Results 191 to 200 of about 526,193 (325)

Degeneracy Sensing Light Detection and Ranging‐Inertial Simultaneous Localization and Mapping with Dual‐Layer Resistant Odometry and Scan‐Context Loop‐Closure Detection Backend in Diverse Environments

open access: yesAdvanced Intelligent Systems, EarlyView.
This paper presents a degeneracy‐aware light detection and ranging (LiDAR)‐inertial framework that enhances LiDAR simultaneous localization and mapping performance in challenging environments. The proposed system integrates a dual‐layer robust odometry frontend with a Scan‐Context‐based loop‐closure detection backend.
Haoming Yang   +4 more
wiley   +1 more source

Prompt-Contrastive Learning for Zero-Shot Relation Extraction. [PDF]

open access: yesEntropy (Basel)
Zhong X   +5 more
europepmc   +1 more source

A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single‐Cell Transcriptomes

open access: yesAdvanced Intelligent Systems, EarlyView.
FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng   +7 more
wiley   +1 more source

Multivariate Contrastive Predictive Coding with Sliding Windows for Disease Prediction from Electronic Health Records

open access: yesAdvanced Intelligent Systems, EarlyView.
Adaptive multi‐indicator contrastive predictive coding is introduced as a self‐supervised pretraining framework for multivariate EHR time series. An adaptive sliding‐window algorithm and 2D convolutional neural network encoder capture localized temporal patterns and global indicator dependencies, enabling label‐efficient disease prediction that ...
Hongxu Yuan   +3 more
wiley   +1 more source

Robust Dysarthric Speech Recognition with GAN Enhancement and LLM Correction

open access: yesAdvanced Intelligent Systems, EarlyView.
This study tackles dysarthric speech recognition by combining generative adversarial network (GAN)‐generated synthetic data with large language model (LLM)‐based error correction. The approach integrates three key elements: an improved CycleGAN to generate synthetic dysarthric speech for data augmentation, a multimodal automatic speech recognition core
Yibo He   +3 more
wiley   +1 more source

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