Tootfinder

Neural Estimation of Information Leakage for Secure Communication System Design
Darius S. Heerklotz, Ingo Schroeder, Pin-Hsun Lin, Christian Deppe, Eduard A. Jorswieck
https://arxiv.org/abs/2508.05176 …

Neural Estimation of Information Leakage for Secure Communication System Design
Underestimating the leakage can compromise secrecy, while overestimating it may lead to inefficient system design. Therefore, a reliable leakage estimator is essential. Neural network-based estimators provide a data-driven way to estimate mutual information without requiring full knowledge of the channel or source distributions. In this work, we aim to scale the blocklength of a wiretap code such that the estimator can still feasibly operate. We propose an improved mutual information estimator …

Robust MIMO Semantic Communication with Imperfect CSI via Knowledge Distillation
Mingze Gong, Shuoyao Wang, Shijian Gao, Jia Yan, Suzhi Bi
https://arxiv.org/abs/2509.04005 https…

Robust MIMO Semantic Communication with Imperfect CSI via Knowledge Distillation
Semantic communication (SemComm) has emerged as a new communication paradigm. To enhance efficiency, multiple-input-multiple-output (MIMO) technology has been further integrated into SemComm systems. However, existing MIMO SemComm systems assume perfect channel matrix estimation for channel-adaptive joint source-channel coding, which is impractical due to hardware and pilot overhead constraints. In this paper, we propose a semantic image transmission system with channel matrix and channel noise…

Higher-genus multiple zeta values
Konstantin Baune, Johannes Broedel, Egor Im, Zhexian Ji, Yannis Moeckli
https://arxiv.org/abs/2507.21765 https://arxiv.or…

Higher-genus multiple zeta values
Multiple zeta values arise as special values of polylogarithms defined on Riemann surfaces of various genera. Building on the vast knowledge for classical and elliptic multiple zeta values, we explore a canonical extension of the formalism to Riemann surfaces of higher genera, which yields higher-genus multiple zeta values. We provide a regularization prescription for higher-genus polylogarithms, which we extend to higher-genus multiple zeta values. Our regularization uses the Schottky uniformi…

Adaptive Divide and Conquer with Two Rounds of Communication
Niladri Kal, Botond Szab\'o, Rajarshi Guhaniyogi, Natesh Pillai, Debdeep Pati
https://arxiv.org/abs/2508.17073 h…

Adaptive Divide and Conquer with Two Rounds of Communication
We introduce a two-round adaptive communication strategy that enables rate-optimal estimation in the white noise model without requiring prior knowledge of the underlying smoothness. In the first round, local machines send summary statistics using $(\log_2(n))^2$ bits to enable the central machine to select the tuning parameters of the procedure. In the second round, another set of statistics are transmitted using optimal number of bits, enabling the central machine to aggregate and produce a f…

LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning
Jelle Luijkx, Runyu Ma, Zlatan Ajanovi\'c, Jens Kober
https://arxiv.org/abs/2509.16615 https://…

LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning
Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive exploration of large state-action spaces. Recent methods leverage the commonsense knowledge and reasoning abilities of large language models (LLMs) to guide exploration toward more meaningful states. However, LLMs can produce plans that are semantically plausible yet physically infeasible, yielding unreliable behavior. We introduce LLM-TALE, a framewo…

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
Ahmed Mujtaba, Gleb Radchenko, Radu Prodan, Marc Masana
https://arxiv.org/abs/2508.14769

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
Federated distillation has emerged as a promising collaborative machine learning approach, offering enhanced privacy protection and reduced communication compared to traditional federated learning by exchanging model outputs (soft logits) rather than full model parameters. However, existing methods employ complex selective knowledge-sharing strategies that require clients to identify in-distribution proxy data through computationally expensive statistical density ratio estimators. Additionally,…

Measuring Implicit Spatial Coordination in Teams: Effects on Collective Intelligence and Performance
Thuy Ngoc Nguyen, Anita Williams Woolley, Cleotilde Gonzalez
https://arxiv.org/abs/2509.09314

Measuring Implicit Spatial Coordination in Teams: Effects on Collective Intelligence and Performance
Coordinated teamwork is essential in fast-paced decision-making environments that require dynamic adaptation, often without an opportunity for explicit communication. Although implicit coordination has been extensively considered in the existing literature, the majority of work has focused on co-located, synchronous teamwork (such as sports teams) or, in distributed teams, primarily on coordination of knowledge work. However, many teams (firefighters, military, law enforcement, emergency respon…

Achieving Robust Channel Estimation Neural Networks by Designed Training Data
Dianxin Luan, John Thompson
https://arxiv.org/abs/2507.12630 https://

Achieving Robust Channel Estimation Neural Networks by Designed Training Data
Channel estimation is crucial in cognitive communications, as it enables intelligent spectrum sensing and adaptive transmission by providing accurate information about the current channel state. However, in many papers neural networks are frequently tested by training and testing on one example channel or similar channels. This is because data-driven methods often degrade on new data which they are not trained on, as they cannot extrapolate their training knowledge. This is despite the fact phy…