arXiv:2512.08671v4 Announce Type: replace-cross Abstract: Recent work cite{arifgroup} introduced Federated Proximal Gradient textbf{(texttt{FedProxGrad})} for solving non-convex composite optimization problems in group fair federated learning. However, the original analysis established convergence only to a textit{noise-dominated neighborhood of stationarity}, with explicit dependence on a variance-induced noise floor. In this work, we provide an improved asymptotic convergence analysis for a generalized texttt{FedProxGrad}-type analytical framework with inexact local proximal solutions and explicit fairness regularization. We call this extended analytical framework textbf{DS texttt{FedProxGrad}} […]

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arXiv:2601.08873v1 Announce Type: new Abstract: The proliferation of AI-generated imagery and sophisticated editing tools has rendered traditional forensic methods ineffective for cross-domain forgery detection. We present ForensicFormer, a hierarchical multi-scale framework that unifies low-level artifact detection, mid-level boundary analysis, and high-level semantic reasoning via cross-attention transformers. Unlike prior single-paradigm approaches, which achieve <75% accuracy on out-of-distribution datasets, our method maintains 86.8% average accuracy across seven diverse test sets, spanning traditional manipulations, GAN-generated images, and diffusion model outputs – […]

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arXiv:2601.07957v1 Announce Type: new Abstract: Maize disease classification plays a vital role in mitigating yield losses and ensuring food security. However, the deployment of traditional disease detection models in resource-constrained environments, such as those using smartphones and drones, faces challenges due to high computational costs. To address these challenges, we propose LWMSCNN-SE, a lightweight convolutional neural network (CNN) that integrates multi-scale feature extraction, depthwise separable convolutions, and squeeze-and-Excitation (SE) attention mechanisms. This novel combination enables the model to […]

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arXiv:2510.23631v2 Announce Type: replace-cross Abstract: Alignment of large language models (LLMs) has predominantly relied on pairwise preference optimization, where annotators select the better of two responses to a prompt. While simple, this approach overlooks the opportunity to learn from richer forms of human feedback, such as multiway comparisons and top-$k$ rankings. We introduce Ranked Choice Preference Optimization (RCPO), a unified framework that bridges preference optimization with (ranked) choice modeling via maximum likelihood estimation. RCPO supports both utility-based and […]

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arXiv:2601.08049v1 Announce Type: new Abstract: The increasing adoption of smart classroom technologies in higher education has mainly focused on automating attendance, with limited attention given to students’ emotional and cognitive engagement during lectures. This limits instructors’ ability to identify disengagement and adapt teaching strategies in real time. This paper presents SCASED (Smart Classroom Attendance System with Emotion Detection), an IoT-based system that integrates automated attendance tracking with facial emotion recognition to support classroom engagement monitoring. The system uses […]

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Coding theory plays a crucial role in enabling reliable communication, storage, and computation. Classical approaches assume a worst-case adversarial model and ensure error correction and data recovery only when the number of honest nodes exceeds the number of adversarial ones by some margin. However, in some emerging decentralized applications, particularly in decentralized machine learning (DeML), participating nodes are rewarded for accepted contributions. This incentive structure naturally gives rise to rational adversaries who act strategically rather than behaving in […]

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arXiv:2510.07750v2 Announce Type: replace Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions. Recent approaches using conformal prediction construct data-driven uncertainty sets with finite-sample coverage guarantees, but they still fix coverage targets a priori and offer little guidance for selecting robustness levels. We propose a new framework that provides […]

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arXiv:2601.19368v1 Announce Type: cross Abstract: Generative diffusion models are extensively used in unsupervised and self-supervised machine learning with the aim to generate new samples from a probability distribution estimated with a set of known samples. They have demonstrated impressive results in replicating dense, real-world contents such as images, musical pieces, or human languages. This work investigates their application to the numerical simulation of incompressible fluid flows, with a view toward incorporating physical constraints such as incompressibility in the […]

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Crypto contract verification is the definitive proof of identity in the DeFi ecosystem, transforming opaque bytecode into trusted logic. However, the process is often misunderstood, leading to frustration when the “Deterministic Black Box” of the compiler produces mismatching fingerprints. This article demystifies verification by visualizing it as a “Mirror Mechanism,” where local compilation environments must precisely replicate the deployment conditions. We move beyond manual web uploads to establish a robust, automated workflow using CLI tools and the “Standard […]

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Hi , I’m much interested into applied RL and looking for a job or a summer internship this summer , I’m a 3rd year undergrad at a tier 1 research institute . However my doubt is my main interest in rl is its ability to create greater impact , speaking about impact what I truly wanted was to use sample efficient rl and create an impact in sustainability and energy grid optimization but I think a greater application […]

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