Tootfinder

Generalizable Federated Learning using Client Adaptive Focal Modulation
Tajamul Ashraf, Iqra Altaf Gillani
https://arxiv.org/abs/2508.10840 https://arxiv.o…

Generalizable Federated Learning using Client Adaptive Focal Modulation
Federated learning (FL) has proven essential for privacy-preserving, collaborative training across distributed clients. Our prior work, TransFed, introduced a robust transformer-based FL framework that leverages a learn-to-adapt hypernetwork to generate personalized focal modulation layers per client, outperforming traditional methods in non-IID and cross-domain settings. In this extended version, we propose AdaptFED, where we deepen the investigation of focal modulation in generalizable FL by …

On Trustworthy Rule-Based Models and Explanations
Mohamed Siala, Jordi Planes, Joao Marques-Silva
https://arxiv.org/abs/2507.07576 https://

On Trustworthy Rule-Based Models and Explanations
A task of interest in machine learning (ML) is that of ascribing explanations to the predictions made by ML models. Furthermore, in domains deemed high risk, the rigor of explanations is paramount. Indeed, incorrect explanations can and will mislead human decision makers. As a result, and even if interpretability is acknowledged as an elusive concept, so-called interpretable models are employed ubiquitously in high-risk uses of ML and data mining (DM). This is the case for rule-based ML models,…

Modified Cubic B-spline Based Differential Quadrature Methods for Time-fractional Black-Scholes Equation
Nizamudheen V, Riyasudheen TK, Noufal Asharaf, Shefeeq T
https://arxiv.org/abs/2508.06780

Modified Cubic B-spline Based Differential Quadrature Methods for Time-fractional Black-Scholes Equation
The time-fractional Black-Scholes equation (TFBSE) is intended to price the options for which the underlying price fluctuates within a correlated fractal transmission system. Although the TFBSE is an influential approach for grasping the long-term memory traits of financial markets, the non-local nature of fractional derivatives makes significant challenges in finding an accurate solution. We perform an efficient use of the differential quadrature method (DQM) based on modified cubic B-splines …

Been to the hospital and had the cast removed.
Should make typing easier.
More pain now than when I first went there though. RSI aggravated by the cast. Thumb fucked up by the cast. Wrist hurts when moved.
Apparently I'm to stretch the wrist slowly and gradually recover mobility like wrist yoga. Physio to come maybe.
Have booked a massage for tomorrow to try and realign my frame, all fucked up by the cast and the sitting around for a month.
Do not recommend fracturing bones in your arm. It's an bad scene man.

Leveraging large language models for SQL behavior-based database intrusion detection
Meital Shlezinger, Shay Akirav, Lei Zhou, Liang Guo, Avi Kessel, Guoliang Li
https://arxiv.org/abs/2508.05690

Leveraging large language models for SQL behavior-based database intrusion detection
Database systems are extensively used to store critical data across various domains. However, the frequency of abnormal database access behaviors, such as database intrusion by internal and external attacks, continues to rise. Internal masqueraders often have greater organizational knowledge, making it easier to mimic employee behavior effectively. In contrast, external masqueraders may behave differently due to their lack of familiarity with the organization. Current approaches lack the granul…

The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams
Parham Khamsepour, Mark Cole, Ish Ashraf, Sandeep Puri, Mehrdad Sabetzadeh, Shiva Nejati
https://arxiv.org/abs/2509.03463

The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams
Large Language Models (LLMs) show strong potential for automating the generation of models from natural-language descriptions. A common approach is an iterative generate-critique-refine loop, where candidate models are produced, evaluated, and updated based on detected issues. This process needs to address: (1) structural correctness - compliance with well-formedness rules - and (2) semantic alignment - accurate reflection of the intended meaning in the source text. We present LADEX (LLM-based …

Sea-Undistort: A Dataset for Through-Water Image Restoration in High Resolution Airborne Bathymetric Mapping
Maximilian Kromer, Panagiotis Agrafiotis, Beg\"um Demir
https://arxiv.org/abs/2508.07760

Sea-Undistort: A Dataset for Through-Water Image Restoration in High Resolution Airborne Bathymetric Mapping
Accurate image-based bathymetric mapping in shallow waters remains challenging due to the complex optical distortions such as wave induced patterns, scattering and sunglint, introduced by the dynamic water surface, the water column properties, and solar illumination. In this work, we introduce Sea-Undistort, a comprehensive synthetic dataset of 1200 paired 512x512 through-water scenes rendered in Blender. Each pair comprises a distortion-free and a distorted view, featuring realistic water effe…

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Xiaobei Zhao, Xingqi Lyu, Xiang Li
https://arxiv.org/abs/2509.06644 https://

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Agricultural robotic agents have been becoming powerful helpers in a wide range of agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement. The AgriVLN method and the A2A benchmark pioneeringly extend Vision-and-Language Navigation (VLN) to the agricultural domain, enabling agents navigate to the target position following the natural language instructions. AgriVLN effectively understands the simple instructions, however, often misundersta…

Scaling Relations of the Dusty Torus with Luminosity and the Broad-Line Region
Ashraf Ayubinia, Jong-Hak Woo, Shu Wang, Amit Kumar Mandal, Donghoon Son
https://arxiv.org/abs/2509.07878

Scaling Relations of the Dusty Torus with Luminosity and the Broad-Line Region
We measure and compare the size of the dusty torus with active galactic nucleus (AGN) luminosity and the size of the broad-line region (BLR), using a sample of 182 AGNs with the best H$β$ lag measurements. After correcting for accretion-disk contamination, torus sizes are determined from the time lags of the WISE W1 and W2 band light curves relative to the optical band variability based on the interpolated cross-correlation function (ICCF) analysis and the Multiple and Inhomogeneous Component …

Video-Based MPAA Rating Prediction: An Attention-Driven Hybrid Architecture Using Contrastive Learning
Dipta Neogi, Nourash Azmine Chowdhury, Muhammad Rafsan Kabir, Mohammad Ashrafuzzaman Khan
https://arxiv.org/abs/2509.06826

Video-Based MPAA Rating Prediction: An Attention-Driven Hybrid Architecture Using Contrastive Learning
The rapid growth of visual content consumption across platforms necessitates automated video classification for age-suitability standards like the MPAA rating system (G, PG, PG-13, R). Traditional methods struggle with large labeled data requirements, poor generalization, and inefficient feature learning. To address these challenges, we employ contrastive learning for improved discrimination and adaptability, exploring three frameworks: Instance Discrimination, Contextual Contrastive Learning, …