arXiv:2603.28921v1 Announce Type: new Abstract: Standard neural network training uses constant momentum (typically 0.9), a convention dating to 1964 with limited theoretical justification for its optimality. We derive a time-varying momentum schedule from the critically damped harmonic oscillator: mu(t) = 1 – 2*sqrt(alpha(t)), where alpha(t) is the current learning rate. This beta-schedule requires zero free parameters beyond the existing learning rate schedule. On ResNet-18/CIFAR-10, beta-scheduling delivers 1.9x faster convergence to 90% accuracy compared to constant momentum. More importantly, […]

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arXiv:2603.25933v1 Announce Type: new Abstract: Social anxiety (SA) is a prevalent mental health challenge that significantly impacts daily social interactions. Imaginal Exposure (IE), a Cognitive Behavioral Therapy (CBT) technique involving imagined anxiety-provoking scenarios, is effective but underutilized, in part because traditional IE homework requires clients to construct and sustain clinically relevant fear narratives. In this work, we explore the feasibility of an LLM-enabled tool that supports IE by generating vivid, personalized exposure scripts. We first co-designed ImaginalExpoBot with […]

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arXiv:2505.21799v4 Announce Type: replace-cross Abstract: The ever-growing scale of deep learning models and training data underscores the critical importance of efficient optimization methods. While preconditioned gradient methods such as Adam and AdamW are the de facto optimizers for training neural networks and large language models, structure-aware preconditioned optimizers like Shampoo and Muon, which utilize the matrix structure of gradients, have demonstrated promising evidence of faster convergence. In this paper, we introduce a unifying framework for analyzing “matrix-aware” preconditioned […]

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This study examines the challenges of modeling complex and noisy data related to socioeconomic factors over time, with a focus on data from various districts in Odisha, India. Traditional time-series models struggle to capture both trends and variations together in this type of data. To tackle this, a Variational Neural Stochastic Differential Equation (V-NSDE) model is designed that combines the expressive dynamics of Neural SDEs with the generative capabilities of Variational Autoencoders (VAEs). This model uses an encoder […]

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arXiv:2604.02446v1 Announce Type: new Abstract: We explore the automatic detection of violin width reduction using 3D photogrammetric meshes. We compare SVM and Decision Trees applied to a geometry-based raw representation built from elevation maps with a more targeted, feature-engineered approach relying on parametric contour lines fitting. Although elevation maps occasionally achieve strong results, their performance does not surpass that of the contour-based inputs.

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arXiv:2602.12421v1 Announce Type: new Abstract: Autonomous satellite servicing missions must execute close-range rendezvous under stringent safety and operational constraints while remaining computationally tractable for onboard use and robust to uncertainty in sensing, actuation, and dynamics. This paper presents CORTEX (Convex Optimization for Rendezvous Trajectory Execution), an autonomous, perception-enabled, real-time trajectory design and guidance framework for close-range rendezvous. CORTEX integrates a deep-learning perception pipeline with convex-optimisation-based trajectory design and guidance, including reference regeneration and abort-to-safe-orbit logic to recover from […]

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Post-quantum migration for ZK proof systems is an architecture problem, not a parameter-tuning problem. This research note proposes a four-layer framework for analyzing where quantum-vulnerable assumptions sit in a proving stack, a three-condition modularity test for whether deployed systems can actually migrate, and applies them to Zcash, ZKsync Era, and Starknet.

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