Time Series Anomaly Detection Using Transformer-Based GAN With Two-Step Masking by Ah-Hyung Shin, Seong Tae Kim, Gyeong-Moon Park

“…Time series anomaly detection is a task that determines whether an unseen signal is normal or abnormal, and it is a crucial function in various real-world applications. Typical approach is to learn normal data representation using generative models, like Generative Adversarial Network (GAN), to discriminate between normal and abnormal signals. …”
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Design of Smart Plant Electrical Signal Monitoring System for Indoor Farming by Afif Saifuddin, Wiratmoko Ardan, Nugroho Andri Prima, Okayasu Takashi, Sutiarso Lilik

“…Calibration for this system needs a function generator. The calibration result is analyzed using statistical methods such as MAPE. …”
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Mechanical product design and manufacturing system based on CNN and server optimization algorithm by Tian Lei, Zhao Ance

“…The training accuracy and loss of the new activation function proposed in the study have always been superior to other functions. …”
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A theory and data-driven method for rapid bottom hole pressure calculation in UGS by Yang Li, Haiwei Guo, Xianfeng Gong, Naixin Lu, Kairui Zhang

“…Furthermore, the method provides a valuable reference for the application of deep learning in environments with limited samples.…”
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The application of high-intensity interval training in college physical education and the improvement of students’ physical and mental quality by Xinghao Wang, Hongyan Wu, Qiao Meng, Jiancong Chen, Taofeng Liu

“…It also improves cardiopulmonary function, enhances heart health, reduces the risk of cardiovascular diseases, and promotes muscle growth, leading to better body composition. …”
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Depth-Induced Intra-to-Inter Transformer network for stereoscopic image retargeting by Xiaoting Fan, Long Sun, Zhong Zhang

“…Finally, a hybrid loss function is applied to improve the stereoscopic image retargeting quality. …”
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The pain of PTSD: integrating persistent or chronic pain within emotional processing theory of posttraumatic stress disorder by Natalie Hellman, Stephanie M. Haft, Anna Woodbury, Andrew M. Sherrill, Sheila A. M. Rauch

“…Discussion details how the comorbidity is maintained through parallel avoidance processes focused on: (1) trauma memories and reminders in PTSD preventing reduction of negative affect (extinction) and inhibitory learning, and (2) physical pain in chronic pain fuelling increased pain and reduced function.Results: A conceptualisation is presented on how PTSD and chronic pain symptomology can be addressed within the EPT framework, increasing the confidence of providers and patients while addressing an important gap in the literature. …”
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Short-Term Wind Power Forecast Based on CNN&LSTM-GRU Model Integrated with CEEMD-SE Algorithm by Guohua YANG, Xin QI, Rui JIA, Yifeng LIU, Fei MENG, Xin MA, Xiaowen XING

“…First, the original wind power output series are decomposed into several intrinsic mode function components and one residual component by complementary set empirical mode decomposition, and those components of similar mode are reconstructed by sample entropy algorithm. …”
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Maximizing theoretical and practical storage capacity in single-layer feedforward neural networks by Zane Z. Chou, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller

“…Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks as a function of these parameters. …”
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A graph transformer with optimized attention scores for node classification by Yu Zhang, Xin Li, Yaoqun Xu, Xitong Xu, Zhe Wang

“…Specifically, we propose an end-to-end attention score optimization loss function, designed to suppress noisy connections and enhance the connection weights between similar nodes. …”
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New Rotation Periods from the Kepler Bonus Background Light Curves by Zachary R. Claytor, Jamie Tayar

“…The new Kepler Bonus Background light curves are deblended using a point-spread function algorithm, providing light curves of over 400,000 new background sources in addition to over 200,000 reanalyzed Kepler prime targets. …”
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Multi-UAV Path Planning for Air-Ground Relay Communication Based on Mix-Greedy MAPPO Algorithm by Yiquan Wang, Yan Cui, Yu Yang, Zhaodong Li, Xing Cui

“…The state space, action space, communication model, training environment, and reward function are designed by comprehensively considering the actual tasks and entity characteristics such as safe distance, no-fly zones, survival in a threatened environment, and energy consumption. …”
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Comparative Analysis of YOLO Models for Bean Leaf Disease Detection in Natural Environments by Diana-Carmen Rodríguez-Lira, Diana-Margarita Córdova-Esparza, José M. Álvarez-Alvarado, Julio-Alejandro Romero-González, Juan Terven, Juvenal Rodríguez-Reséndiz

“…This paper highlights the potential of advanced deep learning models, optimized with second-order optimizers and custom loss functions, in improving pest detection, crop management, and overall agricultural yield.…”
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Understanding dysbiosis and resilience in the human gut microbiome: biomarkers, interventions, and challenges by Azadeh Safarchi, Azadeh Safarchi, Ghanyah Al-Qadami, Ghanyah Al-Qadami, Cuong D. Tran, Michael Conlon

“…Additionally, we discuss the concept of a resilient gut microbiome and highlight potential biomarkers including diversity indices and some bacterial taxa as recovery-associated bacteria, resistance genes, antimicrobial peptides, and functional flexibility. These biomarkers can facilitate the identification and prediction of healthy and resilient microbiomes, particularly in precision medicine, through diagnostic tools or machine learning approaches especially after antimicrobial medications that may cause stable dysbiosis. …”
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Unlocking the structural, vibrational, electronic, optical and thermoelectric properties of K2X (X=S, Se, Te) monolayers via DFT and ML by G. Sneha, R.D. Eithiraj

“…The computational analysis that involves density functional theory (DFT) was achieved by WIEN2k software. …”
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Identification of transcription factor-lipid droplet-related gene biomarkers for the prognosis of post-acute myocardial infarction-induced heart failure by Jianqiao Zhao, Can Guo, Mengyuan Cheng, Jie Li, Yangyang Liu, Huahua Wang, Jianping Shen

“…Machine learning methods were used to screen the genes related to prognosis. …”
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Network self-organization explains the statistics and dynamics of synaptic connection strengths in cortex. by Pengsheng Zheng, Christos Dimitrakakis, Jochen Triesch

“…Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strengths of local excitatory connections in cortex and hippocampus is long-tailed, exhibiting a small number of synaptic connections of very large efficacy. …”
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Dynamic and static analysis of environmental variables in coronavirus spread by Mirko Di Stefano, Leonardo Aragão, Giuseppe Ambrosio, Diego Ciangottini, Cristina Duma, Pasquale Lubrano, Barbara Martelli, Davide Salomoni, Giusy Sergi, Daniele Spiga, Fabrizio Stracci, Elisabetta Ronchieri, Loriano Storchi, Sara Cutini

“…We applied a new procedure, developed for unevenly sampled data (Discrete Correlation Function), to perform the cross-correlation analysis looking at possible correlation between COVID-19 positive and hospitalised cases with the air quality during the first pandemic wave. …”
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Multiple Feature Vectors Based Fault Classification for WSN Integrated Bearing of Rolling Mill by Bo Qin, Luyang Zhang, Heng Yin, Yan Qin

“…Second, the measured signal is filtered by a morphological average filtering algorithm to remove process noise and then the empirical mode decomposition method is applied to extract the intrinsic mode function (IMF) which contains the fault information. …”
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Correctness Coverage Evaluation for Medical Multiple-Choice Question Answering Based on the Enhanced Conformal Prediction Framework by Yusong Ke, Hongru Lin, Yuting Ruan, Junya Tang, Li Li

“…Conformal Prediction (CP) is now recognized as a robust framework within the broader domain of machine learning, offering statistically rigorous guarantees of marginal (average) coverage for prediction sets. …”
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