Tootfinder

Automated Glaucoma Report Generation via Dual-Attention Semantic Parallel-LSTM and Multimodal Clinical Data Integration
Cheng Huang, Weizheng Xie, Zeyu Han, Tsengdar Lee, Karanjit Kooner, Jui-Ka Wang, Ning Zhang, Jia Zhang
https://arxiv.org/abs/2510.10037

Automated Glaucoma Report Generation via Dual-Attention Semantic Parallel-LSTM and Multimodal Clinical Data Integration
Generative AI for automated glaucoma diagnostic report generation faces two predominant challenges: content redundancy in narrative outputs and inadequate highlighting of pathologically significant features including optic disc cupping, retinal nerve fiber layer defects, and visual field abnormalities. These limitations primarily stem from current multimodal architectures' insufficient capacity to extract discriminative structural-textural patterns from fundus imaging data while maintaining pre…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/7]:
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF: A Reproducibility Study
Anirudh Ganesh, Jayavardhan Reddy

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Abdullahi Mohammad, Bdah Eya, Bassant Selim
https://arxiv.org/abs/2510.12179 https://

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Impulsive noise poses a significant challenge to the reliability of wireless communication systems, necessitating accurate estimation of its statistical parameters for effective mitigation. This paper introduces a multitask learning (MTL) framework based on a CNN-LSTM architecture enhanced with an attention mechanism for the joint estimation of impulsive noise parameters. The proposed model leverages a unified weighted-loss function to enable simultaneous learning of multiple parameters within …

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
Alberto Solera-Rico, Carlos Sanmiguel Vila, Stefano Discetti
https://arxiv.org/abs/2510.11600

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
We present an efficient and realisable active flow control framework with few non-intrusive sensors. The method builds upon data-driven, reduced-order predictive models based on Long-Short-Term Memory (LSTM) networks and efficient gradient-based Model Predictive Control (MPC). The model uses only surface-mounted pressure probes to infer the wake state, and is trained entirely offline on a dataset built with open-loop actuations, thus avoiding the complexities of online learning. Sparsification …

Crosslisted article(s) found for astro-ph.SR. https://arxiv.org/list/astro-ph.SR/new
[1/1]:
- Comparing LSTM-Based Sequence-to-Sequence Forecasting Strategies for 24-Hour Solar Proton Flux Pr...
Kangwoo Yi, Bo Shen, Qin Li, Haimin Wang, Yong-Jae Moon, Jaewon Lee, Hwanhee Lee

Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
Vishakha Lall, Yisi Liu
https://arxiv.org/abs/2510.08586 https://ar…

Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
Detecting psychological stress from speech is critical in high-pressure settings. While prior work has leveraged acoustic features for stress detection, most treat stress as a static label. In this work, we model stress as a temporally evolving phenomenon influenced by historical emotional state. We propose a dynamic labelling strategy that derives fine-grained stress annotations from emotional labels and introduce cross-attention-based sequential models, a Unidirectional LSTM and a Transformer…

Diffusion-Based Probabilistic Modeling for Hourly Streamflow Prediction and Assimilation
Wencong Yang, Haoyu Ji, Leo Lonzarich, Yalan Song, Chaopeng Shen
https://arxiv.org/abs/2510.08488

Diffusion-Based Probabilistic Modeling for Hourly Streamflow Prediction and Assimilation
Hourly predictions are critical for issuing flood warnings as the flood peaks on the hourly scale can be distinctly higher than the corresponding daily ones. Currently a popular hourly data-driven prediction scheme is multi-time-scale long short-term memory (MTS-LSTM), yet such models face challenges in probabilistic forecasts or integrating observations when available. Diffusion artificial intelligence (AI) models represent a promising method to predict high-resolution information, e.g., hourl…

Crosslisted article(s) found for astro-ph.SR. https://arxiv.org/list/astro-ph.SR/new
[1/1]:
- Comparing LSTM-Based Sequence-to-Sequence Forecasting Strategies for 24-Hour Solar Proton Flux Pr...
Kangwoo Yi, Bo Shen, Qin Li, Haimin Wang, Yong-Jae Moon, Jaewon Lee, Hwanhee Lee