arXiv:2601.04221v1 Announce Type: new Abstract: This paper presents a new approach to algorithmic composition, called predictive controlled music (PCM), which combines model predictive control (MPC) with music generation. PCM uses dynamic models to predict and optimize the music generation process, where musical notes are computed in a manner similar to an MPC problem by optimizing a performance measure. A feedforward neural network-based assessment function is used to evaluate the generated musical score, which serves as the objective function […]

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Finalist teams advance in the Amazon Nova AI Challenge: Trusted AI Track Top eight university teams move on to head-to-head finals focused on AI security for code generation. Conversational AI Staff writer June 24, 02:11 PM June 30, 02:49 PM Since November 2024, ten top university teams from around the world have competed in the inaugural Amazon Nova AI Challenge: Trusted AI Track, focused on strengthening security in AI coding assistants and developing new automated methods to red-team […]

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Published on January 25, 2026 6:33 PM GMT This post was originally published on November 11th, 2025. I’ve been spending some time reworking and cleaning up the Inkhaven posts I’m most proud of, and completed the process for this one today.   Today, Canada officially lost its measles elimination status. Measles was previously declared eliminated in Canada in 1998, but countries lose that status after 12 months of continuous transmission. Here are some articles about the the fact […]

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The widespread deployment of autonomous driving systems critically depends on guaranteeing safety, especially in complex, dynamic environments. Traditional reactive approaches often fall short in highly uncertain or rapidly evolving situations, leaving minimal margins for error and lacking transparency. To address these limitations, we propose textit{Proactive-Scene}, a novel framework that transitions autonomous driving from passive reaction to active prevention, providing both safety and transparency. Our framework comprises two core components: the Multi-Agent Risk Prediction Network (MARP-Net), which leverages Graph […]

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We introduce the Informational Coherence Index (Icoer), a bounded metric for quantifying the degree of integration and alignment among interconnected artificial intelligence (AI) models. The index combines four interpretable components: normalized processing capacity, a Gaussian informational coupling function that decays with inter-model distance, an entropy-based weight reflecting output uncertainty, and a Lorentzian resonance factor capturing synchronicity. We prove that Icoer ∈ [0, 1] and is monotonically decreasing in both informational distance and entropy. We derive closed-form gradients for […]

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Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes’ probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this first part of a series, we will take a look at the theory of naive Bayes classifiers and introduce the basic concepts of text classification. In following articles, we will implement those concepts to train a naive Bayes spam filter and […]

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arXiv:2601.22195v1 Announce Type: new Abstract: Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) has entered the era of Big Data, and the computational demands for effectively analyzing large EO data with complex deep learning models have become a bottleneck. Motivated by this, we aim to leverage quantum computing for EO data classification and explore its advantages despite the current limitations of […]

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[Editor’s Note: This class was a part of the 2019 DFL Jane Street Fellowship.] This guide would not have been possible without the help and feedback from many people. Special thanks to Yasaman Bahri for her feedback, support, and mentoring. Thank you to Kumar Krishna Agrawal, Sam Schoenholz, and Jeffrey Pennington for their valuable input and guidance. Finally, thank you to our group members Chris Akers, Brian Friedenberg, Sajel Shah, Vincent Su, Witold Szejgis, for their curiosity, commitment […]

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