Article
Mathematics, Applied
Dmitry Ammosov, Aleksandr Grigorev, Sergei Stepanov, Aleksei Tyrylgin
Summary: In this paper, a new approach based on hybrid explicit-implicit learning (HEI) is proposed to solve the poroelasticity problem in a fractured medium. The spatial approximation is done using the finite element method with linear basis functions, while the time approximation is achieved through an explicit-implicit scheme. The method incorporates fixed strain and fixed stress splitting schemes to simplify the calculations.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Neurosciences
Di Tao, Zonglin He, Yuchen Lin, Chang Liu, Qian Tao
Summary: Processing of fear is crucial for human survival and can occur explicitly and implicitly, resulting in different behavioral and neurophysiological outcomes. The core fear network includes the amygdala, pulvinar, and fronto-occipital regions, while explicit fear processing involves visual attention/orientation and contextual association, and implicit fear processing involves an 'alarm' system. These findings illuminate the neural mechanisms underlying fear processing at different levels of awareness.
Article
Neurosciences
Julia Moser, Laura Batterink, Yiwen Li Hegner, Franziska Schleger, Christoph Braun, Ken A. Paller, Hubert Preissl
Summary: Humans are highly sensitive to patterns in the environment and use statistical learning for cognition. This study examined the neural mechanisms of statistical learning using an auditory nonlinguistic paradigm. Neural entrainment reflects implicit learning of patterns, while the emergence of explicit knowledge varies across individuals depending on factors such as attention and exposure time.
Article
Automation & Control Systems
Chang-Dong Wang, Yan-Hui Chen, Wu-Dong Xi, Ling Huang, Guangqiang Xie
Summary: The study introduces a novel neural network model CEICFNet to address the sparsity and cold-start issues in recommender systems. By learning latent factors across domains, the model effectively integrates explicit ratings and implicit interactions.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
YuFei Li, Jia Wu, TianJin Deng
Summary: In this paper, a meta-reinforcement learning method called Meta-GNAS is proposed for Graph Neural Architecture Search (GNAS) to improve learning efficiency on new tasks by leveraging knowledge learned from previous tasks. This is the first work to apply meta-learning to GNAS tasks. Additionally, a predictive model is used to evaluate the accuracy of sampled graph neural architectures instead of training from scratch, further improving efficiency in tackling new tasks. Experimental results demonstrate that the Meta-GNAS designed architecture outperforms manually designed architectures and has faster search speed than other methods, with an average search time of fewer than 210 GPU seconds on 6 datasets.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Psychology, Social
Krishna Savani, Michael W. Morris, Katrina Fincher, Jackson G. Lu, Scott Barry Kaufman
Summary: Newcomers' acculturation rate depends on both their explicit and implicit aptitude, with the latter playing a crucial role in learning interpersonal norms through trial-and-error experience.
JOURNAL OF PERSONALITY AND SOCIAL PSYCHOLOGY
(2022)
Review
Health Care Sciences & Services
Jo-Hsuan Wu, T. Y. Alvin Liu, Wan-Ting Hsu, Jennifer Hui-Chun Ho, Chien-Chang Lee
Summary: This study aimed to systematically examine the diagnostic accuracy of machine learning in diagnosing different categories of diabetic retinopathy based on color fundus photographs and to determine the state-of-the-art machine learning approach. The meta-analysis of 60 studies showed high accuracy of machine learning in diagnosing diabetic retinopathy, indicating that ML-based DR screening algorithms are likely ready for clinical applications.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Marti Lloret-Cabot, Daichao Sheng
Summary: This paper evaluates the computational performance of a first order accurate fully implicit integration scheme and four different order explicit substepping integration schemes, in order to provide practical guidance for solving numerical problems in geotechnical engineering involving critical state models.
COMPUTERS AND GEOTECHNICS
(2022)
Article
Education & Educational Research
Stephen Man-Kit Lee, Yanmengna Cui, Shelley Xiuli Tong
Summary: This study conducted a systematic literature search and meta-analysis to examine the performance of individuals with dyslexia in statistical learning. The results showed that individuals with dyslexia perform worse in statistical learning compared to typically developing controls, regardless of the learning paradigm or orthography. This suggests a domain-general statistical learning weakness in dyslexia across languages.
REVIEW OF EDUCATIONAL RESEARCH
(2022)
Article
Geosciences, Multidisciplinary
Mahdi Panahi, Abolfazl Jaafari, Ataollah Shirzadi, Himan Shahabi, Omid Rahmati, Ebrahim Omidvar, Saro Lee, Dieu Tien Bui
Summary: This study presents the potential application of deep learning neural networks, specifically CNN and RNN, for predicting and mapping flash flood probability in Golestan Province, Iran. The CNN model performed slightly better than the RNN model in predicting future floods, demonstrating improved accuracy compared to previous studies. The study suggests focusing on approximately 40% of the land area identified as highly susceptible to flooding for the design and implementation of flood early warning systems.
GEOSCIENCE FRONTIERS
(2021)
Article
Computer Science, Artificial Intelligence
Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji
Summary: This article proposes a new framework called IECR for learning from contextual information in order to solve complex computational problems. The framework represents each state using contextual key frames and extracts the affordances of the state using loss functions. By developing four new algorithms and evaluating them in five discrete environments, it is shown that all the algorithms that use contextual information significantly outperform the state-of-the-art methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Brian C. Vermeire, Siavash Hedayati Nasab
Summary: This paper introduces a family of accelerated implicit-explicit (AIMEX) schemes for solving stiff systems of equations. AIMEX schemes can significantly improve stability and allowable time step sizes.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Bin Xia, Yapeng Tian, Yulun Zhang, Yucheng Hang, Wenming Yang, Qingmin Liao
Summary: Blind Super Resolution (SR) aims to generate high-resolution (HR) images from low-resolution (LR) input images without knowledge of the degradations. The majority of blind SR methods introduce degradation estimators to help adjust to unknown degradation scenarios. However, it is impractical to provide concrete labels for all combinations of degradations, and specific designs hinder generalization. Therefore, an implicit degradation estimator is proposed to extract discriminative degradation representations without the supervision of ground-truth degradation. Extensive experiments demonstrate the superiority of the proposed Meta-Learning based Region Degradation Aware SR Network (MRDA) in various degradation settings.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Review
Neurosciences
Jordan E. Pierce, Marine Thomasson, Philippe Voruz, Garance Selosse, Julie Peron
Summary: The role of the cerebellum in affective processing is important and involves both the lateral hemispheric lobules and the vermis. The activation of the cerebellum differs between explicit and implicit emotion tasks, with some overlapping clusters and some distinct clusters.
Article
Computer Science, Artificial Intelligence
Bangcheng Zhan, Enmin Song, Hong Liu, Xiangyang Xu, Wencheng Li, Chih-Cheng Hung
Summary: This article introduces a medical image segmentation network called TransMIS. The network combines implicit and explicit attention mechanisms, as well as fuses multi-level features with long-range and local dependencies to construct a robust feature representation and improve segmentation performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Clinical Neurology
Dimitris A. Pinotsis, Roman Loonis, Andre M. Bastos, Earl K. Miller, Karl J. Friston
Article
Neurosciences
Dimitris A. Pinotsis, Timothy J. Buschman, Earl K. Miller
Biographical-Item
Neurosciences
Earl K. Miller, Robert Desimone
Editorial Material
Psychiatry
Alik S. Widge, Earl K. Miller
Article
Biochemistry & Molecular Biology
Caterina Trainito, Constantin von Nicolai, Earl K. Miller, Markus Siegel
Article
Neurosciences
Dimitris A. Pinotsis, Markus Siegel, Earl K. Miller
Article
Biochemical Research Methods
Leo Kozachkov, Mikael Lundqvist, Jean-Jacques Slotine, Earl K. Miller
PLOS COMPUTATIONAL BIOLOGY
(2020)
Article
Biochemical Research Methods
Takuya Ito, Scott L. Brincat, Markus Siegel, Ravi D. Mill, Biyu J. He, Earl K. Miller, Horacio G. Rotstein, Michael W. Cole
PLOS COMPUTATIONAL BIOLOGY
(2020)
Article
Biochemical Research Methods
Indie C. Garwood, Sourish Chakravarty, Jacob Donoghue, Meredith Mahnke, Pegah Kahali, Shubham Chamadia, Oluwaseun Akeju, Earl K. Miller, Emery N. Brown
Summary: This study quantified the neural activity induced by Ketamine and provided detailed descriptions of the spectroscopic features in non-human primates and human patients. The findings can facilitate the development of neurophysiological mechanistic models of Ketamine and biomarker discovery for clinical anesthesia monitoring.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Editorial Material
Neurosciences
David L. Baracka, Earl K. Miller, Christopher Moore, Adam M. Packer, Luiz Pessoa, Lauren N. Ross, Nicole C. Rust
Summary: This article discusses the various concepts and naming of "causality" in neuroscience and proposes four ways to enhance clarity around causality.
TRENDS IN NEUROSCIENCES
(2022)
Article
Biochemical Research Methods
Leo Kozachkov, John Tauber, Mikael Lundqvist, Scott L. Brincat, Jean-Jacques Slotine, Earl K. Miller
Summary: Research suggests that short-term synaptic plasticity (STSP) is important for maintaining working memory and making neural networks more brain-like. Artificial neural networks with STSP showed better performance in maintaining memories and resisting network degradation compared to networks without STSP.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Neurosciences
Roser Sanchez-Todo, Andre M. Bastos, Edmundo Lopez-Sola, Borja Mercadal, Emiliano Santarnecchi, Earl K. Miller, Gustavo Deco, Giulio Ruffini
Summary: In this study, a new framework called laminar neural mass models (LaNMM) is proposed to simulate electrophysiological measurements by combining conduction physics with NMMs. Using this framework, the location of oscillatory generators in the prefrontal cortex of the macaque monkey is inferred from laminar-resolved data. A minimal model capable of generating coupled slow and fast oscillations is defined, and LaNMM-specific parameters are optimized to fit the recorded data. The functional connectivity (FC) of the model and data are evaluated using an optimization function, and the family of best solutions reproduces the observed FC by selecting specific locations of pyramidal cells and their synapses.
Article
Multidisciplinary Sciences
Indie C. Garwood, Alex J. Major, Marc-Joseph Antonini, Josefina Correa, Youngbin Lee, Atharva Sahasrabudhe, Meredith K. Mahnke, Earl K. Miller, Emery N. Brown, Polina Anikeeva
Summary: This study successfully translates multifunctional fiber neurotechnology from rodent studies to macaque studies, enabling cortical and subcortical neural recording and modulation. By recording and analyzing the electrophysiological changes during a working memory task, the researchers uncover the reshaping process of neural activity under local inhibition.
Article
Multidisciplinary Sciences
Sourish Chakravarty, Jacob Donoghue, Ayan S. Waite, Meredith Mahnke, Indie C. Garwood, Sebastian Gallo, Earl K. Miller, Emery N. Brown
Summary: Research has shown that unconsciousness under general anesthesia can be reliably tracked using real-time electroencephalogram processing. To aid patient management during surgery, a closed-loop anesthesia delivery system was implemented in nonhuman primates, which accurately controlled the level of unconsciousness. The system demonstrated superior performance and established critical steps for designing and testing closed-loop anesthesia delivery systems in humans.
Article
Neurosciences
Nathanael A. Cruzado, Zoran Tiganj, Scott L. Brincat, Earl K. Miller, Marc W. Howard
