Multimodal
arxiv arXiv cs.LG · 7d ago

Latent SDEs for Anomaly Detection in Sparse Multivariate Time Series

We propose a generative method using Latent SDEs to detect anomalies in sparse and irregular multivariate time series. The approach projects observed data onto continuous-time stochastic systems, handling missing values and irregular sampling while capturing cyclic patterns. Experiments on six benchmark datasets show our method achieves top performance, outperforming state-of-the-art baselines, especially under severe data sparsity.

arxiv arXiv cs.LG · 7d ago

INDEQS: Graph-Informed Neural Controlled Differential Equations

INDEQS introduces a graph-based neural controlled differential equation framework that incorporates prior directed graph knowledge at architectural levels. It separates inner and outer mixing, offering both graph-constrained and data-adaptive variants, with outer informedness reducing mean absolute error on larger graphs, while inner informedness provides parameter efficiency for known adjacency adherence. Continuous decoders outperform discrete ones in real-world traffic and hydrological forecasting tasks.

arxiv arXiv cs.CL · 7d ago

Morpheus: Neural Tokenizer and Embedder for Turkish

Morpheus is a morphology-aware neural tokenizer and word embedder for Turkish that preserves original text through lossless encoding and decoding. It achieves the lowest bits-per-character (1.425), improves morphological alignment (MorphScore macro-F1 0.61), and uses 19% less GPU memory than 64K-vocabulary subword tokenizers. Frozen Morpheus embeddings outperform BGE-M3 and BERTurk in lexical retrieval, with root-family MAP of 0.85 and ROC-AUC of 1.00.

arxiv arXiv cs.CL · 7d ago

SAMA: Unified Framework for Low-Resource Multimodal Data Augmentation

SAMA introduces a unified framework that generates high-fidelity, task-aware synthetic data by aligning semantic anchors across modalities. It uses a Collaborative Multi-Experts Multimodal Large Language Model with shared and task-specific adapters, and employs an Anchor-Preserving Diffusion mechanism for image synthesis, ensuring semantic consistency while diversifying visual contexts. Extensive experiments show SAMA outperforms state-of-the-art methods in MNER, MRE, and MEE under low-resource conditions.

arxiv arXiv cs.CL · 7d ago

IndicContextEval: Benchmark for Context Utilisation in Audio LLMs

IndicContextEval introduces a 56-hour multilingual benchmark featuring natural speech from 555 speakers across 8 Indian languages and 23 domains. It employs a 7-level prompting framework to progressively test context utilisation, including metadata, descriptions, and adversarial inputs. Evaluation of five models shows significant differences in contextual grounding, underscoring the need for explicit assessment of context use in AudioLLMs.

arxiv arXiv cs.AI · 7d ago

Quantum GAN Augmentation Shows No Benefit in Brain MRI

A controlled benchmark found no significant performance gain from quantum generative models in brain MRI augmentation. Synthetic samples produced by quantum and classical GANs were statistically indistinguishable, with both showing mode collapse and off-distribution samples, especially at low data fractions. The study concludes that quantum augmentation does not provide meaningful data expansion and acts more as regularization.