Zobrazeno 1 - 10
of 339
pro vyhledávání: '"Segarra, Santiago"'
To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog metrics, since they introduce no additional time step-wise signaling
Externí odkaz:
http://arxiv.org/abs/2407.09753
We study the problem of path planning with soft homology constraints on a surface topologically equivalent to a disk with punctures. Specifically, we propose an algorithm, named $\Hstar$, for the efficient computation of a path homologous to a user-p
Externí odkaz:
http://arxiv.org/abs/2406.19551
Score distillation sampling has been pivotal for integrating diffusion models into generation of complex visuals. Despite impressive results it suffers from mode collapse and lack of diversity. To cope with this challenge, we leverage the gradient fl
Externí odkaz:
http://arxiv.org/abs/2406.16683
We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes. Many real-world models exhibit unfair discriminatory behavior due to biases in data. Such discrimination is known to be exacerbated when
Externí odkaz:
http://arxiv.org/abs/2406.09513
We consider fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subpopulations of nodes may not share or receive information equitably. We
Externí odkaz:
http://arxiv.org/abs/2403.15591
Repetitive DNA (repeats) poses significant challenges for accurate and efficient genome assembly and sequence alignment. This is particularly true for metagenomic data, where genome dynamics such as horizontal gene transfer, gene duplication, and gen
Externí odkaz:
http://arxiv.org/abs/2402.09381
We propose a novel algorithm for the support estimation of partially known Gaussian graphical models that incorporates prior information about the underlying graph. In contrast to classical approaches that provide a point estimate based on a maximum
Externí odkaz:
http://arxiv.org/abs/2401.14340
We propose a learning-based framework for efficient power allocation in ad hoc interference networks under episodic constraints. The problem of optimal power allocation -- for maximizing a given network utility metric -- under instantaneous constrain
Externí odkaz:
http://arxiv.org/abs/2401.10297
Computational offloading has become an enabling component for edge intelligence in mobile and smart devices. Existing offloading schemes mainly focus on mobile devices and servers, while ignoring the potential network congestion caused by tasks from
Externí odkaz:
http://arxiv.org/abs/2312.02471
Autor:
Namyar, Pooria, Arzani, Behnaz, Beckett, Ryan, Segarra, Santiago, Raj, Himanshu, Krishnaswamy, Umesh, Govindan, Ramesh, Kandula, Srikanth
Production systems use heuristics because they are faster or scale better than their optimal counterparts. Yet, practitioners are often unaware of the performance gap between a heuristic and the optimum or between two heuristics in realistic scenario
Externí odkaz:
http://arxiv.org/abs/2311.12779