Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Wu, Shaoen"'
Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs). However, to extend SNNs to a Federated
Externí odkaz:
http://arxiv.org/abs/2409.12769
In an era where the Internet of Things (IoT) intersects increasingly with generative Artificial Intelligence (AI), this article scrutinizes the emergent security risks inherent in this integration. We explore how generative AI drives innovation in Io
Externí odkaz:
http://arxiv.org/abs/2404.00139
Reimplementing solutions to previously solved software engineering problems is not only inefficient but also introduces inadequate and error-prone code. Many existing methods achieve impressive performance on this issue by using autoregressive text-g
Externí odkaz:
http://arxiv.org/abs/2210.00328
Autor:
Ziems, Noah, Wu, Shaoen
Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades. Traditional code analysis methods have been proposed, but are often ineffective and inefficient. In this work, we model software vuln
Externí odkaz:
http://arxiv.org/abs/2105.02388
Primary Hyperparathyroidism(PHPT) is a relatively common disease, affecting about one in every 1,000 adults. However, screening for PHPT can be difficult, meaning it often goes undiagnosed for long periods of time. While looking at specific blood tes
Externí odkaz:
http://arxiv.org/abs/2105.02386
Most smart systems such as smart home and smart health response to human's locations and activities. However, traditional solutions are either require wearable sensors or lead to leaking privacy. This work proposes an ambient radar solution which is
Externí odkaz:
http://arxiv.org/abs/1812.07099
Akademický článek
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In-band full duplex wireless is of utmost interest to future wireless communication and networking due to great potentials of spectrum efficiency. IBFD wireless, however, is throttled by its key challenge, namely self-interference. Therefore, effecti
Externí odkaz:
http://arxiv.org/abs/1811.01498
Deep imitation learning enables robots to learn from expert demonstrations to perform tasks such as lane following or obstacle avoidance. However, in the traditional imitation learning framework, one model only learns one task, and thus it lacks of t
Externí odkaz:
http://arxiv.org/abs/1808.04503
Imitation learning holds the promise to address challenging robotic tasks such as autonomous navigation. It however requires a human supervisor to oversee the training process and send correct control commands to robots without feedback, which is alw
Externí odkaz:
http://arxiv.org/abs/1709.07911