arXiv:2601.02561v1 Announce Type: new Abstract: We propose a novel dispersive regularization framework for the numerical simulation of the one-dimensional shallow water equations (SWE). The classical hyperbolic system is regularized by a third-order dispersive term in the momentum equation, which renders the system equivalent, via the Madelung transform, to a defocusing cubic nonlinear Schr”odinger equation with a drift term induced by bottom topography. Instead of solving the shallow water equations directly, we solve the associated Schr”odinger equation and recover […]

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In this tutorial: You will understand that Exploration vs Exploitation is not a button, it is not “epsilon“, but a real data collection strategy, which decides what the agent can learn and how good it can become. You will see why the training reward can lie to you, why an agent without exploration can look “better” on the graph, but actually be weaker in reality. You will learn where exploration actually occurs in an Markov Decision Process(MDP), not […]

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How do you deploy your Python application without getting locked into an expensive cloud-based service? This week on the show, Michael Kennedy from the Talk Python podcast returns to discuss his new book, “Talk Python in Production.” [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

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arXiv:2601.04301v1 Announce Type: new Abstract: As frontier AI systems are pretrained on web-scale data, test set contamination has become a critical concern for accurately assessing their capabilities. While research has thoroughly investigated the impact of test set contamination on discriminative evaluations like multiple-choice question-answering, comparatively little research has studied the impact of test set contamination on generative evaluations. In this work, we quantitatively assess the effect of test set contamination on generative evaluations through the language model lifecycle. […]

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The accelerated development of social media websites has posed intricate security issues in cyberspace, where these sites have increasingly become victims of criminal activities including attempts to intrude into them, abnormal traffic patterns, and organized attacks. The conventional rule-based security systems are not always scalable and dynamic to meet such a threat. This paper introduces a threat detection framework based on machine learning that can be used to classify malicious behavior in the social media network environment based […]

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Mango holds a significant place globally. Considering its importance, it is also called 2 the king of fruits. Accurate price forecasting is essential for market decisions, policy 3 formulation, and agricultural market stability. Traditional time series models struggle 4 to provide effective and accurate forecasts of Mango prices and cannot capture their 5 nonlinear dynamics. The current study integrates machine learning, deep learning, and 6 statistical models to build a robust forecasting model using a dataset from 2001 […]

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arXiv:2601.05909v1 Announce Type: cross Abstract: As machine learning models become increasingly embedded in societal infrastructure, auditing them for bias is of growing importance. However, in real-world deployments, auditing is complicated by the fact that model owners may adaptively update their models in response to changing environments, such as financial markets. These updates can alter the underlying model class while preserving certain properties of interest, raising fundamental questions about what can be reliably audited under such shifts. In this […]

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arXiv:2601.04257v1 Announce Type: new Abstract: We study multilingual speaker attribute prediction under linguistic variation, domain mismatch, and data imbalance across languages. We propose RLMIL-DAT, a multilingual extension of the reinforced multiple instance learning framework that combines reinforcement learning based instance selection with domain adversarial training to encourage language invariant utterance representations. We evaluate the approach on a five language Twitter corpus in a few shot setting and on a VoxCeleb2 derived corpus covering forty languages in a zero […]

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arXiv:2601.00024v1 Announce Type: new Abstract: The 13-card variant of Classic Indian Rummy is a sequential game of incomplete information that requires probabilistic reasoning and combinatorial decision-making. This paper proposes a rule-based framework for strategic play, driven by a new hand-evaluation metric termed MinDist. The metric modifies the MinScore metric by quantifying the edit distance between a hand and the nearest valid configuration, thereby capturing structural proximity to completion. We design a computationally efficient algorithm derived from the MinScore […]

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We present and develop local prospect theory (LPT), a novel framework for understanding consciousness, and, in particular, subjective experience and free will. While predictive processing (PP) theories model the brain as trying to optimize the accuracy of predictions, LPT sees uncertainty as an essential feature of conscious decision-making. This is achieved by creating a “local prospect”—a range of potential developments colored by subjective experience from which an agent can freely choose how to react. Drawing on global workspace […]

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