Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples by Min Gu Kwak, Hyungu Kahng, Seoung Bum Kim

“…Semi-supervised learning (SSL) methods have shown promising results in solving many practical problems when only a few labels are available. …”
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Effects of transcranial alternating current stimulation combined with sertraline on cognitive function in patients with depressive disorder by Li Dan, Xia Zhong, Zhu Wenli, Liang Dandan, Miao Wenwen, Song Chuanfu

“…After intervention， the study group showed significantly higher MCCB scores for attention/vigilance， verbal learning， and overall composite at both week 4 （t=-2.149， -3.530， -2.740， P<0.05） and week 12 （t=-3.534， -3.576， -3.838， P<0.01） when compared to the control group.ConclusionThe combined tACS and sertraline therapy may demonstrate superior efficacy to pharmacotherapy alone in the short term for improving attention/vigilance， verbal learning， overall cognitive function， and anxiety symptoms in patients with depressive disorders. …”
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Unsupervised clustering of biochemical markers reveals health profiles associated with function and survival in active aging by Raquel González-Martos, Javier Galeano, Carmen Ramirez-Castillejo, Narcis Gusi, Eva Gesteiro, German Vicente-Rodriguez, Ignacio Ara, Amelia Guadalupe-Grau

“…Abstract This study explores the relationships between biochemical phenotypes identified using machine learning, and key health outcomes, including body composition, physical function, and mortality risk. …”
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Targeted nano-energetic material exploration through active learning algorithm implementation by Leandro Carreira, Lea Pillemont, Yasser Sami, Nicolas Richard, Alain Esteve, Matthieu Jonckheere, Carole Rossi

“…We introduced a new acquisition function combining linearly two factors with the usual standard deviation of a Gaussian Process Regression algorithm. …”
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Accurate prediction of protein–ligand interactions by combining physical energy functions and graph-neural networks by Yiyu Hong, Junsu Ha, Jaemin Sim, Chae Jo Lim, Kwang-Seok Oh, Ramakrishnan Chandrasekaran, Bomin Kim, Jieun Choi, Junsu Ko, Woong-Hee Shin, Juyong Lee

“…By effectively integrating the outputs of the triplet neural networks with a physics-based scoring function, our model showed a significantly improved performance in hit identification. …”
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High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE. by Stefano Castellana, Caterina Fusilli, Gianluigi Mazzoccoli, Tommaso Biagini, Daniele Capocefalo, Massimo Carella, Angelo Luigi Vescovi, Tommaso Mazza

“…Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. …”
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Adaptive sampling-based optimization of quantics tensor trains for noisy functions: Applications to quantum simulations by Kohtaroh Sakaue, Hiroshi Shinaoka, Rihito Sakurai

“…Tensor cross interpolation (TCI) is a powerful technique for learning a tensor train (TT) by adaptively sampling a target tensor based on an interpolation formula. …”
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Optimal mixture design for organic Rankine cycle using machine learning algorithm by Valerio Mariani, Saverio Ottaviano, Davide Scampamorte, Andrea De Pascale, Giulio Cazzoli, Lisa Branchini, Gian Marco Bianchi

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Research on Q-learning based rate control approach for HTTP adaptive streaming by Li-rong XIONG, Jing-zhi LEI, Xin JIN

“…HTTP adaptive streaming (HAS) has become the standard for adaptive video streaming service.In changing network environments,current hardcoded-based rate adaptation algorithm was less flexible,and it is insufficient to consider the quality of experience (QoE).To optimize the QoE of users,a rate control approach based on Q-learning strategy was proposed.the client environments of HTTP adaptive video streaming was modeled and the state transition rule was defined.Three parameters related to QoE were quantified and a novel reward function was constructed.The experiments were employed by the Q-learning rate control approach in two typical HAS algorithms.The experiments show the rate control approach can enhance the stability of rate switching in HAS clients.…”
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A survey of deep learning-based MRI stroke lesion segmentation methods by Weiyi YU, Tao CHEN, Junping ZHANG, Hongming SHAN

“…Automatic stroke lesion segmentation has become a research hotspot in recent years.In order to comprehensively review current progress of deep learning-based MRI stroke lesion segmentation methods, start with the clinical problems of stroke treatment, we further elaborate the research background and challenges of deep learning-based lesion segmentation, and introduce common public datasets (ISLES and ATLAS) for stroke lesion segmentation.Then, we focus on the innovation and progress of deep learning-based stroke lesion segmentation methods, and summarize the research progress from three perspectives: network structure, training strategy, and loss function, and compare the advantages and disadvantages of various methods.Finally, we discusse the difficulties and challenges in this research and its future development trend.…”
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Error Bounds for lp-Norm Multiple Kernel Learning with Least Square Loss by Shao-Gao Lv, Jin-De Zhu

“…The problem of learning the kernel function with linear combinations of multiple kernels has attracted considerable attention recently in machine learning. …”
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A scalable multi-modal learning fruit detection algorithm for dynamic environments by Liang Mao, Liang Mao, Zihao Guo, Mingzhe Liu, Yue Li, Linlin Wang, Jie Li

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Identification and validation of expression and functions of ferroptosis-related gene HILPDA in early-onset preeclampsia placentas by Qianghua Wang, Xuegu Wang, Jiaojiao Fei, Chuanyue Jiang, Yafen Tao, Nana Yang, Huijuan Chen, Chengli Dou, Biao Ding, Danli Du, Xiang Li

“…We performed functional enrichment (GO and KEGG) and immune infiltration analysis to elucidate molecular mechanisms. …”
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Dairy intake and cognitive function in older adults in three cohorts: a mendelian randomization study by Natalia Ortega, Nick J. Mueller, Abbas Dehghan, Tosca O. E. de Crom, Armin von Gunten, Martin Preisig, Pedro Marques-Vidal, Marco Vinceti, Trudy Voortman, Nicolas Rodondi, Patricia O. Chocano-Bedoya

“…Similarly, lactase persistent participants in CLSA had higher verbal fluency, verbal learning and executive function, but no differences were found in the other cohorts. …”
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CLASSIFICATION SUPPORT VECTOR MACHINE IN BREAST CANCER PATIENTS by Siti Hadijah Hasanah

“…Support vector machine is one of the supervised learning methods in machine learning that is used in classification. …”
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Simplified cerebellum-like spiking neural network as short-range timing function for the talking robot by Vo Nhu Thanh, Hideyuki Sawada

“…In human speech, the timing function is important for determining its duration, stress and rhythm; however, little attention has been paid to these issues when building a speech synthesis system. …”
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Selected advances in nuclear mass predictions based on covariant density functional theory with continuum effects by K. Y. Zhang, C. Pan, X. H. Wu, X. Y. Qu, X. X. Lu, G. A. Sun

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YOLOX-LS: Strong Gravitational Lenses Detection in the DECaLS with Deep Learning by Yangyang Liu, Liangping Tu, Jianxi Li, Jiawei Miao, Gengqi Lin, Chenying Zhao

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CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application by Yu-Wen Chen, Kuo-Hsuan Hung, You-Jin Li, Alexander Chao-Fu Kang, Ya-Hsin Lai, Kai-Chun Liu, Szu-Wei Fu, Syu-Siang Wang, Yu Tsao

“…This study presents a deep learning-based speech signal-processing mobile application known as CITISEN. …”
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The function of “looking-at-nothing” for sequential sensorimotor tasks: Eye movements to remembered action-target locations by Rebecca Martina Foerster

“…This “looking-at-nothing” behavior might be functional, e.g., as “deictic pointer” for manual control or as memory-retrieval cue, or a by-product of automatization. …”
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