technocracy

arXiv:2602.23497v1 Announce Type: new Abstract: Extended Reality (XR) technologies are increasingly tested outside the lab, in homes, schools, and public spaces. While this shift enables more realistic user insights, it also introduces safety challenges that are often overlooked. Physical risks, psychological distress, and accessibility issues can be increased in field studies and unsupervised testing, such as at home or crowdsourced trials. Without clear instructions, safety decisions are left to individual researchers, raising questions of responsibility and consistency. This […]

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arXiv:2603.02267v1 Announce Type: new Abstract: Few-shot text classification aims to recognize unseen classes with limited labeled text samples. Existing approaches focus on boosting meta-learners by developing complex algorithms in the training stage. However, the labeled samples are randomly selected during the testing stage, so they may not provide effective supervision signals, leading to misclassification. To address this issue, we propose a textbf{L}abel-guided textbf{D}istance textbf{S}caling (LDS) strategy. The core of our method is exploiting label semantics as supervision signals […]

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arXiv:2604.15377v1 Announce Type: new Abstract: Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, precipitation prediction remains challenging due to limitations in effectively leveraging diverse multimedia data sources. We introduce M3R, a Meteorology-informed MultiModal attention-based architecture for direct Rainfall prediction that synergistically combines visual NEXRAD radar imagery with numerical Personal Weather Station (PWS) measurements, using a comprehensive pipeline for temporal alignment of heterogeneous meteorological data. With specialized […]

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Check Point’s Cyber Security Report 2026 shows 70% increase in cyber attacks since 2023. 60% of executives reported their organizations faced AI-powered attacks, but only 7% had deployed AI defenses at scale. Moody’s 2026 cyber outlook warns that AI-related threats will “become more prevalent and pronounced”

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:::info Authors: Brendan Nyhan Jaime Settle Emily Thorson Magdalena Wojcieszak Pablo Barberá Annie Y. Chen Hunt Allcott Taylor Brown Adriana Crespo-Tenorio Drew Dimmery Deen Freelon Matthew Gentzkow Sandra González-Bailón Andrew M. Guess Edward Kennedy Young Mie Kim David Lazer Neil Malhotra Devra Moehler Jennifer Pan Daniel Robert Thomas Rebekah Tromble Carlos Velasco Rivera Arjun Wilkins Beixian Xiong Chad Kiewiet de Jonge Annie Franco Winter Mason Natalie Jomini Stroud Joshua A. Tucker ::: Abstract Many critics raise concerns about […]

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This paper investigates whether a single, unified cost function can explain and predict human reaching movements, in contrast with existing approaches that rely on subject- or posture-specific optimization criteria. Using the Minimal Observation Inverse Reinforcement Learning (MO-IRL) algorithm, together with a seven-dimensional set of candidate cost terms, we efficiently estimate time-varying cost weights for a standard planar reaching task. MO-IRL provides orders-of-magnitude faster convergence than bilevel formulations, while using only a fraction of the available data, enabling the […]

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arXiv:2603.07276v1 Announce Type: cross Abstract: Flow maps enable high-quality image generation in a single forward pass. However, unlike iterative diffusion models, their lack of an explicit sampling trajectory impedes incorporating external constraints for conditional generation and solving inverse problems. We put forth Variational Flow Maps, a framework for conditional sampling that shifts the perspective of conditioning from “guiding a sampling path”, to that of “learning the proper initial noise”. Specifically, given an observation, we seek to learn a […]

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arXiv:2510.04602v3 Announce Type: replace Abstract: Wasserstein barycenters provide a principled approach for aggregating probability measures, while preserving the geometry of their ambient space. Existing discrete methods are not scalable as they assume access to the complete set of samples from the input measures. Meanwhile, neural network approaches do scale well, but rely on complex optimization problems and cannot easily incorporate label information. We address these limitations through gradient flows in the space of probability measures. Through time discretization, […]

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