Tootfinder

This https://arxiv.org/abs/2505.17226 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
Federated Learning (FL) enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, who can send misleading updates to corrupt the global model. Traditional aggregation methods, such as simple averaging, are not robust to such attacks. More resilient approaches, like the Krum algorithm…

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
Qiming Zhu, Jialun Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Shing-Chi Cheung
https://arxiv.org/abs/2506.03535

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
Current research on large language models (LLMs) with retrieval-augmented code generation (RACG) mainly focuses on single-language settings, leaving cross-lingual effectiveness and security unexplored. Multi-lingual RACG systems are valuable for migrating code-bases across programming languages (PLs), yet face risks from error (e.g. adversarial data corruption) propagation in cross-lingual transfer. We construct a dataset spanning 13 PLs with nearly 14k instances to explore utility and robustne…

This https://arxiv.org/abs/2406.07186 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Information Aggregation with Costly Information Acquisition
We study information aggregation in a dynamic trading model with partially informed traders. Ostrovsky [2012] showed that 'separable' securities aggregate information in all equilibria, however, separability is not robust to small changes in the traders' private information. To remedy this problem, we enhance the model by allowing traders to acquire signals with cost $κ$, in every period. We show that '$κ$ separable securities' aggregate information and, as the cost decreases, nearly all secu…

This https://arxiv.org/abs/2310.14288 has been replaced.
link: https://scholar.google.com/scholar?q=a

Popular and Dominant Matchings with Uncertain, Multilayer and Aggregated Preferences
We study the Popular Matching problem in multiple models, where the preferences of the agents in the instance may change or may be unknown/uncertain. In particular, we study an Uncertainty model, where each agent has a possible set of preferences, a Multilayer model, where there are layers of preference profiles, a Robust model, where any agent may move some other agents up or down some places in his preference list and an Aggregated Preference model, where votes are summed over multiple instan…

An integrated multi-size collision model for flotation
Benedikt Tiedemann, Moritz Kreuseler, Jochen Fr\"ohlich
https://arxiv.org/abs/2506.03881 https:…

An integrated multi-size collision model for flotation
The accuracy obtained with CFD and process simulations of flotation critically depends on the quality and robustness of the underlying models for the non-resolved sub-processes. An important issue in flotation is the collision between particles and air bubbles. Many models have been developed, but their accuracy for applications in flotation is limited. In particular, the significant size difference between particles and bubbles and their intricate coupling to the turbulent flow field pose seve…

CSI2Vec: Towards a Universal CSI Feature Representation for Positioning and Channel Charting
Victoria Palhares, Sueda Taner, Christoph Studer
https://arxiv.org/abs/2506.05237

CSI2Vec: Towards a Universal CSI Feature Representation for Positioning and Channel Charting
Natural language processing techniques, such as Word2Vec, have demonstrated exceptional capabilities in capturing semantic and syntactic relationships of text through vector embeddings. Inspired by this technique, we propose CSI2Vec, a self-supervised framework for generating universal and robust channel state information (CSI) representations tailored to CSI-based positioning (POS) and channel charting (CC). CSI2Vec learns compact vector embeddings across various wireless scenarios, capturing …

CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather
Minghao Ning, Yufeng Yang, Keqi Shu, Shucheng Huang, Jiaming Zhong, Maryam Salehi, Mahdi Rahmani, Yukun Lu, Chen Sun, Aladdin Saleh, Ehsan Hashemi, Amir Khajepour
https://arxiv.org/abs/2507.02245

CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather
We present CoInfra, a large-scale cooperative infrastructure perception system and dataset designed to advance robust multi-agent perception under real-world and adverse weather conditions. The CoInfra system includes 14 fully synchronized sensor nodes, each equipped with dual RGB cameras and a LiDAR, deployed across a shared region and operating continuously to capture all traffic participants in real-time. A robust, delay-aware synchronization protocol and a scalable system architecture that …

Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?
Yuqian Huo, Jinbiao Wei, Christopher Kverne, Mayur Akewar, Janki Bhimani, Tirthak Patel
https://arxiv.org/abs/2507.01195

Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?
Transpilation, particularly noise-aware optimization, is widely regarded as essential for maximizing the performance of quantum circuits on superconducting quantum computers. The common wisdom is that each circuit should be transpiled using up-to-date noise calibration data to optimize fidelity. In this work, we revisit the necessity of frequent noise-adaptive transpilation, conducting an in-depth empirical study across five IBM 127-qubit quantum computers and 16 diverse quantum algorithms. Our…

A Practical and Secure Byzantine Robust Aggregator
De Zhang Lee, Aashish Kolluri, Prateek Saxena, Ee-Chien Chang
https://arxiv.org/abs/2506.23183 https://

A Practical and Secure Byzantine Robust Aggregator
In machine learning security, one is often faced with the problem of removing outliers from a given set of high-dimensional vectors when computing their average. For example, many variants of data poisoning attacks produce gradient vectors during training that are outliers in the distribution of clean gradients, which bias the computed average used to derive the ML model. Filtering them out before averaging serves as a generic defense strategy. Byzantine robust aggregation is an algorithmic pri…

This https://arxiv.org/abs/2505.12185 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

EVALOOP: Assessing LLM Robustness in Programming from a Self-consistency Perspective
Assessing the programming capabilities of Large Language Models (LLMs) is crucial for their effective use in software engineering. Current evaluations, however, predominantly measure the accuracy of generated code on static benchmarks, neglecting the critical aspect of model robustness during programming tasks. While adversarial attacks offer insights on model robustness, their effectiveness is limited and evaluation could be constrained. Current adversarial attack methods for robustness evalua…