20 papers · ranked by Valyu relevance
Qiuhan Jin, Ding Cui, Koen Nelissen
Recognizing and distinguishing actions is a complex cognitive process that relies on integrating various spatiotemporal information. However, the specific contributions of spatial and temporal features to action recognition remain unclear. To address this gap, we conducted fMRI recordings in monkeys as they observed…
Fengqian Pang, Zhiwen Liu
Background The research and analysis of cellular physiological properties has been an essential approach to studying some biological and biomedical problems. Temporal dynamics of cells therein are used as a quantifiable indicator of cellular response to extracellular cues and physiological stimuli. Methods This work…
Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao + 2 more
'Tao Mei'] Abstract. Video temporal dynamics is conventionally modeled with 3D spatial-temporal kernel or its factorized version comprised of 2D spatial kernel and 1D temporal kernel. The modeling power, nevertheless, is limited by the fixed window size and static weights of a kernel along the temporal dimension. The…
Wyanne A. Noortman, Dennis Vriens, Cornelis H. Slump, Johan Bussink + 4 more
Radiomic feature extraction of the static and parametric images was performed using PyRadiomics version 2.0 in Python 3.6 (Python Software Foundation, Wilmington, Delaware). For every VOI, 90 features were calculated: intensity (18), shape (13), GLCM (22), GLRLM (16), grey level size zone matrix (GLSZM) (16) and…
Jie Miao, Xiangmin Xu, Xiaofen Xing, Dacheng Tao
—Dynamic textures exist in various forms, e.g., fire, smoke, and traffic jams, but recognizing dynamic texture is challenging due to the complex temporal variations. In this paper, we present a novel approach stemmed from slow feature analysis (SFA) for dynamic texture recognition. SFA extracts slowly varying features…
Gwenllian C. Williams, Anna C. Nobre, Sage E.P. Boettcher
Recent research has investigated visual search in dynamic environments and considered how temporal predictions modulate behaviour over time. Spatiotemporal predictions have been shown to adaptively guide behaviour during search to improve target detection at temporally-likely locations. The utility of non-spatial…
Dongha Lee, Seonghyeon Lee, Hwanjo Yu
With the increase of available time series data, predicting their class labels has been one of the most important challenges in a wide range of disciplines. Recent studies on time series classification show that convolutional neural networks (CNN) achieved the state-of-the-art performance as a single classifier. In…
Sungeun Hong, Jongbin Ryu, Woobin Im, Hyun Suk Yang
Recognizing dynamic scenes is one of the fundamental problems in scene understanding, which categorizes moving scenes such as a forest fire, landslide, or avalanche. While existing methods focus on reliable capturing of static and dynamic information, few works have explored frame selection from a dynamic scene…
Kaoutar Mouhcine, Nabila Zrira, Issam Elafi, Ibtissam Benmiloud + 1 more
'Haris Ahmad Khan'] In recent years, dynamic texture classification has become an important task for computer vision. This is a challenging task due to the unknown spatial and temporal nature of dynamic texture. To overcome this challenge, we investigate the potential of deep learning approaches and propose a novel…
Imran Alam, Brendan Harris, Patrick Cahill, Oliver Cliff + 3 more
The interdisciplinary time-series analysis literature encompasses thousands of statistical features for quantifying interpretable properties of dynamical data. But for any given application, it is likely that just a small subset of informative time-series features is required to capture the dynamical quantities of…
Sharon Chiang, Emilian R. Vankov, Hsiang J. Yeh, Michele Guindani + 4 more
'Marina Vannucci' 'Zulfi Haneef' 'John M. Stern' 'Satoru Hayasaka'] Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of…
Manasij Venkatesh, Joseph Jaja, Luiz Pessoa
Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. Yet, a fuller understanding of the mechanisms that support mental…
Marina Gorostiola González, Remco L. van den Broek, Thomas G.M. Braun, Magdalini Chatzopoulou + 4 more
Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive…
Lynn K. A. Sörensen, Sander M. Bohté, Dorina de Jong, Heleen A. Slagter + 1 more
Humans can rapidly recognize objects in a dynamically changing world. This ability is showcased by the fact that observers succeed at recognizing objects in rapidly changing image sequences, at up to 13 ms/image. To date, the mechanisms that govern dynamic object recognition remain poorly understood. Here, we developed…
Carl H Lubba, Sarab S Sethi, Philip Knaute, Simon R Schultz + 2 more
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through…
Authors not listed
Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we…
Jakub Poziemski, Artur Yurkevych, Pawel Siedlecki
The advancement of computational methods in drug discovery, particularly through the use of machine learning (ML) and deep learning (DL), has significantly enhanced the precision of binding affinity predictions. Despite progress in computer-aided drug discovery (CADD) accurate prediction of binding affinity remains a…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…