If you have shipped a serious Angular app in the last few years, you probably have BehaviorSubjects, selector streams, and NgRx slices everywhere—and no one on the team is excited to refactor them. With Angular 19+, Signals are no longer experimental or “nice to have.” They are stable, performant, and increasingly treated as the default way to model local state. As a result, many Angular teams are actively revisiting long-standing RxJS and NgRx patterns—sometimes rewriting them entirely—while community discussions are filled […]

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Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics. We propose SpaTeoGL, a spatiotemporal graph learning framework for interpretable seizure network analysis. SpaTeoGL jointly learns window-level spatial graphs capturing interactions among iEEG electrodes and a temporal graph linking time windows based on similarity of their spatial structure. The method is formulated within a smooth graph signal processing framework and solved via an […]

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The deployment of Machine-Generated Text (MGT) detection systems necessitates processing sensitive user data, creating a fundamental conflict between authorship verification and privacy preservation. Standard anonymization techniques often disrupt linguistic fluency, while rigorous Differential Privacy (DP) mechanisms typically degrade the statistical signals required for accurate detection. To resolve this dilemma, we propose textbf{DP-MGTD}, a framework incorporating an Adaptive Differentially Private Entity Sanitization algorithm. Our approach utilizes a two-stage mechanism that performs noisy frequency estimation and dynamically calibrates privacy budgets, […]

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arXiv:2601.09579v1 Announce Type: cross Abstract: Kernel-based methods are used in the context of Granger Causality to enable the identification of nonlinear causal relationships between time series variables. In this paper, we show that two state of the art kernel-based Granger Causality (GC) approaches can be theoretically unified under the framework of Kernel Principal Component Regression (KPCR), and introduce a method based on this unification, demonstrating that this approach can improve causal identification. Additionally, we introduce a Gaussian Process […]

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arXiv:2603.12270v1 Announce Type: new Abstract: Knowledge distillation from large language models (LLMs) assumes that the teacher’s output distribution is a high-quality training signal. On reasoning tasks, this assumption is frequently violated. A model’s intermediate representations may encode the correct answer, yet this information is lost or distorted through the vocabulary projection, where prompt formatting and answer-token choices creates brittle, noisy outputs. We introduce method{}, a distillation framework that bypasses this bottleneck by training lightweight probes on frozen teacher […]

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Causal relationships play a pivotal role in research within the field of public administration. Ensuring reliable causal inference requires validating the predictability of these relationships, which is a crucial precondition. However, prediction has not garnered adequate attention within the realm of quantitative research in public administration and the broader social sciences. The advent of interpretable machine learning presents a significant opportunity to integrate prediction into quantitative research conducted in public administration. This article delves into the fundamental principles […]

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The SWE-bench task measures AI agents on software engineering tasks at the level of a github issue. It was one of the most important tasks measuring the progress of agents tackling software engineering tasks in 2024. We caught up with two of its creators, Ofir Press and Carlos E. Jimenez, to share their ideas on the state of LLM-backed agents.

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arXiv:2601.22600v1 Announce Type: new Abstract: We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree $mathcal{T}$ and a threshold $theta$, a player must answer whether the root node value of $mathcal{T}$ is at least $theta$ or not. In the given tree, `MAX’ or `MIN’ is labeled on each internal node, and the value of a `MAX’-labeled (`MIN’-labeled) internal node is the maximum (minimum) of its child values. The value of a leaf node is […]

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