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pro vyhledávání: '"P., Seema"'
Publikováno v:
Harvard Data Science Review, Vol 5, Iss 4 (2023)
Externí odkaz:
https://doaj.org/article/e8abb60a11e8449eba7ca9c9f6220120
The world faces increasing challenges related to climate change and shifting geopolitical situations. The COVID 19 pandemic clearly illustrated the need for resilience not just within civil engineering but also within social and economic systems. Whi
Externí odkaz:
http://arxiv.org/abs/2411.00682
Autor:
Aswani, Seema, Shetty, Sujala D.
Explainable AI (XAI) techniques for text summarization provide valuable understanding of how the summaries are generated. Recent studies have highlighted a major challenge in this area, known as the disagreement problem. This problem occurs when diff
Externí odkaz:
http://arxiv.org/abs/2410.18560
Autor:
Applegate, Travis, Brenny, Noah, Chen, Chunhui, Choudhury, Seema, Cochran, James, Kang, Shuaiyan, Khatri, Avinash, Kindo, Haruki, Lam, Tommy, Mitra, Sayan, Mubarak, Adil, Piilonen, Leo, Prell, Soeren, Veronesi, Michele
We have designed and commissioned a new readout board to detect photosensor signals from gas-bubbler panels to continuously monitor the gas flow through the resistive plate chambers of the $K_L^0$ and muon detector of Belle II. The gas flow measureme
Externí odkaz:
http://arxiv.org/abs/2410.10261
Akademický článek
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Autor:
Ai, Xian-Yue, Soldatov, Ivan, Oleschko, Leon, Seema, Müller, Martina, Karpitschka, Stefan, Schäfer, Rudolf, Goennenwein, Sebastian T. B.
Combining wide-field magneto-optical Kerr microscopy with a time-lapse analysis scheme allows investigating magnetization fluctuations with high spatial as well as temporal resolution. We here use this technique to study magnetization fluctuations in
Externí odkaz:
http://arxiv.org/abs/2409.15876
Autor:
Chen, Qingyu, Keenan, Tiarnan D L, Agron, Elvira, Allot, Alexis, Guan, Emily, Duong, Bryant, Elsawy, Amr, Hou, Benjamin, Xue, Cancan, Bhandari, Sanjeeb, Broadhead, Geoffrey, Cousineau-Krieger, Chantal, Davis, Ellen, Gensheimer, William G, Grasic, David, Gupta, Seema, Haddock, Luis, Konstantinou, Eleni, Lamba, Tania, Maiberger, Michele, Mantopoulos, Dimosthenis, Mehta, Mitul C, Nahri, Ayman G, AL-Nawaflh, Mutaz, Oshinsky, Arnold, Powell, Brittany E, Purt, Boonkit, Shin, Soo, Stiefel, Hillary, Thavikulwat, Alisa T, Wroblewski, Keith James, Chung, Tham Yih, Cheung, Chui Ming Gemmy, Cheng, Ching-Yu, Chew, Emily Y, Hribar, Michelle R., Chiang, Michael F., Lu, Zhiyong
Timely disease diagnosis is challenging due to increasing disease burdens and limited clinician availability. AI shows promise in diagnosis accuracy but faces real-world application issues due to insufficient validation in clinical workflows and dive
Externí odkaz:
http://arxiv.org/abs/2409.15087
In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifica
Externí odkaz:
http://arxiv.org/abs/2408.00108
Kniha
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Autor:
Harne, Sarthak, Choudhury, Monjoy Narayan, Rao, Madhav, Srikanth, TK, Mehrotra, Seema, Vashisht, Apoorva, Basu, Aarushi, Sodhi, Manjit
The limited availability of psychologists necessitates efficient identification of individuals requiring urgent mental healthcare. This study explores the use of Natural Language Processing (NLP) pipelines to analyze text data from online mental heal
Externí odkaz:
http://arxiv.org/abs/2406.00314