Tootfinder

Improved Byzantine Agreement under an Adaptive Adversary
Fabien Dufoulon, Gopal Pandurangan
https://arxiv.org/abs/2506.04919 https://…

Improved Byzantine Agreement under an Adaptive Adversary
Byzantine agreement is a fundamental problem in fault-tolerant distributed computing that has been studied intensively for the last four decades. Much of the research has focused on a static Byzantine adversary, where the adversary is constrained to choose the Byzantine nodes in advance of the protocol's execution. This work focuses on the harder case of an adaptive Byzantine adversary that can choose the Byzantine nodes \emph{adaptively} based on the protocol's execution. While efficient $O(\l…

Grey Rhino Warning: IPv6 is Becoming Fertile Ground for Reflection Amplification Attacks
Ling Hu, Tao Yang, Yu Pang, Bingnan Hou, Zhiping Cai, Bo Yu
https://arxiv.org/abs/2506.04768

Grey Rhino Warning: IPv6 is Becoming Fertile Ground for Reflection Amplification Attacks
Distributed Denial-of-Service (DDoS) attacks represent a cost-effective and potent threat to network stability. While extensively studied in IPv4 networks, DDoS implications in IPv6 remain underexplored. The vast IPv6 address space renders brute-force scanning and amplifier testing for all active addresses impractical. Innovatively, this work investigates AS-level vulnerabilities to reflection amplification attacks in IPv6. One prerequisite for amplification presence is that it is located in …

This https://arxiv.org/abs/2503.04091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Generalization in Federated Learning: A Conditional Mutual Information Framework
Federated learning (FL) is a widely adopted privacy-preserving distributed learning framework, yet its generalization performance remains less explored compared to centralized learning. In FL, the generalization error consists of two components: the out-of-sample gap, which measures the gap between the empirical and true risk for participating clients, and the participation gap, which quantifies the risk difference between participating and non-participating clients. In this work, we apply an i…

Robust Distributed Phase Retrieval for Multi-View Compressive Networked Sensing With Outliers
Ming-Hsun Yang
https://arxiv.org/abs/2505.24651 https://

Robust Distributed Phase Retrieval for Multi-View Compressive Networked Sensing With Outliers
This work examines the multi-view compressive phase retrieval problem in a distributed sensor network, where each sensor device, limited by storage and sensing capabilities, can access only intensity measurements from an unknown part of the global sparse vector. The goal is to enable each sensor to recover its observable sparse signal when measurements are corrupted by outliers. To achieve reliable local signal recovery with limited data access, we propose a distributed reconstruction algorithm…

This https://arxiv.org/abs/2309.03678 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Fully Onboard SLAM for Distributed Mapping with a Swarm of Nano-Drones
The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system ba…

CityLens: Benchmarking Large Language-Vision Models for Urban Socioeconomic Sensing
Tianhui Liu, Jie Feng, Hetian Pang, Xin Zhang, Tianjian Ouyang, Zhiyuan Zhang, Yong Li
https://arxiv.org/abs/2506.00530

CityLens: Benchmarking Large Language-Vision Models for Urban Socioeconomic Sensing
Understanding urban socioeconomic conditions through visual data is a challenging yet essential task for sustainable urban development and policy planning. In this work, we introduce $\textbf{CityLens}$, a comprehensive benchmark designed to evaluate the capabilities of large language-vision models (LLVMs) in predicting socioeconomic indicators from satellite and street view imagery. We construct a multi-modal dataset covering a total of 17 globally distributed cities, spanning 6 key domains: e…

Practical Short-Length Coding Schemes for Binary Distributed Hypothesis Testing
Ismaila Salihou Adamou, Elsa Dupraz, Reza Asvadi, Tad Matsumoto
https://arxiv.org/abs/2506.01747

Practical Short-Length Coding Schemes for Binary Distributed Hypothesis Testing
This paper addresses the design of practical shortlength coding schemes for Distributed Hypothesis Testing (DHT). While most prior work on DHT has focused on informationtheoretic analyses, deriving bounds on Type-II error exponents via achievability schemes based on quantization and quantizebinning, the practical implementation of DHT coding schemes has remained largely unexplored. Moreover, existing practical coding solutions for quantization and quantize-binning approaches were developed for …

Stochastic Preconditioning for Neural Field Optimization
Selena Ling, Merlin Nimier-David, Alec Jacobson, Nicholas Sharp
https://arxiv.org/abs/2505.20473 h…

Stochastic Preconditioning for Neural Field Optimization
Neural fields are a highly effective representation across visual computing. This work observes that fitting these fields is greatly improved by incorporating spatial stochasticity during training, and that this simple technique can replace or even outperform custom-designed hierarchies and frequency space constructions. The approach is formalized as implicitly operating on a blurred version of the field, evaluated in-expectation by sampling with Gaussian-distributed offsets. Querying the blurr…

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SZ Sequences: Binary-Based $(0, 2^q)$-Sequences
Abdalla G. M. Ahmed, Matt Pharr, Victor Ostromoukhov, Hui Huang
https://arxiv.org/abs/2505.20434 https://…

SZ Sequences: Binary-Based $(0, 2^q)$-Sequences
Low-discrepancy sequences have seen widespread adoption in computer graphics thanks to their superior convergence rates. Since rendering integrals often comprise products of lower-dimensional integrals, recent work has focused on developing sequences that are also well-distributed in lower-dimensional projections. To this end, we introduce a novel construction of binary-based (0, 4)-sequences; that is, progressive fully multi-stratified sequences of 4D points, and extend the idea to higher powe…