arXiv:2601.00110v1 Announce Type: new Abstract: In this paper, we present textit{CTMAP}, a large language model (LLM) empowered digital twin framework for connectivity-aware route navigation in millimeter-wave (mmWave) wireless networks. Conventional navigation tools optimize only distance, time, or cost, overlooking network connectivity degradation caused by signal blockage in dense urban environments. The proposed framework constructs a digital twin of the physical mmWave network using OpenStreetMap, Blender, and NVIDIA Sionna’s ray-tracing engine to simulate realistic received signal strength (RSS) maps. […]

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Missed a post last week due to the Thanksgiving long weekend :-). We had gone to San Francisco to see the city and try out a couple of hikes). Just FYI – strolling around SF is also as much a hike as any of the real trails at Mt Sutro – with all the uphill & downhill roads! As for Robotics, I am currently on Week 3 of the Mobility course, which is more of physics than ‘computer […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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arXiv:2601.01223v1 Announce Type: cross Abstract: Clinical decision-making demands uncertainty quantification that provides both distribution-free coverage guarantees and risk-adaptive precision, requirements that existing methods fail to jointly satisfy. We present a hybrid Bayesian-conformal framework that addresses this fundamental limitation in healthcare predictions. Our approach integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores while maintaining rigorous coverage validity. Evaluated on 61,538 admissions across 3,793 U.S. hospitals and 4 regions, our method achieves […]

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Priliminaries A Simple Linear Regression Least Squares Estimation linear algebra Square Loss Function for Regression1 For any input (mathbf{x}), our goal in a regression task is to give a prediction (hat{y}=f(mathbf{x})) to approximate target (t) where the function (f(cdot)) is the chosen hypothesis or model as mentioned in the post https://anthony-tan.com/A-Simple-Linear-Regression/. The difference between (t) and (hat{y}) can be called ‘error’ or more precisely ‘loss’. Because in an approximation task, ‘error’ occurs by chance and always exists, and […]

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arXiv:2312.14886v3 Announce Type: replace-cross Abstract: Gaussian processes (GPs) are the most common formalism for defining probability distributions over spaces of functions. While applications of GPs are myriad, a comprehensive understanding of GP sample paths, i.e. the function spaces over which they define a probability measure, is lacking. In practice, GPs are not constructed through a probability measure, but instead through a mean function and a covariance kernel. In this paper we provide necessary and sufficient conditions on the […]

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arXiv:2601.02410v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into software engineering education has driven the emergence of “Vibe Coding,” a paradigm where developers articulate high-level intent through natural language and delegate implementation to AI agents. While proponents argue this approach modernizes pedagogy by emphasizing conceptual design over syntactic memorization, accumulating empirical evidence raises concerns regarding skill retention and deep conceptual understanding. This paper proposes a theoretical framework to investigate the research question: textit{Is Vibe […]

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