arXiv:2601.00237v1 Announce Type: new Abstract: This paper addresses the critical bottleneck of infrared (IR) data scarcity in Printed Circuit Board (PCB) defect detection by proposing a cross-modal data augmentation framework integrating CycleGAN and YOLOv8. Unlike conventional methods relying on paired supervision, we leverage CycleGAN to perform unpaired image-to-image translation, mapping abundant visible-light PCB images into the infrared domain. This generative process synthesizes high-fidelity pseudo-IR samples that preserve the structural semantics of defects while accurately simulating thermal distribution patterns. […]

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arXiv:2601.10904v1 Announce Type: new Abstract: The ARC-AGI benchmark series serves as a critical measure of few-shot generalization on novel tasks, a core aspect of intelligence. The ARC Prize 2025 global competition targeted the newly released ARC-AGI-2 dataset, which features greater task complexity compared to its predecessor. The Kaggle competition attracted 1,455 teams and 15,154 entries, with the top score reaching 24% on the ARC-AGI-2 private evaluation set. Paper submissions nearly doubled year-over-year to 90 entries, reflecting the growing […]

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When Apple revealed the Liquid Glass design system last year, the design community went back to an argument it had been having since iOS 7. Some designers welcomed the return of depth, material, and texture after years of flat design. Others saw it as unnecessary decoration and argued that legibility and convenience were traded for pure form, without adding much function. The argument went nowhere in 2013, and it went nowhere this time, because the debate mostly focused […]

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arXiv:2601.00252v1 Announce Type: new Abstract: Android Permission Model and Application (app) analysis has consistently remained the focus of the investigation of research groups and stakeholders of the Android ecosystem since it was launched in 2008. Even though the Android smartphone operating system (OS) permission model has evolved significantly from `all-or-none access’ to `user-chosen dangerous resource access’, specific challenges and issues remain unresolved even after 15 years after the smartphone OS launch. This study addresses the issues and documents […]

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Most Solidity education focuses on syntax, patterns, and tools. Learn the language, deploy a contract, move on. That used to be enough. In 2026, every major ERC standard is its own ecosystem. Some have bundlers, paymasters, and SDKs. Others run compliance modules and identity registries. This isn’t even in the same category as ERC-20. Each standard is its own open system — a full-blown market for smart account tooling and integrations. Trillions in real-world assets are heading onchain. […]

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Reinforcement learning has shown promise for automating power-grid operation tasks such as topology control and congestion management. However, its deployment in real-world power systems remains limited by strict safety requirements, brittleness under rare disturbances, and poor generalization to unseen grid topologies. In safety-critical infrastructure, catastrophic failures cannot be tolerated, and learning-based controllers must operate within hard physical constraints. This paper proposes a safety-constrained hierarchical control framework for power-grid operation that explicitly decouples long-horizon decision-making from real-time feasibility enforcement. […]

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arXiv:2603.13478v1 Announce Type: cross Abstract: In a spiking neural network, is it enough for each neuron to spike at most once? In recent work, approximation bounds for spiking neural networks have been derived, quantifying how well they can fit target functions. However, these results are only valid for neurons that spike at most once, which is commonly thought to be a strong limitation. Here, we show that the opposite is true for a large class of spiking neuron […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Your AI Model Isn’t Broken. Your Data Is By @melissaindia [ 7 Min read ] Your AI model isn’t failing; your data is. Learn how clean, verified data improves model accuracy and how easy it is to fix with APIs. Read More. The Hidden […]

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This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive, stage-aware defense against Advanced Persistent Threats (APTs). The enterprise environment is modeled as a partially observable Markov decision process (POMDP), where host provenance and network telemetry are fused into unified provenance graphs. Building on our prior work, StageFinder, a graph neural encoder and an LSTM-based stage estimator infer probabilistic attacker stages aligned with the MITRE ATT&CK framework. These stage beliefs, combined with graph embeddings, guide a […]

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