Artificial Intelligence of Things (AIoT) applications increasingly exceed the limits of centralized cloud processing because they require low latency, privacy preservation, scalability, and operational resilience. This review synthesizes distributed intelligence across the edge–fog–cloud continuum through a structured integrative methodology comprising multi-stage literature search, two-stage filtering, and thematic synthesis of more than 100 sources. The analysis covers four representative domains—industrial IoT, smart cities, connected healthcare, and smart agriculture—to identify recurring architectural patterns and shared deployment challenges. The review organizes […]

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In this paper, we propose two novel defective survival models within the Gamma–G family: the Defective Gamma–Gompertz and the Defective Gamma–Da- gum distributions. Unlike classical mixture cure models, our formulation incorporates the cure fraction directly into the survival function through the defective property of the baseline distribution, avoiding the need for an explicit mixing parameter. The motivation for these new models lies in the limited set of defective distributions currently available, despite the increasing demand for flexible and […]

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arXiv:2601.10915v1 Announce Type: new Abstract: In the emerging paradigm of edge inference, neural networks (NNs) are partitioned across distributed edge devices that collaboratively perform inference via wireless transmission. However, standard NNs are generally trained in a noiseless environment, creating a mismatch with the noisy channels during edge deployment. In this paper, we address this issue by characterizing the channel-induced performance deterioration as a generalization error against unseen channels. We introduce an augmented NN model that incorporates channel statistics […]

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arXiv:2602.15877v1 Announce Type: new Abstract: Generalized Additive Models (GAMs) balance predictive accuracy and interpretability, but manually configuring their structure is challenging. We propose using the multi-objective genetic algorithm NSGA-II to automatically optimize GAMs, jointly minimizing prediction error (RMSE) and a Complexity Penalty that captures sparsity, smoothness, and uncertainty. Experiments on the California Housing dataset show that NSGA-II discovers GAMs that outperform baseline LinearGAMs in accuracy or match performance with substantially lower complexity. The resulting models are simpler, smoother, […]

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Scientific machine learning (SciML) is increasingly applied to in-field processing, controlling, and monitoring; however, wide-area sensing, real-time demands, and strict energy and reliability constraints make centralized SciML implementation impractical. Most SciML models assume raw data aggregation at a central node, incurring prohibitively high communication latency and energy costs; yet, distributing models developed for general-purpose ML often breaks essential physical principles, resulting in degraded performance. To address these challenges, we introduce EPIC, a hardware- and physics-co-guided distributed SciML framework, […]

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arXiv:2603.24800v1 Announce Type: new Abstract: In this paper, we uncover the hidden potential of Diffusion Transformers (DiTs) to significantly enhance generative tasks. Through an in-depth analysis of the denoising process, we demonstrate that introducing a single learned scaling parameter can significantly improve the performance of DiT blocks. Building on this insight, we propose Calibri, a parameter-efficient approach that optimally calibrates DiT components to elevate generative quality. Calibri frames DiT calibration as a black-box reward optimization problem, which is […]

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Grokking, the abrupt transition from memorization to generalisation after extended training, suggests the presence of competing solution basins with distinct statistical properties. We study this phenomenon through the lens of Singular Learning Theory (SLT), a Bayesian framework that characterizes the geometry of the loss landscape via the local learning coefficient (LLC), a measure of the local degeneracy of the loss surface. SLT links lower-LLC basins to higher posterior mass concentration and lower expected generalisation error. Leveraging this theory, […]

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Effective IT change management is important for businesses that depend on software and services, particularly in highly regulated sectors such as finance, where operational reliability, auditability, and explainability are essential. A significant portion of IT incidents are caused by changes, making it important to identify high-risk changes before deployment. This study presents a predictive incident risk scoring approach at a large international bank. The approach supports engineers during the assessment and planning phases of change deployments by predicting […]

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Reinforcement learning (RL) has shown promise for trading, yet most open-source backtesting environments assume negligible or fixed transaction costs, causing agents to learn trading behaviors that fail under realistic execution. We introduce three Gymnasium-compatible trading environments — MACE (Market-Adjusted Cost Execution) stock trading, margin trading, and portfolio optimization — that integrate nonlinear market impact models grounded in the Almgren-Chriss framework and the empirically validated square-root impact law. Each environment provides pluggable cost models, permanent impact tracking with exponential […]

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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. ## 6 Ways to Use a Crypto Exchange Aggregator and Save on Swaps By @swapzone [ 7 Min read ] Maximize your crypto swaps! Learn 6 ways an exchange aggregator saves you money. Find the best rates on Bitcoin, Ethereum, & other cryptocurrencies on DEXs. […]

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