CybAI news

San Diego Supercomputer Center/Image courtesy SDSC) Some research universities excel in exploring intellectual possibilities and postulating abstract theories. Those terms aren’t likely to be associated with the University of California, San Diego’s new School of Computing, Information and Data Sciences (SCIDS), which is all about taking the power of data and computer science and applying it to solve problems in the real world. SCIDS was officially formed last month by the union of the San Diego Supercomputer Center (SDSC) and the Halıcıoğlu Data Science Institute (HDSI). As the fourth school on the rapidly expanding UC campus, SCIDS is generating quite…

Digital information has become instrumental to the modern world, evidenced by the increasingly large data centers built to hold and process it. However, electromagnetic interference (EMI) is an ever-present challenge that can result in many issues for devices, their components and the data those devices store.  The worst cases result in lost information, but even less severe instances bring problems such as corruption, performance degradation and hardware malfunctions. These realities mean relevant parties must take proactive measures. The most common one is to incorporate EMI protection. These options provide the necessary shielding to greatly reduce problems.  What Are the Main…

arXiv:2411.14496v1 Announce Type: new Abstract: The thesis proposes a generalized charging framework for multiple mobile chargers to maximize the network lifetime and ensure target coverage and connectivity in large scale WRSNs. Moreover, a multi-point charging model is leveraged to enhance charging efficiency, where the MC can charge multiple sensors simultaneously at each charging location. The thesis proposes an effective Decentralized Partially Observable Semi-Markov Decision Process (Dec POSMDP) model that promotes Mobile Chargers (MCs) cooperation and detects optimal charging locations based on realtime network information. Furthermore, the proposal allows reinforcement algorithms to be applied to different networks without requiring extensive retraining.…

arXiv:2411.14504v1 Announce Type: new Abstract: Night-to-Day translation (Night2Day) aims to achieve day-like vision for nighttime scenes. However, processing night images with complex degradations remains a significant challenge under unpaired conditions. Previous methods that uniformly mitigate these degradations have proven inadequate in simultaneously restoring daytime domain information and preserving underlying semantics. In this paper, we propose textbf{N2D3} (textbf{N}ight-to-textbf{D}ay via textbf{D}egradation textbf{D}isentanglement) to identify different degradation patterns in nighttime images. Specifically, our method comprises a degradation disentanglement module and a degradation-aware contrastive learning module. Firstly, we extract physical priors from a photometric model based on Kubelka-Munk theory. Then, guided by these physical…