Zobrazeno 1 - 10
of 8 784
pro vyhledávání: '"Stapleton, P."'
We compute the algebraic Picard group of the category of $K(n)$-local spectra, for all heights $n$ and all primes $p$. In particular, we show that it is always finitely generated over $\mathbb{Z}_p$ and, whenever $n \geq 2$, is of rank $2$, thereby c
Externí odkaz:
http://arxiv.org/abs/2407.20958
Autor:
Tang, Feilong, Trinh, Matt, Duong, Annita, Ly, Angelica, Stapleton, Fiona, Chen, Zhe, Ge, Zongyuan, Razzak, Imran
Migraine, a prevalent neurological disorder, has been associated with various ocular manifestations suggestive of neuronal and microvascular deficits. However, there is limited understanding of the extent to which retinal imaging may discriminate bet
Externí odkaz:
http://arxiv.org/abs/2408.07293
The Neural Network Field Theory correspondence (NNFT) is a mapping from neural network (NN) architectures into the space of statistical field theories (SFTs). The Bayesian renormalization group (BRG) is an information-theoretic coarse graining scheme
Externí odkaz:
http://arxiv.org/abs/2405.17538
Autor:
Cornelius, Nathan, Dominguez, Lewis, Mehrle, David, Modi, Lakshay, Rose, Millie, Stapleton, Nathaniel
We prove that the image of the total power operation for Burnside rings $A(G) \to A(G\wr\Sigma_n)$ lies inside a relatively small, combinatorial subring $\mathring A(G,n) \subseteq A(G \wr \Sigma_n)$. As $n$ varies, the subrings $\mathring A(G,n)$ as
Externí odkaz:
http://arxiv.org/abs/2405.06661
The field of digital mental health is advancing at a rapid pace. Passively collected data from user engagements with digital tools and services continue to contribute new insights into mental health and illness. As the field of digital mental health
Externí odkaz:
http://arxiv.org/abs/2404.14548
Autor:
Stapleton, Fergal, Galván, Edgar
Evolutionary Algorithms (EAs) play a crucial role in the architectural configuration and training of Artificial Deep Neural Networks (DNNs), a process known as neuroevolution. However, neuroevolution is hindered by its inherent computational expense,
Externí odkaz:
http://arxiv.org/abs/2404.08786
Autor:
Tarantino, Elizabeth, Bolatto, Alberto D., Indebetouw, Rémy, Rubio, Mónica, Sandstrom, Karin M., Smith, J. -D T., Stapleton, Daniel, Wolfire, Mark
We present Cloudy modeling of infrared emission lines in the Wolf-Rayet (WR) nebula N76 caused by one of the most luminous and hottest WR stars in the low metallicity Small Magellanic Cloud. We use spatially resolved mid-infrared Spitzer/IRS and far-
Externí odkaz:
http://arxiv.org/abs/2404.08041
Evolutionary algorithms are increasingly recognised as a viable computational approach for the automated optimisation of deep neural networks (DNNs) within artificial intelligence. This method extends to the training of DNNs, an approach known as neu
Externí odkaz:
http://arxiv.org/abs/2403.19459
Autor:
Aguillard, D. P., Albahri, T., Allspach, D., Anisenkov, A., Badgley, K., Baeßler, S., Bailey, I., Bailey, L., Baranov, V. A., Barlas-Yucel, E., Barrett, T., Barzi, E., Bedeschi, F., Berz, M., Bhattacharya, M., Binney, H. P., Bloom, P., Bono, J., Bottalico, E., Bowcock, T., Braun, S., Bressler, M., Cantatore, G., Carey, R. M., Casey, B. C. K., Cauz, D., Chakraborty, R., Chapelain, A., Chappa, S., Charity, S., Chen, C., Cheng, M., Chislett, R., Chu, Z., Chupp, T. E., Claessens, C., Convery, M. E., Corrodi, S., Cotrozzi, L., Crnkovic, J. D., Dabagov, S., Debevec, P. T., Di Falco, S., Di Sciascio, G., Donati, S., Drendel, B., Driutti, A., Duginov, V. N., Eads, M., Edmonds, A., Esquivel, J., Farooq, M., Fatemi, R., Ferrari, C., Fertl, M., Fienberg, A. T., Fioretti, A., Flay, D., Foster, S. B., Friedsam, H., Froemming, N. S., Gabbanini, C., Gaines, I., Galati, M. D., Ganguly, S., Garcia, A., George, J., Gibbons, L. K., Gioiosa, A., Giovanetti, K. L., Girotti, P., Gohn, W., Goodenough, L., Gorringe, T., Grange, J., Grant, S., Gray, F., Haciomeroglu, S., Halewood-Leagas, T., Hampai, D., Han, F., Hempstead, J., Hertzog, D. W., Hesketh, G., Hess, E., Hibbert, A., Hodge, Z., Hong, K. W., Hong, R., Hu, T., Hu, Y., Iacovacci, M., Incagli, M., Kammel, P., Kargiantoulakis, M., Karuza, M., Kaspar, J., Kawall, D., Kelton, L., Keshavarzi, A., Kessler, D. S., Khaw, K. S., Khechadoorian, Z., Khomutov, N. V., Kiburg, B., Kiburg, M., Kim, O., Kinnaird, N., Kraegeloh, E., Krylov, V. A., Kuchinskiy, N. A., Labe, K. R., LaBounty, J., Lancaster, M., Lee, S., Li, B., Li, D., Li, L., Logashenko, I., Campos, A. Lorente, Lu, Z., Lucà, A., Lukicov, G., Lusiani, A., Lyon, A. L., MacCoy, B., Madrak, R., Makino, K., Mastroianni, S., Miller, J. P., Miozzi, S., Mitra, B., Morgan, J. P., Morse, W. M., Mott, J., Nath, A., Ng, J. K., Nguyen, H., Oksuzian, Y., Omarov, Z., Osofsky, R., Park, S., Pauletta, G., Piacentino, G. M., Pilato, R. N., Pitts, K. T., Plaster, B., Počanić, D., Pohlman, N., Polly, C. C., Price, J., Quinn, B., Qureshi, M. U. H., Ramachandran, S., Ramberg, E., Reimann, R., Roberts, B. L., Rubin, D. L., Sakurai, M., Santi, L., Schlesier, C., Schreckenberger, A., Semertzidis, Y. K., Shemyakin, D., Sorbara, M., Stapleton, J., Still, D., Stöckinger, D., Stoughton, C., Stratakis, D., Swanson, H. E., Sweetmore, G., Sweigart, D. A., Syphers, M. J., Tarazona, D. A., Teubner, T., Tewsley-Booth, A. E., Tishchenko, V., Tran, N. H., Turner, W., Valetov, E., Vasilkova, D., Venanzoni, G., Volnykh, V. P., Walton, T., Weisskopf, A., Welty-Rieger, L., Winter, P., Wu, Y., Yu, B., Yucel, M., Zeng, Y., Zhang, C.
We present details on a new measurement of the muon magnetic anomaly, $a_\mu = (g_\mu -2)/2$. The result is based on positive muon data taken at Fermilab's Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses $3.1$ GeV$/c$ pola
Externí odkaz:
http://arxiv.org/abs/2402.15410
This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric uncertainty, which refers to the inherent variability in the input-output relationships of a system, often characterized by unequal variance or heteroscedastic
Externí odkaz:
http://arxiv.org/abs/2402.13945