Zobrazeno 1 - 10
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pro vyhledávání: '"A, Chin"'
Being able to study the dynamics of quantum systems interacting with several environments is important in many settings ranging from quantum chemistry to quantum thermodynamics, through out-of-equilibrium systems. For such problems tensor network-bas
Externí odkaz:
http://arxiv.org/abs/2409.04145
Drawing parallels between human cognition and artificial intelligence, we explored how large language models (LLMs) internalize identities imposed by targeted prompts. Informed by Social Identity Theory, these identity assignments lead LLMs to distin
Externí odkaz:
http://arxiv.org/abs/2409.03843
Emotional Voice Conversion (EVC) modifies speech emotion to enhance communication by amplifying positive cues and reducing negative ones. This complex task involves entangled factors like voice quality, speaker traits, and content. Traditional deep l
Externí odkaz:
http://arxiv.org/abs/2409.03636
Performing long-term experimentation or large-scale data collection for machine learning in the field of soft robotics is challenging, due to the hardware robustness and experimental flexibility required. In this work, we propose a modular parallel r
Externí odkaz:
http://arxiv.org/abs/2409.03614
Gate quantum computers generate significant interest due to their potential to solve certain difficult problems such as prime factorization in polynomial time. Computer vision researchers have long been attracted to the power of quantum computers. Ro
Externí odkaz:
http://arxiv.org/abs/2409.02006
Ferromagnetic side layers sandwiching a nonmagnetic spacer as a metallic trilayer has become a pivotal platform for achieving spintronic devices. Recent experiments demonstrate that manipulating the width or the nature of conducting spacer induces no
Externí odkaz:
http://arxiv.org/abs/2409.00911
Autor:
Aalbers, J., Akerib, D. S., Musalhi, A. K. Al, Alder, F., Amarasinghe, C. S., Ames, A., Anderson, T. J., Angelides, N., Araújo, H. M., Armstrong, J. E., Arthurs, M., Baker, A., Balashov, S., Bang, J., Bargemann, J. W., Barillier, E. E., Beattie, K., Bhatti, A., Biekert, A., Biesiadzinski, T. P., Birch, H. J., Bishop, E., Blockinger, G. M., Boxer, B., Brew, C. A. J., Brás, P., Burdin, S., Buuck, M., Carmona-Benitez, M. C., Carter, M., Chawla, A., Chen, H., Chin, Y. T., Chott, N. I., Converse, M. V., Coronel, R., Cottle, A., Cox, G., Curran, D., Dahl, C. E., David, A., Delgaudio, J., Dey, S., de Viveiros, L., Di Felice, L., Ding, C., Dobson, J. E. Y., Druszkiewicz, E., Dubey, S., Eriksen, S. R., Fan, A., Fearon, N. M., Fieldhouse, N., Fiorucci, S., Flaecher, H., Fraser, E. D., Fruth, T. M. A., Gaitskell, R. J., Geffre, A., Genovesi, J., Ghag, C., Gibbons, R., Gokhale, S., Green, J., van der Grinten, M. G. D., Haiston, J. J., Hall, C. R., Han, S., Hartigan-O'Connor, E., Haselschwardt, S. J., Hernandez, M. A., Hertel, S. A., Heuermann, G., Homenides, G. J., Horn, M., Huang, D. Q., Hunt, D., Jacquet, E., James, R. S., Johnson, J., Kaboth, A. C., Kamaha, A. C., Kannichankandy, M., Khaitan, D., Khazov, A., Khurana, I., Kim, J., Kim, Y. D., Kingston, J., Kirk, R., Kodroff, D., Korley, L., Korolkova, E. V., Kraus, H., Kravitz, S., Kreczko, L., Kudryavtsev, V. A., Leonard, D. S., Lesko, K. T., Levy, C., Lin, J., Lindote, A., Lippincott, W. H., Lopes, M. I., Lorenzon, W., Lu, C., Luitz, S., Majewski, P. A., Manalaysay, A., Mannino, R. L., Maupin, C., McCarthy, M. E., McDowell, G., McKinsey, D. N., McLaughlin, J., McLaughlin, J. B., McMonigle, R., Mizrachi, E., Monte, A., Monzani, M. E., Morrison, E., Mount, B. J., Murdy, M., Murphy, A. St. J., Naylor, A., Nelson, H. N., Neves, F., Nguyen, A., O'Brien, C. L., Olcina, I., Oliver-Mallory, K. C., Orpwood, J., Oyulmaz, K. Y, Palladino, K. J., Palmer, J., Pannifer, N. J., Parveen, N., Patton, S. J., Penning, B., Pereira, G., Perry, E., Pershing, T., Piepke, A., Qie, Y., Reichenbacher, J., Rhyne, C. A., Riffard, Q., Rischbieter, G. R. C., Ritchey, E., Riyat, H. S., Rosero, R., Rushton, T., Rynders, D., Santone, D., Sazzad, A. B. M. R., Schnee, R. W., Sehr, G., Shafer, B., Shaw, S., Shutt, T., Silk, J. J., Silva, C., Sinev, G., Siniscalco, J., Smith, R., Solovov, V. N., Sorensen, P., Soria, J., Stevens, A., Stifter, K., Suerfu, B., Sumner, T. J., Szydagis, M., Tiedt, D. R., Timalsina, M., Tong, Z., Tovey, D. R., Tranter, J., Trask, M., Tripathi, M., Vacheret, A., Vaitkus, A. C., Valentino, O., Velan, V., Wang, A., Wang, J. J., Wang, Y., Watson, J. R., Weeldreyer, L., Whitis, T. J., Wild, K., Williams, M., Wisniewski, W. J., Wolf, L., Wolfs, F. L. H., Woodford, S., Woodward, D., Wright, C. J., Xia, Q., Xu, J., Xu, Y., Yeh, M., Yeum, D., Zha, W., Zweig, E. A.
The broad physics reach of the LUX-ZEPLIN (LZ) experiment covers rare phenomena beyond the direct detection of dark matter. We report precise measurements of the extremely rare decay of $^{124}$Xe through the process of two-neutrino double electron c
Externí odkaz:
http://arxiv.org/abs/2408.17391
Autor:
Crépisson, Céline, Amouretti, Alexis, Harmand, Marion, Sanloup, Chrystèle, Heighway, Patrick, Azadi, Sam, McGonegle, David, Campbell, Thomas, Chin, David Alexander, Smith, Ethan, Hansen, Linda, Forte, Alessandro, Gawne, Thomas, Lee, Hae Ja, Nagler, Bob, Shi, YuanFeng, Fiquet, Guillaume, Guyot, François, Makita, Mikako, Benuzzi-Mounaix, Alessandra, Vinci, Tommaso, Miyanishi, Kohei, Ozaki, Norimasa, Pikuz, Tatiana, Nakamura, Hirotaka, Sueda, Keiichi, Yabuuchi, Toshinori, Yabashi, Makina, Wark, Justin S., Polsin, Danae N., Vinko, Sam M.
We present measurements on Fe$_2$O$_3$ amorphization and melt under laser-driven shock compression up to 209(10) GPa via time-resolved in situ x-ray diffraction. At 122(3) GPa, a diffuse signal is observed indicating the presence of a non-crystalline
Externí odkaz:
http://arxiv.org/abs/2408.17204
Synthetic Lunar Terrain (SLT) is an open dataset collected from an analogue test site for lunar missions, featuring synthetic craters in a high-contrast lighting setup. It includes several side-by-side captures from event-based and conventional RGB c
Externí odkaz:
http://arxiv.org/abs/2408.16971
The remarkable success of large language models (LLMs) across various multi-modality applications is well established. However, integrating large language models with humans, or brain dynamics, remains relatively unexplored. In this paper, we introdu
Externí odkaz:
http://arxiv.org/abs/2409.00121