Zobrazeno 1 - 10
of 9 084
pro vyhledávání: '"Esquivel, P."'
Autor:
Buckner, Jack H., Meunier, Zechariah D., Arroyo-Esquivel, Jorge, Fitzpatrick, Nathan, Greiner, Ariel, McManus, Lisa C., Watson, James R.
Ecological systems often exhibit complex nonlinear dynamics like oscillations, chaos, and regime shifts. Universal dynamic equations have shown promise in modeling complex dynamics by combining known functional forms with neural networks that represe
Externí odkaz:
http://arxiv.org/abs/2410.09233
We introduce a novel strategy for multi-robot sorting of waste objects using Reinforcement Learning. Our focus lies on finding optimal picking strategies that facilitate an effective coordination of a multi-robot system, subject to maximizing the was
Externí odkaz:
http://arxiv.org/abs/2409.13511
This work presents a novel framework for numerically simulating the depressurization of tanks and pipelines containing carbon dioxide (CO2). The framework focuses on efficient solution strategies for the coupled system of fluid flow equations and the
Externí odkaz:
http://arxiv.org/abs/2408.09164
We study the anisotropy of centroid and integrated intensity maps with synthetic observations. We perform post-process radiative transfer including the optically thick regime that was not covered in Hern\'andez-Padilla et al. (2020). We consider the
Externí odkaz:
http://arxiv.org/abs/2406.13926
Publikováno v:
CHI '24, Proceedings of the CHI Conference on Human Factors in Computing Systems, May 11-16 2024, Honolulu, HI, USA
Collaborating across dissimilar, distributed spaces presents numerous challenges for computer-aided spatial communication. Mixed reality (MR) can blend selected surfaces, allowing collaborators to work in blended f-formations (facing formations), eve
Externí odkaz:
http://arxiv.org/abs/2405.04873
Autor:
Esquivel, J. Arturo, Shen, Yunyi, Leos-Barajas, Vianey, Eadie, Gwendolyn, Speagle, Joshua, Craiu, Radu V, Medina, Amber, Davenport, James
We present a hidden Markov model (HMM) for discovering stellar flares in light curve data of stars. HMMs provide a framework to model time series data that are not stationary; they allow for systems to be in different states at different times and co
Externí odkaz:
http://arxiv.org/abs/2404.13145
Autor:
Aleo, P. D., Engel, A. W., Narayan, G., Angus, C. R., Malanchev, K., Auchettl, K., Baldassare, V. F., Berres, A., de Boer, T. J. L., Boyd, B. M., Chambers, K. C., Davis, K. W., Esquivel, N., Farias, D., Foley, R. J., Gagliano, A., Gall, C., Gao, H., Gomez, S., Grayling, M., Jones, D. O., Lin, C. -C., Magnier, E. A., Mandel, K. S., Matheson, T., Raimundo, S. I., Shah, V. G., Soraisam, M. D., de Soto, K. M., Vicencio, S., Villar, V. A., Wainscoat, R. J.
We present LAISS (Lightcurve Anomaly Identification and Similarity Search), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly ZTF Alert Stream via the AN
Externí odkaz:
http://arxiv.org/abs/2404.01235
Given an affine Poisson algebra, that is singular one may ask whether there is an associated symplectic form. In the smooth case the answer is obvious: for the symplectic form to exist the Poisson tensor has to be invertible. In the singular case, ho
Externí odkaz:
http://arxiv.org/abs/2403.14921
Autor:
Barrera, Gerardo, Esquivel, Liliana
The present manuscript is devoted to the study of the convergence to equilibrium as the noise intensity $\varepsilon>0$ tends to zero for ergodic random systems out of equilibrium of the type \begin{align*} \mathrm{d} X^{\varepsilon}_t(x) = (\mathfra
Externí odkaz:
http://arxiv.org/abs/2402.15457
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