arXiv:2602.13268v1 Announce Type: new Abstract: Moral cognition is a crucial yet underexplored aspect of decision-making in AI models. Regardless of the application domain, it should be a consideration that allows for ethically aligned decision-making. This paper presents a multifaceted contribution to this research space. Firstly, a comparative analysis of techniques to instill ethical competence into AI models has been presented to gauge them on multiple performance metrics. Second, a novel mathematical discretization of morality and a demonstration of […]

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Motivation: Pathway enrichment analysis is widely used to interpret gene expression data. Standard approaches, such as GSEA, rely on predefined phenotypic labels and pairwise comparisons, which limits their applicability in unsupervised settings. Existing unsupervised extensions, including single-sample methods, provide pathway-level summaries but primarily capture linear relationships and do not explicitly model gene-pathway associations. More recently, deep learning models have been explored to capture non-linear transcriptomic structure. However, their interpretation has typically relied on generic explainable AI (XAI) techniques […]

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This paper introduces Bayesian R-LayerNorm, a novel normalization layer that extends the previously proposed R-LayerNorm with formal mathematical foundations and uncertainty quantification. Building upon the empirical success of R-LayerNorm, we present a complete mathematical formalism using sta-tistical field theory, renormalization group methods, and information geometry. Our approach provides provable stability guarantees through three theorems: numerical stability, gradient stability, and training convergence. The Bayesian extension incorporates uncertainty estimation through a stable ψ-function, enabling adaptive noise suppression based on local […]

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arXiv:2602.06085v1 Announce Type: new Abstract: FPGAs offer high performance, low latency, and energy efficiency for accelerated computing, yet adoption in scientific and edge settings is limited by the specialized hardware expertise required. High-level synthesis (HLS) boosts productivity over HDLs, but competitive designs still demand hardware-aware optimizations and careful dataflow design. We introduce LAAFD, an agentic workflow that uses large language models to translate general-purpose C++ into optimized Vitis HLS kernels. LAAFD automates key transfor mations: deep pipelining, vectorization, […]

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Diffusion models generate samples through an iterative denoising process, guided by a neural network. While training the denoiser on real-world data is computationally demanding, the sampling procedure itself is more flexible. This adaptability serves as a key lever in practice, enabling improvements in both the quality of generated samples and the efficiency of the sampling process. In this work, we introduce an inverse reinforcement learning framework for learning sampling strategies without retraining the denoiser. We formulate the diffusion […]

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As developers, we often need to tweak screenshots – blur out sensitive info, add annotations, or even change the text. But have you ever wanted to replace a word in a screenshot, and have it look exactly like the original? Same font, same size, same color, same style? I did, and that’s why I built Screenshot Editor – a free, open-source web app that does exactly that. In this article, I’ll walk you through the journey, the challenges, and the […]

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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. ## Designing API Contracts for Legacy System Modernization By @jamescaron [ 6 Min read ] A practical look at designing API contracts during legacy system modernization, focusing on real production failures and strategies to prevent silent regression Read More. How Supercell Powers its Massive Social […]

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[Updated on 2018-12-20: Remove YOLO here. Part 4 will cover multiple fast object detection algorithms, including YOLO.] [Updated on 2018-12-27: Add bbox regression and tricks sections for R-CNN.] In the series of “Object Detection for Dummies”, we started with basic concepts in image processing, such as gradient vectors and HOG, in Part 1. Then we introduced classic convolutional neural network architecture designs for classification and pioneer models for object recognition, Overfeat and DPM, in Part 2. In the […]

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