to justify. Inspired by bioinformatics, this study examines how the nature of clusters varies with cluster number, presenting a method for determining cluster robustness, and providing a systematic method for deciding on the cluster number. The study focuses specifically on short-text clustering, involving 30,000 political Twitter bios, where the sparse co-occurrence of words between texts makes finding meaningful clusters challenging. A metric of proportional stability is introduced to uncover the stability of specific clusters between cluster resolutions, and the results are visualised using Sankey diagrams to provide an interrogative tool for understanding the nature of the dataset. The visualisation provides an intuitive way to track cluster subdivision and reorganisation as cluster number increases, offering insights that static, single-resolution metrics cannot capture. The results show that instead of seeking a single 'optimal' solution, choosing a cluster number involves balancing informativeness and complexity., Comment: 11 pages, 3 figures
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- 2025
34. Beyond Pattern Recognition: Probing Mental Representations of LMs
- Author
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Miller, Moritz and Shridhar, Kumar
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Computer Science - Computation and Language - Abstract
Language Models (LMs) have demonstrated impressive capabilities in solving complex reasoning tasks, particularly when prompted to generate intermediate explanations. However, it remains an open question whether these intermediate reasoning traces represent a dynamic, evolving thought process or merely reflect sophisticated pattern recognition acquired during large scale pre training. Drawing inspiration from human cognition, where reasoning unfolds incrementally as new information is assimilated and internal models are continuously updated, we propose to delve deeper into the mental model of various LMs. We propose a new way to assess the mental modeling of LMs, where they are provided with problem details gradually, allowing each new piece of data to build upon and refine the model's internal representation of the task. We systematically compare this step by step mental modeling strategy with traditional full prompt methods across both text only and vision and text modalities. Experiments on the MathWorld dataset across different model sizes and problem complexities confirm that both text-based LLMs and multimodal LMs struggle to create mental representations, questioning how their internal cognitive processes work.
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- 2025
35. Learning Long-Horizon Robot Manipulation Skills via Privileged Action
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Mao, Xiaofeng, Xu, Yucheng, Sun, Zhaole, Miller, Elle, Layeghi, Daniel, and Mistry, Michael
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Computer Science - Robotics - Abstract
Long-horizon contact-rich tasks are challenging to learn with reinforcement learning, due to ineffective exploration of high-dimensional state spaces with sparse rewards. The learning process often gets stuck in local optimum and demands task-specific reward fine-tuning for complex scenarios. In this work, we propose a structured framework that leverages privileged actions with curriculum learning, enabling the policy to efficiently acquire long-horizon skills without relying on extensive reward engineering or reference trajectories. Specifically, we use privileged actions in simulation with a general training procedure that would be infeasible to implement in real-world scenarios. These privileges include relaxed constraints and virtual forces that enhance interaction and exploration with objects. Our results successfully achieve complex multi-stage long-horizon tasks that naturally combine non-prehensile manipulation with grasping to lift objects from non-graspable poses. We demonstrate generality by maintaining a parsimonious reward structure and showing convergence to diverse and robust behaviors across various environments. Additionally, real-world experiments further confirm that the skills acquired using our approach are transferable to real-world environments, exhibiting robust and intricate performance. Our approach outperforms state-of-the-art methods in these tasks, converging to solutions where others fail.
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- 2025
36. Mechanistic Understanding of Language Models in Syntactic Code Completion
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Miller, Samuel, Rai, Daking, and Yao, Ziyu
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Computer Science - Software Engineering ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,I.2.7 - Abstract
Recently, language models (LMs) have shown impressive proficiency in code generation tasks, especially when fine-tuned on code-specific datasets, commonly known as Code LMs. However, our understanding of the internal decision-making processes of Code LMs, such as how they use their (syntactic or semantic) knowledge, remains limited, which could lead to unintended harm as they are increasingly used in real life. This motivates us to conduct one of the first Mechanistic Interpretability works to understand how Code LMs perform a syntactic completion task, specifically the closing parenthesis task, on the CodeLlama-7b model (Roziere et al. 2023). Our findings reveal that the model requires middle-later layers until it can confidently predict the correct label for the closing parenthesis task. Additionally, we identify that while both multi-head attention (MHA) and feed-forward (FF) sub-layers play essential roles, MHA is particularly crucial. Furthermore, we also discover attention heads that keep track of the number of already closed parentheses precisely but may or may not promote a correct number of closing parentheses that are still missing, leading to a positive or negative impact on the model's performance., Comment: 10 pages, 4 figures, accepted to the AAAI 2025 Workshop on Towards Knowledgeable Foundation Models
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- 2025
37. MONSTER: Monash Scalable Time Series Evaluation Repository
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Dempster, Angus, Foumani, Navid Mohammadi, Tan, Chang Wei, Miller, Lynn, Mishra, Amish, Salehi, Mahsa, Pelletier, Charlotte, Schmidt, Daniel F., and Webb, Geoffrey I.
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Computer Science - Machine Learning - Abstract
We introduce MONSTER-the MONash Scalable Time Series Evaluation Repository-a collection of large datasets for time series classification. The field of time series classification has benefitted from common benchmarks set by the UCR and UEA time series classification repositories. However, the datasets in these benchmarks are small, with median sizes of 217 and 255 examples, respectively. In consequence they favour a narrow subspace of models that are optimised to achieve low classification error on a wide variety of smaller datasets, that is, models that minimise variance, and give little weight to computational issues such as scalability. Our hope is to diversify the field by introducing benchmarks using larger datasets. We believe that there is enormous potential for new progress in the field by engaging with the theoretical and practical challenges of learning effectively from larger quantities of data., Comment: 45 pages; 38 figures
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- 2025
38. Using tournaments to calculate AUROC for zero-shot classification with LLMs
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Yoon, Wonjin, Bulovic, Ian, and Miller, Timothy A.
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Computer Science - Computation and Language - Abstract
Large language models perform surprisingly well on many zero-shot classification tasks, but are difficult to fairly compare to supervised classifiers due to the lack of a modifiable decision boundary. In this work, we propose and evaluate a method that converts binary classification tasks into pairwise comparison tasks, obtaining relative rankings from LLMs. Repeated pairwise comparisons can be used to score instances using the Elo rating system (used in chess and other competitions), inducing a confidence ordering over instances in a dataset. We evaluate scheduling algorithms for their ability to minimize comparisons, and show that our proposed algorithm leads to improved classification performance, while also providing more information than traditional zero-shot classification.
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- 2025
39. Performance of an Optical TPC Geant4 Simulation with Opticks GPU-Accelerated Photon Propagation
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NEXT Collaboration, Parmaksiz, I., Mistry, K., Church, E., Adams, C., Asaadi, J., Baeza-Rubio, J., Bailey, K., Byrnes, N., Jones, B. J. P., Moya, I. A., Navarro, K. E., Nygren, D. R., Oyedele, P., Rogers, L., Samaniego, F., Stogsdill, K., Almazán, H., Álvarez, V., Aparicio, B., Aranburu, A. I., Arazi, L., Arnquist, I. J., Auria-Luna, F., Ayet, S., Azevedo, C. D. R., Ballester, F., del Barrio-Torregrosa, M., Bayo, A., Benlloch-Rodríguez, J. M., Borges, F. I. G. M., Brodolin, A., Cárcel, S., Castillo, A., Cid, L., Conde, C. A. N., Contreras, T., Cossío, F. P., Coupe, R., Dey, E., Díaz, G., Echevarria, C., Elorza, M., Escada, J., Esteve, R., Felkai, R., Fernandes, L. M. P., Ferrario, P., Ferreira, A. L., Foss, F. W., Freixa, Z., García-Barrena, J., Gómez-Cadenas, J. J., Grocott, J. W. R., Guenette, R., Hauptman, J., Henriques, C. A. O., Morata, J. A. Hernando, Herrero-Gómez, P., Herrero, V., Carrete, C. Hervés, Ifergan, Y., Kellerer, F., Larizgoitia, L., Larumbe, A., Lebrun, P., Lopez, F., López-March, N., Madigan, R., Mano, R. D. P., Marques, A. P., Martín-Albo, J., Martínez-Lema, G., Martínez-Vara, M., Miller, R. L., Molina-Canteras, J., Monrabal, F., Monteiro, C. M. B., Mora, F. J., Novella, P., Nuñez, A., Oblak, E., Palacio, J., Palmeiro, B., Para, A., Pazos, A., Pelegrin, J., Maneiro, M. Pérez, Querol, M., Renner, J., Rivilla, I., Rogero, C., Romeo, B., Romo-Luque, C., Nacienciano, V. San, Santos, F. P., Santos, J. M. F. dos, Seemann, M., Shomroni, I., Silva, P. A. O. C., Simón, A., Soleti, S. R., Sorel, M., Soto-Oton, J., Teixeira, J. M. R., Teruel-Pardo, S., Toledo, J. F., Tonnelé, C., Torelli, S., Torrent, J., Trettin, A., Usón, A., Valle, P. R. G., Veloso, J. F. C. A., Waiton, J., and Yubero-Navarro, A.
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High Energy Physics - Experiment ,Physics - Instrumentation and Detectors - Abstract
We investigate the performance of Opticks, an NVIDIA OptiX API 7.5 GPU-accelerated photon propagation compared with a single-threaded Geant4 simulation. We compare the simulations using an improved model of the NEXT-CRAB-0 gaseous time projection chamber. Performance results suggest that Opticks improves simulation speeds by between $58.47\pm{0.02}$ and $181.39\pm{0.28}$ times relative to a CPU-only Geant4 simulation and these results vary between different types of GPU and CPU. A detailed comparison shows that the number of detected photons, along with their times and wavelengths, are in good agreement between Opticks and Geant4., Comment: 12 pages, 8 Figures
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- 2025
40. MultiFlow: A unified deep learning framework for multi-vessel classification, segmentation and clustering of phase-contrast MRI validated on a multi-site single ventricle patient cohort
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Yao, Tina, Clair, Nicole St., Miller, Gabriel F., Investigators, FORCE, Steeden, Jennifer A., Rathod, Rahul H., and Muthurangu, Vivek
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Computer Science - Computer Vision and Pattern Recognition - Abstract
This study presents a unified deep learning (DL) framework, MultiFlowSeg, for classification and segmentation of velocity-encoded phase-contrast magnetic resonance imaging data, and MultiFlowDTC for temporal clustering of flow phenotypes. Applied to the FORCE registry of Fontan procedure patients, MultiFlowSeg achieved 100% classification accuracy for the aorta, SVC, and IVC, and 94% for the LPA and RPA. It demonstrated robust segmentation with a median Dice score of 0.91 (IQR: 0.86-0.93). The automated pipeline processed registry data, achieving high segmentation success despite challenges like poor image quality and dextrocardia. Temporal clustering identified five distinct patient subgroups, with significant differences in clinical outcomes, including ejection fraction, exercise tolerance, liver disease, and mortality. These results demonstrate the potential of combining DL and time-varying flow data for improved CHD prognosis and personalized care., Comment: 6 Figures, 1 Table
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- 2025
41. The X-ray Integral Field Unit at the end of the Athena reformulation phase
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Peille, Philippe, Barret, Didier, Cucchetti, Edoardo, Albouys, Vincent, Piro, Luigi, Simionescu, Aurora, Cappi, Massimo, Bellouard, Elise, Cénac-Morthé, Céline, Daniel, Christophe, Pradines, Alice, Finoguenov, Alexis, Kelley, Richard, Mas-Hesse, J. Miguel, Paltani, Stéphane, Rauw, Gregor, Rozanska, Agata, Svoboda, Jiri, Wilms, Joern, Audard, Marc, Bozzo, Enrico, Costantini, Elisa, Dadina, Mauro, Dauser, Thomas, Decourchelle, Anne, Herder, Jan-Willem den, Goldwurm, Andrea, Jonker, Peter, Markowitz, Alex, Mendez, Mariano, Miniutti, Giovanni, Molendi, Silvano, Nicastro, Fabrizio, Pajot, François, Pointecouteau, Etienne, Pratt, Gabriel W., Schaye, Joop, Vink, Jacco, Webb, Natalie, Bandler, Simon, Barbera, Marco, Ceballos, Maria Teresa, Charles, Ivan, Hartog, Roland den, Doriese, W. Bertrand, Duval, Jean-Marc, Gatti, Flavio, Jackson, Brian, Kilbourne, Caroline, Macculi, Claudio, Martin, Sylvain, Parot, Yann, Porter, Frederick, Prêle, Damien, Ravera, Laurent, Smith, Stephen, Soucek, Jan, Thibert, Tanguy, Tuominen, Eija, Acero, Fabio, Ettori, Stefano, Grosso, Nicolas, Kaastra, Jelle, Mazzotta, Pasquale, Miller, Jon, Sciortino, Salvatore, Beaumont, Sophie, D'Andrea, Matteo, de Plaa, Jelle, Eckart, Megan, Gottardi, Luciano, Leutenegger, Maurice, Lotti, Simone, Molin, Alexei, Natalucci, Lorenzo, Adil, Muhammad Qazi, Argan, Andrea, Cavazzuti, Elisabetta, Fiorini, Mauro, Khosropanah, Pourya, Villegas, Eduardo Medinaceli, Minervini, Gabriele, Perry, James, Pinsard, Frederic, Raulin, Desi, Rigano, Manuela, Roelfsema, Peter, Schwander, Denis, Terron, Santiago, Torrioli, Guido, Ullom, Joel, Zuchniak, Monika, Chaoul, Laurence, Torrejon, Jose Miguel, Brachet, Frank, Cobo, Beatriz, Durkin, Malcolm, Fioretti, Valentina, Geoffray, Hervé, Jacques, Lionel, Kirsch, Christian, Cicero, Ugo Lo, Adams, Joseph, Gloaguen, Emilie, Gonzalez, Manuel, Hull, Samuel, Jellyman, Erik, Kiviranta, Mikko, Sakai, Kazuhiro, Taralli, Emanuele, Vaccaro, Davide, van der Hulst, Paul, van der Kuur, Jan, van Leeuwen, Bert-Joost, van Loon, Dennis, Wakeham, Nicholas, Auricchio, Natalia, Brienza, Daniele, Cheatom, Oscar, Franssen, Philippe, Julien, Sabine, Mer, Isabelle Le, Moirin, David, Silva, Vitor, Todaro, Michela, Clerc, Nicolas, Coleiro, Alexis, Ptak, Andy, Puccetti, Simonetta, Surace, Christian, Abdoelkariem, Shariefa, Adami, Christophe, Aicardi, Corinne, André, Jérôme, Angelinelli, Matteo, Anvar, Shebli, Arnaldi, Luis Horacio, Attard, Anthony, Audley, Damian, Bancel, Florian, Banks, Kimberly, Bernard, Vivian, de Vaate, Jan Geralt Bij, Bonino, Donata, Bonnamy, Anthony, Bonny, Patrick, Boreux, Charles, Bounab, Ayoub, Brigitte, Maïmouna, Bruijn, Marcel, Brysbaert, Clément, Bulgarelli, Andrea, Calarco, Simona, Camus, Thierry, Canourgues, Florent, Capobianco, Vito, Cardiel, Nicolas, Celasco, Edvige, Chen, Si, Chervenak, James, Chiarello, Fabio, Clamagirand, Sébastien, Coeur-Joly, Odile, Corcione, Leonardo, Coriat, Mickael, Coulet, Anais, Courty, Bernard, Coynel, Alexandre, D'Ai, Antonino, Dambrauskas, Eugenio, D'anca, Fabio, Dauner, Lea, De Gerone, Matteo, DeNigris, Natalie, Dercksen, Johannes, de Wit, Martin, Dieleman, Pieter, DiPirro, Michael, Doumayrou, Eric, Duband, Lionel, Dubbeldam, Luc, Dupieux, Michel, Dupourqué, Simon, Durand, Jean Louis, Eckert, Dominique, Ferrando, Philippe, Barusso, Lorenzo Ferrari, Finkbeiner, Fred, Fiocchi, Mariateresa, Fossecave, Hervé, Gabici, Stefano, Gallucci, Giovanni, Gant, Florent, Gao, Jian-Rong, Gastaldello, Fabio, Genolet, Ludovic, Ghizzardi, Simona, Giovannini, Elisa, Giustini, Margherita, Givaudan, Alain, Godet, Olivier, Gomez, Alicia, Gonzalez, Raoul, Gozaliasl, Ghassem, Grandsire, Laurent, Granena, David, Gros, Michel, Guerin, Corentin, Guilhem, Emmanuel, Guizzo, Gian Paolo, Gu, Liyi, Irwin, Kent, Jacquey, Christian, Janiuk, Agnieszka, Jaubert, Jean, Jolly, Antoine, Jourdan, Thierry, Knödlseder, Jürgen, König, Ole, Korb, Andrew, Kreykenbohm, Ingo, Lafforgue, David, Lan, Radek, Larrieu, Maélyss, Laudet, Philippe, Laurent, Philippe, Laurent, Sylvain, Laurenza, Monica, Cam, Maël Le, Lesrel, Jean, Ligori, Sebastiano, Lorenz, Maximilian, Luminari, Alfredo, Madsen, Kristin, Maisonnave, Océane, Marelli, Lorenzo, Marty, Wilfried, Massida, Zoé, Massonet, Didier, Maussang, Irwin, Alonso, Pablo Eleazar Merino, Mesquida, Jean, Mineo, Teresa, Montinaro, Nicola, Murat, David, Nagayoshi, Kenichiro, Nazé, Yaël, Noguès, Loïc, Nouals, François, Ortega, Cristina, Panessa, Francesca, Parodi, Luigi, Piconcelli, Enrico, Pinto, Ciro, Porquet, Delphine, Prouvé, Thomas, Punch, Michael, Rioland, Guillaume, Riollet, Marc-Olivier, Rodriguez, Louis, Roig, Anton, Roncarelli, Mauro, Roucayrol, Lionel, Roudil, Gilles, de Ocenda, Lander Ruiz, Sciortino, Luisa, Simonella, Olivier, Sordet, Michael, Taubenschuss, Ulrich, Terrasa, Guilhem, Terrier, Régis, Ubertini, Pietro, Uhlir, Ludek, Uslenghi, Michela, van Weers, Henk, Varisco, Salvatore, Varniere, Peggy, Volpe, Angela, Walmsley, Gavin, Wise, Michael, Wolnievik, Andreas, and Woźniak, Grzegorz
- Subjects
Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Solar and Stellar Astrophysics - Abstract
The Athena mission entered a redefinition phase in July 2022, driven by the imperative to reduce the mission cost at completion for the European Space Agency below an acceptable target, while maintaining the flagship nature of its science return. This notably called for a complete redesign of the X-ray Integral Field Unit (X-IFU) cryogenic architecture towards a simpler active cooling chain. Passive cooling via successive radiative panels at spacecraft level is now used to provide a 50 K thermal environment to an X-IFU owned cryostat. 4.5 K cooling is achieved via a single remote active cryocooler unit, while a multi-stage Adiabatic Demagnetization Refrigerator ensures heat lift down to the 50 mK required by the detectors. Amidst these changes, the core concept of the readout chain remains robust, employing Transition Edge Sensor microcalorimeters and a SQUID-based Time-Division Multiplexing scheme. Noteworthy is the introduction of a slower pixel. This enables an increase in the multiplexing factor (from 34 to 48) without compromising the instrument energy resolution, hence keeping significant system margins to the new 4 eV resolution requirement. This allows reducing the number of channels by more than a factor two, and thus the resource demands on the system, while keeping a 4' field of view (compared to 5' before). In this article, we will give an overview of this new architecture, before detailing its anticipated performances. Finally, we will present the new X-IFU schedule, with its short term focus on demonstration activities towards a mission adoption in early 2027., Comment: 44 pages, 14 figures, accepted for publication in Experimental Astronomy
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- 2025
42. Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction
- Author
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Yoon, WonJin, Ren, Boyu, Thomas, Spencer, Kim, Chanwhi, Savova, Guergana, Hall, Mei-Hua, and Miller, Timothy
- Subjects
Computer Science - Computation and Language - Abstract
Recent progress in large language models (LLMs) has enabled the automated processing of lengthy documents even without supervised training on a task-specific dataset. Yet, their zero-shot performance in complex tasks as opposed to straightforward information extraction tasks remains suboptimal. One feasible approach for tasks with lengthy, complex input is to first summarize the document and then apply supervised fine-tuning to the summary. However, the summarization process inevitably results in some loss of information. In this study we present a method for processing the summaries of long documents aimed to capture different important aspects of the original document. We hypothesize that LLM summaries generated with different aspect-oriented prompts contain different \textit{information signals}, and we propose methods to measure these differences. We introduce approaches to effectively integrate signals from these different summaries for supervised training of transformer models. We validate our hypotheses on a high-impact task -- 30-day readmission prediction from a psychiatric discharge -- using real-world data from four hospitals, and show that our proposed method increases the prediction performance for the complex task of predicting patient outcome.
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- 2025
43. Reconstructing neutrinoless double beta decay event kinematics in a xenon gas detector with vertex tagging
- Author
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NEXT Collaboration, Martínez-Vara, M., Mistry, K., Pompa, F., Jones, B. J. P., Martín-Albo, J., Sorel, M., Adams, C., Almazán, H., Álvarez, V., Aparicio, B., Aranburu, A. I., Arazi, L., Arnquist, I. J., Auria-Luna, F., Ayet, S., Azevedo, C. D. R., Bailey, K., Ballester, F., del Barrio-Torregrosa, M., Bayo, A., Benlloch-Rodríguez, J. M., Borges, F. I. G. M., Brodolin, A., Byrnes, N., Cárcel, S., Castillo, A., Church, E., Cid, L., Conde, C. A. N., Contreras, T., Cossío, F. P., Coupe, R., Dey, E., Díaz, G., Echevarria, C., Elorza, M., Escada, J., Esteve, R., Felkai, R., Fernandes, L. M. P., Ferrario, P., Ferreira, A. L., Foss, F. W., Freixa, Z., García-Barrena, J., Gómez-Cadenas, J. J., Grocott, J. W. R., Guenette, R., Hauptman, J., Henriques, C. A. O., Morata, J. A. Hernando, Herrero-Gómez, P., Herrero, V., Carrete, C. Hervés, Ifergan, Y., Kellerer, F., Larizgoitia, L., Larumbe, A., Lebrun, P., Lopez, F., López-March, N., Madigan, R., Mano, R. D. P., Marques, A. P., Martínez-Lema, G., Miller, R. L., Molina-Canteras, J., Monrabal, F., Monteiro, C. M. B., Mora, F. J., Navarro, K. E., Novella, P., Nuñez, A., Nygren, D. R., Oblak, E., Palacio, J., Palmeiro, B., Para, A., Parmaksiz, I., Pazos, A., Pelegrin, J., Maneiro, M. Pérez, Querol, M., Renner, J., Rivilla, I., Rogero, C., Rogers, L., Romeo, B., Romo-Luque, C., Nacienciano, V. San, Santos, F. P., Santos, J. M. F. dos, Seemann, M., Shomroni, I., Silva, P. A. O. C., Simón, A., Soleti, S. R., Soto-Oton, J., Teixeira, J. M. R., Teruel-Pardo, S., Toledo, J. F., Tonnelé, C., Torelli, S., Torrent, J., Trettin, A., Usón, A., Valle, P. R. G., Veloso, J. F. C. A., Waiton, J., and Yubero-Navarro, A.
- Subjects
High Energy Physics - Experiment ,High Energy Physics - Phenomenology ,Physics - Instrumentation and Detectors - Abstract
If neutrinoless double beta decay is discovered, the next natural step would be understanding the lepton number violating physics responsible for it. Several alternatives exist beyond the exchange of light neutrinos. Some of these mechanisms can be distinguished by measuring phase-space observables, namely the opening angle $\cos\theta$ among the two decay electrons, and the electron energy spectra, $T_1$ and $T_2$. In this work, we study the statistical accuracy and precision in measuring these kinematic observables in a future xenon gas detector with the added capability to precisely locate the decay vertex. For realistic detector conditions (a gas pressure of 10 bar and spatial resolution of 4 mm), we find that the average $\overline{\cos\theta}$ and $\overline{T_1}$ values can be reconstructed with a precision of 0.19 and 110 keV, respectively, assuming that only 10 neutrinoless double beta decay events are detected., Comment: 19 pages, 8 figures
- Published
- 2025
44. Evidence of the P_ccbars(4459)0 in Upsilon(1S, 2S) inclusive decays at Belle
- Author
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Adachi, I., Aggarwal, L., Ahmed, H., Ahn, J. K., Aihara, H., Akopov, N., Alhakami, M., Aloisio, A., Althubiti, N., Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhardwaj, V., Bhuyan, B., Bianchi, F., Biswas, D., Bodrov, D., Bolz, A., Boschetti, A., Bozek, A., Bracko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Capua, F., Dingfelder, J., Dolezal, Z., Jimenez, I. Domnguez, Dong, T. V., Dong, X., Dossett, D., Dugic, K., Dujany, G., Ecker, P., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Goldenzweig, P., Graziani, E., Greenwald, D., Gruberova, Z., Guan, Y., Gudkova, K., Haide, I., Han, Y., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernandez, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobi, D., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodys, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Krizan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, D., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Lewis, P. M., Li, C., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Mehta, R., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, H., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Onuki, Y., Otani, F., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schmitz, J., Schneider, S., Schnell, G., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Song, W., Staric, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wakai, M., Wallner, S., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yuan, J., Zani, L., Zeng, F., Zhang, B., Zhou, J. S., Zhou, Q. D., Zhu, L., Zhukova, V. I., Zlebck, R., and Zou, S.
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High Energy Physics - Experiment - Abstract
Using data samples of 102 million Upsilon(1S) events and 158 million Upsilon(2S) events collected by the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider, we search for [udsccbar] pentaquark states decaying to Jpsi Lambda. Using the first observations of Upsilon(1S, 2S) inclusive decays to Jpsi Lambda, we find evidence of the P_ccbars(4459)0 state with a significance of 3.3 standard deviations, including statistical and systematic uncertainties. We measure the mass and width of the Pccbars(4459)0 to be (4471.7 +- 4.8 +- 0.6) MeV/c2 and (21.9 +- 13.1 +- 2.7) MeV, respectively. The branching fractions for P_ccbars(4459)0 production are measured to be B[Upsilon(1S) -> P_ccbars(4459)0/ Pbar_ccbars(4459)0 + anything] = (3.5 +- 2.0 +- 0.2)*10-6 and B[Upsilin(2S) -> P_ccbars(4459)0/ Pbar_ccbars(4459)0 +anything] = (2.9 +- 1.7 +- 0.4)*10-6. The inclusive branching fractions of Upsilon(1S, 2S) -> Jpsi Lambda/Lambdabar are measured to be B[Upsilin(1S) -> Jpsi Lambda/Lambdabar + anything] = (36.9 +- 5.3 +- 2.4)*10-6 and B[Upsilon(2S) -> Jpsi Lambda/Lambdabar + anything] = (22.3 +- 5.7 +- 3.1)*10-6. We measure the visible cross section $\sigma(e^+e^- \to J/psi \Lambda/\bar\Lambda$ + anything) = (90 +- 14 +- 6) fb for the continuum production at $\sqrt{s} = 10.52$ GeV. In all cases, the first uncertainties are statistical and the second are systematic., Comment: 8 pages, 3 figures
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- 2025
45. Towards personalised assessment of abdominal aortic aneurysm structural integrity
- Author
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Jamshidian, Mostafa, Wittek, Adam, Mufty, Hozan, Maleux, Geert, Fourneau, Inge, Gizewski, Elke R., Gassner, Eva, Loizides, Alexander, Lutz, Maximilian, Enzmann, Florian K., Liepvre, Donatien Le, Bernard, Florian, Minvielle, Ludovic, Fondanèche, Antoine, and Miller, Karol
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Computer Science - Computational Engineering, Finance, and Science - Abstract
Abdominal aortic aneurysm (AAA) is a life-threatening condition involving the permanent dilation of the aorta, often detected incidentally through imaging for some other condition. The standard clinical approach to managing AAA follows a one-size-fits-all model based on aneurysm size and growth rate, leading to underestimation or overestimation of rupture risk in individual patients. The widely studied stress-based rupture risk estimation using computational biomechanics requires wall strength information. However, non-invasive methods for local patient-specific wall strength measurement have not yet been developed. Recently, we introduced an image-based approach for patient-specific, in vivo, non-invasive AAA kinematic analysis using time-resolved 3D computed tomography angiography (4D-CTA) images to measure wall strain throughout the cardiac cycle. In the present study, we integrated wall tension computation and strain measurement to develop a novel measure of local structural integrity of AAA wall - Relative Structural Integrity Index (RSII), independent of material properties and thickness of the wall and conditions of blood pressure measurement. Our methods provide a visual map of AAA wall structural integrity for individual patients using only their medical images and blood pressure data. We applied our methods to twelve patients. Additionally, we compared our measure of structural integrity of aneurysmal and non-aneurysmal aortas. Our results show similar values of the wall structural integrity measure across the patients, indicating the reliability of our methods. In line with experimental observations reported in the literature, our analysis revealed that localized low stiffness areas are primarily found in the most dilated AAA regions. Our results clearly demonstrate that the AAA wall is stiffer than the non-aneurysmal aorta., Comment: 25 Pages
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- 2025
46. Project portfolio planning in the pharmaceutical industry -- strategic objectives and quantitative optimization
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Wiklund, Stig Johan, Ytterstad, Magnus, and Miller, Frank
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Statistics - Applications - Abstract
Many pharmaceutical companies face concerns with the maintenance of desired revenue levels. Sales forecasts for the current portfolio of products and projects may indicate a decline in revenue as the marketed products approach patent expiry. To counteract the potential downturn in revenue, and to establish revenue growth, an in-flow of new projects into the development phases is required. In this article, we devise an approach with which the in-flow of new projects could be optimized, while adhering to the objectives and constraints set on revenue targets, budget limitations and strategic considerations on the composition of the company's portfolio.
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- 2025
47. Performance of the Stellar Abundances and atmospheric Parameters Pipeline adapted for M dwarfs I. Atmospheric parameters from the spectroscopic module
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Olander, Terese, Gent, Matthew R., Heiter, Ulrike, Kochukhov, Oleg, Bergemann, Maria, Magg, Ekaterina, Cassisi, Santi, Kovalev, Mikhail, Morel, Thierry, Miller, Nicola J., Souto, Diogo, Shan, Yutong, Rojas-Ayala, Bárbara, Delgado-Mena, Elisa, and Wang, Haiyang S.
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Astrophysics - Solar and Stellar Astrophysics ,Astrophysics - Earth and Planetary Astrophysics ,Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
M dwarfs are important targets in the search for Earth-like exoplanets due to their small masses and low luminosities. Several ongoing and upcoming space missions are targeting M dwarfs for this reason, and the ESA PLATO mission is one of these. In order to fully characterise a planetary system the properties of the host star must be known. For M dwarfs we can derive effective temperature, surface gravity, metallicity, and abundances of various elements from spectroscopic observations in combination with photometric data. The Stellar Abundances and atmospheric Parameters Pipeline (SAPP) has been developed as a prototype for one of the stellar science softwares within the PLATO consortium, it is aimed at FGK stars. We have modified it to be able to analyse the M dwarf among the PLATO targets. The current version of the pipeline for M dwarfs mostly relies on spectroscopic observations. The data processing is based on the machine learning algorithm The Payne and fits a grid of model spectra to an observed spectrum to derive effective temperature and metallicity. We use spectra in the H-band, as the near-infrared region is beneficial for M dwarfs. A method based on synthetic spectra was developed for the continuum normalisation of the spectra, taking into account the pseudo-continuum formed by numerous lines of the water molecule. Photometry is used to constrain the surface gravity. We tested the modified SAPP on spectra of M dwarfs from the APOGEE survey. Our validation sample of 26 stars includes stars with interferometric observations and binaries. We found a good agreement between our values and reference values from a range of studies. The overall uncertainties in the derived effective temperature, surface gravity, and metallicity is 100 K, 0.1 dex, and 0.15 dex, respectively. We find that the modified SAPP performs well on M dwarfs and identify possible areas of future development., Comment: Accepted in A&A
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- 2025
48. RTBAS: Defending LLM Agents Against Prompt Injection and Privacy Leakage
- Author
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Zhong, Peter Yong, Chen, Siyuan, Wang, Ruiqi, McCall, McKenna, Titzer, Ben L., Miller, Heather, and Gibbons, Phillip B.
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Computer Science - Cryptography and Security ,Computer Science - Artificial Intelligence - Abstract
Tool-Based Agent Systems (TBAS) allow Language Models (LMs) to use external tools for tasks beyond their standalone capabilities, such as searching websites, booking flights, or making financial transactions. However, these tools greatly increase the risks of prompt injection attacks, where malicious content hijacks the LM agent to leak confidential data or trigger harmful actions. Existing defenses (OpenAI GPTs) require user confirmation before every tool call, placing onerous burdens on users. We introduce Robust TBAS (RTBAS), which automatically detects and executes tool calls that preserve integrity and confidentiality, requiring user confirmation only when these safeguards cannot be ensured. RTBAS adapts Information Flow Control to the unique challenges presented by TBAS. We present two novel dependency screeners, using LM-as-a-judge and attention-based saliency, to overcome these challenges. Experimental results on the AgentDojo Prompt Injection benchmark show RTBAS prevents all targeted attacks with only a 2% loss of task utility when under attack, and further tests confirm its ability to obtain near-oracle performance on detecting both subtle and direct privacy leaks.
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- 2025
49. High Contrast Nulling in Photonic Meshes Through Architectural Redundancy
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Valdez, Carson, Sun, Zhanghao, Kroo, Anne R., Miller, David A. B., and Solgaard, Olav
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Physics - Optics - Abstract
We demonstrate a silicon photonic architecture comprised of Double Mach-Zehnder Interferometers (DMZIs) designed for high-contrast photonic applications. This configuration significantly enhances the achievable extinction ratio of photonic integrated circuits (PICs), reaching levels exceeding 80 dB. By leveraging the tunable properties of DMZIs and implementing a systematic configuration algorithm, the proposed mesh effectively compensates for fabrication imperfections and mitigates non-idealities such as back reflections. Experimental validation on a silicon-on-insulator platform demonstrates the potential of this approach for applications requiring high contrast nulling such as astronomical sensing.
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- 2025
50. NDAI Agreements
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Stephenson, Matthew, Miller, Andrew, Sun, Xyn, Annem, Bhargav, and Parikh, Rohan
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Economics - Theoretical Economics ,Computer Science - Artificial Intelligence - Abstract
We study a fundamental challenge in the economics of innovation: an inventor must reveal details of a new idea to secure compensation or funding, yet such disclosure risks expropriation. We present a model in which a seller (inventor) and buyer (investor) bargain over an information good under the threat of hold-up. In the classical setting, the seller withholds disclosure to avoid misappropriation, leading to inefficiency. We show that trusted execution environments (TEEs) combined with AI agents can mitigate and even fully eliminate this hold-up problem. By delegating the disclosure and payment decisions to tamper-proof programs, the seller can safely reveal the invention without risking expropriation, achieving full disclosure and an efficient ex post transfer. Moreover, even if the invention's value exceeds a threshold that TEEs can fully secure, partial disclosure still improves outcomes compared to no disclosure. Recognizing that real AI agents are imperfect, we model "agent errors" in payments or disclosures and demonstrate that budget caps and acceptance thresholds suffice to preserve most of the efficiency gains. Our results imply that cryptographic or hardware-based solutions can function as an "ironclad NDA," substantially mitigating the fundamental disclosure-appropriation paradox first identified by Arrow (1962) and Nelson (1959). This has far-reaching policy implications for fostering R&D, technology transfer, and collaboration., Comment: 21 pages, 1 figure
- Published
- 2025
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