First Searches for Dark Matter with the KM3NeT Neutrino Telescopes

Autor: KM3NeT Collaboration, Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Benfenati, F., Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Bertin, V., Biagi, S., Boettcher, M., Bonanno, D., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzăş, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Martino, B., De Wasseige, G., Decoene, V., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gagliardini, S., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Ibnsalih, W. Idrissi, Illuminati, G., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lemaître, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Paesani, D., González, J. Palacios, Papalashvili, G., Parisi, V., Gómez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaş, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Piattelli, P., Plavin, A., Poiré, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Romanov, A., Ros, E., Šaina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Sevle, P., Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Stavropoulos, D., Štekl, I., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tudorache, A., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., Zywucka, N.

Indirect dark matter detection methods are used to observe the products of dark matter annihilations or decays originating from astrophysical objects where large amounts of dark matter are thought to accumulate. With neutrino telescopes, an excess of

Externí odkaz: http://arxiv.org/abs/2411.10092

Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models

Autor: NVIDIA, Atzmon, Yuval, Bala, Maciej, Balaji, Yogesh, Cai, Tiffany, Cui, Yin, Fan, Jiaojiao, Ge, Yunhao, Gururani, Siddharth, Huffman, Jacob, Isaac, Ronald, Jannaty, Pooya, Karras, Tero, Lam, Grace, Lewis, J. P., Licata, Aaron, Lin, Yen-Chen, Liu, Ming-Yu, Ma, Qianli, Mallya, Arun, Martino-Tarr, Ashlee, Mendez, Doug, Nah, Seungjun, Pruett, Chris, Reda, Fitsum, Song, Jiaming, Wang, Ting-Chun, Wei, Fangyin, Zeng, Xiaohui, Zeng, Yu, Zhang, Qinsheng

We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space diffusion models trained using a novel Laplacian diffusion process, in wh

Externí odkaz: http://arxiv.org/abs/2411.07126

On the Role of Constraints in the Complexity of Min-Max Optimization

Autor: Bernasconi, Martino, Castiglioni, Matteo, Celli, Andrea, Farina, Gabriele

We investigate the role of constraints in the computational complexity of min-max optimization. The work of Daskalakis, Skoulakis, and Zampetakis [2021] was the first to study min-max optimization through the lens of computational complexity, showing

Externí odkaz: http://arxiv.org/abs/2411.03248

Ferroelectric terpolymer films with enhanced cooling efficiency: An integrated approach considering electrocaloric response and dielectric losses

Autor: Zeggai, Nouh, LoBue, Martino, Almanza, Morgan

Publikováno v: Physical Review Applied, 2024, 22 (4), pp.044059

In response to the growing demand for more efficient and compact refrigeration and energy conversion devices, electrocaloric poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) is among the most promising active substances. However, desp

Externí odkaz: http://arxiv.org/abs/2410.23708

Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data

Autor: Beeri, Gal, Chamot, Benoit, Latchem, Elena, Venkatesh, Shruthi, Whalan, Sarah, Kruger, Van Zyl, Martino, David

This exploratory pilot study investigated the potential of combining a domain-specific model, BERN2, with large language models (LLMs) to enhance automated disease phenotyping from research survey data. Motivated by the need for efficient and accurat

Externí odkaz: http://arxiv.org/abs/2410.20695