arXiv:2602.22350v1 Announce Type: new Abstract: We introduce the concept of engineered simultaneity: a system design that (1) requires comparing events at spacelike-separated locations, (2) implements this comparison via an implicit simultaneity convention, and (3) represents the result as objective rather than conventional. The United States National Best Bid and Offer (NBBO), mandated by SEC Regulation NMS Rule 611, is shown to be an instance of engineered simultaneity. We prove that the NBBO is frame-dependent: its value depends on […]

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arXiv:2601.08891v1 Announce Type: new Abstract: Early-exit neural networks enable adaptive inference by allowing predictions at intermediate layers, reducing computational cost. However, early exits often lack interpretability and may focus on different features than deeper layers, limiting trust and explainability. This paper presents Explanation-Guided Training (EGT), a multi-objective framework that improves interpretability and consistency in early-exit networks through attention-based regularization. EGT introduces an attention consistency loss that aligns early-exit attention maps with the final exit. The framework jointly optimizes […]

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arXiv:2601.19811v1 Announce Type: new Abstract: Processing high-volume, streaming data is increasingly common in modern statistics and machine learning, where batch-mode algorithms are often impractical because they require repeated passes over the full dataset. This has motivated incremental stochastic estimation methods, including the incremental stochastic Expectation-Maximization (EM) algorithm formulated via stochastic approximation. In this work, we revisit and analyze an incremental stochastic variant of the Majorization-Minimization (MM) algorithm, which generalizes incremental stochastic EM as a special case. Our approach […]

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arXiv:2602.11332v1 Announce Type: new Abstract: In recent years, artificial neural networks have been increasingly studied as feedback controllers for guidance problems. While effective in complex scenarios, they lack the verification guarantees found in classical guidance policies. Their black-box nature creates significant concerns regarding trustworthiness, limiting their adoption in safety-critical spaceflight applications. This work addresses this gap by developing a method to assess the safety of a trained neural network feedback controller via automatic domain splitting and polynomial bounding. […]

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arXiv:2604.09814v1 Announce Type: new Abstract: Medical image segmentation models built on Segment Anything Model (SAM) achieve strong performance on clean benchmarks, yet their reliability often degrades under realistic image corruptions such as noise, blur, motion artifacts, and modality-specific distortions. Existing approaches address either medical-domain adaptation or corruption robustness, but not both jointly. In SAM, we find that these capabilities are concentrated in complementary modules: the image encoder preserves medical priors, while the mask decoder governs corruption robustness. Motivated […]

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arXiv:2601.19029v1 Announce Type: new Abstract: Automated piano performance evaluation traditionally relies on symbolic (MIDI) representations, which capture note-level information but miss the acoustic nuances that characterize expressive playing. I propose using pre-trained audio foundation models, specifically MuQ and MERT, to predict 19 perceptual dimensions of piano performance quality. Using synthesized audio from PercePiano MIDI files (rendered via Pianoteq), I compare audio and symbolic approaches under controlled conditions where both derive from identical source data. The best model, MuQ […]

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arXiv:2603.24742v1 Announce Type: new Abstract: AI safety is an increasingly urgent concern as the capabilities and adoption of AI systems grow. Existing evolutionary models of AI governance have primarily examined incentives for safe development and effective regulation, typically representing users’ trust as a one-shot adoption choice rather than as a dynamic, evolving process shaped by repeated interactions. We instead model trust as reduced monitoring in a repeated, asymmetric interaction between users and AI developers, where checking AI behaviour […]

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:::info Astounding Stories of Super-Science, October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. Title: Astounding Stories of Super-Science, October, 1994 Author: Astounding Stories Release Date: October 1, 1994 [eBook #174] Updated: September 17, 2025 Language: English The Picture of Dorian Gray ::: TABLE OF LINKS Chapter I Chapter II Chapter III Chapter IV Chapter V Chapter VI Chapter VII Chapter VIII Chapter IX Chapter X Chapter XI Chapter XII Chapter XIII Chapter XIV Chapter […]

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arXiv:2602.17693v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) is crucial for efficient model deployment, yet its effectiveness on Ascend NPU remains under-explored compared to GPU architectures. This paper presents a case study of representative PTQ baselines applied to reasoning-oriented models such as DeepSeek-R1-Distill-Qwen series (1.5B/7B/14B) and QwQ-32B. We evaluate four distinct algorithms, including AWQ, GPTQ, SmoothQuant, and FlatQuant, to cover the spectrum from weight-only compression to advanced rotation-based methods. Our empirical results reveal significant platform sensitivity. While 4-bit […]

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