20 papers · ranked by Valyu relevance
Sasideep Pasumarthi, Satwik Bathula, Nitya Tiwari, Himanshu Padole
Objective Recent advances in neuroimaging have highlighted the growing utility of resting-state functional magnetic resonance imaging (rs-fMRI) as an alternative to task-based fMRI. In addition to being simpler, cost-effective, and time-efficient, rs-fMRI is particularly advantageous for non-compliant populations such…
Junbeom Kwon, Jungwoo Seo, Heehwan Wang, Taesup Moon + 2 more
'Jiook Cha'] Title: Abstract Accurate prediction of the brain’s task reactivity from resting-state functional magnetic resonance imaging (fMRI) data remains a significant challenge in neuroscience. Traditional statistical approaches often fail to capture the complex, nonlinear spatiotemporal patterns of brain function.…
Qingyuan Wu, Qi Huang, Chao Liu, Haiyan Wu
Oxytocin (OT) is a neuropeptide that modulates social behaviors and the social brain. The effects of OT on the social brain can be tracked by assessing the neural activity in the resting and task states, providing a system-level framework for characterizing state-based functional relationships of its distinct effect.…
Soren J. Madsen, Young-Eun Lee, Lucina Q. Uddin, Jeanette A. Mumford + 6 more
Deep learning has been proven effective in predicting brain activation patterns from resting-state features. In this work, using resting state and task fMRI data from the Human Connectome Project (HCP), we replicate the state-of-the-art deep learning model BrainSurfCNN and examine new model architectures for…
Sasideep Pasumarthi, Nitya Tiwari, Himanshu Padole
Predicting task-induced brain activation from resting-state fMRI (rs-fMRI) remains a significant challenge in computational neuroimaging, primarily due to the difficulty in simultaneously modeling the detailed temporal evolution and high spatial resolution of intrinsic neural activity. Most existing literature relies…
Junbeom Kwon, Jungwoo Seo, Heehwan Wang, Taesup Moon + 2 more
Accurate prediction of the brain’s task reactivity from resting-state functional magnetic resonance imaging (fMRI) data remains a significant challenge in neuroscience. Traditional statistical approaches often fail to capture the complex, nonlinear spatiotemporal patterns of brain function. This study introduces SwiFUN…
Kazuma Nagashima, Jumpei Nishikawa, Junya Morita
Immersion in a task is a prerequisite for creativity. However, excessive arousal in a single task has drawbacks, such as overlooking events outside of the task. To examine such a negative aspect, this study constructs a computational model of arousal dynamics where the excessively increased arousal makes the task…
Authors not listed
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this…
Jen Lewis, Kevin Gurney
The performance of routine action sequences constitutes a significant proportion of human behaviour, and has received much attention in the cognitive psychology literature. However, a neuroanatomically plausible explanation of the cognitive processes underlying this routine sequential behaviour has hitherto remained…
Sai Mahit Vadaddi, Qiyuan Zhao, Brett M Savoie
Activation energy characterization of competing reactions is a costly, but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a…
Robert Kasumba, Dennis Barbour, Chien-Ju Ho
Adaptive systems often need to make task-specific decisions about people from limited evidence: a tutor may need to anticipate how a learner will approach a new problem, a game may need to adapt when a player enters a new level, and a human-AI system may need to infer whether a partner will persist with a plan or…
Thi Nguyet Que Nguyen, Alejandro García-Rudolph, Joan Saurí, John D. Kelleher
'John D. Kelleher'] A health-related (HR) profile is a set of multiple health-related items recording the status of the patient at different follow-up times post-stroke. In order to support clinicians in designing rehabilitation treatment programs, we propose a novel multi-task learning (MTL) strategy for predicting…
Ismael Espinoza Jaramillo, Channabasava Chola, Jin-Gyun Jeong, Ji-Heon Oh + 9 more
'Ji-Heon Oh' 'Hwanseok Jung' 'Jin-Hyuk Lee' 'Won Hee Lee' 'Tae-Seong Kim' 'Athina Tsanousa' 'Georgios Meditskos' 'Stefanos Vrochidis' 'Periklis Chatzimisios' 'Ioannis Yiannis Kompatsiaris'] Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare…
Jet Lageman, Atsushi Kikumoto, David Badre, Johannes J. Fahrenfort + 1 more
Goal-directed actions are performed to obtain specific sensory outcomes. However, theories of action control diverge on whether anticipated outcomes are only consequences of motor command specification or serve as instrumental determinants of action selection. Here, we directly tested whether predicted sensory outcomes…
Michele Fiori, Gabriele Civitarese, Marco Colussi, Cláudio Bettini
—Unobtrusive sensor-based recognition of Activities of Daily Living (ADLs) in smart homes by processing data collected from IoT sensing devices supports applications such as healthcare, safety, and energy management. Recent zero-shot methods based on Large Language Models (LLMs) have the advantage of removing the…
Abduallah Mohamed, Fernando Lejarza, Stephanie Cahail, Christian Claudel + 1 more
'Christian Claudel' 'Edison Thomaz'] Abstract—The problem of human activity recognition from mobile sensor data applies to multiple domains, such as health monitoring, personal fitness, daily life logging, and senior care. A critical challenge for training human activity recognition models is data quality. Acquiring…
Damien Bouchabou, Sao Mai Nguyen, Christophe Lohr, Benoît Leduc + 1 more
'Ioannis Kanellos'] Abstract: Long Short Term Memory (LSTM)-based structures have demonstrated their efficiency for daily living recognition activities in smart homes by capturing the order of sensor activations and their temporal dependencies. Nevertheless, they still fail in dealing with the semantics and the context…
Kunal Lodaya, Nathan Ricke, Kelly Chen, Troy Van Voorhis
Graphite-conjugated catalysts (GCCs) are a promising class of materials that combine many of the advantages of heterogenous and homogeneous catalysts. In particular, GCCs containing an aryl-pyridinium active site appear to be effective nonmetal catalysts for the oxygen reduction reaction (ORR). In this study, we…
Connor Taylor, Kobi Felton, Daniel Wigh, Mohammed Jeraal + 4 more
Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations need to be executed in the presence of polar functionality necessary for fragment-protein binding. New technologies such as high-throughput experimentation and…
Sanku Satya Uday, Satti Thanuja Pavani, T. Jaya Lakshmi, Rohit Chivukula
'Rohit Chivukula'] Human Activity Recognition (HAR) describes the machine's ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smartphones or Smart Watches, which can help them manage their daily routines in a…