Zobrazeno 1 - 10
of 2 516
pro vyhledávání: '"Wu, Xinyu"'
We consider the problem of network stability in finite-buffer systems. We observe that finite buffer may affect stability even in simplest network structure, and we propose an ordinary differential equation (ODE) model to capture the queuing dynamics
Externí odkaz:
http://arxiv.org/abs/2411.03780
Autor:
Wu, Xinyu, Modiano, Eytan
We quantify the threat of network adversaries to inducing \emph{network overload} through \emph{routing attacks}, where a subset of network nodes are hijacked by an adversary. We develop routing attacks on the hijacked nodes for two objectives relate
Externí odkaz:
http://arxiv.org/abs/2411.03749
Autor:
Wu, Xinyu
Network overload occurs when the demand of network users exceeds the network capacity. The increasing gap between the growth of network demand and capacity, resulting from the surge in data-intensive machine learning applications and the slowdown in
Externí odkaz:
https://hdl.handle.net/1721.1/155364
We consider the problem of predicting power failure cascades due to branch failures. We propose a flow-free model based on graph neural networks that predicts grid states at every generation of a cascade process given an initial contingency and power
Externí odkaz:
http://arxiv.org/abs/2404.16134
Autor:
Reihanisaransari, Reza, Gajjela, Chalapathi Charan, Wu, Xinyu, Ishrak, Ragib, Corvigno, Sara, Zhong, Yanping, Liu, Jinsong, Sood, Anil K., Mayerich, David, Berisha, Sebastian, Reddy, Rohith
Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks
Externí odkaz:
http://arxiv.org/abs/2402.17960
This paper focuses on time-varying delayed stochastic differential systems with stochastically switching parameters formulated by a unified switching behavior combining a discrete adapted process and a Cox process. Unlike prior studies limited to sta
Externí odkaz:
http://arxiv.org/abs/2401.15252
We develop link rate control policies to minimize the queueing delay of packets in overloaded networks. We show that increasing link rates does not guarantee delay reduction during overload. We consider a fluid queueing model that facilitates explici
Externí odkaz:
http://arxiv.org/abs/2312.04054
Millimeter-wave (mmWave) radar is increasingly being considered as an alternative to optical sensors for robotic primitives like simultaneous localization and mapping (SLAM). While mmWave radar overcomes some limitations of optical sensors, such as o
Externí odkaz:
http://arxiv.org/abs/2311.11260
In this paper, a novel Multi-agent Reinforcement Learning (MARL) approach, Multi-Agent Continuous Dynamic Policy Gradient (MACDPP) was proposed to tackle the issues of limited capability and sample efficiency in various scenarios controlled by multip
Externí odkaz:
http://arxiv.org/abs/2309.14727
This paper addresses the prediction stability, prediction accuracy and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach dropout-based probabilistic ensembles with tra
Externí odkaz:
http://arxiv.org/abs/2309.11089