Zobrazeno 1 - 10
of 40 246
pro vyhledávání: '"A. Baris"'
Autor:
C. Varone, G. Carbone, A. Baris, M. C. Caciolli, S. Fabozzi, C. Fortunato, I. Gaudiosi, S. Giallini, M. Mancini, L. Paolella, M. Simionato, P. Sirianni, R. L. Spacagna, F. Stigliano, D. Tentori, L. Martelli, G. Modoni, M. Moscatelli
Publikováno v:
Natural Hazards and Earth System Sciences, Vol 23, Pp 1371-1382 (2023)
In 2012, the Emilia-Romagna region (Italy) was struck by a seismic crisis characterized by two main shocks (ML 5.9 and 5.8) which triggered relevant liquefaction events. Terre del Reno is one of the municipalities that experienced the most extensive
Externí odkaz:
https://doaj.org/article/efb0a0da9672435c8340ed1b033e6789
Autor:
Nath, Vishwesh, Li, Wenqi, Yang, Dong, Myronenko, Andriy, Zheng, Mingxin, Lu, Yao, Liu, Zhijian, Yin, Hongxu, Law, Yee Man, Tang, Yucheng, Guo, Pengfei, Zhao, Can, Xu, Ziyue, He, Yufan, Heinrich, Greg, Aylward, Stephen, Edgar, Marc, Zephyr, Michael, Molchanov, Pavlo, Turkbey, Baris, Roth, Holger, Xu, Daguang
Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is essential. In traditional computer vision tasks, creative or approximate a
Externí odkaz:
http://arxiv.org/abs/2411.12915
Autor:
Yao, Xing, Yu, Runxuan, Hu, Dewei, Yang, Hao, Lou, Ange, Wang, Jiacheng, Lu, Daiwei, Arenas, Gabriel, Oguz, Baris, Pouch, Alison, Schwartz, Nadav, Byram, Brett C, Oguz, Ipek
Ultrasound (US) image stitching can expand the field-of-view (FOV) by combining multiple US images from varied probe positions. However, registering US images with only partially overlapping anatomical contents is a challenging task. In this work, we
Externí odkaz:
http://arxiv.org/abs/2411.06750
Autor:
Ye, Haojie, Xia, Yuchen, Chen, Yuhan, Chen, Kuan-Yu, Yuan, Yichao, Deng, Shuwen, Kasikci, Baris, Mudge, Trevor, Talati, Nishil
Oblivious RAM (ORAM) hides the memory access patterns, enhancing data privacy by preventing attackers from discovering sensitive information based on the sequence of memory accesses. The performance of ORAM is often limited by its inherent trade-off
Externí odkaz:
http://arxiv.org/abs/2411.05400
This paper presents a novel reinforcement learning framework for trajectory tracking of unmanned aerial vehicles in cluttered environments using a dual-agent architecture. Traditional optimization methods for trajectory tracking face significant comp
Externí odkaz:
http://arxiv.org/abs/2410.23571
Autor:
Srba, Ivan, Razuvayevskaya, Olesya, Leite, João A., Moro, Robert, Schlicht, Ipek Baris, Tonelli, Sara, García, Francisco Moreno, Lottmann, Santiago Barrio, Teyssou, Denis, Porcellini, Valentin, Scarton, Carolina, Bontcheva, Kalina, Bielikova, Maria
In the current era of social media and generative AI, an ability to automatically assess the credibility of online social media content is of tremendous importance. Credibility assessment is fundamentally based on aggregating credibility signals, whi
Externí odkaz:
http://arxiv.org/abs/2410.21360
Autor:
Çağatan, Ömer Veysel, Akgün, Barış
In this study, we investigate the effect of SSL objective modifications within the SPR framework, focusing on specific adjustments such as terminal state masking and prioritized replay weighting, which were not explicitly addressed in the original de
Externí odkaz:
http://arxiv.org/abs/2410.17428
We study a federated version of multi-objective optimization (MOO), where a single model is trained to optimize multiple objective functions. MOO has been extensively studied in the centralized setting but is less explored in federated or distributed
Externí odkaz:
http://arxiv.org/abs/2410.16398
Publikováno v:
MICCAI TGI3 2024
Microscopic examination of slides prepared from tissue samples is the primary tool for detecting and classifying cancerous lesions, a process that is time-consuming and requires the expertise of experienced pathologists. Recent advances in deep learn
Externí odkaz:
http://arxiv.org/abs/2410.09818
Graph embeddings play a critical role in graph representation learning, allowing machine learning models to explore and interpret graph-structured data. However, existing methods often rely on opaque, high-dimensional embeddings, limiting interpretab
Externí odkaz:
http://arxiv.org/abs/2410.01778