Tootfinder

BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
Taesoo Park, Mungwi Jeong, Mingyu Park, Narae Kim, Junyoung Kim, Mujung Kim, Jisang Yoo, Hoyun Lee, Sanghoon Kim, Soonchul Kwon
https://arxiv.org/abs/2506.09487

BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
This paper presents a tutorial-style survey and implementation guide of BemaGANv2, an advanced GAN-based vocoder designed for high-fidelity and long-term audio generation. Built upon the original BemaGAN architecture, BemaGANv2 incorporates major architectural innovations by replacing traditional ResBlocks in the generator with the Anti-aliased Multi-Periodicity composition (AMP) module, which internally applies the Snake activation function to better model periodic structures. In the discrimin…

On the role of non-linear latent features in bipartite generative neural networks
Tony Bonnaire, Giovanni Catania, Aur\'elien Decelle, Beatriz Seoane
https://arxiv.org/abs/2506.10552

On the role of non-linear latent features in bipartite generative neural networks
We investigate the phase diagram and memory retrieval capabilities of bipartite energy-based neural networks, namely Restricted Boltzmann Machines (RBMs), as a function of the prior distribution imposed on their hidden units - including binary, multi-state, and ReLU-like activations. Drawing connections to the Hopfield model and employing analytical tools from statistical physics of disordered systems, we explore how the architectural choices and activation functions shape the thermodynamic pro…

This https://arxiv.org/abs/2505.02580 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Temperature and pressure reconstruction in turbulent Rayleigh-Bénard convection by Lagrangian velocities using PINN
Velocity, pressure, and temperature are the key variables for understanding thermal convection, and measuring them all is a complex task. In this paper, we demonstrate a method to reconstruct temperature and pressure fields based on given Lagrangian velocity data. A physics-informed neural network (PINN) based on a multilayer perceptron architecture and a periodic sine activation function is used to reconstruct both the temperature and the pressure for two cases of turbulent Rayleigh-Bénard co…

This https://arxiv.org/abs/2506.05197 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Overlap Gap and Computational Thresholds in the Square Wave Perceptron
Square Wave Perceptrons (SWPs) form a class of neural network models with oscillating activation function that exhibit intriguing ``hardness'' properties in the high-dimensional limit at a fixed signal-to-noise ratio $α= O(1)$. In this work, we examine two key aspects of these models. The first is related to the so-called overlap-gap property, that is a disconnectivity feature of the geometry of the solution space of combinatorial optimization problems proven to cause the failure of a large fa…

Immunometabolism at the Crossroads of Infection: Mechanistic and Systems-Level Perspectives from Host and Pathogen
Sunayana Malla, Nabia Shahreen, Rajib Saha
https://arxiv.org/abs/2506.02236

Immunometabolism at the Crossroads of Infection: Mechanistic and Systems-Level Perspectives from Host and Pathogen
The emerging field of immunometabolism has underscored the central role of metabolic pathways in orchestrating immune cell function. Far from being passive background processes, metabolic activities actively regulate key immune responses. Fundamental pathways such as glycolysis, the tricarboxylic acid (TCA) cycle, and oxidative phosphorylation critically shape the behavior of immune cells, influencing macrophage polarization, T cell activation, and dendritic cell function. In this review, we sy…

Overlap Gap and Computational Thresholds in the Square Wave Perceptron
Marco Benedetti, Andrej Bogdanov, Enrico M. Malatesta, Marc M\'ezard, Gianmarco Perrupato, Alon Rosen, Nikolaj I. Schwartzbach, Riccardo Zecchina
https://arxiv.org/abs/2506.05197

Overlap Gap and Computational Thresholds in the Square Wave Perceptron
Square Wave Perceptrons (SWPs) form a class of neural network models with oscillating activation function that exhibit intriguing ``hardness'' properties in the high-dimensional limit at a fixed signal-to-noise ratio $α= O(1)$. In this work, we examine two key aspects of these models. The first is related to the so-called overlap-gap property, that is a disconnectivity feature of the geometry of the solution space of combinatorial optimization problems proven to cause the failure of a large fa…