April 2026

Batch processes are inherently transient and typically nonlinear, motivating nonlinear model predictive control (NMPC). However, adopting NMPC is hindered by the cost and unavailability of dynamic models. Thus, we propose to use Gaussian Processes (GP) in a model-learning NMPC scheme (GP-MLMPC) for batch processes. We initialize the GP-MLMPC using data from a single initial trajectory, e.g., from a PI controller. We iteratively apply the NMPC embedded with GPs to run batches and update the GP with new observations […]

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Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better guidance for test improvement, while realism is essential when mutants are used to simulate real bugs. Building on these roles, selective mutation for deep learning (DL) aims to reduce the cost of mutant generation and execution by choosing operator configurations that yield resistant and realistic mutants. However, the DL literature lacks a unified measure that […]

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Self-supervised learning in healthcare has largely relied on invariance-based objectives, which maximize similarity between different views of the same patient. While effective for static anatomy, this paradigm is fundamentally misaligned with clinical diagnosis, as it mathematically compels the model to suppress the transient pathological changes it is intended to detect. We propose a shift towards Action-Conditioned World Models that learn to simulate the dynamics of disease progression, or Event-Conditioned. Adapting the LeJEPA framework to physiological time-series, we define […]

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Client contribution estimation in Federated Learning is necessary for identifying clients’ importance and for providing fair rewards. Current methods often rely on server-side validation data or self-reported client information, which can compromise privacy or be susceptible to manipulation. We introduce a data-free signal based on the matrix von Neumann (spectral) entropy of the final-layer updates, which measures the diversity of the information contributed. We instantiate two practical schemes: (i) SpectralFed, which uses normalized entropy as aggregation weights, and […]

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As Multimodal Large Language Models (MLLMs) mature, GUI agents are evolving from static interactions to complex navigation. While Reinforcement Learning (RL) has emerged as a promising paradigm for training MLLM agents on dynamic GUI tasks, its effective application faces a dilemma. Standard Offline RL often relies on static step-level data, neglecting global trajectory semantics such as task completion and execution quality. Conversely, Online RL captures the long-term dynamics but suffers from high interaction costs and potential environmental instability. […]

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In this quiz, you’ll test your understanding of AI Coding Agents Guide: A Map of the Four Workflow Types. By working through this quiz, you’ll revisit the four common workflow types for AI coding agents: IDE, terminal, pull request, and cloud. You’ll also get a chance to review how to match the right workflow to the task in front of you. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick […]

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How can learning Rust help make you a better Python Developer? How do techniques required by a compiled language translate to improving your Python code? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

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For more than a century, freight rail has evolved around one dominant constraint: energy cost. From steam to coal to diesel, each generation of rail has been defined by the same underlying challenge: how to move massive freight loads while managing one of the industry’s largest and most persistent operating expenses. Despite improvements in engine efficiency and logistics, energy has remained structurally difficult to reduce. In many rail systems, it can still account for up to ~20% of […]

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AI systems are increasingly built around data that does not really pause. Financial markets are an obvious example, where inputs keep updating, not arriving in fixed batches. In that kind of setup, something like the BNB price stops being a single figure and starts to look more like a stream that keeps changing. Cryptocurrency markets tend to exaggerate that effect. Movement is not always smooth and patterns do not always repeat in a clean way. For AI models, […]

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