Uncertain choices with asymmetric information: how clear evidence and ambiguity interact?

Real-world decisions often involve partial ambiguity, where the complete picture of potential risks is unclear. In such situations, individuals must make choices by balancing the value of available information against the uncertainty of unknown risks. Our study investigates this challenge by examining how people navigate the trade-off between the favorability of limited evidence and the degree of ambiguity when making decisions under partial ambiguity.

Discovering the effective connectome of the brain with dynamic Bayesian DAG learning.

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data.

A sex-dependent computer-aided diagnosis system for autism spectrum disorder using connectivity of resting-state fMRI.

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with the main symptoms of social communication disabilities. ASD is more than four times more common among males than females. The diagnosis of ASD is currently a subjective process by experts the same for males and females.

Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity.

Autism spectrum disorder (ASD) is a developmental disorder characterized by defects in social interaction. The past functional connectivity studies using resting-state fMRI have found both patterns of hypo-connectivity and hyper-connectivity in ASD and proposed the age as an important factor on functional connectivity disorders. However, this influence is not clearly characterized yet.

An information-based approach to handle various types of uncertainty in fuzzy bodies of evidence.

Fuzzy evidence theory, or fuzzy Dempster-Shafer Theory captures all three types of uncertainty, i.e. fuzziness, non-specificity, and conflict, which are usually contained in a piece of information within one framework.

Component-related BOLD response to localize epileptic focus using simultaneous EEG-fMRI recordings at 3T.

Background: Simultaneous EEG-fMRI experiments record spatiotemporal dynamics of epileptic activity. A shortcoming of spike-based EEG-fMRI studies is their inability to provide information about behavior of epileptic generators when no spikes are visible.

New Method: We extract time series of epileptic components identified on EEG and fit them with Generalized Linear Model (GLM) model.

Coincidence Detection within the Excitable Rat Olfactory Bulb Granule Cell Spines.

In the mammalian olfactory bulb, the inhibitory axonless granule cells (GCs) feature reciprocal synapses that interconnect them with the principal neurons of the bulb, mitral, and tufted cells. These synapses are located within large excitable spines that can generate local action potentials (APs) upon synaptic input ("spine spike"). Moreover, GCs can fire global APs that propagate throughout the dendrite.

A time local subset feature selection for prediction of sudden cardiac death from ECG signal.

Prediction of sudden cardiac death continues to gain universal attention as a promising approach to saving millions of lives threatened by sudden cardiac death (SCD). This study attempts to promote the literature from mere feature extraction analysis to developing strategies for manipulating the extracted features to target improvement of classification accuracy. To this end, a novel approach to local feature subset selection is applied using meticulous methodologies developed in previous studies of this team for extracting features from non-linear, time-frequency, and classical processes.

Rapid face adaptation distributes representation in inferior-temporal cortex across time and neuronal dimensions.

Neuronal networks of the brain adapt their information processing according to the history of stimuli. Whereas most studies have linked adaptation to repetition suppression, recurrent connections within a network and disinhibition due to adaptation predict more complex response patterns. The main questions of this study are as follows: what is the effect of the selectivity of neurons on suppression/enhancement of neural responses? What are the consequences of adaptation on information representation in neural population and the temporal structure of response patterns? We studied rapid face adaptation using spiking activities of neurons in the inferior-temporal (IT) cortex.

Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine.

Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists.

Temporal dynamics of visual category representation in the macaque inferior temporal cortex.

Object categories are recognized at multiple levels of hierarchical abstractions. Psychophysical studies have shown a more rapid perceptual access to the mid-level category information (e.g.

A Computational Approach towards Visual Object Recognition at Taxonomic Levels of Concepts.

It has been argued that concepts can be perceived at three main levels of abstraction. Generally, in a recognition system, object categories can be viewed at three levels of taxonomic hierarchy which are known as superordinate, basic, and subordinate levels. For instance, "horse" is a member of subordinate level which belongs to basic level of "animal" and superordinate level of "natural objects.

Evidence theoretic protein fold classification based on the concept of hyperfold.

In current computational biology, assigning a protein domain to a fold class is a complicated and controversial task. It can be more challenging in the much harder task of correct identification of protein domain fold pattern solely through using extracted information from protein sequence. To deal with such a challenging problem, the concepts of hyperfold and interlaced folds are introduced for the first time.

Differences in white matter reflect atypical developmental trajectory in autism: A Tract-based Spatial Statistics study.

Autism is a neurodevelopmental disorder in which white matter (WM) maturation is affected. We assessed WM integrity in 16 adolescents and 14 adults with high-functioning autism spectrum disorder (ASD) and in matched neurotypical controls (NT) using diffusion weighted imaging and Tract-based Spatial Statistics. Decreased fractional anisotropy (FA) was observed in adolescents with ASD in tracts involved in emotional face processing, language, and executive functioning, including the inferior fronto-occipital fasciculus and the inferior and superior longitudinal fasciculi.

Dynamical analysis of yeast protein interaction network during the sake brewing process.

Proteins interact with each other for performing essential functions of an organism. They change partners to get involved in various processes at different times or locations. Studying variations of protein interactions within a specific process would help better understand the dynamic features of the protein interactions and their functions.

A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM.

Protein function is related to its chemical reaction to the surrounding environment including other proteins. On the other hand, this depends on the spatial shape and tertiary structure of protein and folding of its constituent components in space. The correct identification of protein domain fold solely using extracted information from protein sequence is a complicated and controversial task in the current computational biology.

Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae.

Background: It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors.

Learning active fusion of multiple experts' decisions: an attention-based approach.

In this letter, we propose a learning system, active decision fusion learning (ADFL), for active fusion of decisions. Each decision maker, referred to as a local decision maker, provides its suggestion in the form of a probability distribution over all possible decisions. The goal of the system is to learn the active sequential selection of the local decision makers in order to consult with and thus learn the final decision based on the consultations.

A study on expertise of agents and its effects on cooperative Q-learning.

Cooperation in learning (CL) can be realized in a multiagent system, if agents are capable of learning from both their own experiments and other agents' knowledge and expertise. Extra resources are exploited into higher efficiency and faster learning in CL as compared to that of individual learning (IL). In the real world, however, implementation of CL is not a straightforward task, in part due to possible differences in area of expertise (AOE).