Tootfinder

‘Indian democracy fought back’: Modi humbled as opposition gains ground | India elections 2024 | The Guardian
https://www.theguardian.com/world/article/2024/jun/09/indian-elections-democracy-modi-opposition-gains-anal…

14% for Reform! Jesus.

Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
Kangwei Qi, Qiong Wu, Pingyi Fan, Nan Cheng, Qiang Fan, Jiangzhou Wang
https://arxiv.org/abs/2406.11318 https://arxiv.org/pdf/2406.11318
arXiv:2406.11318v1 Announce Type: new
Abstract: Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices. However, obstacles such as buildings may degrade the communications and incur communication interruptions, and thus the vehicle may not meet the requirement for task offloading. Reconfigurable intelligent surfaces (RIS) is introduced to support vehicle communication and provide an alternative communication path. The system performance can be improved by flexibly adjusting the phase-shift of the RIS. For RIS-assisted VEC system where tasks arrive randomly, we design a control scheme that considers offloading power, local power allocation and phase-shift optimization. To solve this non-convex problem, we propose a new deep reinforcement learning (DRL) framework that employs modified multi-agent deep deterministic policy gradient (MADDPG) approach to optimize the power allocation for vehicle users (VUs) and block coordinate descent (BCD) algorithm to optimize the phase-shift of the RIS. Simulation results show that our proposed scheme outperforms the centralized deep deterministic policy gradient (DDPG) scheme and random scheme.

Corpus Poisoning via Approximate Greedy Gradient Descent
Jinyan Su, John X. Morris, Preslav Nakov, Claire Cardie
https://arxiv.org/abs/2406.05087 https://<…

Corpus Poisoning via Approximate Greedy Gradient Descent
Dense retrievers are widely used in information retrieval and have also been successfully extended to other knowledge intensive areas such as language models, e.g., Retrieval-Augmented Generation (RAG) systems. Unfortunately, they have recently been shown to be vulnerable to corpus poisoning attacks in which a malicious user injects a small fraction of adversarial passages into the retrieval corpus to trick the system into returning these passages among the top-ranked results for a broad set of…