Article
Engineering, Civil
Carlos H. R. Lima, Hyun-Han Kwon, Yong-Tak Kim
Summary: A hierarchical Bayesian mixture model was developed for daily rainfall forecasts using stochastic weather models, improving forecast skills through the inclusion of predictors and reduction of parameter uncertainties. The model structure allows for better understanding and estimation of regional parameters, tested with 47 years of data from 60 gauges in South Korea. The model showed improvements in skill scores over climatology and persistence reference models up to a three days lead time, with potential applications in real-time daily rainfall forecasts globally.
JOURNAL OF HYDROLOGY
(2021)
Article
Engineering, Geological
Yu Feng, Ke Gao, Arnaud Mignan, Jiawei Li
Summary: A novel Bayesian hierarchical model is proposed to quantify uncertainty in local mean stress estimation and improve estimation results at different locations. The improved probabilistic estimation not only can be applied to other analyses involving mean stresses, but also provides more accurate results.
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
(2021)
Article
Multidisciplinary Sciences
Casey Doyle, Thushara Gunda, Asmeret Naugle
Summary: In this paper, the effects of corporate hierarchies on innovation spread across multilayer networks are explored using an elaborated SIR framework. It is found that adding management layers can significantly improve spreading processes, and utilizing a more centralized working relationship network can further increase innovation reach. The selection of seed nodes also affects the stability of the adopted community, with nodes near high positions sometimes producing larger and more stable peak adoption.
Article
Business
Jiawen Chen, Linlin Liu
Summary: This paper investigates the impact of entrepreneurs' social media use on entrepreneurial reinvestment. Based on the information-based view, the study proposes an inverted U-shaped relationship between social media use and entrepreneurial investment due to the competing forces of information gathering and processing. This relationship is more prominent in newly founded ventures or high-tech industries. Empirical findings from a large sample of Chinese private firms confirm the core assertions. This study provides important insights into the complex consequences of social media use for entrepreneurship activities.
JOURNAL OF BUSINESS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Yongqiang Ma, Wen Zhang, Ming Du, Haodong Jing, Nanning Zheng
Summary: In this paper, the researchers used functional MRI to explore the brain's visual perception processes. By designing visual stimulus experiments and establishing a causal network model, they successfully extracted matching information from fMRI and obtained the causal relationship between matching information and fMRI. The results suggest that the model can effectively extract high-level semantic information from brain signals and model visual perception processes in the brain's visual cortex.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Environmental Sciences
Maria Fernanda Morales Oreamuno, Sergey Oladyshkin, Wolfgang Nowak
Summary: Bayesian model selection (BMS) and Bayesian model justifiability analysis (BMJ) provide a statistically rigorous framework for comparing competing models using Bayesian model evidence (BME). However, BME-based analysis has limitations in accounting for a model's predictive performance after calibration and in comparing models using different calibration subsets. To address these limitations, we propose augmenting BMS and BMJ analyses with information-theoretic measures such as expected log-predictive density (ELPD), relative entropy (RE), and information entropy (IE). We demonstrate how these measures, alongside BME, enhance the understanding of the Bayesian updating process and enable objective model comparison using different calibration datasets.
WATER RESOURCES RESEARCH
(2023)
Article
Computer Science, Information Systems
Weiming Qiu, Yonghao Chen, Dihu Chen, Tao Su, Simei Yang
Summary: This paper studies how to appropriately apply three system configurations to reduce energy consumption on heterogeneous cluster-based systems. By formulating the dependence of the configurations as an ILP model and comparing different runtime strategies, the effectiveness of various management strategies on different platform sizes is analyzed.
Article
Engineering, Electrical & Electronic
Jun Xiao, Rui Zhao, Kin-Man Lam
Summary: Sparse models have been successful in image denoising and have advantages over deep-learning-based methods, such as not requiring a large amount of training data and having better generalization capability.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2021)
Article
Microbiology
Alma Revers, Xiang Zhang, Aeilko H. Zwinderman
Summary: This study incorporated phylogenetic relationships between operational taxonomic units (OTUs) into diet-microbe association analysis using a Bayesian hierarchical negative binomial (NB) model. The approach showed improved performance in terms of mean squared error (MSE) of slope estimates compared to other models. The results demonstrated that including phylogenetic relationships can lead to decreased variances in slope estimates for certain phylogenetic families.
FRONTIERS IN MICROBIOLOGY
(2021)
Review
Multidisciplinary Sciences
Yair Ziv, Bat Sheva Hadad
Summary: The paper proposes ways to assess currently unattended levels of processing to further understand the mental mechanisms driving social information processing and consequent social behaviors.
Article
Computer Science, Artificial Intelligence
Wenyi Tang, Bei Hui, Ling Tian, Guangchun Luo, Zaobo He, Zhipeng Cai
Summary: User representation learning is a critical task in social network analysis, with the proposed adversarial fusion framework utilizing multi-view information for robust and interpretable user representations. The framework includes a generator and discriminator, with the generator using a variational autoencoder to capture and disentangle latent factors behind user intentions. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed model.
INFORMATION FUSION
(2021)
Article
Engineering, Marine
Mingyuan Wang, Sunjuexu Pan, Yuanqin Tao, Honglei Sun, Xinyi Li
Summary: Determining soil parameters is crucial for the safety of marine geotechnical engineering. However, due to limited borehole samples, parameter estimation involves uncertainty. This paper proposes a porosity prediction method using hierarchical Bayesian modeling (HBM) to predict porosity in regions with limited data. Experimental results show that the proposed method can accurately predict porosity in specific regions with small datasets and effectively reduce uncertainty.
Article
Ecology
Alison Ke, Rahel Sollmann, Luke O. Frishkoff, Daniel S. Karp
Summary: Understanding how animals use their environments is essential for behavioral ecology and species conservation. This study developed a behavior N-mixture model to estimate the probability of specific behaviors occurring in different environments while accounting for imperfect detection. The model was validated through simulations and applied to bird observation data, revealing behavioral differences between species in forested and agricultural habitats. The behavior N-mixture model accurately characterized uncertainty and identified critical habitats for a species' life cycle.
ECOLOGICAL APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jia Li, Yongfeng Huang, Heng Chang, Yu Rong
Summary: This paper addresses the problems of node classification and graph classification. It proposes a hierarchical graph modeling approach for the node classification problem, where nodes are graph instances. A novel semi-supervised solution named SEAL-CI is designed to improve accuracy by updating two modules at the graph instance level and the hierarchical graph level.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Ecology
Hanna M. McCaslin, Abigail B. Feuka, Mevin B. Hooten
Summary: Bayesian hierarchical models play a crucial role in ecology, but can be computationally intensive. Recursive Bayesian computing and transformation-assisted RB methods help improve the efficiency and interpretability of Bayesian models, reducing computation time for fitting complex ecological statistical models.
METHODS IN ECOLOGY AND EVOLUTION
(2021)
Article
Neurosciences
Kelsey R. McDonald, John M. Pearson, Scott A. Huettel
SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE
(2020)
Article
Neurosciences
Jeff T. Mohl, John M. Pearson, Jennifer M. Groh
JOURNAL OF NEUROPHYSIOLOGY
(2020)
Article
Neurosciences
Merideth A. Addicott, John M. Pearson, Julia C. Schechter, Jeffrey J. Sapyta, Margaret D. Weiss, Scott H. Kollins
Summary: This study found that individuals with ADHD tend to make more exploratory decisions and methylphenidate does not alter this behavior. Exploratory choices are positively associated with ADHD symptoms, suggesting that unexplained variance in ADHD decisions may be due to insufficient value tracking.
NEUROPSYCHOPHARMACOLOGY
(2021)
Article
Biochemistry & Molecular Biology
Cathy S. Chen, R. Becket Ebitz, Sylvia R. Bindas, A. David Redish, Benjamin Y. Hayden, Nicola M. Grissom
Summary: In value-based decision-making tasks, individuals may make decisions based on the feature dimension that reward probabilities vary on. However, in complex, multidimensional environments, stimuli can vary on multiple dimensions simultaneously, making it unclear which feature deserves the most credit for outcomes. This study found that sex was associated with divergent strategies for sampling and learning about the world in mice, with female mice acquiring correct image-value associations more quickly than males.
Article
Multidisciplinary Sciences
Suva Roy, Na Young Jun, Emily L. Davis, John Pearson, Greg D. Field
Summary: The study found that the output of the retina is organized into detector grids that signal different visual features to the brain, and using an efficient coding model, it was determined that anti-aligned mosaic pairs optimize the encoding of natural scenes. ON and OFF RGC pairs with similar feature selectivity had anti-aligned receptive field mosaics, while RGC types encoding distinct features had independent mosaics, extending efficient coding theory to predict the spatial arrangement of diverse RGC populations.
Article
Biology
Jack Goffinet, Samuel Brudner, Richard Mooney, John Pearson
Summary: As the scale and complexity of behavioral data increase, utilizing unsupervised learning methods to directly learn features from data proves to be highly useful in quantifying complex and high-dimensional vocal behavior.
Article
Behavioral Sciences
Robert C. Wilson, Elizabeth Bonawitz, Vincent D. Costa, R. Becket Ebitz
Summary: Explore-exploit decisions involve balancing the benefits of exploring unknown options with exploiting known options for immediate reward. Many organisms use two distinct strategies to solve this dilemma: a bias for information and the randomization of choice. Understanding how this decision-making process occurs in humans and animals is an area of growing research interest.
CURRENT OPINION IN BEHAVIORAL SCIENCES
(2021)
Review
Neurosciences
R. Becket Ebitz, Benjamin Y. Hayden
Summary: Neurophysiology is shifting towards population-level thinking, which has had a significant impact in motor neuroscience and holds promise for addressing cognitive questions. Core concepts such as state spaces, manifolds, coding dimensions, subspaces, and dynamics provide a foundation for exploring cognition through population-level thinking. The progress and potential of population-level thinking in cognitive neuroscience is demonstrated in studies investigating attention, working memory, decision-making, executive function, learning, and reward processing.
Article
Multidisciplinary Sciences
Jonnathan Singh Alvarado, Jack Goffinet, Valerie Michael, William Liberti, Jordan Hatfield, Timothy Gardner, John Pearson, Richard Mooney
Summary: In male zebra finches, neural activity in the basal ganglia shows distinct patterns during song practice and courtship song performance, leading to reduced vocal variability. Understanding complex behaviors like song production and performance requires insight into brain function during both practice and performance. Calcium signals in spiny neurons vary greatly during song practice, but are suppressed during performance, leading to stereotyped and precise song production for an audience.
Article
Biology
Cathy S. Chen, Evan Knep, Autumn Han, R. Becket Ebitz, Nicola M. Grissom
Summary: Research on male and female mice in decision-making tasks revealed that males tend to make more exploratory choices while females learn more quickly during exploration. These sex differences are more prominent during periods of learning and exploration than during stable choices.
Article
Biochemical Research Methods
Samuel Brudner, John Pearson, Richard Mooney
Summary: This paper analyzes the song of juvenile male zebra finches and finds a relationship between vocal variation and performance quality. Specifically, heightened morning variability reintroduces immature performance variations that were avoided the evening before. This may allow birds to avoid overcommitting to recently learned solutions.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Neurosciences
Divya Subramanian, John M. Pearson, Marc A. Sommer
Summary: The brain uses both Bayesian and discriminative models of perception during active vision, depending on task requirements (continuous vs categorical) and the source of uncertainty (image noise vs motor-driven noise). Human subjects were found to be Bayesian for continuous judgments but anti-Bayesian for categorical judgments, while macaques showed similar results. This comparative analysis offers insights into the neural organization of the saccadic system.
Meeting Abstract
Neurosciences
Alexander Herman, Collin Meyer, Cathy Chen, Nicola Grissom, Dameon Harrell, Becket Ebitz, David Darrow
NEUROPSYCHOPHARMACOLOGY
(2021)
Meeting Abstract
Neurosciences
Gabriel Marrocco, Katarzyna Jurewicz, Tirin Moore, Becket Ebitz
NEUROPSYCHOPHARMACOLOGY
(2021)
Article
Multidisciplinary Sciences
Na Young Jun, Greg D. Field, John Pearson
Summary: Optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and antialigned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)