Tootfinder

From latent factors to language: a user study on LLM-generated explanations for an inherently interpretable matrix-based recommender system
Maxime Manderlier, Fabian Lecron, Olivier Vu Thanh, Nicolas Gillis
https://arxiv.org/abs/2509.18980

Shilling Recommender Systems by Generating Side-feature-aware Fake User Profiles
Yuanrong Wang, Yingpeng Du
https://arxiv.org/abs/2509.17918 https://arxiv.…

Shilling Recommender Systems by Generating Side-feature-aware Fake User Profiles
Recommender systems (RS) greatly influence users' consumption decisions, making them attractive targets for malicious shilling attacks that inject fake user profiles to manipulate recommendations. Existing shilling methods can generate effective and stealthy fake profiles when training data only contain rating matrix, but they lack comprehensive solutions for scenarios where side features are present and utilized by the recommender. To address this gap, we extend the Leg-UP framework by enhanci…

A model free approach for continuous-time optimal tracking control with unknown user-define cost and constrained control input via advantage function
Duc Cuong Nguyen, Quang Huy Dao, Phuong Nam Dao
https://arxiv.org/abs/2509.16821

A model free approach for continuous-time optimal tracking control with unknown user-define cost and constrained control input via advantage function
This paper presents a pioneering approach to solving the linear quadratic regulation (LQR) and linear quadratic tracking (LQT) problems with constrained inputs using a novel off-policy continuous-time Q-learning framework. The proposed methodology leverages a novel concept of the Advantage function for linear continuous systems, enabling solutions to be obtained without the need for prior knowledge of the reward matrix weights, state resetting, or assuming the existence of a predefined admissib…

Distributed Batch Matrix Multiplication: Trade-Offs in Download Rate, Randomness, and Privacy
Amirhosein Morteza, Remi A. Chou
https://arxiv.org/abs/2509.15047 https://

Distributed Batch Matrix Multiplication: Trade-Offs in Download Rate, Randomness, and Privacy
We study the trade-off between communication rate and privacy for distributed batch matrix multiplication of two independent sequences of matrices $\bold{A}$ and $\bold{B}$ with uniformly distributed entries. In our setting, $\bold{B}$ is publicly accessible by all the servers while $\bold{A}$ must remain private. A user is interested in evaluating the product $\bold{AB}$ with the responses from the $k$ fastest servers. For a given parameter $α\in [0, 1]$, our privacy constraint must ensure th…

Core-elements Subsampling for Alternating Least Squares
Dunyao Xue, Mengyu Li, Cheng Meng, Jingyi Zhang
https://arxiv.org/abs/2509.18024 https://arxiv.org/…

Core-elements Subsampling for Alternating Least Squares
In this paper, we propose a novel element-wise subset selection method for the alternating least squares (ALS) algorithm, focusing on low-rank matrix factorization involving matrices with missing values, as commonly encountered in recommender systems. While ALS is widely used for providing personalized recommendations based on user-item interaction data, its high computational cost, stemming from repeated regression operations, poses significant challenges for large-scale datasets. To enhance t…

Privacy Preserving Inference of Personalized Content for Out of Matrix Users
Michael Sun, Tai Vu, Andrew Wang
https://arxiv.org/abs/2508.14905 https://arxi…

Privacy Preserving Inference of Personalized Content for Out of Matrix Users
Recommender systems for niche and dynamic communities face persistent challenges from data sparsity, cold start users and items, and privacy constraints. Traditional collaborative filtering and content-based approaches underperform in these settings, either requiring invasive user data or failing when preference histories are absent. We present DeepNaniNet, a deep neural recommendation framework that addresses these challenges through an inductive graph-based architecture combining user-item in…

MPS-JuliQAOA: User-friendly, Scalable MPS-based Simulation for Quantum Optimization
Sean Feeney, Reuben Tate, John Golden, Stephan Eidenbenz
https://arxiv.org/abs/2508.05883 htt…

MPS-JuliQAOA: User-friendly, Scalable MPS-based Simulation for Quantum Optimization
We present the MPS-JuliQAOA simulator, a user-friendly, open-source tool to simulate the Quantum Approximate Optimization Algorithm (QAOA) of any optimization problem that can be expressed as diagonal Hamiltonian. By leveraging Julia-language constructs and the ITensor package to implement a Matrix Product State (MPS) approach to simulating QAOA, MPS-Juli-QAOA effortlessly scales to 512 qubits and 20 simulation rounds on the standard de-facto benchmark 3-regular MaxCut QAOA problem. MPS-JuliQAO…

Optimizing Rate-CRB Performance for Beyond Diagonal Reconfigurable Intelligent Surface Enabled ISAC
Xiaoqi Zhang, Liang Liu, Shuowen Zhang, Weifeng Zhu, Haijun Zhang
https://arxiv.org/abs/2508.11295

Optimizing Rate-CRB Performance for Beyond Diagonal Reconfigurable Intelligent Surface Enabled ISAC
This letter considers a beyond diagonal reconfigurable intelligent surface (BD-RIS) aided integrated sensing and communication (ISAC) system, where the BD-RIS can help a multi-antenna base station (BS) serve multiple user equipments (UEs) and localize a target simultaneously. We formulate an optimization problem that designs the BS beamforming matrix and the BD-RIS scattering matrix to maximize UEs' sum rate subject to a localization Cramer-Rao bound (CRB) constraint and an additional unitary m…

On the Consistency and Performance of the Iterative Bayesian Update
Ehab ElSalamouny, Catuscia Palamidessi
https://arxiv.org/abs/2508.09980 https://arxiv.o…

On the Consistency and Performance of the Iterative Bayesian Update
For many social, scientific, and commercial purposes, it is often important to estimate the distribution of the users' data regarding a sensitive attribute, e.g., their ages, locations, etc. To allow this estimation while protecting the users' privacy, every user applies a local privacy protection mechanism that releases a noisy (sanitized) version of their original datum to the data collector; then the original distribution is estimated using one of the known methods, such as the matrix invers…

Spacetime Wavelet Method for Linear Boundary-Value Problems in Sylvester Matrix Equation Form
Cody D. Cochran, Karel Matous
https://arxiv.org/abs/2509.02720 https://

Spacetime Wavelet Method for Linear Boundary-Value Problems in Sylvester Matrix Equation Form
We present a high-order spacetime numerical method for discretizing and solving linear initial-boundary value problems using wavelet-based techniques with user-prescribed error estimates. The spacetime wavelet discretization yields a system of algebraic equations resulting in a Sylvester matrix equation. We solve this system with a Global Generalized Minimal Residual (GMRES) method in conjunction with a wavelet-based recursive algorithm to improve convergence. We perform rigorous verification s…

Audible Networks: Deconstructing and Manipulating Sounds with Deep Non-Negative Autoencoders
Juan Jos\'e Burred, Carmine-Emanuele Cella
https://arxiv.org/abs/2510.08816 http…

Audible Networks: Deconstructing and Manipulating Sounds with Deep Non-Negative Autoencoders
We propose the use of Non-Negative Autoencoders (NAEs) for sound deconstruction and user-guided manipulation of sounds for creative purposes. NAEs offer a versatile and scalable extension of traditional Non-Negative Matrix Factorization (NMF)-based approaches for interpretable audio decomposition. By enforcing non-negativity constraints through projected gradient descent, we obtain decompositions where internal weights and activations can be directly interpreted as spectral shapes and temporal …

Energy Efficiency Maximization for Movable Antenna-Enhanced MIMO Downlink System Based on S-CSI
Xintai Chen, Biqian Feng, Yongpeng Wu, Xiang-Gen Xia, Chengshan Xiao
https://arxiv.org/abs/2509.12036

Energy Efficiency Maximization for Movable Antenna-Enhanced MIMO Downlink System Based on S-CSI
This paper presents an innovative movable antenna (MA)-enhanced multi-user multiple-input multiple-output (MIMO) downlink system. We aim to maximize the energy efficiency (EE) under statistical channel state information (S-CSI) through a joint optimization of the precoding matrix and the antenna position vectors (APVs). To solve the resulting stochastic problem, we first resort to deterministic equivalent (DE) tecnology to formulate the deterministic minorizing function of the system EE and the…

Picking NPA constraints from a randomly sampled quantum moment matrix
G. Viola, A. Chaturvedi
https://arxiv.org/abs/2509.02309 https://arxiv.org/pdf/2509.0…

Picking NPA constraints from a randomly sampled quantum moment matrix
We describe a simple and flexible method for implementing semi-definite programming relaxations for bounding the set of quantum correlations. The method relies on obtaining equality constraints from randomly sampled moment matrices and hence allows the user to easily access the set of quantum behavior in diverse operational scenarios.

TeMFpy: a Python library for converting fermionic mean-field states into tensor networks
Simon H. Hille, Attila Szab\'o
https://arxiv.org/abs/2510.05227 https://

TeMFpy: a Python library for converting fermionic mean-field states into tensor networks
We introduce TeMFpy, a Python library for converting fermionic mean-field states to finite or infinite matrix product state (MPS) form. TeMFpy includes new, efficient, and easy-to-understand algorithms for both Slater determinants and Pfaffian states. Together with Gutzwiller projection, these also allow the user to build variational wave functions for various strongly correlated electron systems, such as quantum spin liquids. We present all implemented algorithms in detail and describe how the…

From Motifs to L\'evy Flights: Modeling Urban Mobility in Bogot\'a's Public Transport System
Juan F. Alay\'on-Mart\'inez, Alejandro P. Riascos
https://arxiv.org/abs/2510.02621

From Motifs to Lévy Flights: Modeling Urban Mobility in Bogotá's Public Transport System
In this paper, we study two years of access card validation records from Bogotá's multimodal public transport system, comprising over 2.3 billion trips across bus rapid transit, feeder buses, dual-service buses, and an aerial cable network. By reconstructing user trajectories as motifs, we identify recurrent mobility patterns that extend beyond simple round trips, enabling the construction of an integrated origin-destination (OD) matrix covering 2,828 urban zones. Similarity analysis using the…

Visualizing Quantum Circuits: State Vector Difference Highlighting and the Half-Matrix
Michael J. McGuffin, Jean-Marc Robert
https://arxiv.org/abs/2510.00895 https://

Visualizing Quantum Circuits: State Vector Difference Highlighting and the Half-Matrix
Existing graphical user interfaces for circuit simulators often show small visual summaries of the reduced state of each qubit, showing the probability, phase, purity, and/or Bloch sphere coordinates associated with each qubit. These necessarily provide an incomplete picture of the quantum state of the qubits, and can sometimes be confusing for students or newcomers to quantum computing. We contribute two novel visual approaches to provide more complete information about small circuits. First, …

Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs
Minda Zhao
https://arxiv.org/abs/2508.21259 https://arxiv.org/pdf/2508…

Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs
Recommender systems struggle to provide accurate suggestions to new users with limited interaction history, a challenge known as the cold-user problem. This paper proposes a reinforcement learning approach using Double and Dueling Deep Q-Networks (DQN) to dynamically learn user preferences from sparse feedback, enhancing recommendation accuracy without relying on sensitive demographic data. By integrating these advanced DQN variants with a matrix factorization model, we achieve superior perform…

Computing Linear Combinations of $\varphi$-Function Actions for Exponential Integrators
Awad H. Al-Mohy
https://arxiv.org/abs/2509.26475 https://arxiv.org/…

Computing Linear Combinations of $φ$-Function Actions for Exponential Integrators
We propose a matrix-free algorithm for evaluating linear combinations of $φ$-function actions, $w_i := \sum_{j=0}^{p} α_i^{\,j}\,φ_j(t_i A)v_j$ for $i=1\colon r$, arising in exponential integrators. The method combines the scaling and recovering method with a truncated Taylor series, choosing a spectral shift and a scaling parameter by minimizing a power-based objective of the shifted operator. Accuracy is user-controlled and ultimately limited by the working precision. The algorithm decoupl…