Tootfinder

This https://arxiv.org/abs/2410.13738 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Instance-dependent Convergence Theory for Diffusion Models
Score-based diffusion models have demonstrated outstanding empirical performance in machine learning and artificial intelligence, particularly in generating high-quality new samples from complex probability distributions. Improving the theoretical understanding of diffusion models, with a particular focus on the convergence analysis, has attracted significant attention. In this work, we develop a convergence rate that is adaptive to the smoothness of different target distributions, referred to …

A comprehensive analysis of PINNs: Variants, Applications, and Challenges
Afila Ajithkumar Sophiya, Akarsh K Nair, Sepehr Maleki, Senthil K. Krishnababu
https://arxiv.org/abs/2505.22761

A comprehensive analysis of PINNs: Variants, Applications, and Challenges
Physics Informed Neural Networks (PINNs) have been emerging as a powerful computational tool for solving differential equations. However, the applicability of these models is still in its initial stages and requires more standardization to gain wider popularity. Through this survey, we present a comprehensive overview of PINNs approaches exploring various aspects related to their architecture, variants, areas of application, real-world use cases, challenges, and so on. Even though existing surv…

This https://arxiv.org/abs/2504.07472 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

HACMony: Automatically Detecting Hopping-related Audio-stream Conflict Issues on HarmonyOS
HarmonyOS is emerging as a popular distributed operating system for diverse mobile devices. One of its standout features is app-hopping, which allows users to seamlessly transition apps across different HarmonyOS devices. However, when apps playing audio streams hop between devices, they can easily trigger Hopping-related Audio-stream Conflict (HAC) scenarios. Improper resolution of HAC will lead to significant HAC issues, which are harder to detect compared to single-device audio-stream confli…

This https://arxiv.org/abs/2505.13688 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Gaze-Enhanced Multimodal Turn-Taking Prediction in Triadic Conversations
Turn-taking prediction is crucial for seamless interactions. This study introduces a novel, lightweight framework for accurate turn-taking prediction in triadic conversations without relying on computationally intensive methods. Unlike prior approaches that either disregard gaze or treat it as a passive signal, our model integrates gaze with speaker localization, structuring it within a spatial constraint to transform it into a reliable predictive cue. Leveraging egocentric behavioral cues, our…

artificial intelligence “enables the cost of misinformation to come close to zero.” 
And it is starts with flooding Canadian city council mailboxes with climate misinformation https://www.nationalobserver.com/2025/05/2

A weaponized AI chatbot is flooding city councils with climate misinformation
Internal instructions accessed by Canada’s National Observer show that the chatbot produces tailored scripts, petitions, reports and even speeches for council chambers. The messaging is all framed to resonate with municipal officials’ duty to represent local interests. The chatbot drops the cost of misinformation to "close to zero."

This https://arxiv.org/abs/2408.01347 has been replaced.
initial toot: https://mastoxiv.page/@a…

Through-Thickness Modelling of Metal Rolling using Multiple-Scale Asymptotics
A new semi-analytic model of the metal rolling process is introduced, which, for the first time, is able to predict the through-thickness stress and strain oscillations present in long thin roll-gaps. The model is based on multiple-scales asymptotics, assuming a long thin roll-gap and a comparably small Coulomb friction coefficient. The leading-order solution varies only on a long lengthscale corresponding to the roll-gap length and matches with slab models. The next-order correction varies on …

This https://arxiv.org/abs/2502.18710 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Bridging Critical Gaps in Convergent Learning: How Representational Alignment Evolves Across Layers, Training, and Distribution Shifts
Understanding convergent learning -- the degree to which independently trained neural systems -- whether multiple artificial networks or brains and models -- arrive at similar internal representations -- is crucial for both neuroscience and AI. Yet, the literature remains narrow in scope -- typically examining just a handful of models with one dataset, relying on one alignment metric, and evaluating networks at a single post-training checkpoint. We present a large-scale audit of convergent lear…

This https://arxiv.org/abs/2304.11476 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Maximum Spherical Mean Value (mSMV) Filtering for Whole Brain Quantitative Susceptibility Mapping
To develop a tissue field filtering algorithm, called maximum Spherical Mean Value (mSMV), for reducing shadow artifacts in quantitative susceptibility mapping (QSM) of the brain without requiring brain tissue erosion. Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field values near the border, where the maximum value of the harmonic background field is located. The effectiveness of mSMV for artifact removal was evaluated by comparing wi…

This https://arxiv.org/abs/2505.08408 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A three-term Polak-Ribière-Polyak conjugate gradient method for vector optimization
A three-term Polak-Ribière-Polyak conjugate gradient method is first proposed in this paper for solving vector optimization problems. This method can autonomously generate descent directions independent of line search procedures, while retaining the conjugate property. The global convergence of the proposed scheme is established by the generalized Wolfe line search procedure without self-adjusting strategies, regular restarts and convexity assumptions. Finally, numerical experiments illustrati…

This https://arxiv.org/abs/2505.08408 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A three-term Polak-Ribière-Polyak conjugate gradient method for vector optimization
A three-term Polak-Ribière-Polyak conjugate gradient method is first proposed in this paper for solving vector optimization problems. This method can autonomously generate descent directions independent of line search procedures, while retaining the conjugate property. The global convergence of the proposed scheme is established by the generalized Wolfe line search procedure without self-adjusting strategies, regular restarts and convexity assumptions. Finally, numerical experiments illustrati…