arXiv:2602.07219v1 Announce Type: new Abstract: We study the approximation of the median of $d$ inputs using ReLU neural networks. We present depth-width tradeoffs under several settings, culminating in a constant-depth, linear-width construction that achieves exponentially small approximation error with respect to the uniform distribution over the unit hypercube. By further establishing a general reduction from the maximum to the median, our results break a barrier suggested by prior work on the maximum function, which indicated that linear width […]

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arXiv:2602.09202v1 Announce Type: new Abstract: The accelerating militarization of artificial intelligence has transformed the ethics, politics, and governance of warfare. This article interrogates how AI-driven targeting systems function as epistemic infrastructures that classify, legitimize, and execute violence, using Israel’s conduct in Gaza as a paradigmatic case. Through the lens of responsibility, the article examines three interrelated dimensions: (a) political responsibility, exploring how states exploit AI to accelerate warfare while evading accountability; (b) professional responsibility, addressing the complicity of […]

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I’ve been experimenting with real-time pipelines that combine OCR + TTS + voice conversion, and I ended up building a desktop app that can “voice” game subtitles dynamically. The idea is simple: – Capture subtitles from screen (OCR) – Convert them into speech (TTS) – Transform the voice per character (RVC) But the hard parts were: – Avoiding repeated subtitle spam (similarity filtering) – Keeping latency low (~0.3s) – Handling multiple characters with different voice models without reloading […]

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A Digital Twin (DT) may protect information that is considered private to its associated physical system. For a mobile device, this may include its mobility profile, recent location(s), and experienced channel conditions. Online schedulers, however, typically use this type of information to perform tasks such as shared bandwidth and channel time slot assignments. In this paper, we consider three transmission scheduling problems with energy constraints, where such information is needed, and yet must remain private: minimizing total transmission […]

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Table of links Abstract 1 Introduction 2 Background and Related Work Software Security Coding Weaknesses Security Shift-Left Modern Code Review Code Review for Software Security Security Concern Handling Process in Code Review 3 Motivating Examples 4 Case Study Design Research Questions Studied Projects Data Collection Coding Weakness Taxonomy Study Overview Security Concern Identification Approach (RQ1) Alignment Analysis of Known Vulnerabilities (RQ2) Handling Process Identification (RQ3) 5 Preliminary Analysis PA1: Prevalence of Coding Weakness Comments PA2: Preliminary Evaluation of […]

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Magnetic resonance spectroscopy (MRS) is used to quantify metabolites in vivo and estimate biomarkers for conditions ranging from neurological disorders to cancers. Quantifying low-concentration metabolites such as GABA ($γ$-aminobutyric acid) is challenging due to low signal-to-noise ratio (SNR) and spectral overlap. We investigate and validate deep learning for quantifying complex, low-SNR, overlapping signals from MEGA-PRESS spectra, devise a convolutional neural network (CNN) and a Y-shaped autoencoder (YAE), and select the best models via Bayesian optimisation on 10,000 simulated […]

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arXiv:2602.17729v1 Announce Type: new Abstract: Across academia, industry, and government, “AI” has become central in research and development, regulatory debates, and promises of ever faster and more capable decision-making and action. In numerous domains, especially safety-critical ones, there are significant concerns over how “AI” may affect decision-making, responsibility, or the likelihood of mistakes (to name only a few categories of critique). However, for most critiques, the target is generally “AI”, a broad term admitting many (types of) systems […]

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arXiv:2601.04277v1 Announce Type: new Abstract: Post-training improves large language models (LLMs) but often worsens confidence calibration, leading to systematic overconfidence. Recent unsupervised post-hoc methods for post-trained LMs (PoLMs) mitigate this by aligning PoLM confidence to that of well-calibrated pre-trained counterparts. However, framing calibration as static output-distribution matching overlooks the inference-time dynamics introduced by post-training. In particular, we show that calibration errors arise from two regimes: (i) confidence drift, where final confidence inflates despite largely consistent intermediate decision processes, […]

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In this article, we will take deploy a Super Resolution App on the cloud using lightning.ai. The primary goal here is to see how easy it is to create and share a research demo. However, the cloud is for more than just model sharing: we will also learn how we can tap into additional GPU resources for model training.

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arXiv:2602.21302v1 Announce Type: new Abstract: Dynamic manipulation of deformable objects is challenging for humans and robots because they have infinite degrees of freedom and exhibit underactuated dynamics. We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of deformable objects. We demonstrate this method on a non-planar rope manipulation task called the flying knot. Using a single human demonstration and a simplified rope model, the method learns directly on hardware without reliance on large amounts of demonstration […]

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