Tootfinder

Interpretable Kernels
Patrick J. F. Groenen, Michael Greenacre
https://arxiv.org/abs/2508.15932 https://arxiv.org/pdf/2508.15932

Interpretable Kernels
The use of kernels for nonlinear prediction is widespread in machine learning. They have been popularized in support vector machines and used in kernel ridge regression, amongst others. Kernel methods share three aspects. First, instead of the original matrix of predictor variables or features, each observation is mapped into an enlarged feature space. Second, a ridge penalty term is used to shrink the coefficients on the features in the enlarged feature space. Third, the solution is not obtain…

The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs
B. Paroli, F. Borghi, M. A. C. Potenza, P. Milani
https://arxiv.org/abs/2506.19642

The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs
Threshold logic gates (TLGs) have been proposed as artificial counterparts of biological neurons with classification capabilities based on a linear predictor function combining a set of weights with the feature vector. The linearity of TLGs limits their classification capabilities requiring the use of networks for the accomplishment of complex tasks. A generalization of the TLG model called receptron, characterized by input-dependent weight functions allows for a significant enhancement of clas…

Analyzing PDFs like Binaries: Adversarially Robust PDF Malware Analysis via Intermediate Representation and Language Model
Side Liu, Jiang Ming, Guodong Zhou, Xinyi Liu, Jianming Fu, Guojun Peng
https://arxiv.org/abs/2506.17162

Analyzing PDFs like Binaries: Adversarially Robust PDF Malware Analysis via Intermediate Representation and Language Model
Malicious PDF files have emerged as a persistent threat and become a popular attack vector in web-based attacks. While machine learning-based PDF malware classifiers have shown promise, these classifiers are often susceptible to adversarial attacks, undermining their reliability. To address this issue, recent studies have aimed to enhance the robustness of PDF classifiers. Despite these efforts, the feature engineering underlying these studies remains outdated. Consequently, even with the appli…

Fundamental limits of distributed covariance matrix estimation via a conditional strong data processing inequality
Mohammad Reza Rahmani, Mohammad Hossein Yassaee, Mohammad Reza Aref
https://arxiv.org/abs/2507.16953

Fundamental limits of distributed covariance matrix estimation via a conditional strong data processing inequality
Estimating high-dimensional covariance matrices is a key task across many fields. This paper explores the theoretical limits of distributed covariance estimation in a feature-split setting, where communication between agents is constrained. Specifically, we study a scenario in which multiple agents each observe different components of i.i.d. samples drawn from a sub-Gaussian random vector. A central server seeks to estimate the complete covariance matrix using a limited number of bits communica…

Pseudodifferential Weyl calculus on vector bundles
Lars Andersson, Benjamin Moser, Marius A. Oancea, Claudio F. Paganini, Gabriel Schmid
https://arxiv.org/abs/2507.11965

Pseudodifferential Weyl calculus on vector bundles
We develop a geometric framework for Weyl quantization on pseudo-Riemannian manifolds, in which pseudodifferential operators act on sections of vector bundles equipped with arbitrary connections. We construct the associated star product and compute its semiclassical expansion up to third order in the expansion parameter. A central feature of our approach is a one-to-one correspondence between formally self-adjoint symbols and formally self-adjoint operators, extending known results from flat sp…

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Yoon Huh, Bumjun Kim, Wan Choi
https://arxiv.org/abs/2508.03248 https://

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Recent advancements in semantic communication have primarily focused on image transmission, where neural network (NN)-based joint source-channel coding (JSCC) modules play a central role. However, such systems often experience semantic communication errors due to mismatched knowledge bases between users and performance degradation from outdated models, necessitating regular model updates. To address these challenges in vector quantization (VQ)-based image semantic communication systems, we prop…

Decay estimates of wave equations in un-isotropic media
Sergiu Klainerman, Xuecheng Wang
https://arxiv.org/abs/2508.12049 https://arxiv.org/pdf/2508.12049

Decay estimates of wave equations in un-isotropic media
We prove decay estimates for solutions to non-isotropic linear systems of wave equations. The defining feature of these estimates is that they depend only on the commutation properties of the system with the scaling vector field. As application we give two surprisingly simple proofs for small data global regularity results non-isotropic systems of wave equations in $\mathbb{R}^{1+3}$ with cubic semilinear nonlinearities. We hope that the techniques presented here are relevant for the more diffi…

Context-Aware Search and Retrieval Over Erasure Channels
Sara Ghasvarianjahromi, Yauhen Yakimenka, J\"org Kliewer
https://arxiv.org/abs/2507.11894 htt…

Context-Aware Search and Retrieval Over Erasure Channels
This paper introduces and analyzes a search and retrieval model that adopts key semantic communication principles from retrieval-augmented generation. We specifically present an information-theoretic analysis of a remote document retrieval system operating over a symbol erasure channel. The proposed model encodes the feature vector of a query, derived from term-frequency weights of a language corpus by using a repetition code with an adaptive rate dependent on the contextual importance of the t…

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 …

Phenomenology of two-photon interaction at high energies: accessing dilute and high parton density of the photon structure
G. Zardo Becker
https://arxiv.org/abs/2508.01043 https…

Phenomenology of two-photon interaction at high energies: accessing dilute and high parton density of the photon structure
In this work, gamma-gamma interactions in electron-positron collisions are studied across both low- and high-energy regimes. The analysis includes contributions from the Vector Meson Dominance (VMD) model (via Reggeon exchange), the Quark Parton Model (via box diagrams), and the gluonic component (described using the dipole formalism), which becomes dominant at high energies. A key feature of the dipole picture is that a photon can fluctuate into a quark-antiquark (qqbar) pair, forming a color …

Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance
Chi-Sheng Chen, Aidan Hung-Wen Tsai
https://arxiv.org/abs/2508.02685 https://

Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance
The rise of decentralized finance (DeFi) has created a growing demand for accurate yield and performance forecasting to guide liquidity allocation strategies. In this study, we benchmark six models, XGBoost, Random Forest, LSTM, Transformer, quantum neural networks (QNN), and quantum support vector machines with quantum feature maps (QSVM-QNN), on one year of historical data from 28 Curve Finance pools. We evaluate model performance on test MAE, RMSE, and directional accuracy. Our results show …

Bounds on the Excess Minimum Risk via Generalized Information Divergence Measures
Ananya Omanwar, Fady Alajaji, Tam\'as Linder
https://arxiv.org/abs/2505.24117

Bounds on the Excess Minimum Risk via Generalized Information Divergence Measures
Given finite-dimensional random vectors $Y$, $X$, and $Z$ that form a Markov chain in that order (i.e., $Y \to X \to Z$), we derive upper bounds on the excess minimum risk using generalized information divergence measures. Here, $Y$ is a target vector to be estimated from an observed feature vector $X$ or its stochastically degraded version $Z$. The excess minimum risk is defined as the difference between the minimum expected loss in estimating $Y$ from $X$ and from $Z$. We present a family of …

This https://arxiv.org/abs/2411.16666 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors
We introduce CatNet, an algorithm that effectively controls False Discovery Rate (FDR) and selects significant features in LSTM. CatNet employs the derivative of SHAP values to quantify the feature importance, and constructs a vector-formed mirror statistic for FDR control with the Gaussian Mirror algorithm. To avoid instability due to nonlinear or temporal correlations among features, we also propose a new kernel-based independence measure. CatNet performs robustly on different model settings …

BS-1-to-N: Diffusion-Based Environment-Aware Cross-BS Channel Knowledge Map Generation for Cell-Free Networks
Zhuoyin Dai, Di Wu, Yong Zeng, Xiaoli Xu, Xinyi Wang, Zesong Fei
https://arxiv.org/abs/2507.23236

BS-1-to-N: Diffusion-Based Environment-Aware Cross-BS Channel Knowledge Map Generation for Cell-Free Networks
Channel knowledge map (CKM) inference across base stations (BSs) is the key to achieving efficient environmentaware communications. This paper proposes an environmentaware cross-BS CKM inference method called BS-1-to-N based on the generative diffusion model. To this end, we first design the BS location embedding (BSLE) method tailored for cross-BS CKM inference to embed BS location information in the feature vector of CKM. Further, we utilize the cross- and self-attention mechanism for the pro…