Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regression targets the conditional mean, which is often geometrically misleading for circular responses under multimodal, skewed, or asymmetric data structures. To address these limitations, a lightweight deep generative framework, namely ANGLE, is introduced for non-parametric distributional regression on the circle. The full conditional distribution of an angular response, given Euclidean and circular covariates, is learned through a generative map optimized via […]

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arXiv:2602.10304v1 Announce Type: new Abstract: While cervical arthroplasty using Total Disc Replacement (TDR) implants is an established treatment for persistent neck and arm pain, revision rates limit it from reaching its full potential. To address the underlying complications, we developed finite element simulation-driven design optimizations for a TDR’s bone-implant interface and motion-preservation features. These automated processes explored high-dimensional design spaces iteratively through analysis of design variations interplay with spinal structures. The optimizations were metamodel-based using artificial neural networks […]

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arXiv:2603.00616v1 Announce Type: new Abstract: Modern cyber-physical systems, such as automotive control, rely on feedback controllers that regulate the system towards desired a setpoint. In practice, however, the controller must also be scheduled efficiently on resource-constrained processors, where the choice of numerical precision for controller implementation directly affects both control quality and computational cost. This trade-off is critical: higher precision improves control performance but increases runtime, while lower precision executes faster in the processor but may degrade overall […]

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Predict-then-Optimize (PTO) pipelines are widely employed in computing and networked systems, where Machine Learning (ML) models are used to predict critical contextual information for downstream decision-making tasks such as cloud LLM serving, data center demand response, and edge workload scheduling. However, these ML predictors are often vulnerable to out-of-distribution (OOD) samples at test time, leading to significant decision performance degradation due to large prediction errors. To address the generalization challenges under OOD conditions, we present the framework of […]

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TL;DR. PMB (Personal Memory Brain) is an open-source MCP server that gives AI coding agents persistent, long-term memory. Everything runs on your machine – SQLite + LanceDB, zero cloud, zero API keys. On the LoCoMo benchmark it hits 94.5% recall@10 at 70ms p50, matching or beating cloud-based memory services. This post is mostly the techniques that got it there – predicate-aware reranking, multilingual verb expansion, no-LLM atomic fact extraction, RRF query splitting, and a durable async embed queue […]

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