Zobrazeno 1 - 10
of 51 530
pro vyhledávání: '"P. HANSON"'
Autor:
P.S. Deo, T.M. Barber, C. Gotts, M. Villarreal, H. Randeva, S. Brown, J. Bath, P. O’Hare, S. Chaggar, P. Hanson
Publikováno v:
Complementary Therapies in Medicine, Vol 83, Iss , Pp 103059- (2024)
Objective: The study aimed to investigate the feasibility of a remote mindfulness based self-management intervention for individuals with type 2 diabetes. It is important to further our understanding of how to improve self-management to improve healt
Externí odkaz:
https://doaj.org/article/d0c4d5fee57a4329be74f2e658c7169c
Building upon work of L\"{u}cke and Schlicht, we study (higher) Kurepa trees through the lens of higher descriptive set theory, focusing in particular on various perfect set properties and representations of sets of branches through trees as continuo
Externí odkaz:
http://arxiv.org/abs/2411.19839
With the rapid increase in wildfires in the past decade, it has become necessary to detect and predict these disasters to mitigate losses to ecosystems and human lives. In this paper, we present a novel solution -- Hyper-Drive3D -- consisting of snap
Externí odkaz:
http://arxiv.org/abs/2411.16107
Autor:
Suresh, Smruti, Carvajal, Michael Angelo, Hanson, Nathaniel, Holand, Ethan, Hibbard, Samuel, Padir, Taskin
The inspection of confined critical infrastructure such as attics or crawlspaces is challenging for human operators due to insufficient task space, limited visibility, and the presence of hazardous materials. This paper introduces a prototype of PARI
Externí odkaz:
http://arxiv.org/abs/2411.16511
Autor:
Casarosa, Matteo, Lambie-Hanson, Chris
The derived functors $\lim^n$ of the inverse limit find many applications in algebra and topology. In particular, the vanishing of certain derived limits $\lim^n \mathbf{A}[H]$, parametrized by an abelian group $H$, has implications for strong homolo
Externí odkaz:
http://arxiv.org/abs/2411.15856
Autor:
Capote, E., Jia, W., Aritomi, N., Nakano, M., Xu, V., Abbott, R., Abouelfettouh, I., Adhikari, R. X., Ananyeva, A., Appert, S., Apple, S. K., Arai, K., Aston, S. M., Ball, M., Ballmer, S. W., Barker, D., Barsotti, L., Berger, B. K., Betzwieser, J., Bhattacharjee, D., Billingsley, G., Biscans, S., Blair, C. D., Bode, N., Bonilla, E., Bossilkov, V., Branch, A., Brooks, A. F., Brown, D. D., Bryant, J., Cahillane, C., Cao, H., Clara, F., Collins, J., Compton, C. M., Cottingham, R., Coyne, D. C., Crouch, R., Csizmazia, J., Cumming, A., Dartez, L. P., Davis, D., Demos, N., Dohmen, E., Driggers, J. C., Dwyer, S. E., Effler, A., Ejlli, A., Etzel, T., Evans, M., Feicht, J., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fuentes-Garcia, M., Fulda, P., Fyffe, M., Ganapathy, D., Gateley, B., Gayer, T., Giaime, J. A., Giardina, K. D., Glanzer, J., Goetz, E., Goetz, R., Goodwin-Jones, A. W., Gras, S., Gray, C., Griffith, D., Grote, H., Guidry, T., Gurs, J., Hall, E. D., Hanks, J., Hanson, J., Heintze, M. C., Helmling-Cornell, A. F., Holland, N. A., Hoyland, D., Huang, H. Y., Inoue, Y., James, A. L., Jamies, A., Jennings, A., Jones, D. H., Kabagoz, H. B., Karat, S., Karki, S., Kasprzack, M., Kawabe, K., Kijbunchoo, N., King, P. J., Kissel, J. S., Komori, K., Kontos, A., Kumar, Rahul, Kuns, K., Landry, M., Lantz, B., Laxen, M., Lee, K., Lesovsky, M., Villarreal, F. Llamas, Lormand, M., Loughlin, H. A., Macas, R., MacInnis, M., Makarem, C. N., Mannix, B., Mansell, G. L., Martin, R. M., Mason, K., Matichard, F., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McRae, T., Mera, F., Merilh, E. L., Meylahn, F., Mittleman, R., Moraru, D., Moreno, G., Mullavey, A., Nelson, T. J. N., Neunzert, A., Notte, J., Oberling, J., OHanlon, T., Osthelder, C., Ottaway, D. J., Overmier, H., Parker, W., Patane, O., Pele, A., Pham, H., Pirello, M., Pullin, J., Quetschke, V., Ramirez, K. E., Ransom, K., Reyes, J., Richardson, J. W., Robinson, M., Rollins, J. G., Romel, C. L., Romie, J. H., Ross, M. P., Ryan, K., Sadecki, T., Sanchez, A., Sanchez, E. J., Sanchez, L. E., Savage, R. L., Schaetzl, D., Schiworski, M. G., Schnabel, R., Schofield, R. M. S., Schwartz, E., Sellers, D., Shaffer, T., Short, R. W., Sigg, D., Slagmolen, B. J. J., Soike, C., Soni, S., Srivastava, V., Sun, L., Tanner, D. B., Thomas, M., Thomas, P., Thorne, K. A., Todd, M. R., Torrie, C. I., Traylor, G., Ubhi, A. S., Vajente, G., Vanosky, J., Vecchio, A., Veitch, P. J., Vibhute, A. M., von Reis, E. R. G., Warner, J., Weaver, B., Weiss, R., Whittle, C., Willke, B., Wipf, C. C., Wright, J. L., Yamamoto, H., Zhang, L., Zucker, M. E.
On May 24th, 2023, the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), joined by the Advanced Virgo and KAGRA detectors, began the fourth observing run for a two-year-long dedicated search for gravitational waves. The LIGO Hanfor
Externí odkaz:
http://arxiv.org/abs/2411.14607
Autor:
Yu, Runlong, Qiu, Chonghao, Ladwig, Robert, Hanson, Paul C., Xie, Yiqun, Li, Yanhua, Jia, Xiaowei
This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) concentrations in lakes, which is crucial for sustainin
Externí odkaz:
http://arxiv.org/abs/2411.12973
Autor:
Agarwal, S., Aguilar, J. A., Alden, N., Ali, S., Allison, P., Betts, M., Besson, D., Bishop, A., Botner, O., Bouma, S., Buitink, S., Camphyn, R., Cataldo, M., Chiche, S., Clark, B. A., Coleman, A., Couberly, K., de Kockere, S., de Vries, K. D., Deaconu, C., Glaser, C., Glüsenkamp, T., Hallgren, A., Hallmann, S., Hanson, J. C., Hendricks, B., Henrichs, J., Heyer, N., Hornhuber, C., Hughes, K., Karg, T., Karle, A., Kelley, J. L., Kerr, C., Klein, C., Korntheuer, M., Kowalski, M., Kravchenko, I., Krebs, R., Lahmann, R., Latif, U., Laub, P., Liu, C. -H., Marsee, M. J., Meyers, Z. S., Mikhailova, M., Mulrey, K., Muzio, M., Nelles, A., Novikov, A., Nozdrina, A., Oberla, E., Oeyen, B., Polfrey, S., Punsuebsay, N., Pyras, L., Ravn, M., Reichert, M., Rix, J., Ryckbosch, D., Schlüter, F., Scholten, O., Seckel, D., Seikh, M. F. H., Smith, D., Stoffels, J., Terveer, K., Toscano, S., Tosi, D., Tutt, J., Broeck, D. J. Van Den, van Eijndhoven, N., Vieregg, A. G., Vijai, A., Welling, C., Williams, D. R., Windischhofer, P., Veale, J., Wissel, S., Young, R., Zink, A.
The Radio Neutrino Observatory in Greenland (RNO-G) is the first in-ice radio array in the northern hemisphere for the detection of ultra-high energy neutrinos via the coherent radio emission from neutrino-induced particle cascades within the ice. Th
Externí odkaz:
http://arxiv.org/abs/2411.12922
Autor:
Hanson, Nathaniel, Manke, Philip, Birkholz, Simon, Mühlbauer, Maximilian, Heine, Rene, Brandes, Arnd
Machine learning is an important tool for analyzing high-dimension hyperspectral data; however, existing software solutions are either closed-source or inextensible research products. In this paper, we present cuvis.ai, an open-source and low-code so
Externí odkaz:
http://arxiv.org/abs/2411.11324
Autor:
McFarland, Ciera, Dhawan, Ankush, Kumari, Riya, Council, Chad, Coad, Margaret, Hanson, Nathaniel
Soft, growing vine robots are well-suited for exploring cluttered, unknown environments, and are theorized to be performant during structural collapse incidents caused by earthquakes, fires, explosions, and material flaws. These vine robots grow from
Externí odkaz:
http://arxiv.org/abs/2411.06615