SoftGrasp: Adaptive grasping for dexterous hand based on multimodal imitation learning by Yihong Li, Ce Guo, Junkai Ren, Bailiang Chen, Chuang Cheng, Hui Zhang, Huimin Lu

“…Next, we utilize a multi-head attention mechanism to align and integrate multimodal features, dynamically allocating attention to ensure comprehensive learning. On this basis, we design a behavior cloning method based on an angle-torque loss function, enabling multimodal imitation learning. …”
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Development of deep-learning-based autonomous agents for low-speed maneuvering in Unity by Riccardo Berta, Luca Lazzaroni, Alessio Capello, Marianna Cossu, Luca Forneris, Alessandro Pighetti, Francesco Bellotti

“…This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). …”
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A Reinforcement Learning-Based Generative Approach for Event Temporal Relation Extraction by Zhonghua Wu, Wenzhong Yang, Meng Zhang, Fuyuan Wei, Xinfang Liu

“…To resolve these limitations, we introduce a reinforcement learning-based generative framework for event temporal relation extraction. …”
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SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

“…Thus, the development of lightweight detection technologies suitable for portable devices has become essential.MethodsThis paper presents SGSNet, a lightweight deep learning model designed for the fast and accurate detection of various strawberry growth stages. …”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…The independent treatment variables were two climatic factors and five human activity factors affecting vegetation changes in East Africa. Six machine learning algorithms were used to establish NDVI prediction models: random forest (RF), BP neural networks (BP), support vector machines (SVM), genetic algorithm (GA), radial basis function (RBF), and convolutional neural networks (CNN). …”
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Enhancing Learning-Based Cross-Modality Prediction for Lossless Medical Imaging Compression by Daniel S. Nicolau, Lucas A. Thomaz, Luis M. N. Tavora, Sergio M. M. Faria

“…The scheme uses a deep learning-based approach with Image-to-Image translation based on a Generative Adversarial Network architecture to generate an estimated image of one modality from its cross-modal pair. …”
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TSMAL: Target-Shadow Mask Assistance Learning Network for SAR Target Recognition by Shuai Guo, Ting Chen, Penghui Wang, Junkun Yan, Hongwei Liu

“…Finally, a multitask learning loss is designed depending on the recognition and feature optimization tasks to guide the network learning. …”
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Patterns of Change in Functional Connectivity and Motor Performance Are Different in Youth Recently Recovered from Concussion by Nishta R. Amin, Mary Beth Nebel, Hsuan-Wei Chen, Tyler A. Busch, Elizabeth D. Rosenthal, Stewart Mostofsky, Stacy J. Suskauer, Adrian Svingos

“…Collectively, these findings suggest that longitudinal changes in somatomotor-dorsal attention functional connectivity may be associated with lingering motor learning deficits after clinical recovery from pediatric mTBI. …”
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Neuroanatomical and functional substrates of the short video addiction and its association with brain transcriptomic and cellular architecture by Yuanyuan Gao, Ying Hu, Jinlian Wang, Chang Liu, Hohjin Im, Weipeng Jin, Wenwei Zhu, Wei Ge, Guang Zhao, Qiong Yao, Pinchun Wang, Manman Zhang, Xin Niu, Qinghua He, Qiang Wang

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Additively Manufactured RF Electronics With Structurally Integrated Physically Unclonable Functions for Wireless System Security by Aaron Pendino, Nghia Nguyen, Saif E. Nouma, Jing Wang, Attila A. Yavuz, Yasin Yilmaz, Gokhan Mumcu

“…Physically unclonable functions (PUFs) are alternatives to secret keys stored in non-volatile memory. …”
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An automatic pruning method for SAR target detection based on multitask reinforcement learning by Huiyao Wan, Jie Chen, Pazlat Nurmamat, Hongcheng Zeng, Wei Yang, Tao Xu, Bocai Wu, Jie Chen, Zhixiang Huang, Paulo S.R. Diniz

“…Second, an automatic pruning strategy search method based on multitask reinforcement learning (RLAS) is designed. This method automatically searches for the best pruning strategy for each network structure, and an entropy-based multitask reward function MOR is constructed to guide RLAS in searching for the optimal pruning strategy considering the edge accuracy, sparsity, and model feature channels entropy. …”
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An interpretable physics-informed deep learning model for estimating multiple air pollutants by Binjie Chen, Jiacong Hu, Yumiao Wang, Tian Feng, Weiwei Sun, Zunlei Feng, Gang Yang, Huiqiong Wang

“…Moreover, widely applied machine learning models have demonstrated a lack of interpretability and a weak physical basis. …”
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VCINet: Visual Cue-Inspired Feature Learning Network for SAR Building Segmentation by Mengyu Wang, Zhiyuan Yan, Wenhui Diao, Xudong Hu, Yingchao Feng, Linlin Xin, Junxi Li, Xian Sun

“…We systematically study the stable spatial pattern of buildings and leverage the variogram function to model the characteristic to constrain deep network training. …”
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Modeling of Battery Storage of Photovoltaic Power Plants Using Machine Learning Methods by Rad Stanev, Tanyo Tanev, Venizelos Efthymiou, Chrysanthos Charalambous

“…Also, as part of the deep learning FNN analysis study, a customized early stopping function is introduced. …”
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Global projections of heat stress at high temporal resolution using machine learning by P. Georgiades, P. Georgiades, T. Economou, T. Economou, Y. Proestos, J. Araya, J. Lelieveld, J. Lelieveld, M. Neira

“…Dairy cattle, a crucial component of the livestock sector, are particularly vulnerable to heat stress, which can adversely affect milk production, immune function, and feed intake and, in extreme cases, lead to mortality. …”
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New roles and competencies of teachers in the ICT-mediated learning environment of Russian universities by I. N. Ainoutdinova, T. M. Tregubova, J. Ng, V. A. Kopnov

“…The research considers organisational and functional changes in the learning environment of Russian universities mediated by the spread of ICTs in education. …”
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Using machine learning to identify Parkinson’s disease severity subtypes with multimodal data by Hwayoung Park, Changhong Youm, Sang-Myung Cheon, Bohyun Kim, Hyejin Choi, Juseon Hwang, Minsoo Kim

“…This study aims to address the clinical applicability and heterogeneity of PD using PD severity subtypes classification and digital biomarker development by combining objective multimodal data with machine learning (ML) approaches. Methods We analyzed datasets that combine clinical characteristics, physical function and lifestyle data, gait parameters in motion analysis systems, and wearable sensors collected from persons with PD (n = 102) to perform clustering for subtype classification. …”
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Deep learning based automation of mean linear intercept quantification in COPD research by Lars Leyendecker, Anna Louisa Weltin, Florian Nienhaus, Michaela Matthey, Bastian Nießing, Daniela Wenzel, Daniela Wenzel, Robert H. Schmitt, Robert H. Schmitt

“…Chronic obstructive pulmonary disease (COPD), a major cause of global mortality, necessitates novel therapies targeting lung function and remodeling. Their effect on emphysema formation is initially investigated using mouse models by analyzing histological lung sections. …”
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The Robust Vessel Segmentation and Centerline Extraction: One-Stage Deep Learning Approach by Rostislav Epifanov, Yana Fedotova, Savely Dyachuk, Alexandr Gostev, Andrei Karpenko, Rustam Mullyadzhanov

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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…Objectives:With the emergence and development of new network structures such as polymorphic network,the demand for network resource capacity is becoming more and more diverse.It is very difficult to adjust the distribution of virtual network functions (VNFs) carried on network slices for on-demand,dynamic and efficient resource capacity matching.This paper uses a data-driven VNF resource capacity prediction method to explore the pre-deployment of VNFs carried on network slices. …”
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