Zobrazeno 1 - 10
of 5 059
pro vyhledávání: '"P. Branson"'
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 14, Pp n/a-n/a (2024)
Abstract The present paper develops a new framework to predict the mean flow through an array of cylinders in which the flow around the array (array‐scale) and the flow around individual cylinders (element‐scale) are modeled separately using actu
Externí odkaz:
https://doaj.org/article/25720aa81d694061b3b4c6dd280061b5
In causal inference, sensitivity models assess how unmeasured confounders could alter causal analyses, but the sensitivity parameter -- which quantifies the degree of unmeasured confounding -- is often difficult to interpret. For this reason, researc
Externí odkaz:
http://arxiv.org/abs/2405.08738
Autor:
Chauhan, Vinod Kumar, Clifton, Lei, Salaün, Achille, Lu, Huiqi Yvonne, Branson, Kim, Schwab, Patrick, Nigam, Gaurav, Clifton, David A.
While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited. One critical factor contributing to this restraint is sample selection bias (SSB) which refers to the study population being less repre
Externí odkaz:
http://arxiv.org/abs/2405.07841
Autor:
Schindl, Kyle, Branson, Zach
In the design stage of a randomized experiment, one way to ensure treatment and control groups exhibit similar covariate distributions is to randomize treatment until some prespecified level of covariate balance is satisfied. This experimental design
Externí odkaz:
http://arxiv.org/abs/2403.12815
Autor:
Bai, Yatong, Garg, Utsav, Shanker, Apaar, Zhang, Haoming, Parajuli, Samyak, Bas, Erhan, Filipovic, Isidora, Chu, Amelia N., Fomitcheva, Eugenia D, Branson, Elliot, Kim, Aerin, Sojoudi, Somayeh, Cho, Kyunghyun
Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collecting processes. This time-consuming endeavor hinders the emergence of
Externí odkaz:
http://arxiv.org/abs/2401.04575
Propensity score trimming, which discards subjects with propensity scores below a threshold, is a common way to address positivity violations that complicate causal effect estimation. However, most works on trimming assume treatment is discrete and m
Externí odkaz:
http://arxiv.org/abs/2309.00706
Autor:
Røst, Håkon I., Cooil, Simon P., Åsland, Anna Cecilie, Hu, Jinbang, Ali, Ayaz, Taniguchi, Takashi, Watanabe, Kenji, Belle, Branson D., Holst, Bodil, Sadowski, Jerzy T., Mazzola, Federico, Wells, Justin W.
Publikováno v:
Nano Lett. 23, 16 (2023), 7539-7545
Understanding the collective behavior of the quasiparticles in solid-state systems underpins the field of non-volatile electronics, including the opportunity to control many-body effects for well-desired physical phenomena and their applications. Hex
Externí odkaz:
http://arxiv.org/abs/2308.13846
Autor:
Schreiber, B. A., Denholm, J., Jaeckle, F., Arends, M. J., Branson, K. M., Schönlieb, C. -B., Soilleux, E. J.
We present an innovative method for rapidly segmenting hematoxylin and eosin (H&E)-stained tissue in whole-slide images (WSIs) that eliminates a wide range of undesirable artefacts such as pen marks and scanning artefacts. Our method involves taking
Externí odkaz:
http://arxiv.org/abs/2308.13304
Sequence modelling approaches for epigenetic profile prediction have recently expanded in terms of sequence length, model size, and profile diversity. However, current models cannot infer on many experimentally feasible tissue and assay pairs due to
Externí odkaz:
http://arxiv.org/abs/2308.11671
Autor:
Allaire, C., Ammendola, R., Aschenauer, E. -C., Balandat, M., Battaglieri, M., Bernauer, J., Bondì, M., Branson, N., Britton, T., Butter, A., Chahrour, I., Chatagnon, P., Cisbani, E., Cline, E. W., Dash, S., Dean, C., Deconinck, W., Deshpande, A., Diefenthaler, M., Ent, R., Fanelli, C., Finger, M., Finger, Jr., M., Fol, E., Furletov, S., Gao, Y., Giroux, J., Waduge, N. C. Gunawardhana, Harish, R., Hassan, O., Hegde, P. L., Hernández-Pinto, R. J., Blin, A. Hiller, Horn, T., Huang, J., Jayakodige, D., Joo, B., Junaid, M., Karande, P., Kriesten, B., Elayavalli, R. Kunnawalkam, Lin, M., Liu, F., Liuti, S., Matousek, G., McEneaney, M., McSpadden, D., Menzo, T., Miceli, T., Mikuni, V., Montgomery, R., Nachman, B., Nair, R. R., Niestroy, J., Oregon, S. A. Ochoa, Oleniacz, J., Osborn, J. D., Paudel, C., Pecar, C., Peng, C., Perdue, G. N., Phelps, W., Purschke, M. L., Rajput, K., Ren, Y., Renteria-Estrada, D. F., Richford, D., Roy, B. J., Roy, D., Sato, N., Satogata, T., Sborlini, G., Schram, M., Shih, D., Singh, J., Singh, R., Siodmok, A., Stone, P., Stevens, J., Suarez, L., Suresh, K., Tawfik, A. -N., Acosta, F. Torales, Tran, N., Trotta, R., Twagirayezu, F. J., Tyson, R., Volkova, S., Vossen, A., Walter, E., Whiteson, D., Williams, M., Wu, S., Zachariou, N., Zurita, P.
The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at
Externí odkaz:
http://arxiv.org/abs/2307.08593