April 2026

You have alerts for everything. Uptime, latency, error rates. So why is nobody tracking whether a clinician can complete a workflow without opening a compensatory spreadsheet in a second tab? Medtech doesn’t have a UX problem. It has a measurement problem that everyone quietly agreed to call a UX problem — because a UX problem is someone else’s job to fix. I’ve spent eight years embedded in clinical products. The large ones, the specialised ones, the ones where […]

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Learning the underlying potential energy of stochastic gradient systems from partial and noisy observations is a fundamental problem arising in physics, chemistry, and data-driven modeling. Classical approaches often rely on direct regression of governing equations or velocity fields, which can be sensitive to noise and external perturbations and may fail when observations are incomplete. In this work, we propose a structure-aware, energy-based learning framework for inferring unknown potential functions in generalized diffusion processes, grounded in the energetic variational […]

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For years, undervolting has been the holy grail of laptop thermal management. It is the first thing any enthusiast does with a new machine. Fine-tune the voltage curve in MSI Afterburner, run a stress test, and enjoy lower temperatures and potentially higher boost clocks. I have been doing it for every laptop I have owned. But when I started running long, multi-hour Stable Diffusion sessions, I discovered a frustrating truth: undervolting is not enough. It is a fantastic […]

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Tech media did not collapse. It scaled. That may be the bigger story. Over the years, tech journalism became cleaner, faster, smarter, more organized, and more commercially efficient. The websites have improved. The headlines got sharper. The coverage became more structured. The business models got tighter. On the surface, that looks like progress. In many ways, it is. But something else happened at the same time. A lot of tech media stopped feeling like a living internet space […]

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Running a multi-tiered cloud setup without an Architecture diagram is pretty much like driving a car blind. Individual components might be working together just fine, but when multiple systems are interacting, there are issues regarding overarching security boundaries , it is a Total guesswork. When visibility tanks, you’re left dealing with fragile integrations, isolated decisions, and a mountain of technical debt that just keeps growing. Diagrams act as ultimate translation layer between raw, abstract code and actual human […]

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We love to anthropomorphize our tools. When a Large Language Model produces a brilliant piece of system architecture or a perfectly structured product requirement document, it is tempting to pause and wonder: Is the machine actually thinking? The philosophical counterpunch is well-rehearsed. Aren’t humans just advanced prediction engines? Aren’t we drawing on our own training data, memories and experiences, to guess what we should do or say next, perhaps just running on a noisier temperature setting? It is […]

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Accurate prediction of thermal runaway in lithium-ion batteries is essential for ensuring the safety, efficiency, and reliability of modern energy storage systems. Conventional data-driven approaches, such as Long Short-Term Memory (LSTM) networks, can capture complex temporal dependencies but often violate thermodynamic principles, resulting in physically inconsistent predictions. Conversely, physics-based thermal models provide interpretability but are computationally expensive and difficult to parameterize for real-time applications. To bridge this gap, this study proposes a Physics-Informed Long Short-Term Memory (PI-LSTM) framework […]

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Multi-task learning is effective for related applications, but its performance can deteriorate when the target sample size is small. Transfer learning can borrow strength from related studies; yet, many existing methods rely on restrictive bounded-difference assumptions between the source and target models. We propose SMART, a spectral transfer method for multi-task linear regression that instead assumes spectral similarity: the target left and right singular subspaces lie within the corresponding source subspaces and are sparsely aligned with the source […]

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arXiv:2604.18819v1 Announce Type: new Abstract: The integration of Fog Computing with Flying Ad-Hoc Networks (FANETs) offers promising capabilities for decentralized, low-latency intelligence in UAV-based applications. However, the distributed nature, mobility, and resource constraints of FANETs expose them to significant security and privacy challenges, particularly against quantum threats. To address these issues, this work introduces a blockchain-based, AI-enhanced key management framework designed for fog-enabled FANETs. The proposed scheme employs a Post-Quantum Multivariate Identity-Based Signature Scheme (PQ-MISS) and Zero-Knowledge Proofs […]

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arXiv:2604.18816v1 Announce Type: new Abstract: Principal Component Analysis (PCA) is a fundamental tool for representation learning, but its global linear formulation fails to capture the structure of data supported on curved manifolds. In contrast, manifold learning methods model nonlinearity but often sacrifice the spectral structure and stability of PCA. We propose emph{Geodesic Tangent Space Aggregation PCA (GTSA-PCA)}, a geometric extension of PCA that integrates curvature awareness and geodesic consistency within a unified spectral framework. Our approach replaces the […]

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