Tootfinder

On the Generalization Limits of Quantum Generative Adversarial Networks with Pure State Generators
Jasmin Frkatovic, Akash Malemath, Ivan Kankeu, Yannick Werner, Matthias Tsch\"ope, Vitor Fortes Rey, Sungho Suh, Paul Lukowicz, Nikolaos Palaiodimopoulos, Maximilian Kiefer-Emmanouilidis
https://arxiv.org/abs/2508.09844

On the Generalization Limits of Quantum Generative Adversarial Networks with Pure State Generators
We investigate the capabilities of Quantum Generative Adversarial Networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator. Through extensive numerical testing of current main architectures, we find that QGANs struggle to generalize across datasets, converging on merely the average representation of the training data. When the output of the generator is a pure-state, we analytically derive a lower bound for the di…

On the Constant-Factor Approximability of Minimum Cost Constraint Satisfaction Problems
Ian DeHaan, Neng Huang, Euiwoong Lee
https://arxiv.org/abs/2507.08693

On the Constant-Factor Approximability of Minimum Cost Constraint Satisfaction Problems
We study minimum cost constraint satisfaction problems (MinCostCSP) through the algebraic lens. We show that for any constraint language $Γ$ which has the dual discriminator operation as a polymorphism, there exists a $|D|$-approximation algorithm for MinCostCSP$(Γ)$ where $D$ is the domain. Complementing our algorithmic result, we show that any constraint language $Γ$ where MinCostCSP$(Γ)$ admits a constant-factor approximation must have a \emph{near-unanimity} (NU) polymorphism …

Improved Binary Black Hole Search Discriminator from the Singular Value Decomposition of Non-Gaussian Noise Transients
Tathagata Ghosh, Sukanta Bose, Sanjeev Dhurandhar, Sunil Choudhary
https://arxiv.org/abs/2507.04628

Improved Binary Black Hole Search Discriminator from the Singular Value Decomposition of Non-Gaussian Noise Transients
The sensitivity of current gravitational wave (GW) detectors to transient GW signals is severely affected by a variety of non-Gaussian and non-stationary noise transients, such as the blip and tomte "glitches". These glitches share some time-frequency resemblance with GW signals from massive binary black holes. In earlier works [Joshi et al., Phys. Rev. D 103, 044035 (2021); Choudhary et al., Phys. Rev. D 110, 044051 (2024)], the authors presented a method for constructing a $χ^2$-distributed …

SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation
Guido Manni, Clemente Lauretti, Loredana Zollo, Paolo Soda
https://arxiv.org/abs/2508.06429

SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation
Deep learning has revolutionized medical imaging, but its effectiveness is severely limited by insufficient labeled training data. This paper introduces a novel GAN-based semi-supervised learning framework specifically designed for low labeled-data regimes, evaluated across settings with 5 to 50 labeled samples per class. Our approach integrates three specialized neural networks -- a generator for class-conditioned image translation, a discriminator for authenticity assessment and classificatio…

Structure Maintained Representation Learning Neural Network for Causal Inference
Yang Sun, Wenbin Lu, Yi-Hui Zhou
https://arxiv.org/abs/2508.01865 https://…

Structure Maintained Representation Learning Neural Network for Causal Inference
Recent developments in causal inference have greatly shifted the interest from estimating the average treatment effect to the individual treatment effect. In this article, we improve the predictive accuracy of representation learning and adversarial networks in estimating individual treatment effects by introducing a structure keeper which maintains the correlation between the baseline covariates and their corresponding representations in the high dimensional space. We train a discriminator at …

Design and implementation of the constant fraction discriminator for glass MRPC timing
L. L. Kurchaninov (TRIUMF), E. A. Ladygin (Joint Institute for Nuclear Research), V. P. Ladygin (Joint Institute for Nuclear Research), A. A. Semak (Institute for High Energy Physics, National Research Center Kurchatov Institute)
https://arxiv…

Design and implementation of the constant fraction discriminator for glass MRPC timing
The analog front-end electronics based on the constant fraction discrimination method is designed and optimized for the Multigap Resistive Plate Chamber (MRPC) timing measurements. The total time resolution of 40 ps has been obtained for 10 and 12 gaps MRPCs using cosmic setup and a muon beam at the IHEP U-70 accelerator in Protvino, which complies with the conditions of the SPD experiment at NICA.

Super-resolution of turbulent velocity fields in two-way coupled particle-laden flows
Ali Shamooni, Ruyue Cheng, Thorsten Zirwes, Hesam Tofighian, Oliver T. Stein, Andreas Kronenburg
https://arxiv.org/abs/2507.03567

Super-resolution of turbulent velocity fields in two-way coupled particle-laden flows
This paper introduces a deep learning-based super-resolution (SR) framework specifically developed for accurately reconstructing high-resolution velocity fields in two-way coupled particle-laden turbulent flows. Leveraging conditional generative adversarial networks (cGANs), the generator network architecture incorporates explicit conditioning on physical parameters, such as effective particle mass density and subgrid kinetic energy, while the discriminator network is conditioned on low-resolut…

HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management
Jiadong He, Liang Yu, Zhiqiang Chen, Dawei Qiu, Dong Yue, Goran Strbac, Meng Zhang, Yujian Ye, Yi Wang
https://arxiv.org/abs/2506.00898

HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management
This letter proposes an Adversarial Inverse Reinforcement Learning (AIRL)-based energy management method for a smart home, which incorporates an implicit thermal dynamics model. In the proposed method, historical optimal decisions are first generated using a neural network-assisted Hierarchical Model Predictive Control (HMPC) framework. These decisions are then used as expert demonstrations in the AIRL module, which aims to train a discriminator to distinguish expert demonstrations from transit…

Nonlinear Framework for Speech Bandwidth Extension
Tarikul Islam Tamiti, Nursad Mamun, Anomadarshi Barua
https://arxiv.org/abs/2507.15970 https://

Nonlinear Framework for Speech Bandwidth Extension
Recovering high-frequency components lost to bandwidth constraints is crucial for applications ranging from telecommunications to high-fidelity audio on limited resources. We introduce NDSI-BWE, a new adversarial Band Width Extension (BWE) framework that leverage four new discriminators inspired by nonlinear dynamical system to capture diverse temporal behaviors: a Multi-Resolution Lyapunov Discriminator (MRLD) for determining sensitivity to initial conditions by capturing deterministic chaos, …