January 2026

arXiv:2601.19040v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. Existing augmentation methods, however, typically rely on heuristics and static paradigms. Motivated by dynamic data optimization, which shows that the contribution of samples varies across training stages, we propose OATS (Online Data Augmentation for Time Series Foundation Models), a principled strategy that generates synthetic data tailored […]

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arXiv:2601.19036v1 Announce Type: new Abstract: High-quality motion data underpins games, film, XR, and robotics. Vision-based motion capture tools have made significant progress, offering accessible and visually convincing results, yet often fall short in the final stretch — the last mile — when it comes to physical realism and production readiness, due to various artifacts introduced during capture. In this paper, we summarize key issues through case studies and feedback from professional animators to set a stepping stone for […]

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arXiv:2601.19035v1 Announce Type: new Abstract: A key challenge in employing data, algorithms and data-driven systems is to adhere to the principle of fairness and justice. Statistical fairness measures belong to an important category of technical/formal mechanisms for detecting fairness issues in data and algorithms. In this contribution we study the relations between two types of statistical fairness measures namely Statistical-Parity and Equalized-Odds. The Statistical-Parity measure does not rely on having ground truth, i.e., (objectively) labeled target attributes. This […]

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arXiv:2601.19029v1 Announce Type: new Abstract: Automated piano performance evaluation traditionally relies on symbolic (MIDI) representations, which capture note-level information but miss the acoustic nuances that characterize expressive playing. I propose using pre-trained audio foundation models, specifically MuQ and MERT, to predict 19 perceptual dimensions of piano performance quality. Using synthesized audio from PercePiano MIDI files (rendered via Pianoteq), I compare audio and symbolic approaches under controlled conditions where both derive from identical source data. The best model, MuQ […]

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arXiv:2601.19027v1 Announce Type: new Abstract: The evolution of the Radio Access Network (RAN) in 5G and 6G technologies marks a shift toward open, programmable, and softwarized architectures, driven by the Open RAN paradigm. This approach emphasizes open interfaces for telemetry sharing, intelligent data-driven control loops for network optimization, and virtualization and disaggregation of multi-vendor RAN components. While promising, this transition introduces significant challenges, including the need to design interoperable solutions, acquire datasets to train and test AI/ML algorithms […]

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arXiv:2601.19026v1 Announce Type: new Abstract: Microscaling data formats leverage per-block tensor quantization to enable aggressive model compression with limited loss in accuracy. Unlocking their potential for efficient training and inference necessitates hardware-friendly implementations that handle matrix multiplications in a native format and adopt efficient error-mitigation strategies. Herein, we report the emergence of a surprising behavior associated with microscaling quantization, whereas the output of a quantized model degrades as block size is decreased below a given threshold. This behavior […]

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arXiv:2601.19022v1 Announce Type: new Abstract: Forecasting rare events in multivariate time-series data is challenging due to severe class imbalance, long-range dependencies, and distributional uncertainty. We introduce EVEREST, a transformer-based architecture for probabilistic rare-event forecasting that delivers calibrated predictions and tail-aware risk estimation, with auxiliary interpretability via attention-based signal attribution. EVEREST integrates four components: (i) a learnable attention bottleneck for soft aggregation of temporal dynamics; (ii) an evidential head for estimating aleatoric and epistemic uncertainty via a Normal–Inverse–Gamma distribution; […]

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arXiv:2601.19021v1 Announce Type: new Abstract: Research with dementia caregivers poses persistent methodological and ethical challenges, particularly when interview-based studies are designed without sufficient grounding in lived caregiving realities. Questions framed through clinical or deficit-oriented assumptions risk alienating participants, undermining rapport, and producing shallow or ethically fraught data. While human-computer interaction (HCI) research increasingly adopts participatory approaches in technology design, participation rarely extends to the design of research methods themselves. This paper examines the role of lived-experience advisors as […]

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arXiv:2601.19020v1 Announce Type: new Abstract: This paper presents an adaptive numerical framework for solving exterior “sound-soft” scattering problems governed by the Helmholtz equation. By interpreting the Method of Fundamental Solutions through the lens of rational approximation, we introduce an automated strategy for singularity placement based on the analytic continuation of boundary data. The proposed AAALS-Helmholtz algorithm leverages a “continuum” variant of the AAA algorithm to identify the singularities limiting analytic extension, and to ensure an optimal source distribution […]

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arXiv:2601.19017v1 Announce Type: new Abstract: Explainable AI (XAI) is commonly applied to anomalous sound detection (ASD) models to identify which time-frequency regions of an audio signal contribute to an anomaly decision. However, most audio explanations rely on qualitative inspection of saliency maps, leaving open the question of whether these attributions accurately reflect the spectral cues the model uses. In this work, we introduce a new quantitative framework for evaluating XAI faithfulness in machine-sound analysis by directly linking attribution […]

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