Zobrazeno 1 - 10
of 18 360
pro vyhledávání: '"Shiri, A."'
Autor:
Artstein-Avidan, Shiri, Putterman, Eli
The longstanding Godbersen's conjecture states that for any convex body $K \subset \mathbb R^n$ of volume $1$ and any $j \in \{0, \ldots, n\}$, the mixed volume $V_j = V(K[j], -K[n - j])$ is bounded by $\binom{n}{j}$, with equality if and only if $K$
Externí odkaz:
http://arxiv.org/abs/2412.05308
Autor:
Shiri, Fatemeh, Guo, Xiao-Yu, Far, Mona Golestan, Yu, Xin, Haffari, Gholamreza, Li, Yuan-Fang
Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to compreh
Externí odkaz:
http://arxiv.org/abs/2411.06048
Autor:
Collins, Jazmin, Jung, Crescentia, Jang, Yeonju, Montour, Danielle, Won, Andrea Stevenson, Azenkot, Shiri
Publikováno v:
ASSETS 2023
As social VR applications grow in popularity, blind and low vision users encounter continued accessibility barriers. Yet social VR, which enables multiple people to engage in the same virtual space, presents a unique opportunity to allow other people
Externí odkaz:
http://arxiv.org/abs/2410.21659
Autor:
Jung, Crescentia, Collins, Jazmin, Penuela, Ricardo E. Gonzalez, Segal, Jonathan Isaac, Won, Andrea Stevenson, Azenkot, Shiri
Publikováno v:
ASSETS 2024
Social VR has increased in popularity due to its affordances for rich, embodied, and nonverbal communication. However, nonverbal communication remains inaccessible for blind and low vision people in social VR. We designed accessible cues with audio a
Externí odkaz:
http://arxiv.org/abs/2410.21652
Autor:
Collins, Jazmin, Nicholson, Kaylah Myranda, Khadir, Yusuf, Won, Andrea Stevenson, Azenkot, Shiri
The rapid growth of virtual reality (VR) has led to increased use of social VR platforms for interaction. However, these platforms lack adequate features to support blind and low vision (BLV) users, posing significant challenges in navigation, visual
Externí odkaz:
http://arxiv.org/abs/2410.14058
Publikováno v:
Physica A, Statistical Mechanics and its Applications (2024), 130166
Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computati
Externí odkaz:
http://arxiv.org/abs/2410.11384
Autor:
Harzevili, Nima Shiri, Mohajer, Mohammad Mahdi, Shin, Jiho, Wei, Moshi, Uddin, Gias, Yang, Jinqiu, Wang, Junjie, Wang, Song, Ming, Zhen, Jiang, Nagappan, Nachiappan
Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and can lead to silent failures, incorrect results, or unexpected program
Externí odkaz:
http://arxiv.org/abs/2410.06440
Autor:
Chen, Liangyu, Fors, Simon Pettersson, Yan, Zixian, Ali, Anaida, Abad, Tahereh, Osman, Amr, Moschandreou, Eleftherios, Lienhard, Benjamin, Kosen, Sandoko, Li, Hang-Xi, Shiri, Daryoush, Liu, Tong, Hill, Stefan, Amin, Abdullah-Al, Rehammar, Robert, Dahiya, Mamta, Nylander, Andreas, Rommel, Marcus, Roudsari, Anita Fadavi, Caputo, Marco, Leif, Grönberg, Govenius, Joonas, Dobsicek, Miroslav, Giannelli, Michele Faucci, Kockum, Anton Frisk, Bylander, Jonas, Tancredi, Giovanna
The realization of fault-tolerant quantum computing requires the execution of quantum error-correction (QEC) schemes, to mitigate the fragile nature of qubits. In this context, to ensure the success of QEC, a protocol capable of implementing both qub
Externí odkaz:
http://arxiv.org/abs/2409.16748
We study the communication complexity of truthful combinatorial auctions, and in particular the case where valuations are either subadditive or single-minded, which we denote with $\mathsf{SubAdd}\cup\mathsf{SingleM}$. We show that for three bidders
Externí odkaz:
http://arxiv.org/abs/2409.08241
Autor:
Butts, Gavin, Emdad, Pegah, Lee, Jethro, Song, Shannon, Salavati, Chiman, Diaz, Willmar Sosa, Dori-Hacohen, Shiri, Murai, Fabricio
There have been growing concerns around high-stake applications that rely on models trained with biased data, which consequently produce biased predictions, often harming the most vulnerable. In particular, biased medical data could cause health-rela
Externí odkaz:
http://arxiv.org/abs/2409.07424