Results 191 to 200 of about 104,768 (268)

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

Advanced Robotics Research, EarlyView.
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki, Ilche Georgievski, Marco Aiello   +2 more
wiley   +1 more source

Causal Mediation and Functional Outcome Analysis with Process Data. [PDF]

Psychometrika
Suk Y, Park C.
europepmc   +1 more source

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

VIRSE: a variational Bayesian framework for RNA structural ensemble inference. [PDF]

Brief Bioinform
Liang J, Wang Y, Fan X, Xie M, Song Q.
europepmc   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Gap Measurement Method for Railway Switch Machines Based on the Fusion of Deep Vision and Geometric Features. [PDF]

Sensors (Basel)
Zhi W, Feng Q, Xiao S, He X, Liu H, Zou Y, Li H.   +6 more
europepmc   +1 more source

Deep Contrastive Learning for High‐Throughput Prediction of Drug Resistance Mutations from Sequences

Advanced Science, EarlyView.
This study presents DeepMutDTA, a deep learning framework aimed at predicting mutation‐induced changes in protein‐drug interactions and prioritizing variants potentially linked to drug resistance. Trained on large‐scale data, it incorporates SimSiam‐MuTF, a label‐aware contrastive fine‐tuning strategy that encourages separation between WT and MT ...
Xiaowen Hu, Pan Zhang, Shangqian Wu, Hao Sun, Minwei Li, Sophia Tsoka, Zizhang Sheng, Lei Deng   +7 more
wiley   +1 more source

Beyond Linear Risk: A Machine Learning Approach to Understanding Perinatal Depression in Context. [PDF]

Multivariate Behav Res
Sherlock P, Mansolf M, Hofheimer J, Hockett CW, O'Connor TG, Roubinov D, Graff JC, Lai JS, Bush NR, Wright RJ, Chiu YM, program collaborators for Environmental Influences on Child Health Outcomes.   +11 more
europepmc   +1 more source

Genome‐Wide CRISPR Screen Identifies a microRNA Orchestrating Pleiotropic Resistance to Targeted Therapy and T Cell Immunity in Melanoma

Advanced Science, EarlyView.
A genome‐wide microRNA CRISPR screen identifies miR‐18a as a master regulator of cross‐resistance in melanoma. Loss of miR‐18a activates the AJUBA–YAP/Hippo axis to confer BRAFi resistance and enhances THBS1–CD47 interaction to impair CD8+ T cell immunity. hnRNP A1 is identified as an upstream regulator of miR‐18a processing.
Zhao Wang, Haotian Liu, Xueqi Wang, Jianjin Teng, Zihan Zheng, Jingya Zhang, Wanyi Xiao, Qian Liang, Jiaxin Li, Xiaomeng Jia, Xin Feng, Hongzhang Cui, Menghan Luo, Tielong Yang, Liuxing Wu, Ke Zhao, Weilong Yang, Mulin Jun Li, Dandan Huang, Jilong Yang   +19 more
wiley   +1 more source

Resolving non-identifiability mitigates systematic errors in simultaneous models of neural tuning and functional coupling. [PDF]

J Comput Neurosci
Sachdeva P, Bak JH, Livezey J, Kirst C, Frank L, Bhattacharyya S, Bouchard KE.   +6 more
europepmc   +1 more source