arXiv:2603.26677v1 Announce Type: new Abstract: Souls-like games exemplify how digital play can produce radical forms of pleasure through sustained challenge: players voluntarily invest tens or hundreds of hours in experiences designed to kill them repeatedly. This paper theorizes ordeal pleasure as a community-level phenomenon that emerges most fully when three mechanisms reinforce one another: Ludic Cultivation (mastery through fair adversity), Aspirational Deferment (delayed gratification oriented toward future growth), and Communal Mythopoesis (collective construction of shared meaning). Drawing on […]

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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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arXiv:2603.24733v1 Announce Type: new Abstract: Quantifying human movement (kinematics) and musculoskeletal forces (kinetics) at scale, such as estimating quadriceps force during a sit-to-stand movement, could transform prediction, treatment, and monitoring of mobility-related conditions. However, quantifying kinematics and kinetics traditionally requires costly, time-intensive analysis in specialized laboratories, limiting clinical translation. Scalable, accurate tools for biomechanical assessment are needed. We introduce OpenCap Monocular, an algorithm that estimates 3D skeletal kinematics and kinetics from a single smartphone video. The method refines […]

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arXiv:2604.06289v1 Announce Type: new Abstract: In this paper, we analyze and improve the adversarial robustness of a convolutional neural network (CNN) that assists crystal-collimator alignment at CERN’s Large Hadron Collider (LHC) by classifying a beam-loss monitor (BLM) time series during crystal rotation. We formalize a local robustness property for this classifier under an adversarial threat model based on real-world plausibility. Building on established parameterized input-transformation patterns used for transformation- and semantic-perturbation robustness, we instantiate a preprocessing-aware wrapper for […]

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arXiv:2602.10248v1 Announce Type: new Abstract: We study the problem of selecting the shape parameter in Radial Basis function (RBF) interpolation using leave-one-out-cross-validation (LOOCV). Since the classical LOOCV formula requires repeated solves with a dense $N times N$ kernel matrix, we combine a Nystr”{o}m approximation with the Woodbury identity to obtain an efficient surrogate objective that avoids large matrix inversions. Based on this reduced form, we compare a grid-based search with a gradient descent strategy and examine their behavior […]

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arXiv:2603.15651v1 Announce Type: new Abstract: The early prediction of sepsis in intensive care unit (ICU) patients is crucial for improving survival rates. However, the development of accurate predictive models is hampered by data fragmentation across healthcare institutions and the complex, temporal nature of medical data, all under stringent privacy constraints. To address these challenges, we propose a novel framework that uniquely integrates federated learning (FL) with a medical knowledge graph and a temporal transformer model, enhanced by meta-learning […]

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arXiv:2602.20306v1 Announce Type: new Abstract: High-fidelity computational models of cardiac mechanics provide mechanistic insight into the heart function but are computationally prohibitive for routine clinical use. Surrogate models can accelerate simulations, but generalization across diverse anatomies is challenging, particularly in data-scarce settings. We propose a two-step framework that decouples geometric representation from learning the physics response, to enable shape-informed surrogate modeling under data-scarce conditions. First, a shape model learns a compact latent representation of left ventricular geometries. The […]

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arXiv:2601.21027v1 Announce Type: new Abstract: This paper presents a global trajectory optimization framework for minimizing lap time in autonomous racing under uncertain vehicle dynamics. Optimizing the trajectory over the full racing horizon is computationally expensive, and tracking such a trajectory in the real world hardly assures global optimality due to uncertain dynamics. Yet, existing work mostly focuses on dynamics learning at the tracking level, without updating the trajectory itself to account for the learned dynamics. To address these […]

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TL;DR: Zomato’s “Friend Recommendations” API allows unilateral contact syncing. By uploading a phone number, bad actors can extract a user’s restaurant recommendation history and restaurant coordinates. By mapping overlapping delivery radii, an attacker can estimate a user’s approximate physical location without their consent. GitHub Repository & Full Proof of Concept Code: https://github.com/jatin-dot-py/zomato-intelligence (Status: As of 26 Feb, the endpoints remain unpatched) Last week, I reported a privacy flaw in Zomato’s “Friend Recommendations” feature with a proof-of-concept video and […]

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arXiv:2602.07160v1 Announce Type: cross Abstract: Standard attention stores keys/values losslessly but reads them via a per-head convex average, blocking channel-wise selection. We propose the Free Energy Mixer (FEM): a free-energy (log-sum-exp) read that applies a value-driven, per-channel log-linear tilt to a fast prior (e.g., from queries/keys in standard attention) over indices. Unlike methods that attempt to improve and enrich the $(q,k)$ scoring distribution, FEM treats it as a prior and yields a value-aware posterior read at unchanged complexity, […]

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