SSH-Net is a structured deep neural network designed to predict failure time distribution functions under competing risks. It uses separate sub-networks for different covariate groups, improving accuracy by aligning neural structure with data hierarchy. The model is validated through simulation studies and applied to Titan GPU failure data.
A new framework addresses data bias in machine learning by incorporating coverage constraints to ensure sufficient representation of intersectional subgroups. It trades small bias errors for greater data efficiency and formulates bias mitigation as an integer linear program, characterizing the price of fairness as a function of fairness tolerance to guide data governance and legal compliance.
A study evaluates EEG Foundation Models for event-based burst-suppression detection in ICU settings without patient-specific calibration. REVE-base achieved the highest event-based F1-score of 0.868 and reduced burst-per-minute error by 52.1% compared to EEGNet and 36.2% compared to adaptive thresholding, demonstrating superior performance. Ablation results show full fine-tuning outperforms other strategies, and pretrained REVE-base surpasses random initialization by 0.723 F1 points at 25% labeled data, highlighting the value of pretraining for limited datasets.
A model-agnostic framework combines flow-based generative editing with evolutionary algorithms to enable data editing in non-differentiable settings. It operates in residual space, using self-pollination for local refinement and cross-pollination for broad exploration, validated on MorphoMNIST and crystal data to balance target alignment, instance preservation, and diversity.
This study analyzes and evaluates concept drift detection algorithms across various categories using synthetic and real-world streaming datasets. It examines drift characteristics and evaluates detector performance under abrupt and gradual drift scenarios to improve understanding of drift behavior and detector applicability.
A new deep transfer learning method leverages systems' non-linearities to generate diagnostic data under severe data scarcity. This approach uses a periodic multi-excitation procedure and a novel data visualization technique to augment limited vibration data, enabling effective fault diagnosis via pre-trained CNNs. Experimental results on a railway pantograph validate the method's effectiveness.
A new module called Self-Adaptive Scale-handling (AS) addresses scale heterogeneity in time series forecasting. It uses Scale Calibrating and Scaling Selection to adaptively adjust scaling factors, preserving semantic discriminability and reducing inverse-scaling errors. Experiments on fund sales data show improved performance when integrated into existing forecasting models.
A study across five Swiss cities compares TESSERA and AlphaEarth embeddings with traditional Sentinel data to upscale Local Climate Zone maps to 10-meter resolution using an attention-based U-Net. TESSERA consistently outperforms both Sentinel-1/2 and AlphaEarth, achieving IoU scores of 0.59–0.69 and 0.77–0.82. The results show embeddings reduce manual preprocessing and support scalable, reproducible LCZ mapping, though improved reference data is key for further accuracy gains.
A comparative study evaluates four neural architectures—MLP, ResNet, U-Net, and FNO—as autoregressive predictors of internal battery states using the Doyle-Fuller-Newman model. The U-Net achieves a mean final-step nRMSE of 3% across all state variables and provides a 5.38x speed-up over numerical solvers, demonstrating the importance of spatial inductive bias in surrogate performance.
A study evaluates EEG Foundation Models for event-based burst-suppression detection in ICU EEG without patient-specific calibration. REVE-base achieved the highest event-based F1-score of 0.868 and reduced burst-per-minute error by 52.1% compared to EEGNet. Ablation experiments show full fine-tuning outperforms other strategies, and pretrained REVE-base surpasses random initialization by 0.723 F1 points at 25% labeled data.
This study analyzes and evaluates concept drift detection algorithms across multiple categories using synthetic and real-world streaming datasets. It examines drift characteristics and evaluates detector performance under abrupt and gradual drift scenarios to improve understanding of drift behavior and detector applicability.
The VibrantForests framework uses satellite data trained on lidar samples to generate annual, wall-to-wall maps of canopy cover, height, biomass, basal area, and quadratic mean diameter at 10-meter resolution across the contiguous U.S. It improves accuracy by reducing overestimation in sparse forests and underestimation in dense forests, extending the range of reliable predictions beyond traditional passive-sensor models.
A new hybrid modeling framework uses genomic data and neural networks to predict biokinetic parameters in soil organic matter turnover models. It incorporates ecological constraints to ensure realistic microbial dynamics, even for unobserved variables, and outperforms existing methods on both synthetic and real datasets with minimal training data.
TerraMARS is an end-to-end pipeline that uses a domain-adapted small language model to extract structured information from Mars science literature. It converts unstructured text into JSON format and supports Mars terraforming-related question answering, enabling integration into habitability modeling and digital twin applications. The pipeline uses Google Gemma 3 1B fine-tuned with QLoRA on Mars-specific datasets, though further work is needed to improve accuracy and factual consistency.
A new algorithm estimates note names, key signatures, and local scales from MIDI-like input by jointly optimizing modal and tonal stages. It has been evaluated on jazz lead sheets, solo transcriptions, traditional tunes, and classical piano scores, with additional distances defined between common jazz scales for musicological research.
CzechDocs is a multiway parallel dataset of formatted documents in HTML, DOCX, and PDF formats, covering Czech and minority languages such as Ukrainian, English, Vietnamese, and Russian. It supports evaluation of machine translation systems that preserve document formatting, with a validation subset and evaluation toolkit publicly released. A held-out test split will be used for a future shared task on document-level translation with formatting preservation.
A user asks if anyone with sufficient computing resources can create a large distillation dataset of 70-1 million examples from GLM5.2. The goal is to enable better training of smaller models like Qwen3.5, benefiting the broader community.
A community initiative suggests creating a crowdsourced coding dataset to enable local LLM development. The proposal aims to allow anyone with hardware to contribute data, with more powerful users helping to fine-tune or quantize models, thus reducing reliance on company-released models.
A new automated system addresses extreme class imbalance and asymmetric label noise in Autonomous Emergency Braking data. It uses targeted data augmentation and noise suppression to identify rare delayed and false triggers with 80% improved recall and 50% reduced manual annotation effort, enabling continuous self-improvement in on-vehicle AEB optimization.
XGBoost-Forget enables efficient machine unlearning for XGBoost models on tabular network intrusion datasets. It maintains model performance while achieving faster unlearning compared to full retraining, addressing a gap in unlearning research for tabular data in network intrusion detection.