Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning by Wenzheng Tao, Sarang Joshi, Ross Whitaker

Subjects: “…functional data analysis…”
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Associations between Executive Functions and Sensorimotor Performance in Children at Risk for Learning Disabilities by Cecília Tószegi, Andras N. Zsido, Beatrix Lábadi

“…The aim of the current study is to provide new insights into the relationship between executive functions and sensorimotor development by considering the risks associated with learning difficulties. …”
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An Extension of the Quadratic Error Function for Learning Imprecise Data in Multivariate Nonlinear Regression by Castro Gbêmêmali Hounmenou, Kossi Essona Gneyou, Romain Glélé Kakaï

“…We give an extension of the generalized least squares error function in a context of multivariate nonlinear regression to learn imprecise data. …”
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Compensatory Relation Between Executive Function and Fluid Intelligence in Predicting Math Learning by Marina Vasilyeva, Linxi Lu, Kennedy Damoah, Elida V. Laski

Subjects: “…math learning…”
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Functional connectivity and GABAergic signaling modulate the enhancement effect of neurostimulation on mathematical learning. by George Zacharopoulos, Masoumeh Dehghani, Beatrix Krause-Sorio, Jamie Near, Roi Cohen Kadosh

“…Our multimodal approach elucidates the causal role of the dlPFC and frontoparietal network in a critical academic learning skill, shedding light on the interplay between functional connectivity and GABAergic modulation in the efficacy of brain-based interventions to augment learning outcomes, particularly benefiting individuals who would learn less optimally based on their neurobiological profile.…”
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Sb-PiPLU: A Novel Parametric Activation Function for Deep Learning by Ayan Mondal, Vimal K. Shrivastava, Ayan Chatterjee, Raghavendra Ramachandra

“…In recent years, a variety of non-linear activation functions have been proposed. However, many of these suffer from drawbacks that limit the effectiveness of deep learning models. …”
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Multi-Function Working Mode Recognition Based on Multi-Feature Joint Learning by Lei Liu, Minghua Wu, Dongyang Cheng, Wei Wang

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Options for Strengthening the Ensemble of Hypotheses under Uncertainty of the Objective Learning Function Formation by A. F. Chernyavsky, A. I. Kazlova

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Effects of aminooxyacetic acid on learning and memory function and neurochemical changes in chronic alcoholism by Hongbo Jiang, Xunling Wang, Yingwei Liang, Yinghan Hou, Xinping Yue, Zhiyi Zhang, Dan Chen, Xinyi Fan, Ailin Du

“…Objective: This study aimed to investigate the effect of aminooxyacetic acid (AOAA) on cognitive function, particularly learning and memory, in a rat model of chronic alcoholism. …”
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Deployment method for vEPC virtualized network function via Q-learning by Quan YUAN, Hong-bo TANG, Kai-zhi HUANG, Xiao-lei WANG, Yu ZHAO

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Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling by Taseef Hasan Farook, James Dudley

“…This narrative review explores the application of predictive modeling and deep learning to identify clinical trends and associations related to occlusion and TMJ function. …”
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Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…This study introduces a multi-scale machine learning framework leveraging whole-body magnetic resonance imaging (MRI) and clinical data to predict regional, muscle, joint, and functional progression in FSHD. …”
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Research on intelligent government question answering system with autonomous learning and memory function by Fang Haiquan, Deng Mingming

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The Effect of Goal-Oriented Physical Exercises on Cognitive Functions of Children with Learning Disorder by Hanieh Ghasemian Moghadam, Hasan Mohamadzadeh

“…Introduction: The present study aimed to investigate the effect of a course of Taekwondo goal-oriented physical exercises on the cognitive functions of children with learning disorders.Methods: In this quasi-experimental study, which was conducted with a pre-test-post-test control group design, 20 boys with learning disorders were selected from the centers for learning disabilities by referring to special education in Mashhad. …”
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LRO biogenesis and function: what can we learn from mast cells? by Juan Eduardo Montero-Hernández, Kerui Zhang, Ulrich Blank, Ulrich Blank, Gaël Ménasché

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361 Automated assessment of facial nerve function using multimodal machine learning by Oren Wei, Diana Lopez, Ioan Lina, Kofi Boahene

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Digital image representation by atomic functions: features for computer vision and machine learning by Viktor Makarichev, Vladimir Lukin, Sergii Kryvenko, Iryna Brysina

“…In this study, we explore the discrete atomic transform (DAT), which is based on atomic functions, as a potential solution to this problem. …”
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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“… The study is dedicated to the problem of classification of structural and functional development stage of cardiomyocytes derived from the induced pluripotent stem cells with application of the digital image processing methods and machine learning algorithms, in particular, neural networks. …”
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Improved enzyme functional annotation prediction using contrastive learning with structural inference by Yuxin Yang, Abby Jerger, Song Feng, Zixu Wang, Christina Brasfield, Margaret S. Cheung, Jeremy Zucker, Qiang Guan

“…A prominent challenge within this domain has been the task of predicting enzyme function, a complex problem that has seen the development of numerous computational methods, particularly those rooted in deep learning techniques. …”
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Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease by Lindsay Fadel, Elizabeth Hipskind, Steen E. Pedersen, Jonathan Romero, Caitlyn Ortiz, Caitlyn Ortiz, Eric Shin, Md Abul Hassan Samee, Robia G. Pautler, Robia G. Pautler, Robia G. Pautler

“…Notably, FC changes were also observed in the Default Mode Network, exhibiting a loss of hyperconnectivity over time. Modeling revealed functional connections that support learning and memory performance differ between the 6- and 10-month groups.DiscussionThese ML models show potential for early disease detection by identifying connectivity patterns associated with cognitive decline. …”
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