arXiv:2603.16983v1 Announce Type: new Abstract: Machine learning models for geotechnical hazard prediction can achieve high accuracy while learning physically inconsistent relationships from sparse or biased training data. Current remedies (post-hoc explainability, such as SHAP and LIME, and training-time constraints) either diagnose individual predictions approximately or restrict model capacity without providing exhaustive guarantees. This paper encodes trained tree ensembles as logical formulas in a Satisfiability Modulo Theories (SMT) solver and checks physical specifications across the entire input domain, not […]

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:::info Astounding Stories of Super-Science, April, 2004, by Astounding Stories is part of HackerNoon’s Book Blog Post series. Title: Astounding Stories of Super-Science, October, 2022 Author: Astounding Stories Release Date: April 1, 2004 [eBook #11870] Updated: October 28, 2024 Language: English THE COUNTRY OF THE BLIND ::: TABLE OF LINKS I. — THE JILTING OF JANE. II. — THE CONE. III. — THE STOLEN BACILLUS. IV. — THE FLOWERING OF THE STRANGE ORCHID. V. — IN THE AVU OBSERVATORY. […]

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Graph neural networks (GNNs) are designed to use attributed graphs to learn representations. Such representations are beneficial in the unsupervised learning of clusters and community detection. Nonetheless, such inference may reveal sensitive groups, clustered systems, or collective behaviors, raising concerns regarding group-level privacy. Community attribution in social and critical infrastructure networks, for example, can expose coordinated asset groups, operational hierarchies, and system dependencies that could be used for profiling or intelligence gathering. We study a defensive setting in […]

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arXiv:2603.23523v1 Announce Type: new Abstract: Recent 3D Large-Language Models (3D-LLMs) claim to understand 3D worlds, especially spatial relationships among objects. Yet, we find that simply fine-tuning a language model on text-only question-answer pairs can perform comparably or even surpass these methods on the SQA3D benchmark without using any 3D input. This indicates that the SQA3D benchmark may not be able to detect if the model exploits textual shortcuts rather than engages in 3D-aware reasoning. To address this issue, […]

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arXiv:2602.08980v1 Announce Type: cross Abstract: Neural ordinary differential equations (neural ODEs) can effectively learn dynamical systems from time series data, but their behavior on graph-structured data remains poorly understood, especially when applied to graphs with different size or structure than encountered during training. We study neural ODEs ($mathtt{nODE}$s) with vector fields following the Barab’asi-Barzel form, trained on synthetic data from five common dynamical systems on graphs. Using the $mathbb{S}^1$-model to generate graphs with realistic and tunable structure, we […]

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arXiv:2602.02602v1 Announce Type: new Abstract: 3D content acquisition and creation are expanding rapidly in the new era of machine learning and AI. 3D Gaussian Splatting (3DGS) has become a promising high-fidelity and real-time representation for 3D content. Similar to the initial wave of digital audio-visual content at the turn of the millennium, the demand for intellectual property protection is also increasing, since explicit and editable 3D parameterization makes unauthorized use and dissemination easier. In this position paper, we […]

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arXiv:2601.06730v2 Announce Type: replace-cross Abstract: The Rashomon effect — the existence of multiple, distinct models that achieve nearly equivalent predictive performance — has emerged as a fundamental phenomenon in modern machine learning and statistics. In this paper, we explore the causes underlying the Rashomon effect, organizing them into three categories: statistical sources arising from finite samples and noise in the data-generating process; structural sources arising from non-convexity of optimization objectives and unobserved variables that create fundamental non-identifiability; and […]

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arXiv:2602.04012v1 Announce Type: new Abstract: Understanding self-organization in natural collectives such as bird flocks inspires swarm robotics, yet most flocking models remain reactive, overlooking anticipatory cues that enhance coordination. Motivated by avian postural and wingbeat signals, as well as multirotor attitude tilts that precede directional changes, this work introduces a principled, bio-inspired anticipatory augmentation of reactive flocking termed Future Direction-Aware (FDA) flocking. In the proposed framework, agents blend reactive alignment with a predictive term based on short-term estimates […]

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arXiv:2603.06687v1 Announce Type: new Abstract: Geo-temporal understanding, the ability to infer location, time, and contextual properties from visual input alone, underpins applications such as disaster management, traffic planning, embodied navigation, world modeling, and geography education. Although recent vision-language models (VLMs) have advanced image geo-localization using cues like landmarks and road signs, their ability to reason about temporal signals and physically grounded spatial cues remains limited. To address this gap, we introduce TimeSpot, a benchmark for evaluating real-world geo-temporal […]

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The intersection of many-body physics and deep learning has opened a new frontier: Neural Quantum States (NQS). While traditional methods struggle with high-dimensional frustrated systems, the global attention mechanism of Transformers provides a powerful tool for capturing complex quantum correlations. In this tutorial, we implement a research-grade Variational Monte Carlo (VMC) pipeline using NetKet and JAX to solve the frustrated J1–J2 Heisenberg spin chain. We will: Build a custom Transformer-based NQS architecture. Optimize the wavefunction using Stochastic Reconfiguration […]

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