Sparse Learning of the Disease Severity Score for High-Dimensional Data by Ivan Stojkovic, Zoran Obradovic

“…Some steps in that direction have been taken and machine learning algorithms for extracting scoring functions from data have been proposed. …”
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A machine learning approach to population pharmacokinetic modelling automation by Sam Richardson, Itziar Irurzun Arana, Andrzej Nowojewski, Diansong Zhou, Jacob Leander, Weifeng Tang, Richard Dearden, Megan Gibbs

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Network Dismantling on Signed Network by Evolutionary Deep Reinforcement Learning by Yuxuan Ou, Fujing Xiong, Hairong Zhang, Huijia Li

“…To address these questions, in this paper, we design a new objective function and further propose an effective algorithm named as DSEDR, which aims to search for the best dismantling strategy based on evolutionary deep reinforcement learning. …”
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Ventricular segmentation algorithm for echocardiography based on transfer learning and GAN by Jin Wang, Xiaoning Bo, Guoqin Li, Yanli Tan

“…With the swift progression of computer technology, utilizing deep learning for left ventricular image segmentation in echocardiography is of great significance for automated cardiac function assessment. …”
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Learning Transferable Convolutional Proxy by SMI-Based Matching Technique by Wei Jin, Nan Jia

“…By minimizing an objective function of SMI, classification error, and manifold regularization, we learn the convolutional networks of both source and target domains. …”
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A Method for Detecting Tomato Maturity Based on Deep Learning by Song Wang, Jianxia Xiang, Daqing Chen, Cong Zhang

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Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly by Benjamín Tapia Sal Paz, Gorka Sorrosal, Aitziber Mancisidor, Carlos Calleja, Itziar Cabanes

“…This paper presents a reinforcement learning (RL)-based control strategy for the robotic disassembly of flexible elements. …”
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A Deep Learning Model for the Thermospheric Nitric Oxide Emission by Xuetao Chen, Jiuhou Lei, Dexin Ren, Wenbin Wang

“…A 3‐D NO emission model (referred to as NOE3D) that is based on the convolutional neural network with a context loss function is developed to estimate the 3‐D distribution of NO emission. …”
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Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks by Hasan A. A. Al-Rawi, Kok-Lim Alvin Yau, Hafizal Mohamad, Nordin Ramli, Wahidah Hashim

“…This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. …”
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Knowledge-Based Deep Learning for Time-Efficient Inverse Dynamics by Shuhao Ma, Yu Cao, Ian D. Robertson, Chaoyang Shi, Jindong Liu, Zhi-Qiang Zhang

“…Prior physical knowledge from forward dynamics and pre-selected inverse dynamics based physiological criteria are integrated into the loss function to guide the training of neural networks. …”
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Microstructural asymmetries of the planum temporale predict functional lateralization of auditory-language processing by Peipei Qin, Qiuhui Bi, Zeya Guo, Liyuan Yang, Haokun Li, Peng Li, Xinyu Liang, Junhao Luo, Xiangyu Kong, Yirong Xiong, Bo Sun, Sebastian Ocklenburg, Gaolang Gong

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Enhancing temporal learning in recurrent spiking networks for neuromorphic applications by Ismael Balafrej, Soufiyan Bahadi, Jean Rouat, Fabien Alibart

“…Subsequently, we apply a biologically inspired branching factor regularization rule to stabilize the network’s dynamics and make training easier by incorporating a time-local error in the loss function. Lastly, we modify a commonly used surrogate gradient function by increasing its support to facilitate learning over longer timescales when using binary spikes. …”
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Detection of Maize Pathogenic Fungal Spores Based on Deep Learning by Yijie Ren, Ying Xu, Huilin Tian, Qian Zhang, Mingxiu Yang, Rongsheng Zhu, Dawei Xin, Qingshan Chen, Qiaorong Wei, Shuang Song

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Adaptive pilot design for OFDM based on deep reinforcement learning by Qiaoshou LIU, Xiong ZHOU, Shuang LIU, Yifeng DENG

“…For orthogonal frequency division multiplexing (OFDM) systems, an adaptive pilot design algorithm based on deep reinforcement learning was proposed.The pilot design problem was formulated as a Markov decision process, where the index of pilot positions was defined as actions.A reward function based on mean squared error (MSE) reduction strategy was formulated, and deep reinforcement learning was employed to update the pilot positions.The pilot was adaptively and dynamically allocated based on channel conditions, thereby utilizing channel characteristics to combat channel fading.The simulation results show that the proposed algorithm has significantly improved channel estimation performance compared with the traditional pilot uniform allocation scheme under three typical multipath channels of 3GPP.…”
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Self-learning differential evolution algorithm for dynamic polycentric problems by Xing-bao LIU, Jian-ping YIN, Chun-hua HU, Rong-yuan CHEN

“…A novel self-learning differential evolution algorithm is proposed to solve dynamical multi-center optimization problems.The approach of re-evaluating some specific individuals is used to monitor environmental changes.The proposed self-learning operator guides the evolutionary group to a new environment,meanwhile maintains the stable topology structure of group to maintain the current evolutionary trend.A neighborhood search mechanism and a random immigrant mechanism are adapted to make a tradeoff between algorithmic convergence and population diversity.The experiment studies on a periodic dynamic function set suits are done,and the comparisons with peer algorithms show that the self-learning differential algorithm outperforms other algorithms in term of convergence and adaptability under dynamical environment.…”
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Functional divergence between the two cerebral hemispheres contributes to human fluid intelligence by Xinyu Liang, Junhao Luo, Qiuhui Bi, Yaya Jiang, Liyuan Yang, Deniz Vatansever, Elizabeth Jefferies, Gaolang Gong

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Briefing in Application of Machine Learning Methods in Ion Channel Prediction by Hao Lin, Wei Chen

“…Ion channels can be classified into numerous classes and different types of ion channels exhibit different functions. Thus, the correct identification of ion channels and their types using computational methods will provide in-depth insights into their function in various biological processes. …”
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Association between cognitive function, antioxidants, and clinical variables in Chinese patients with schizophrenia by Dan Li, Yuanyuan Huang, Hongxin Lu, Sumiao Zhou, Shixuan Feng, Hehua Li, Xuejing Li, Yi Guo, Chunlian Fu, Guiying Chen, Yuping Ning, Fengchun Wu, Lianqi Liu

“…The study aimed to investigate cognitive function and its influencing factors in Chinese patients with SCZ. …”
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Influence-Balanced XGBoost: Improving XGBoost for Imbalanced Data Using Influence Functions by Akiyoshi Sutou, Jinfang Wang

“…Decision tree boosting algorithms, such as XGBoost, have demonstrated superior predictive performance on tabular data for supervised learning compared to neural networks. However, recent studies on loss functions for imbalanced data have primarily focused on deep learning. …”
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Reinforcement Learning for Computational Guidance of Launch Vehicle Upper Stage by Shiyao Li, Yushen Yan, Hao Qiao, Xin Guan, Xinguo Li

“…This manuscript investigates the use of a reinforcement learning method for the guidance of launch vehicles and a computational guidance algorithm based on a deep neural network (DNN). …”
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