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174,253 results on '"A Pia"'

1. Double Higgs Production in Vector Boson Fusion at NLO QCD in HEFT

Author: Braun, Jens, Bredt, Pia, Heinrich, Gudrun, and Höfer, Marius
Subjects: High Energy Physics - Phenomenology
Abstract: We present the next-to-leading order QCD corrections to Higgs boson pair production in vector boson fusion, including the leading operators in the framework of Higgs Effective Field Theory (HEFT). The corresponding calculation is based on an automated interface between the Monte Carlo event generator Whizard and the one-loop amplitude generator GoSam. The QCD corrections also include non-factorising diagrams and diagrams of Higgs-Strahlung type, thus going beyond the structure function approach. We find that some constellations of anomalous couplings, while being well within the current experimental constraints, can have a significant impact on the shape of typical observables for this process., Comment: 24 pages, 6 figures, 2 tables
Published: 2025

2. $\mathrm{O}(a)$ improvement of the flavour singlet scalar density in a setup with Wilson fermions

Author: Petrak, Pia Leonie Jones, Heitger, Jochen, and Joswig, Fabian
Subjects: High Energy Physics - Lattice
Abstract: We report on our Ward identity determination of the $\mathrm{O}(a)$ improvement coefficient for the flavour singlet scalar density, namely $g_\mathrm{S}$, from three-flavour lattice QCD with Wilson-clover fermions and the tree-level Symanzik improved gauge action. We employ five couplings, $g_0^2 \in [1.5,1.77]$, that cover the range used in large-volume CLS simulations. While $g_\mathrm{S}$ itself is for instance relevant for the $\mathrm{O}(a)$ improvement of meson and baryon sigma terms, a relation to $b_\mathrm{g}$, the $\mathrm{O}(a)$ improvement parameter of the gauge coupling, can also be established, allowing for its non-perturbative extraction as well. With Wilson fermions, $b_\mathrm{g}$ is in principle required for full $\mathrm{O}(a)$ improvement at non-vanishing sea quark masses. We outline our procedure for extracting $b_\mathrm{g}$., Comment: 10 pages, 1 figure, Proceedings (The 41st International Symposium on Lattice Field Theory (Lattice 2024), Liverpool, UK)
Published: 2025
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3. Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning

Author: 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. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Pletcher, K., Plows, K., Pollack, C., Pollmann, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paixão, L. G. Porto, Potekhin, M., Pozzato, M., Pradhan, R., Prakash, T., Prest, M., Psihas, F., Pugnere, D., Pullia, D., Qian, X., Queen, J., Raaf, J. L., Rabelhofer, M., Radeka, V., Rademacker, J., Radics, B., Raffaelli, F., Rafique, A., Raguzin, E., Rahe, A., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralaikoto, M. A., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Randriamanampisoa, S. S., Rappoldi, A., Raselli, G., Rath, T., Ratoff, P., Ray, R., Razafinime, H., Razakamiandra, R. F., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reichenbacher, J., Reitzner, S. D., Renner, E., Repetto, S., Rescia, S., Resnati, F., Restrepo, Diego, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rikalo, A., Rincón, E. 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. H., Whittington, D., Wieler, F., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wresilo, K., Wrobel, M., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Wynne, B. M., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, T., Yankelevich, A., Yates, L., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zettlemoyer, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
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
Published: 2025

4. Tensor-Programmable Quantum Circuits for Solving Differential Equations

Author: Siegl, Pia, Reese, Greta Sophie, Hashizume, Tomohiro, van Hülst, Nis-Luca, and Jaksch, Dieter
Subjects: Quantum Physics
Abstract: We present a quantum solver for partial differential equations based on a flexible matrix product operator representation. Utilizing mid-circuit measurements and a state-dependent norm correction, this scheme overcomes the restriction of unitary operators. Hence, it allows for the direct implementation of a broad class of differential equations governing the dynamics of classical and quantum systems. The capabilities of the framework are demonstrated for an example system governed by Euler equations with absorbing boundaries., Comment: 5 pages + 5 supplemental, 4 figures + 1 supplemental
Published: 2025

5. Is fixed-node diffusion quantum Monte Carlo reproducible?

Author: Della Pia, Flaviano, Shi, Benjamin, Al-Hamdani, Yasmine S., Alfè, Dario, Anderson, Tyler, Barborini, Matteo, Benali, Anouar, Casula, Michele, Drummond, Neil, Dubecký, Matúš, Filippi, Claudia, Kent, Paul, Krogel, Jaron, Rios, Pablo Lopez, Lüchow, Arne, Luo, Ye, Michaelides, Angelos, Mitas, Lubos, Nakano, Kosuke, Needs, Richard, Per, Manolo, Scemama, Anthony, Schultze, Jil, Shinde, Ravindra, Slootman, Emiel, Sorella, Sandro, Tkatchenko, Alexandre, Towler, Mike, Umrigar, Cyrus, Wagner, Lucas, Wheeler, William Ashwin, Zhou, Haihan, and Zen, Andrea
Subjects: Physics - Computational Physics, Condensed Matter - Materials Science, Physics - Chemical Physics
Abstract: Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schr\"{o}dinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to address the titular question, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. This work lays the foundation to establish accurate and reproducible FN-DMC estimates for all future studies across applications in materials science, physics, chemistry, and biology.
Published: 2025

6. To BEE or not to BEE: Estimating more than Entropy with Biased Entropy Estimators

Author: la Torre, Ilaria Pia, Kelly, David A., Menendez, Hector D., and Clark, David
Subjects: Computer Science - Information Theory, Computer Science - Software Engineering
Abstract: Entropy estimation plays a significant role in biology, economics, physics, communication engineering and other disciplines. It is increasingly used in software engineering, e.g. in software confidentiality, software testing, predictive analysis, machine learning, and software improvement. However accurate estimation is demonstrably expensive in many contexts, including software. Statisticians have consequently developed biased estimators that aim to accurately estimate entropy on the basis of a sample. In this paper we apply 18 widely employed entropy estimators to Shannon measures useful to the software engineer: entropy, mutual information and conditional mutual information. Moreover, we investigate how the estimators are affected by two main influential factors: sample size and domain size. Our experiments range over a large set of randomly generated joint probability distributions and varying sample sizes, rather than choosing just one or two well known probability distributions as in previous investigations. Our most important result is identifying that the Chao-Shen and Chao-Wang-Jost estimators stand out for consistently converging more quickly to the ground truth, regardless of domain size and regardless of the measure used. They also tend to outperform the others in terms of accuracy as sample sizes increase. This discovery enables a significant reduction in data collection effort without compromising performance.
Published: 2025

7. Deep learning for temporal super-resolution 4D Flow MRI

Author: Callmer, Pia, Bonini, Mia, Ferdian, Edward, Nordsletten, David, Giese, Daniel, Young, Alistair A., Fyrdahl, Alexander, and Marlevi, David
Subjects: Computer Science - Machine Learning
Abstract: 4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, apparent trade-offs between acquisition time, image noise, and resolution limit clinical applicability. In particular, in regions of highly transient flow, coarse temporal resolution can hinder accurate capture of physiologically relevant flow variations. To overcome these issues, post-processing techniques using deep learning have shown promising results to enhance resolution post-scan using so-called super-resolution networks. However, while super-resolution has been focusing on spatial upsampling, temporal super-resolution remains largely unexplored. The aim of this study was therefore to implement and evaluate a residual network for temporal super-resolution 4D Flow MRI. To achieve this, an existing spatial network (4DFlowNet) was re-designed for temporal upsampling, adapting input dimensions, and optimizing internal layer structures. Training and testing were performed using synthetic 4D Flow MRI data originating from patient-specific in-silico models, as well as using in-vivo datasets. Overall, excellent performance was achieved with input velocities effectively denoised and temporally upsampled, with a mean absolute error (MAE) of 1.0 cm/s in an unseen in-silico setting, outperforming deterministic alternatives (linear interpolation MAE = 2.3 cm/s, sinc interpolation MAE = 2.6 cm/s). Further, the network synthesized high-resolution temporal information from unseen low-resolution in-vivo data, with strong correlation observed at peak flow frames. As such, our results highlight the potential of utilizing data-driven neural networks for temporal super-resolution 4D Flow MRI, enabling high-frame-rate flow quantification without extending acquisition times beyond clinically acceptable limits., Comment: 12 pages, 8 figures
Published: 2025

8. Low-rank variance reduction for uncertain radiative transfer with control variates

Author: Patwardhan, Chinmay, Stammer, Pia, Løvbak, Emil, Kusch, Jonas, and Krumscheid, Sebastian
Subjects: Mathematics - Numerical Analysis, 65M75, 65C05, 35Q49
Abstract: The radiative transfer equation models various physical processes ranging from plasma simulations to radiation therapy. In practice, these phenomena are often subject to uncertainties. Modeling and propagating these uncertainties requires accurate and efficient solvers for the radiative transfer equations. Due to the equation's high-dimensional phase space, fine-grid solutions of the radiative transfer equation are computationally expensive and memory-intensive. In recent years, dynamical low-rank approximation has become a popular method for solving kinetic equations due to the development of computationally inexpensive, memory-efficient and robust algorithms like the augmented basis update \& Galerkin integrator. In this work, we propose a low-rank Monte Carlo estimator and combine it with a control variate strategy based on multi-fidelity low-rank approximations for variance reduction. We investigate the error analytically and numerically and find that a joint approach to balance rank and grid size is necessary. Numerical experiments further show that the efficiency of estimators can be improved using dynamical low-rank approximation, especially in the context of control variates., Comment: 16 pages, 2 figures, 1 table, Submitted to Proceedings of MCQMC 2024
Published: 2025

9. Low-Resource Text-to-Speech Synthesis Using Noise-Augmented Training of ForwardTacotron

Author: Lakshminarayana, Kishor Kayyar, Zalkow, Frank, Dittmar, Christian, Pia, Nicola, and Habets, Emanuel A. P.
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: In recent years, several text-to-speech systems have been proposed to synthesize natural speech in zero-shot, few-shot, and low-resource scenarios. However, these methods typically require training with data from many different speakers. The speech quality across the speaker set typically is diverse and imposes an upper limit on the quality achievable for the low-resource speaker. In the current work, we achieve high-quality speech synthesis using as little as five minutes of speech from the desired speaker by augmenting the low-resource speaker data with noise and employing multiple sampling techniques during training. Our method requires only four high-quality, high-resource speakers, which are easy to obtain and use in practice. Our low-complexity method achieves improved speaker similarity compared to the state-of-the-art zero-shot method HierSpeech++ and the recent low-resource method AdapterMix while maintaining comparable naturalness. Our proposed approach can also reduce the data requirements for speech synthesis for new speakers and languages., Comment: Accepted for publication at the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025) to be held at Hyderabad, India
Published: 2025

10. Extended formulations for the multilinear polytope of acyclic hypergraphs

Author: Del Pia, Alberto and Khajavirad, Aida
Subjects: Mathematics - Optimization and Control, Computer Science - Discrete Mathematics
Abstract: This article provides an overview of our joint work on binary polynomial optimization over the past decade. We define the multilinear polytope as the convex hull of the feasible region of a linearized binary polynomial optimization problem. By representing the multilinear polytope with hypergraphs, we investigate the connections between hypergraph acyclicity and the complexity of the facial structure of the multilinear polytope. We characterize the acyclic hypergraphs for which a polynomial-size extended formulation for the multilinear polytope can be constructed in polynomial time., Comment: arXiv admin note: text overlap with arXiv:2212.11239
Published: 2025

11. Design and Implementation of the Cosmic Ray Tagger System for the ICARUS detector at FNAL

Author: Aduszkiewicz, A., Bagby, L., Behera, B., Bernardini, P., Bertolucci, S., Betancourt, M., Budd, H., Boone, T., Campos, A., Casazza, D., Cicero, V., Cherdack, D., Coan, T. E., Esposti, L. Degli, Di Ferdinando, D., Di Noto, L., Guandalini, C., Guerzoni, M., Heggestuen, A., Hilgenberg, C., Howell, R., Iliescu, M., Ingratta, G., Kim, T., Kose, U., Laurenti, G., Mariani, C., Mauri, N., McFarland, K. S., Montagna, E., Montanari, A., Moggi, N., Murphy, M., Pasqualini, L., Paolone, V., Patrizii, L., Pellegrini, G., Pia, V., Poppi, F., Pozzato, M., Ruggeri, A., Sala, P., Sapienza, P., Schukraft, A., Sirri, G., Stanco, L., Surdo, A., Tenti, M., Togo, V., Tosi, N., Vicenzi, M., Wilson, R. J., and Zucchelli, S.
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: The ICARUS-T600 Liquid Argon Time Projection Chamber is operating at Fermilab at shallow depth and thus exposed to a high flux of cosmic rays that can fake neutrino interactions. A cosmic ray tagging (CRT) system ($\sim$1100 m$^2$), surrounding the cryostat with two layers of fiber embedded plastic scintillators, was developed to mitigate the cosmic ray induced background. Using nanosecond-level timing information, the CRT can distinguish incoming cosmic rays from outgoing particles from neutrino interactions in the TPC. In this paper an overview of the CRT system, its installation and commissioning at Fermilab, and its performance are discussed.
Published: 2025

12. Search for continuous gravitational wave signals from luminous dark photon superradiance clouds with LVK O3 observations

Author: Mirasola, Lorenzo, Mondino, Cristina, Amicucci, Francesco, Siemonsen, Nils, Palomba, Cristiano, D'Antonio, Sabrina, Leaci, Paola, D'Onofrio, Luca, Astone, Pia, Egana-Ugrinovic, Daniel, Huang, Junwu, Baryakhtar, Masha, and East, William E.
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: Superradiance clouds of kinetically-mixed dark photons around spinning black holes can produce observable multi-messenger electromagnetic and gravitational wave signals. The cloud generates electric fields of up to a Teravolt-per-meter, which lead to a cascade production of charged particles, yielding a turbulent quasi-equilibrium plasma around the black hole, and resulting in electromagnetic fluxes ranging from supernova to pulsar-like luminosities. For stellar mass black holes, such systems resemble millisecond pulsars and are expected to emit pulsating radio waves and continuous gravitational waves (CWs) within the LIGO-Virgo-KAGRA (LVK) sensitivity band. We select 44 sources with approximately coincident frequencies or positive frequency drifts from existing pulsar catalogs as potential candidates of long-lasting superradiance clouds around old galactic black holes. For a subset of 34 sources that are well measured and have not been previously targeted, we perform the first search for CW emission in LVK data from the third observing run. We find no evidence of a CW signal and place 95% confidence level upper limits on the emitted strain amplitude. We interpret these results, together with limits from previous searches, in terms of the underlying dark photon theory by performing an analysis of the expected signals from superradiance clouds from galactic black holes. We find that, even for moderately spinning black holes, the absence of an observed CW signal disfavors a discrete set of dark photon masses between about $10^{-13}$ $\rm{eV}/c^2$ and $10^{-12}$ $\rm{eV}/c^2$ and kinetic mixing couplings in the range of $10^{-9}$-$10^{-7}$, subject to assumptions about the properties of the black hole population and the cloud's electromagnetic emission., Comment: 26 pages, 8 figures
Published: 2025

13. Gaps in biomedical research in frontotemporal dementia: A call for diversity and disparities focused research

Author: Nuytemans, Karen, Franzen, Sanne, Broce, Iris J, Caramelli, Paulo, Ellajosyula, Ratnavalli, Finger, Elizabeth, Gupta, Veer, Gupta, Vivek, Illán‐Gala, Ignacio, Loi, Samantha M, Morhardt, Darby, Pijnenburg, Yolande, Rascovsky, Katya, Williams, Monique M, Yokoyama, Jennifer S, Acosta‐Uribe, Juliana, Akinyemi, Rufus, Alladi, Suvarna, Ayele, Biniyam A, Ayhan, Yavuz, Bourdage, Renelle, Castro‐Suarez, Sheila, de Souza, Leonardo Cruz, Dacks, Penny, de Boer, Sterre CM, de Leon, Jessica, Dodge, Shana, Grasso, Stephanie, Ghoshal, Nupur, Kamath, Vidyulata, Kumfor, Fiona, Matias‐Guiu, Jordi A, Narme, Pauline, Nielsen, T Rune, Okhuevbie, Daniel, Piña‐Escudero, Stefanie, Ruiz‐Garcia, Ramiro, Ryan, Brigid, Scarioni, Marta, Slachevsky, Andrea, Suarez‐Gonzalez, Aida, Tee, Boon Lead, Tsoy, Elena, Ulugut, Hulya, Onyike, Chiadi U, Babulal, Ganesh M, and PIA, ISTAART Diversity and Disparities PIA ISTAART Frontotemporal Dementia and Related Disorders
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Frontotemporal Dementia (FTD), Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Brain Disorders, Alzheimer's Disease Related Dementias (ADRD), Rare Diseases, Acquired Cognitive Impairment, Alzheimer's Disease, Aphasia, Aging, Behavioral and Social Science, Neurological, Humans, Frontotemporal Dementia, Biomedical Research, Healthcare Disparities, Cultural Diversity, biomarkers, cultural diversity, epidemiology, ethnicity, frontotemporal dementia, genetics, infrastructure, ISTAART Frontotemporal Dementia and Related Disorders PIA, ISTAART Diversity and Disparities PIA, Geriatrics, Clinical sciences, Biological psychology
Abstract: Frontotemporal dementia (FTD) is one of the leading causes of young-onset dementia before age 65, typically manifesting as abnormal behavior (in behavioral variant FTD) or language impairment (in primary progressive aphasia). Although FTD affects all populations across the globe, knowledge regarding the pathophysiology and genetics derives primarily from studies conducted in North America and Western Europe. Globally, biomedical research for FTD is hindered by variable access to diagnosis, discussed in this group's earlier article, and by reduced access to expertise, funding, and infrastructure. This perspective paper was produced by two professional interest areas of the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) and discusses the field's current status on the cross-cultural aspects of basic and translational research in FTD (including that focused on epidemiology, genetics, biomarkers, and treatment). It subsequently provides a summary of gaps and needs to address the disparities and advance global FTD biomedical research.
Published: 2024

14. Systematic discrepancies between reference methods for non-covalent interactions within the S66 dataset

Author: Shi, Benjamin X., Della Pia, Flaviano, Al-Hamdani, Yasmine S., Michaelides, Angelos, Alfè, Dario, and Zen, Andrea
Subjects: Physics - Chemical Physics, Quantum Physics
Abstract: The accurate treatment of non-covalent interactions is necessary to model a wide range of applications, from molecular crystals to surface catalysts to aqueous solutions and many more. Quantum diffusion Monte Carlo (DMC) and coupled cluster theory with single, double and perturbative triple excitations [CCSD(T)] are considered two widely-trusted methods for treating non-covalent interactions. However, while they have been well-validated for small molecules, recent work has indicated that these two methods can disagree by more than $7.5\,$kcal/mol for larger systems. The origin of this discrepancy remains unknown. Moreover, the lack of systematic comparisons, particularly for medium-sized complexes, has made it difficult to identify which systems may be prone to such disagreements and the potential scale of these differences. In this work, we leverage the latest developments in DMC to compute interaction energies for the entire S66 dataset, containing 66 medium-sized complexes with a balanced representation of dispersion and electrostatic interactions. Comparison to previous CCSD(T) references reveals systematic trends, with DMC predicting stronger binding than CCSD(T) for electrostatic-dominated systems, while the binding becomes weaker for dispersion-dominated systems. We show that the relative strength of this discrepancy is correlated to the ratio of electrostatic and dispersion interactions, as obtained from energy decomposition analysis methods. Finally, we pinpoint systems in the S66 dataset where these discrepancies are particularly prominent, offering cost-effective benchmarks to guide future developments in DMC, CCSD(T) as well as the wider electronic structure theory community.
Published: 2024

15. A Deterministic Dynamical Low-rank Approach for Charged Particle Transport

Author: Stammer, Pia, Burlacu, Tiberiu, Wahl, Niklas, Lathouwers, Danny, and Kusch, Jonas
Subjects: Mathematics - Numerical Analysis, Physics - Computational Physics, Physics - Medical Physics
Abstract: Deterministically solving charged particle transport problems at a sufficient spatial and angular resolution is often prohibitively expensive, especially due to their highly forward peaked scattering. We propose a model order reduction approach which evolves the solution on a low-rank manifold in time, making computations feasible at much higher resolutions and reducing the overall run-time and memory footprint. For this, we use a hybrid dynamical low-rank approach based on a collided-uncollided split, i.e., the transport equation is split through a collision source method. Uncollided particles are described using a ray tracer, facilitating the inclusion of boundary conditions and straggling, whereas collided particles are represented using a moment method combined with the dynamical low-rank approximation. Here the energy is treated as a pseudo-time and a rank adaptive integrator is chosen to dynamically adapt the rank in energy. We can reproduce the results of a full-rank reference code at a much lower rank and thus computational cost and memory usage. The solution further achieves comparable accuracy with respect to TOPAS MC as previous deterministic approaches., Comment: to be published in proceedings of M&C 2025
Published: 2024

16. New developments in the Whizard event generator

Author: Reuter, Jürgen, Bredt, Pia, Höfer, Marius, Kilian, Wolfgang, Kreher, Nils, Löschner, Maximilian, Mękała, Krzysztof, Ohl, Thorsten, Striegl, Tobias, and Żarnecki, Aleksander Filip
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract: We give a status report on new developments within the Whizard event generator. Important new features comprise NLO electroweak automation (incl. extension to BSM processes like SMEFT), loop-induced processes and new developments in the UFO interface. We highlight work in progress and further plans, such as the implementation of electroweak PDFs, photon radiation, the exclusive top threshold and features for exotic new physics searches., Comment: Presented at the International Workshop on Future Linear Colliders (LCWS 2024), 8-11 July 2024, contribution accepted by EPJ Web of Conferences
Published: 2024
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17. Building Europe's first space-based Quantum Key Distribution system -- The German Aerospace Center's role in the EAGLE-1 mission

Author: Rivera, Gabriela Calistro, Heirich, Oliver, Shrestha, Amita, Ferenczi, Agnes, Duliu, Alexandru, Eppinger, Jakob, Castella, Bruno Femenia, Fuchs, Christian, Garbagnati, Elisa, Laidlaw, Douglas, Lützen, Pia, De Marco, Innocenzo, Moll, Florian, Prell, Johannes, Reeves, Andrew, Nonay, Jorge Rosano, Roubal, Christian, Torres, Joana S., and Wagner, Matthias
Subjects: Quantum Physics
Abstract: The EAGLE-1 mission aims to develop Europe's first sovereign, end-to-end space-based quantum key distribution (QKD) system. The mission is led by the European Space Agency (ESA) and SES in collaboration with several European National Space Agencies and private partners. The state-of-the-art QKD system will consist of a payload on board the EAGLE-1 low Earth orbit (LEO) satellite, optical ground stations, quantum operational networks, and key management system. The EAGLE-1 mission represents a major step for next-generation quantum communication infrastructures, delivering valuable technical results and mission data. The Institute of Communications and Navigation (IKN) of the German Aerospace Center (DLR) is a key partner in the EAGLE-1 mission and is involved in the research and development of elements in both space and ground segments. Here we report on the development of the QKD transmitter, a vital part of the QKD payload, and the customization of the Optical Ground Station Oberpfaffenhofen (OGS-OP) to conduct the IOT phase of EAGLE-1. For the space segment, DLR-IKN is in charge of the design of the QKD transmitter, including the development of the software and firmware. This transmitter generates quantum states which are used to implement a QKD protocol based on an optical signal, that will be transmitted to ground. For the ground segment, The OGS-OP will serve as the in-orbit testing ground station for EAGLE-1. Building upon the expertise with a range of satellites for quantum communication, as well as new implementations, OGS-OP will validate the performance of the payload, optical link and QKD system for the first time. We present the main developments of OGS-OP for the mission, which includes the implementation of an upgraded adaptive optics system to correct for atmospheric distortions and optimize the coupling of the incoming light into a single mode optical fiber., Comment: 9 pages, 5 figures. Manuscript presented at the 75th International Astronautical Congress (IAC), Milan, Italy, 14-18 October 2024
Published: 2024

18. Perceived brightness and resolution of holographic augmented reality retinal scan glasses

Author: Rutz, Maximilian, Neuberger, Pia, Pick, Simon, and Straßer, Torsten
Subjects: Physics - Optics, Physics - Biological Physics
Abstract: Augmented reality display performance depends strongly on features of the human visual system. This is especially true for retinal scan glasses, which use laser beam scanning and transparent holographic optical combiners. Human-centered approaches allow us to go beyond conventional optical metrology and evaluate display performance as it is perceived in actual augmented reality use cases. Here, we first present a theoretical formula for the retinal scan luminance and ambient contrast ratio calculated from optical powers, wavelengths, field of view, and human pupil diameter. As a promising insight we found that the pupil diameter dependence is beneficial in assimilating the virtual image luminance to the ambient luminance. Second, we designed and performed a psychophysical experiment to assess perceived resolution in augmented reality settings using a fully functional retinal scan glasses prototype. We present the results of the trials and illustrate how this approach can be useful in the further development of augmented reality smart glasses., Comment: 21 pages, 9 figures, 2 tables
Published: 2024

19. Extramural English in Scandinavia and Asia: Scale Development, Learner Engagement, and Perceived Speaking Ability

Author: Pia Sundqvist and M. Sercan Uztosun
Abstract: This article comprises two international studies. Study 1 aimed to develop a scale to measure the frequency of learners' voluntary, informal, out-of-school engagement with English, so-called Extramural English (EE) activities. It involved three stages -- pilot study, exploratory factor analysis, and confirmatory factor analysis -- followed by measuring the test-retest reliability and known-groups validity of the scale. L2 English learners (N = 907; mean age: 17) from Scandinavia (Denmark, Norway, and Sweden) and Turkey participated. The analyses led to a 32-item EE Scale that loaded onto eight factors: "EE Digital Creativity," "Gaming," "Internalized," "Music," "Niche Activities," "Reading" and "Listening," "Social Interaction," and "Viewing." Study 2, in which the scale was implemented, aimed to explore the frequency of EE activities and examined whether EE predicts learners' perceived speaking ability in different settings. Learners from Scandinavia (N = 197) and Asia (N = 125; China and Turkey) participated. Data analyses showed that both samples engaged most frequently in "EE Music," "Viewing" and "Reading and Listening." Ordinal regression analysis revealed that EE predicts perceived speaking ability in both contexts, but differently so. Thus, EE seems to play different roles for learning English in the different settings. Implications are discussed regarding the context-specific nature of EE.
Published: 2024
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20. Gender Disparities and Burnout Among Emergency Physicians: A Systematic Review by the World Academic Council of Emergency Medicine–Female Leadership Academy for Medical Excellence

Author: Thakur, Suman, Chauhan, Vivek, Galwankar, Sagar, Lateef, Fatimah, Daniel, Pia, Cakir, Zeynep, Lugo, Katia M., Basnet, Samjhana, Bildik, Busra, Azahaf, Siham, Vural, Sevilay, Difyeli, Busra H., and Moreno-Walton, Lisa
Subjects: burnout, Emergency Physicians, gender, Female
Abstract: Background: The Female Leadership Academy for Medical Excellence, members of the World Academic Council of Emergency Medicine, conducted this systematic review, which explores gender disparities in burnout among emergency physicians (EP) using the Maslach Burnout Inventory-Human Services Survey (MBI-HSS). Burnout is a critical issue in healthcare, particularly in emergency medicine where high stress and demanding work environments prevail.Methods: Following PRISMA guidelines, we searched PubMed and Epistemonikos for studies using MBI-HSS to measure burnout in EPs. Inclusion criteria encompassed peer-reviewed, English-language articles reporting burnout by sex. Data extraction focused on proportions of burnout and its subcomponents, mean scores, and odds ratios, with quality assessed using Joanna Briggs Institute criteria.Results: We included 18 studies spanning 26,939 EPs from 10 countries. While overall burnout rates did not signiﬁcantly differ between the sexes, the proportion of female EPs with high emotional exhaustion (EE) (69%) and low sense of personal accomplishment (PA) (45%) were signiﬁcantly higher compared to males with high EE in 57% and low PA in 29%, respectively (P < 0.001 for both). Proportion with high depersonalization (DP) score was 44% in both male and female EPs. Mean scores revealed females experiencing higher mean EE (26.8 ± 15.7) scores vs males (25.4 ± 15.9) P < 0.001. Males had mean DP scores (8.6 ± 8.0) and mean PA scores (26.6 ± 12.7) compared to females with lower mean DP scores (7.4 ± 7.2) and higher PA scores (27.7 ± 11.9), respectively P < 0.001 for both. Odds ratios indicated varying risks, predominantly higher EE odds among females, varying from 0.72 to 2.3.Conclusion: This review underscores gender-speciﬁc manifestations of burnout among emergency physicians, with females more susceptible to emotional exhaustion and lower sense of personal accomplishment. Standardized reporting methods are crucial for future meta-analyses to reﬁne gender-speciﬁc interventions combating burnout in emergency medicine. Targeted strategies addressing distinct manifestations of burnout are imperative to support the well-being and retention of EPs, fostering sustainable healthcare delivery.
Published: 2024

21. Multivariate Adjustments for Average Equivalence Testing

Author: Boulaguiem, Younes, Insolia, Luca, Victoria-Feser, Maria-Pia, Couturier, Dominique-Laurent, and Guerrier, Stéphane
Subjects: Statistics - Methodology
Abstract: Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are `equivalent' for different outcomes simultaneously. The multivariate Two One-Sided Tests (TOST) procedure is typically used in this context by checking if, outcome by outcome, the marginal $100(1-2\alpha$)\% confidence intervals for the difference in means between the two conditions of interest lie within pre-defined lower and upper equivalence limits. This procedure, known to be conservative in the univariate case, leads to a rapid power loss when the number of outcomes increases, especially when one or more outcome variances are relatively large. In this work, we propose a finite-sample adjustment for this procedure, the multivariate $\alpha$-TOST, that consists in a correction of $\alpha$, the significance level, taking the (arbitrary) dependence between the outcomes of interest into account and making it uniformly more powerful than the conventional multivariate TOST. We present an iterative algorithm allowing to efficiently define $\alpha^{\star}$, the corrected significance level, a task that proves challenging in the multivariate setting due to the inter-relationship between $\alpha^{\star}$ and the sets of values belonging to the null hypothesis space and defining the test size. We study the operating characteristics of the multivariate $\alpha$-TOST both theoretically and via an extensive simulation study considering cases relevant for real-world analyses -- i.e.,~relatively small sample sizes, unknown and heterogeneous variances, and different correlation structures -- and show the superior finite-sample properties of the multivariate $\alpha$-TOST compared to its conventional counterpart. We finally re-visit a case study on ticlopidine hydrochloride and compare both methods when simultaneously assessing bioequivalence for multiple pharmacokinetic parameters., Comment: 34 pages, 13 figures. To be published in: Statistics in Medicine
Published: 2024

22. Correlated Structural and Optical Characterization of Hexagonal Boron Nitride

Author: Gusdorff, Jordan A., Bhatia, Pia, Shin, Trey T., Uy-Tioco, Alexandra Sofia, Sailors, Benjamin N., Keneipp, Rachael N., Drndić, Marija, and Bassett, Lee C.
Subjects: Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics, Quantum Physics
Abstract: Hexagonal boron nitride (hBN) plays a central role in nanoelectronics and nanophotonics. Moreover, hBN hosts room-temperature quantum emitters and optically addressable spins, making it promising for quantum sensing and quantum photonics. Despite many investigations of their optical properties, however, the emitters' chemical structure remains unclear, as does the role of contamination at surfaces and interfaces in forming the emitters or modifying their properties. We prepare hBN samples that are compatible with confocal photoluminescence (PL) microscopy, transmission electron microscopy (TEM), and atomic-force microscopy (AFM), and we use those techniques to quantitatively investigate correlations between fluorescent emission, flake morphology, and surface residue. We find that the microscopy techniques themselves induce changes in hBN's optical activity and residue morphology: PL measurements induce photobleaching, whereas TEM measurements alter surface residue and emission characteristics. We also study the effects of common treatments $\unicode{x2014}$ annealing and oxygen plasma cleaning $\unicode{x2014}$ on the structure and optical activity of hBN. The methods can be broadly applied to study two-dimensional materials, and the results illustrate the importance of correlative studies to elucidate structural factors that influence hBN's functionality as a host for quantum emitters and spin defects., Comment: 27 pages, 16 figures, 3 tables
Published: 2024

23. Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows

Author: Duma, Zina-Sabrina, Sihvonen, Tuomas, Susiluoto, Jouni, Lamminpää, Otto, Haario, Heikki, and Reinikainen, Satu-Pia
Subjects: Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science, Statistics - Methodology
Abstract: Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the optimization studies on kernel-based chemometric methods is limited and often reduced to grid searching. Previously, the authors introduced Kernel Flows (KF) to learn kernel parameters for Kernel Partial Least-Squares (K-PLS) regression. KF is easy to implement and helps minimize overfitting. In cases of high collinearity between spectra and biogeophysical quantities in spectroscopy, simpler methods like Principal Component Regression (PCR) may be more suitable. In this study, we propose a new KF-type approach to optimize Kernel Principal Component Regression (K-PCR) and test it alongside KF-PLS. Both methods are benchmarked against non-linear regression techniques using two hyperspectral remote sensing datasets.
Published: 2024

24. Search for a Hidden Sector Scalar from Kaon Decay in the Di-Muon Final State at ICARUS

Author: ICARUS Collaboration, Alrahman, F. Abd, Abratenko, P., Abrego-Martinez, N., Aduszkiewicz, A., Akbar, F., Soplin, L. Aliaga, Garrote, R. Alvarez, Pons, M. Artero, Asaadi, J., Badgett, W. F., Baibussinov, B., Behera, B., Bellini, V., Benocci, R., Berger, J., Berkman, S., Bertolucci, S., Betancourt, M., Bonesini, M., Boone, T., Bottino, B., Braggiotti, A., Brailsford, D., Brice, S. J., Brio, V., Brizzolari, C., Budd, H. S., Campani, A., Campos, A., Carber, D., Carneiro, M., Terrazas, I. Caro, Carranza, H., Fernandez, F. Castillo, Castro, A., Centro, S., Cerati, G., Chatterjee, A., Cherdack, D., Cherubini, S., Chithirasreemadam, N., Cicerchia, M., Coan, T. E., Cocco, A., Convery, M. R., Cooper-Troendle, L., Copello, S., Da Motta, H., Dallolio, M., Dange, A. A., de Roeck, A., Di Domizio, S., Di Noto, L., Di Stefano, C., Di Ferdinando, D., Diwan, M., Dolan, S., Domine, L., Donati, S., Drielsma, F., Dyer, J., Dytman, S., Falcone, A., Farnese, C., Fava, A., Ferrari, A., Gallice, N., Garcia, F. G., Gatto, C., Gibin, D., Gioiosa, A., Gu, W., Guglielmi, A., Gurung, G., Hassinin, K., Hausner, H., Heggestuen, A., Howard, B., Howell, R., Ingratta, I., James, C., Jang, W., Jung, M., Jwa, Y. -J., Kashur, L., Ketchum, W., Kim, J. S., Koh, D. -H., Larkin, J., Li, Y., Mariani, C., Marshall, C. M., Martynenko, S., Mauri, N., McFarland, K. S., Mé9ndez, D. P., Menegolli, A., Meng, G., Miranda, O. G., Mogan, A., Moggi, N., Montagna, E., Montanari, C., Montanari, A., Mooney, M., Moreno-Granados, G., Mueller, J., Murphy, M., Naples, D., Nguyen, V. C. L, Palestini, S., Pallavicini, M., Paolone, V., Papaleo, R., Pasqualini, L., Patrizii, L., Paudel, L., Pelegrina-Gutiérrez, L., Petrillo, G., Petta, C., Pia, V., Pietropaolo, F., Poppi, F., Pozzato, M., Putnam, G., Qian, X., Rappoldi, A., Raselli, G. L., Repetto, S., Resnati, F., Ricci, A. M., Riccobene, G., Richards, E., Rosenberg, M., Rossella, M., Rowe, N., Roy, P., Rubbia, C., Saad, M., Safa, I., Saha, S., Sala, P., Salmoria, G., Samanta, S., Sapienza, P., Scaramelli, A., Scarpelli, A., Schmitz, D., Schukraft, A., Senadheera, D., Seo, S-H., Sergiampietri, F., Sirri, G., Smedley, J. S., Smith, J., Stanco, L., Stewart, J., Tanaka, H. A., Tapia, F., Tenti, M., Terao, K., Terranova, F., Togo, V., Torretta, D., Torti, M., Tortorici, F., Triozzi, R., Tsai, Y. -T., Tufanli, S., Usher, T., Varanini, F., Ventura, S., Vicenzi, M., Vignoli, C., Viren, B., Wieler, F. A., Williams, Z., Wilson, R. J., Wilson, P., Wolfs, J., Wongjirad, T., Wood, A., Worcester, E., Worcester, M., Wospakrik, M., Yadav, S., Yu, H., Yu, J., Zani, A., Zennamo, J., Zettlemoyer, J., Zhang, C., and Zucchelli, S.
Subjects: High Energy Physics - Experiment
Abstract: We present a search for long-lived particles (LLPs) produced from kaon decay that decay to two muons inside the ICARUS neutrino detector. This channel would be a signal of hidden sector models that can address outstanding issues in particle physics such as the strong CP problem and the microphysical origin of dark matter. The search is performed with data collected in the Neutrinos at the Main Injector (NuMI) beam at Fermilab corresponding to $2.41\times 10^{20}$ protons-on-target. No new physics signal is observed, and we set world-leading limits on heavy QCD axions, as well as for the Higgs portal scalar among dedicated searches. Limits are also presented in a model-independent way applicable to any new physics model predicting the process $K\to \pi+S(\to\mu\mu)$, for a long-lived particle S. This result is the first search for new physics performed with the ICARUS detector at Fermilab. It paves the way for the future program of long-lived particle searches at ICARUS.
Published: 2024

25. Beyond hypergraph acyclicity: limits of tractability for pseudo-Boolean optimization

Author: Del Pia, Alberto and Khajavirad, Aida
Subjects: Mathematics - Optimization and Control, Computer Science - Discrete Mathematics
Abstract: In this paper, we study the problem of minimizing a polynomial function with literals over all binary points, often referred to as pseudo-Boolean optimization. We investigate the fundamental limits of computation for this problem by providing new necessary conditions and sufficient conditions for tractability. On the one hand, we obtain the first intractability results, in the best-case sense, for pseudo-Boolean optimization problems on signed hypergraphs with bounded rank, in terms of the treewidth of the intersection graph. Namely, first, under some mild assumptions, we show that for every sequence of hypergraphs indexed by the treewidth and with bounded rank, the complexity of solving the associated pseudo-Boolean optimization problem grows super-polynomially in the treewidth. Second, we show that any hypergraph of bounded rank is the underlying hypergraph of some signed hypergraph for which the corresponding pseudo-Boolean polytope has an exponential extension complexity in the treewidth. On the other hand, we introduce the nest-set gap, a new hypergraph-theoretic notion that enables us to define a notion of "distance" from the hypergaph acyclicity. We prove that if this distance is bounded, the pseudo-Boolean polytope admits a polynomial-size extended formulation. This in turn enables us to obtain a polynomial-time algorithm for a large class of pseudo-Boolean optimization problems whose underlying hypergraphs contain beta-cycles.
Published: 2024

26. Active Event Alignment for Monocular Distance Estimation

Author: Cai, Nan and Bideau, Pia
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Event cameras provide a natural and data efficient representation of visual information, motivating novel computational strategies towards extracting visual information. Inspired by the biological vision system, we propose a behavior driven approach for object-wise distance estimation from event camera data. This behavior-driven method mimics how biological systems, like the human eye, stabilize their view based on object distance: distant objects require minimal compensatory rotation to stay in focus, while nearby objects demand greater adjustments to maintain alignment. This adaptive strategy leverages natural stabilization behaviors to estimate relative distances effectively. Unlike traditional vision algorithms that estimate depth across the entire image, our approach targets local depth estimation within a specific region of interest. By aligning events within a small region, we estimate the angular velocity required to stabilize the image motion. We demonstrate that, under certain assumptions, the compensatory rotational flow is inversely proportional to the object's distance. The proposed approach achieves new state-of-the-art accuracy in distance estimation - a performance gain of 16% on EVIMO2. EVIMO2 event sequences comprise complex camera motion and substantial variance in depth of static real world scenes.
Published: 2024

27. Long-term stability and oxidation of ferroelectric AlScN devices: An operando HAXPES study

Author: Rehm, Oliver, Baumgarten, Lutz, Guido, Roberto, Düring, Pia Maria, Gloskovskii, Andrei, Schlueter, Christoph, Mikolajick, Thomas, Schroeder, Uwe, and Müller, Martina
Subjects: Condensed Matter - Materials Science
Abstract: Aluminum scandium nitride (Al$_{1-x}$Sc$_x$N) is a promising material for ferroelectric devices due to its large remanent polarization, scalability, and compatibility with semiconductor technology. By doping AlN with Sc, the bonds in the polar AlN structure are weakened, which enables ferroelectric switching below the dielectric breakdown field. However, one disadvantage of Sc doping is that it increases the material's tendency towards oxidation. In the present study, the oxidation process of tungsten-capped and uncapped Al$_{0.83}$Sc$_{0.17}$N thin films is investigated by hard X-ray photoelectron spectroscopy (HAXPES). The samples had been exposed to air for either two weeks or 6 months. HAXPES spectra indicate the replacement of nitrogen by oxygen, and the tendency of oxygen to favor oxidation with Sc rather than Al. The appearance of an N$_2$ spectral feature thus can be directly related to the oxidation process. We present an oxidation model that mimics these spectroscopic results of the element-specific oxidation processes within Al$_{1-x}$Sc$_x$N. Finally, in operando HAXPES data of uncapped and capped AlScN-capacitor stacks are interpreted using the proposed model.
Published: 2024

28. Comparative Study of RF Heating in Deep Brain Stimulation Devices During MRI at 1.5 T and 0.55 T: Challenging the Assumption of Safety at Low Field Strengths

Author: Bhusal, Bhumi, Sanpitak, Pia Panravi, Vu, Jasmine, Jiang, Fuchang, Richardson, Jacob, Seiberlich, Nicole, Rosenow, Joshua M., Elahi, Behzad, and Golestanirad, Laleh
Subjects: Physics - Medical Physics
Abstract: Purpose: Low-field MRI has been assumed to be implant-friendly based on limited studies. However, RF-induced heating due to an implant is a complex resonance phenomenon, highly dependent on the implant's configurations and the applied RF frequencies. This study aims to evaluate the RF heating of DBS implants during MRI at low-field strengths compared to higher field 1.5 T MRI. Methods: A commercial deep brain stimulation (DBS) implant was used in full system as well as lead only configurations to evaluate and compare RF heating during MR imaging at 0.55 T and 1.5 T. The transfer function of the device at both configurations was measured and validated at each of the frequencies, which was then used for the in vivo prediction of RF heating for realistic DBS configurations at head, chest and abdomen imaging landmarks. Results: For the lead only case, the RF heating due to the DBS was substantially smaller during imaging at 0.55 T compared to that at 1.5 T. However, for the full DBS system (longer implant), the RF heating at 0.55 T was comparable to and for some cases even higher than that at 1.5 T, reaching a level that poses risk of tissue damage in patients. Conclusions: While RF heating generally tends to be lower at low-field MRI, the case with longer implanted leads demands extra caution, due to the higher possibility of matching resonant condition at low-field-strength frequencies. Thus, specific risk evaluation for each implant and configuration is required rather than assuming that lower field strength imaging is safer.
Published: 2024

29. Doppler correlation-driven vetoes for the Frequency Hough analysis in continuous gravitational-wave searches

Author: Di Giovanni, Matteo, Leaci, Paola, Astone, Pia, Pra, Stefano Dal, D'Antonio, Sabrina, D'Onofrio, Luca, Frasca, Sergio, Muciaccia, Federico, Palomba, Cristiano, Pierini, Lorenzo, and Tehrani, Francesco Safai
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Data Analysis, Statistics and Probability
Abstract: We present an improved method for vetoing candidates of continuous gravitational-wave sources during all-sky searches utilizing the Frequency Hough pipeline. This approach leverages linear correlations between source parameters induced by the Earth Doppler effect, which can be effectively identified through the Hough Transform. Candidates that do not align with these patterns are considered spurious and can thus be vetoed, enhancing the depth and statistical significance of follow-up analyses. Additionally, we provide a comprehensive explanation of the method calibration, which intrinsically linked to the total duration of the observing run. On average, the procedure successfully vetoes $56\%$ of candidates. To assess the method performance, we conducted a Monte-Carlo simulation injecting fake continuous-wave signals into data from the third observing run of the LIGO detectors. This analysis allowed us to infer strain amplitude upper limits at a $90\%$ confidence level. We found that the optimal sensitivity is $h_0^{90\%} = 3.62^{+0.23}_{-0.22}\times 10^{-26}$ in the [128, 200] Hz band, which is within the most sensible frequency band of the LIGO detectors., Comment: 13 pages, 9 figures, 5 tables
Published: 2024

30. Efficient Sparse PCA via Block-Diagonalization

Author: Del Pia, Alberto, Zhou, Dekun, and Zhu, Yinglun
Subjects: Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract: Sparse Principal Component Analysis (Sparse PCA) is a pivotal tool in data analysis and dimensionality reduction. However, Sparse PCA is a challenging problem in both theory and practice: it is known to be NP-hard and current exact methods generally require exponential runtime. In this paper, we propose a novel framework to efficiently approximate Sparse PCA by (i) approximating the general input covariance matrix with a re-sorted block-diagonal matrix, (ii) solving the Sparse PCA sub-problem in each block, and (iii) reconstructing the solution to the original problem. Our framework is simple and powerful: it can leverage any off-the-shelf Sparse PCA algorithm and achieve significant computational speedups, with a minor additive error that is linear in the approximation error of the block-diagonal matrix. Suppose $g(k, d)$ is the runtime of an algorithm (approximately) solving Sparse PCA in dimension $d$ and with sparsity value $k$. Our framework, when integrated with this algorithm, reduces the runtime to $\mathcal{O}\left(\frac{d}{d^\star} \cdot g(k, d^\star) + d^2\right)$, where $d^\star \leq d$ is the largest block size of the block-diagonal matrix. For instance, integrating our framework with the Branch-and-Bound algorithm reduces the complexity from $g(k, d) = \mathcal{O}(k^3\cdot d^k)$ to $\mathcal{O}(k^3\cdot d \cdot (d^\star)^{k-1})$, demonstrating exponential speedups if $d^\star$ is small. We perform large-scale evaluations on many real-world datasets: for exact Sparse PCA algorithm, our method achieves an average speedup factor of 93.77, while maintaining an average approximation error of 2.15%; for approximate Sparse PCA algorithm, our method achieves an average speedup factor of 6.77 and an average approximation error of merely 0.37%.
Published: 2024

31. Phase transitions for polyadic epidemic and voter models with multiscale groups

Author: Steinmeyer, Pia, Mölter, Jan, and Kuehn, Christian
Subjects: Nonlinear Sciences - Adaptation and Self-Organizing Systems, Physics - Physics and Society
Abstract: Polyadic (or higher-order) interactions can significantly impact the dynamics of interacting particle systems. However, so far, the group size interactions have been relatively small. In this work, we examine the influence of multiscale polyadic group interactions, where some groups are small and others very large. We consider two paradigmatic examples, an SIS-epidemic and the adaptive voter model. On the level of the mean field, we specifically discuss the impact of the multiscale polyadic interactions on equilibrium dynamics and phase transitions. For the SIS-epidemic model, we find a region of bistability that protects the disease-free state over a wide range beyond the classical epidemic threshold from a significant outbreak. For the adaptive voter model, we show that multiscale polyadic interactions can stabilize the network or increase the convergence rate to an unbiased equilibrium.
Published: 2024

32. Production of the Fisher information for the Landau-Coulomb equation with L1 initial data

Author: Desvillettes, Laurent, Golding, William, Gualdani, Maria Pia, and Loher, Amelie
Subjects: Mathematics - Analysis of PDEs, Mathematical Physics
Abstract: We consider the Landau-Coulomb equation for initial data with bounded mass, finite numbers of moments, and entropy. We show the existence of a global weak solution that has bounded Fisher information for positive times. This solution is therefore a global strong solution away from the initial time. We propose an alternative approach, based on already existing estimates, to the study of the appearance of Fisher information recently performed by Ji in [12].
Published: 2024

33. Opacity Enforcement by Edit Functions Under Incomparable Observations

Author: Duan, Wei, Liu, Ruotian, Fanti, Maria Pia, Hadjicostis, Christoforos N., and Li, Zhiwu
Subjects: Computer Science - Formal Languages and Automata Theory, Electrical Engineering and Systems Science - Systems and Control
Abstract: As an information-flow privacy property, opacity characterizes whether a malicious external observer (referred to as an intruder) is able to infer the secret behavior of a system. This paper addresses the problem of opacity enforcement using edit functions in discrete event systems modeled by partially observed deterministic finite automata. A defender uses the edit function as an interface at the output of a system to manipulate actual observations through insertion, substitution, and deletion operations so that the intruder will be prevented from inferring the secret behavior of the system. Unlike existing work which usually assumes that the observation capabilities of the intruder and the defender are identical, we consider a more general setting where they may observe incomparable subsets of events generated by the system.To characterize whether the defender has the ability to enforce opacity of the system under this setting, the notion of \emph{$ic$-enforceability} is introduced. Then, the opacity enforcement problem is transformed to a two-player game, with imperfect information between the system and the defender, which can be used to determine a feasible decision-making strategy for the defender. Within the game scheme, an edit mechanism is constructed to enumerate all feasible edit actions following system behavior. We further show that an $ic$-enforcing edit function (if one exists) can be synthesized from the edit mechanism to enforce opacity.
Published: 2024

34. Beyond Fundamental Building Blocks: Plasticity in Structurally Complex Crystals

Author: Stollenwerk, Tobias, Huckfeldt, Pia Carlotta, Ulumuddin, Nisa Zakia Zahra, Schneider, Malik, Xie, Zhuocheng, and Korte-Kerzel, Sandra
Subjects: Condensed Matter - Materials Science
Abstract: Intermetallics, which encompass a wide range of compounds, often exhibit similar or closely related crystal structures, resulting in various intermetallic systems with structurally derivative phases. This study examines the hypothesis that deformation behavior can be transferred from fundamental building blocks to structurally related phases using the binary samarium-cobalt system. We investigate SmCo$_2$ and SmCo$_5$ as fundamental building blocks and compare them to the structurally related SmCo$_3$ and Sm$_2$Co$_{17}$ phases. Nanoindentation and micropillar compression tests were performed to characterize the primary slip systems, complemented by generalized stacking fault energy calculations via atomic-scale modeling. Our results show that while elastic properties of the structurally complex phases follow a rule of mixtures, their plastic deformation mechanisms are more intricate, influenced by the stacking and bonding nature within the crystal's building blocks. These findings underscore the importance of local bonding environments in predicting the mechanical behavior of structurally related intermetallics, providing crucial insights for the development of high-performance intermetallic materials.
Published: 2024

35. The track-length extension fitting algorithm for energy measurement of interacting particles in liquid argon TPCs and its performance with ProtoDUNE-SP data

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

36. FlowMAC: Conditional Flow Matching for Audio Coding at Low Bit Rates

Author: Pia, Nicola, Strauss, Martin, Multrus, Markus, and Edler, Bernd
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Machine Learning, Computer Science - Sound
Abstract: This paper introduces FlowMAC, a novel neural audio codec for high-quality general audio compression at low bit rates based on conditional flow matching (CFM). FlowMAC jointly learns a mel spectrogram encoder, quantizer and decoder. At inference time the decoder integrates a continuous normalizing flow via an ODE solver to generate a high-quality mel spectrogram. This is the first time that a CFM-based approach is applied to general audio coding, enabling a scalable, simple and memory efficient training. Our subjective evaluations show that FlowMAC at 3 kbps achieves similar quality as state-of-the-art GAN-based and DDPM-based neural audio codecs at double the bit rate. Moreover, FlowMAC offers a tunable inference pipeline, which permits to trade off complexity and quality. This enables real-time coding on CPU, while maintaining high perceptual quality., Comment: Submitted to ICASSP 2025
Published: 2024

37. Effect of valence electrons on the core level x-ray photoelectron spectra of niobium oxide thin films prepared by molecular beam epitaxy

Author: Palakkal, Jasnamol, Henning, Pia, and Alff, Lambert
Subjects: Condensed Matter - Materials Science
Abstract: X-ray photoelectron spectroscopy (XPS) is a versatile tool for identifying the chemical and electronic state of an element. However, the presence of various initial- and final-state effects makes the interpretation of XPS spectra tricky and erroneous. Metal oxides show multipeak XPS spectra when the charge carriers are coupled with the core hole of the photoion after photoemission. The extra peaks are often misconstrued to originate from other oxidation states of the cation. With a systematic approach to the analysis of Nb 3d XPS spectra of partially oxidized NbOx thin films prepared by molecular beam epitaxy, we found that the valence band electrons close to the Fermi level contribute to a satellite peak in Nb-oxides. Insulating Nb-oxide containing Nb5+ (d0) with no or fewer electrons near the Fermi level has no such peaks; on the other hand, Nb5+ containing thin films exhibit this satellite peak when sufficient surface charge carriers contribute to the core hole-electron coupling., Comment: 18 pages including supplementary information
Published: 2024

38. How Do You Perceive My Face? Recognizing Facial Expressions in Multi-Modal Context by Modeling Mental Representations

Author: Blume, Florian, Qu, Runfeng, Bideau, Pia, Maier, Martin, Rahman, Rasha Abdel, and Hellwich, Olaf
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Facial expression perception in humans inherently relies on prior knowledge and contextual cues, contributing to efficient and flexible processing. For instance, multi-modal emotional context (such as voice color, affective text, body pose, etc.) can prompt people to perceive emotional expressions in objectively neutral faces. Drawing inspiration from this, we introduce a novel approach for facial expression classification that goes beyond simple classification tasks. Our model accurately classifies a perceived face and synthesizes the corresponding mental representation perceived by a human when observing a face in context. With this, our model offers visual insights into its internal decision-making process. We achieve this by learning two independent representations of content and context using a VAE-GAN architecture. Subsequently, we propose a novel attention mechanism for context-dependent feature adaptation. The adapted representation is used for classification and to generate a context-augmented expression. We evaluate synthesized expressions in a human study, showing that our model effectively produces approximations of human mental representations. We achieve State-of-the-Art classification accuracies of 81.01% on the RAVDESS dataset and 79.34% on the MEAD dataset. We make our code publicly available., Comment: GCPR 2024
Published: 2024

39. Does a Centralized Written Final Examination in Mathematics Indeed Improve Pupils' Subject-Related Study Ability?

Author: Pia Tscholl, Florian Stampfer, and Tobias Hell
Abstract: Since 2015/16, a standardized written final examination in mathematics or applied mathematics has been compulsory for nearly all pupils at the upper secondary level in Austria. While this standardized competence-oriented maturity examination is intended to increase pupils' subject-related study ability, empirical research in this regard is scarce. Therefore, the subject-related study ability for six partially different control and experimental groups containing between 11 and 17 first-year STEM students is compared using a one-tailed two-sample Wilcoxon rank sum test. No significant differences in the subject-related study ability are detected between the control groups, comprising first-year Austrian STEM students who did not participate in the standardized written final examination in mathematics, and the experimental groups, comprising first-year Austrian STEM students who did participate in the standardized written final examination in mathematics. However, post hoc power analyses show that the sample sizes for each of the six sample cases would have to be much larger to prove significant results with a power of at least 80%. Additionally, no evidence for teaching-to-the test practices could be found in the experimental groups.
Published: 2024

40. Students' Thriving and Well-Being in Online Learning Environments in Vocational Education and Training

Author: Pia Kiikeri, Satu Uusiautti, and Sirpa Purtilo-Nieminen
Abstract: Context: Recent research on students' experiences with the quality of online learning during the COVID-19 pandemic has increased the challenge to the development of online learning. During the pandemic, anxiety, depression, and fatigue occurred in online studies, which also weakened students' well-being. In this quantitative study, we examined how students thrive in synchronous and asynchronous online implementations. The goal of the research was to support students' well-being in online studies; therefore, the study uses the PERMA well-being theory. Approach: Finnish vocational education and training students (N = 363) participated in the study and answered the questions on positive emotions, engagement, relationships, meaning, and accomplishment from both asynchronous and synchronous online delivery perspectives using a questionnaire. Findings: This study highlighted the importance of the teacher's online learning facilitation skills and task design skills, and the emotions generated in an online course. Based on the results, the respondents were divided into three student profiles: (1) Students thriving in online courses in general; (2) Students thriving, especially in asynchronous online courses; and (3) Students not thriving in online courses in general. Conclusions: According to the findings, students need different options for completing online courses. The role of the teacher in creating a positive and supportive atmosphere, fostering relationships, and creating clear work-related and versatile tasks is of great importance in the generation of emotions, whether it is an asynchronous or synchronous online implementation.
Published: 2024

41. Digital tools for knowledge exchange and Sustainable Public Food Procurement in community kindergartens: A case study of Slupsk, Poland

Author: Suchomska, Joanna, Laborgne, Pia, Pierce, Andrea, Porto de Albuquerque, Joao, Jirka, Simon, Goszczynski, Wojciech, Camara-Menoyo, Carlos, and Wroblewski, Michał
Published: 2024

42. No Effects of Auditory and Visual White Noise on Oculomotor Control in Children with ADHD

Author: Erica Jostrup, Emma Claesdotter-Knutsson, Pia Tallberg, Göran Söderlund, Peik Gustafsson, and Marcus Nyström
Abstract: Background: White noise stimulation has demonstrated efficacy in enhancing working memory in children with ADHD. However, its impact on other executive functions commonly affected by ADHD, such as inhibitory control, remains largely unexplored. This research aims to explore the effects of two types of white noise stimulation on oculomotor inhibitory control in children with ADHD. Method: Memory guided saccade (MGS) and prolonged fixation (PF) performance was compared between children with ADHD (N = 52) and typically developing controls (TDC, N = 45), during auditory and visual white noise stimulation as well as in a no noise condition. Results: Neither the auditory nor the visual white noise had any beneficial effects on performance for either group. Conclusions: White noise stimulation does not appear to be beneficial for children with ADHD in tasks that target oculomotor inhibitory control. Potential explanations for this lack of noise benefit will be discussed.
Published: 2024
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43. Emotions in Engineering Education: A Configurative Meta-Synthesis Systematic Review

Author: Johanna Lönngren, Alberto Bellocchi, Maria Berge, Pia Bøgelund, Inês Direito, James L. Huff, Khairiyah Mohd-Yusof, Homero Murzi, Nor Farahwahidah Abdul Rahman, and Roland Tormey
Abstract: Background: The study of emotions in engineering education (EEE) has increased in recent years, but this emerging, multidisciplinary body of research is dispersed and not well consolidated. This paper reports on the first systematic review of EEE research and scholarship. Purpose: The review aimed to critically assess how researchers and scholars in engineering education have conceptualized emotions and how those conceptualizations have been used to frame and conduct EEE research and scholarship. Scope/Method: The systematic review followed the procedures of a configurative meta-synthesis, mapping emotion theories and concepts, research purposes and methods, and citation patterns in the EEE literature. The review proceeded through five stages: (i) scoping and database searching; (ii) abstract screening, full text sifting, and full text review; (iii) pearling; (iv) scoping review, and (v) in-depth analysis for the meta-synthesis review. Two hundred and thirteen publications were included in the final analysis. Results: The results show that the EEE literature has not extensively engaged with the wide range of conceptualizations of emotion available in the educational, psychological, and sociological literature. Further, the focus on emotion often seems to have been unintentional and of secondary importance in studies whose primary goals were to study other phenomena. Conclusions: More research adopting intentional, theorized approaches to emotions will be crucial in further developing the field. To do justice to complex emotional phenomena in teaching and learning, future EEE research will also need to engage a broader range of conceptualizations of emotion and research methods, drawing on diverse disciplinary traditions.
Published: 2024
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44. Addressing Democracy and Its Threats in Education: Exploring a Pluralist Perspective in Light of Finnish Social Studies Textbooks

Author: Pia Mikander and Henri Satokangas
Abstract: Democracy is increasingly being challenged, by disengagement and by anti-pluralist movements (Levitsky and Ziblatt in How Democracies Die: What History Reveals About Our Future, Viking, New York, 2018; Wikforss in "Därför demokrati. Om kunskapen och folkstyret" [Because of this, democracy. On knowledge and people's rule] Fri Tanke, 2021; Svolik et al. in J Democr 34(1):5-20, 2023). This article draws upon a theoretical discussion about democracy, pluralism, and threats to democracy. Departing from Dewey, Laclau, Mouffe, Young and Allen, we address democracy as an ideology that centers around pluralism, or an ever-increasing inclusion of voices from the margins as its goal. We argue that perceiving democracy pedagogically as a pluralistic ideology would support students' democratic citizenship and equip them for a world where threats to democracy are being reported. Employing a case study on Finnish social studies textbooks, we analyze how democracy as well as threats to democracy are discursively portrayed. Our study shows that the textbooks present democracy as predominantly institutional and static. We also find that while disengagement is portrayed as a problem for democracy, anti-pluralist movements are generally not referred to as a threat. Additionally, we examine a discourse in the textbooks that connects freedom of speech with democracy in a way that favors a multitude of opinions, even antidemocratic ones, over creating space for marginalized voices. Drawing on the theoretical discussion and the results of the analysis, we argue that a focus on pluralism as the core of democracy makes the opposition between restricting hate speech and advocating for democracy redundant.
Published: 2024
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45. 'Every Morning I Take Two Steps to My Desk…': Students' Perspectives on Distance Learning during the COVID-19 Pandemic

Author: Marco Chiodaroli, Lisa Freyhult, Andreas Solders, Diego Tarrío, and Katerina Pia Günter
Abstract: During the COVID-19 pandemic, distance learning became the predominant teaching method at most universities, exposing students and teachers alike to novel and unexpected challenges and learning opportunities. Our study is situated in the context of higher physics education at a large Swedish university and adopts a mixed-methods approach to explore how students perceive shifts to distance learning. Quantitative student survey responses comparing distance learning during the pandemic with previous in-person learning are analyzed with k-means cluster analysis and with a random-intercept multilevel linear model. Combined analyses produce a consistent picture of students who report having experienced the greatest challenges. They are on average younger, report being less autonomous in their learning, and find it harder than peers to ask questions to the instructor. They are also less likely to have access to a place where they can study without interruptions. Variation across courses is small with students being largely subjected to the same set of challenges. Qualitative data from semi-structured focus group interviews and open-ended questions supports these findings, provides a deeper understanding of the struggles, and reveals possibilities for future interventions. Students report an overall collapse of structure in their learning that takes place along multiple dimensions. Our findings highlight a fundamental role played by informal peer-to-peer and student-instructor interactions, and by the exchange of what we refer to as "structural information." We discuss implications for teachers and institutions regarding the possibility of providing support structures, such as study spaces, as well as fostering student autonomy.
Published: 2024
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46. Neurospora intermedia from a traditional fermented food enables waste-to-food conversion

Author: Maini Rekdal, Vayu, Villalobos-Escobedo, José Manuel, Rodriguez-Valeron, Nabila, Olaizola Garcia, Mikel, Prado Vásquez, Diego, Rosales, Alexander, Sörensen, Pia M, Baidoo, Edward EK, Calheiros de Carvalho, Ana, Riley, Robert, Lipzen, Anna, He, Guifen, Yan, Mi, Haridas, Sajeet, Daum, Christopher, Yoshinaga, Yuko, Ng, Vivian, Grigoriev, Igor V, Munk, Rasmus, Wijaya, Christofora Hanny, Nuraida, Lilis, Damayanti, Isty, Cruz-Morales, Pablo, and Keasling, Jay D
Subjects: Microbiology, Biological Sciences, Zero Hunger, Fermentation, Fermented Foods, Neurospora, Phylogeny, Indonesia, Food Microbiology, Metagenomics, Humans, Metabolomics, Medical Microbiology
Abstract: Fungal fermentation of food and agricultural by-products holds promise for improving food sustainability and security. However, the molecular basis of fungal waste-to-food upcycling remains poorly understood. Here we use a multi-omics approach to characterize oncom, a fermented food traditionally produced from soymilk by-products in Java, Indonesia. Metagenomic sequencing of samples from small-scale producers in Western Java indicated that the fungus Neurospora intermedia dominates oncom. Further transcriptomic, metabolomic and phylogenomic analysis revealed that oncom-derived N. intermedia utilizes pectin and cellulose degradation during fermentation and belongs to a genetically distinct subpopulation associated with human-generated by-products. Finally, we found that N. intermedia grew on diverse by-products such as fruit and vegetable pomace and plant-based milk waste, did not encode mycotoxins, and could create foods that were positively perceived by consumers outside Indonesia. These results showcase the traditional significance and future potential of fungal fermentation for creating delicious and nutritious foods from readily available by-products.
Published: 2024

47. Post-acute COVID-19 outcomes including participant-reported long COVID: amubarvimab/romlusevimab versus placebo in the ACTIV-2 trial

Author: Evering, Teresa H, Moser, Carlee, Jilg, Nikolaus, Ritz, Justin, Wohl, David A, Li, Jonathan Z, Margolis, David, Javan, Arzhang Cyrus, Eron, Joseph J, Currier, Judith S, Daar, Eric S, Smith, Davey M, Hughes, Michael D, Chew, Kara W, Chew, Kara, Smith, David, Daar, Eric, Wohl, David, Currier, Judith, Eron, Joseph, Hughes, Michael, Giganti, Mark, Hosey, Lara, Roa, Jhoanna, Patel, Nilam, Colsh, Kelly, Rwakazina, Irene, Beck, Justine, Sieg, Scott, Li, Jonathan, Fletcher, Courtney, Fischer, William, Ignacio, Rachel Bender, Cardoso, Sandra, Corado, Katya, Jagannathan, Prasanna, Perelson, Alan, Pillay, Sandy, Riviere, Cynthia, Singh, Upinder, Taiwo, Babafemi, Gottesman, Joan, Newell, Matthew, Pedersen, Susan, Dragavon, Joan, Jennings, Cheryl, Greenfelder, Brian, Murtaugh, William, Kosmyna, Jan, Gapara, Morgan, Shahkolahi, Akbar, Lacal, Verónica, Salusso, Diego, Nuñez, Sebastian, Rodriguez, Marcelo Rodrigo, Laborde, Luciana, Papasidero, Marcelo, Wehbe, Luis, Gonzalez, Mariana, Voena, Felicitas Fernandez, Alvarez, Tomas, Lopez, Amaru, Huhn, Virginia, Nores, Ulises D'Andrea, Dieser, Pablo, Bordese, Fernando, Mussi, Marisa, de Carvalho Santana, Rodrigo, Bárbaro, Adriana Aparecida Tiraboschi, Santos, Breno, de Cássia Alves Lira, Rita, da Silva, Andre Luiz Machado, Cardoso, Sandra Wagner, Ribeiro, Maria Pia Diniz, Soliva, Nathália, Vasconcellos, Eduardo, Ribeiro, Jorge Eurico, Enéas, Miriam Amaral, Pinto, Jorge, de Morais Caporali, Julia Fonseca, Ferreira, Flávia Gomes Faleiro, Martinez, Norma Erendira Rivera, Lopez, Victor Casildo Bohorquez, Frias, Melchor Victor, Fetalvero, Krystle, Maranan, Alyxzza, Rosa, Jennifer, Coetzer, Thomas, Mohata, Maureen, Lalloo, Umesh, Madlala, Penelope, Pillay-Ramaya, Larisha, and Bennet, Jaclyn Ann
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Coronaviruses, Clinical Trials and Supportive Activities, Emerging Infectious Diseases, Infectious Diseases, Clinical Research, Prevention, 6.1 Pharmaceuticals, Good Health and Well Being, COVID-19, Monoclonal antibodies, Outpatient treatment, Clinical trial, Post COVID conditions, Long COVID, Post-acute sequelae of SARS-CoV-2 infection, ACTIV-2/A5401 Study Team, Clinical sciences, Health services and systems, Public health
Abstract: BackgroundIt is unknown if early COVID-19 monoclonal antibody (mAb) therapy can reduce risk of Long COVID. The mAbs amubarvimab/romlusevimab were previously demonstrated to reduce risk of hospitalization/death by 79%. This study assessed the impact of amubarvimab/romlusevimab on late outcomes, including Long COVID.MethodsNon-hospitalized high-risk adults within 10 days of COVID-19 symptom onset enrolled in a randomized, double-blind, placebo-controlled phase 2/3 trial of amubarvimab/romlusevimab for COVID-19 treatment. Late symptoms, assessed using a participant-completed symptom diary, were a pre-specified exploratory endpoint. The primary outcome for this analysis was the composite of Long COVID by participant self-report (presence of COVID-19 symptoms as recorded in the diary at week 36) or hospitalization or death by week 36. Inverse probability weighting (IPW) was used to address incomplete outcome ascertainment, giving weighted risk ratios (wRR) comparing amubarvimab/romlusevimab to placebo.FindingsParticipants received amubarvimab/romlusevimab (n = 390) or placebo (n = 390) between January and July 2021. Median age was 49 years, 52% were female, 18% Black/African American, 49% Hispanic/Latino, and 9% COVID-19-vaccinated at entry. At week 36, 103 (13%) had incomplete outcome ascertainment, and 66 (17%) on amubarvimab/romlusevimab and 92 (24%) on placebo met the primary outcome (wRR = 0.70, 95% confidence interval (CI) 0.53-0.93). The difference was driven by fewer hospitalizations/deaths with amubarvimab/romlusevimab (4%) than placebo (13%). Among 652 participants with available diary responses, 53 (16%) on amubarvimab/romlusevimab and 44 (14%) on placebo reported presence of Long COVID.InterpretationAmubarvimab/romlusevimab treatment, while highly effective in preventing hospitalizations/deaths, did not reduce risk of Long COVID. Additional interventions are needed to prevent Long COVID.FundingNational Institute of Allergy and Infectious Diseases of the National Institutes of Health. Amubarvimab and romlusevimab supplied by Brii Biosciences.
Published: 2024

48. Cellwise robust and sparse principal component analysis

Author: Pfeiffer, Pia, Vana-Gür, Laura, and Filzmoser, Peter
Subjects: Statistics - Computation, Statistics - Applications, Statistics - Methodology
Abstract: A first proposal of a sparse and cellwise robust PCA method is presented. Robustness to single outlying cells in the data matrix is achieved by substituting the squared loss function for the approximation error by a robust version. The integration of a sparsity-inducing $L_1$ or elastic net penalty offers additional modeling flexibility. For the resulting challenging optimization problem, an algorithm based on Riemannian stochastic gradient descent is developed, with the advantage of being scalable to high-dimensional data, both in terms of many variables as well as observations. The resulting method is called SCRAMBLE (Sparse Cellwise Robust Algorithm for Manifold-based Learning and Estimation). Simulations reveal the superiority of this approach in comparison to established methods, both in the casewise and cellwise robustness paradigms. Two applications from the field of tribology underline the advantages of a cellwise robust and sparse PCA method.
Published: 2024

49. DUNE Phase II: Scientific Opportunities, Detector Concepts, Technological Solutions

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D. M., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Barham~Alzás, P., Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chakraborty, S., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cortez, A. F. V., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Fernández-Posada, D., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., M~Gago, A., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. 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Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.
Published: 2024

50. Learning the Latent dynamics of Fluid flows from High-Fidelity Numerical Simulations using Parsimonious Diffusion Maps

Author: Della Pia, Alessandro, Patsatzis, Dimitris, Russo, Lucia, and Siettos, Constantinos
Subjects: Physics - Fluid Dynamics, Mathematics - Numerical Analysis, Nonlinear Sciences - Chaotic Dynamics, 65Pxx, 68T20, 76Fxx, 76-10
Abstract: We use parsimonious diffusion maps (PDMs) to discover the latent dynamics of high-fidelity Navier-Stokes simulations with a focus on the 2D fluidic pinball problem. By varying the Reynolds number, different flow regimes emerge, ranging from steady symmetric flows to quasi-periodic asymmetric and turbulence. We show, that the proposed non-linear manifold learning scheme, identifies in a crisp manner the expected intrinsic dimension of the underlying emerging dynamics over the parameter space. In particular, PDMs, estimate that the emergent dynamics in the oscillatory regime can be captured by just two variables, while in the chaotic regime, the dominant modes are three as anticipated by the normal form theory. On the other hand, proper orthogonal decomposition (POD)/PCA, most commonly used for dimensionality reduction in fluid mechanics, does not provide such a crisp separation between the dominant modes. To validate the performance of PDMs, we also computed the reconstruction error, by constructing a decoder using Geometric Harmonics. We show that the proposed scheme outperforms the POD/PCA over the whole Reynolds number range. Thus, we believe that the proposed scheme will allow for the development of more accurate reduced order models for high-fidelity fluid dynamics simulators, thus relaxing the curse of dimensionality in numerical analysis tasks such as bifurcation analysis, optimization and control.
Published: 2024
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