Zobrazeno 1 - 10
of 650
pro vyhledávání: '"PAPPAS, Nikolaos"'
Autor:
Feng, Yu, Htut, Phu Mon, Qi, Zheng, Xiao, Wei, Mager, Manuel, Pappas, Nikolaos, Halder, Kishaloy, Li, Yang, Benajiba, Yassine, Roth, Dan
Quantifying the uncertainty in the factual parametric knowledge of Large Language Models (LLMs), especially in a black-box setting, poses a significant challenge. Existing methods, which gauge a model's uncertainty through evaluating self-consistency
Externí odkaz:
http://arxiv.org/abs/2412.09572
Using multiple sensors to update the status process of interest is promising in improving the information freshness. The unordered arrival of status updates at the monitor end poses a significant challenge in analyzing the timeliness performance of p
Externí odkaz:
http://arxiv.org/abs/2412.08277
We study the age of information (AoI) in a random access network consisting of multiple source-destination pairs, where each source node is empowered by energy harvesting capability. Every source node transmits a sequence of data packets to its desti
Externí odkaz:
http://arxiv.org/abs/2412.01192
Autor:
Mishra, Anup, Pappas, Nikolaos, Stefanović, Čedomir, Ayan, Onur, An, Xueli, Wu, Yiqun, Popovski, Petar, Leyva-Mayorga, Israel
Achieving a flexible and efficient sharing of wireless resources among a wide range of novel applications and services is one of the major goals of the sixth-generation of mobile systems (6G). Accordingly, this work investigates the performance of a
Externí odkaz:
http://arxiv.org/abs/2411.13192
We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use t
Externí odkaz:
http://arxiv.org/abs/2411.08413
Autor:
Shahriar, Sadat, Qi, Zheng, Pappas, Nikolaos, Doss, Srikanth, Sunkara, Monica, Halder, Kishaloy, Mager, Manuel, Benajiba, Yassine
Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require ful
Externí odkaz:
http://arxiv.org/abs/2410.19206
We study the real-time remote tracking of a two-state Markov process by an energy harvesting source. The source decides whether to transmit over an unreliable channel based on the state. We formulate this scenario as a Markov decision process (MDP) t
Externí odkaz:
http://arxiv.org/abs/2410.11521
Autor:
Liu, Qin, Shang, Chao, Liu, Ling, Pappas, Nikolaos, Ma, Jie, John, Neha Anna, Doss, Srikanth, Marquez, Lluis, Ballesteros, Miguel, Benajiba, Yassine
The safety alignment ability of Vision-Language Models (VLMs) is prone to be degraded by the integration of the vision module compared to its LLM backbone. We investigate this phenomenon, dubbed as ''safety alignment degradation'' in this paper, and
Externí odkaz:
http://arxiv.org/abs/2410.09047
Autor:
Luo, Jiping, Pappas, Nikolaos
This paper considers the semantics-aware remote state estimation of an asymmetric Markov chain with prioritized states. Due to resource constraints, the sensor needs to trade between estimation quality and communication cost. The aim is to exploit th
Externí odkaz:
http://arxiv.org/abs/2410.03637
Diffusion models have been extensively utilized in AI-generated content (AIGC) in recent years, thanks to the superior generation capabilities. Combining with semantic communications, diffusion models are used for tasks such as denoising, data recons
Externí odkaz:
http://arxiv.org/abs/2407.18468