scholarly journals The role of action effects in motor sequence planning and execution: exploring the influence of temporal and spatial effect anticipation

2021 ◽  
Author(s):  
Rachel M. Brown ◽  
Erik Friedgen ◽  
Iring Koch
Keyword(s):  
Action Planning ◽  
Spatial Effects ◽  
Motor Sequence ◽  
Action Sequence ◽  
Sequential Action ◽  
Spatial Features ◽  
Temporal Features ◽  
Action Sequences ◽  
Temporal And Spatial ◽  
Anticipation Effect

AbstractActions we perform every day generate perceivable outcomes with both spatial and temporal features. According to the ideomotor principle, we plan our actions by anticipating the outcomes, but this principle does not directly address how sequential movements are influenced by different outcomes. We examined how sequential action planning is influenced by the anticipation of temporal and spatial features of action outcomes. We further explored the influence of action sequence switching. Participants performed cued sequences of button presses that generated visual effects which were either spatially compatible or incompatible with the sequences, and the spatial effects appeared after a short or long delay. The sequence cues switched or repeated across trials, and the predictability of action sequence switches was varied across groups. The results showed a delay-anticipation effect for sequential action, whereby a shorter anticipated delay between action sequences and their outcomes speeded initiation and execution of the cued action sequences. Delay anticipation was increased by predictable action switching, but it was not strongly modified by the spatial compatibility of the action outcomes. The results extend previous demonstrations of delay anticipation to the context of sequential action. The temporal delay between actions and their outcomes appears to be retrieved for sequential planning and influences both the initiation and the execution of actions.

scholarly journals Dynamic response of microglia/macrophage polarization following demyelination in mice

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Tianci Chu ◽  
Yi Ping Zhang ◽  
Zhisen Tian ◽  
Chuyuan Ye ◽  
Mingming Zhu ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Macrophage Polarization ◽  
Functional Decline ◽  
Oligodendrocyte Progenitor Cells ◽  
Spatial Features ◽  
Lesion Pattern ◽  
Temporal Features ◽  
Temporal And Spatial ◽  
Highly Correlated ◽  
Focal Demyelination

Abstract Background The glial response in multiple sclerosis (MS), especially for recruitment and differentiation of oligodendrocyte progenitor cells (OPCs), predicts the success of remyelination of MS plaques and return of function. As a central player in neuroinflammation, activation and polarization of microglia/macrophages (M/M) that modulate the inflammatory niche and cytokine components in demyelination lesions may impact the OPC response and progression of demyelination and remyelination. However, the dynamic behaviors of M/M and OPCs during demyelination and spontaneous remyelination are poorly understood, and the complex role of neuroinflammation in the demyelination-remyelination process is not well known. In this study, we utilized two focal demyelination models with different dynamic patterns of M/M to investigate the correlation between M/M polarization and the demyelination-remyelination process. Methods The temporal and spatial features of M/M activation/polarization and OPC response in two focal demyelination models induced by lysolecithin (LPC) and lipopolysaccharide (LPS) were examined in mice. Detailed discrimination of morphology, sensorimotor function, diffusion tensor imaging (DTI), inflammation-relevant cytokines, and glial responses between these two models were analyzed at different phases. Results The results show that LPC and LPS induced distinctive temporal and spatial lesion patterns. LPS produced diffuse demyelination lesions, with a delayed peak of demyelination and functional decline compared to LPC. Oligodendrocytes, astrocytes, and M/M were scattered throughout the LPS-induced demyelination lesions but were distributed in a layer-like pattern throughout the LPC-induced lesion. The specific M/M polarization was tightly correlated to the lesion pattern associated with balance beam function. Conclusions This study elaborated on the spatial and temporal features of neuroinflammation mediators and glial response during the demyelination-remyelination processes in two focal demyelination models. Specific M/M polarization is highly correlated to the demyelination-remyelination process probably via modulations of the inflammatory niche, cytokine components, and OPC response. These findings not only provide a basis for understanding the complex and dynamic glial phenotypes and behaviors but also reveal potential targets to promote/inhibit certain M/M phenotypes at the appropriate time for efficient remyelination.

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 1327
Author(s):  
Rui Zhang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Siyang Zhou
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Parallel Structure ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Spatial Features ◽  
Temporal Features ◽  
Different Types ◽  
In Series ◽  
Parallel Network

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

Composite load modeling by spatial-temporal deep attention network based on wide-area monitoring systems

2021 ◽  
pp. 1-12
Author(s):  
Omid Izadi Ghafarokhi ◽  
Mazda Moattari ◽  
Ahmad Forouzantabar
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Wide Area ◽  
Load Modeling ◽  
Wide Area Monitoring ◽  
Training Performance ◽  
Spatial Features ◽  
Temporal Features ◽  
Frequency Domains ◽  
Area Monitoring

With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods.

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

scholarly journals Multiple Evidence for Climate Patterns Influencing Ecosystem Productivity across Spatial Gradients in the Venice Lagoon

2021 ◽  
Vol 9 (4) ◽  
pp. 363
Author(s):  
Camilla Bertolini ◽  
Edouard Royer ◽  
Roberto Pastres
Keyword(s):  
Time Series ◽  
Primary Productivity ◽  
Clustering Analysis ◽  
Venice Lagoon ◽  
Spatial Effects ◽  
Spatial Gradients ◽  
Climate Forecasting ◽  
Oxygen Production Rate ◽  
Temporal And Spatial

Effects of climatic changes in transitional ecosystems are often not linear, with some areas likely experiencing faster or more intense responses, which something important to consider in the perspective of climate forecasting. In this study of the Venice lagoon, time series of the past decade were used, and primary productivity was estimated from hourly oxygen data using a published model. Temporal and spatial patterns of water temperature, salinity and productivity time series were identified by applying clustering analysis. Phytoplankton and nutrient data from long-term surveys were correlated to primary productivity model outputs. pmax, the maximum oxygen production rate in a given day, was found to positively correlate with plankton variables measured in surveys. Clustering analysis showed the occurrence of summer heatwaves in 2008, 2013, 2015 and 2018 and three warm prolonged summers (2012, 2017, 2019) coincided with lower summer pmax values. Spatial effects in terms of temperature were found with segregation between confined and open areas, although the patterns varied from year to year. Production and respiration differences showed that the lagoon, despite seasonality, was overall heterotrophic, with internal water bodies having greater values of heterotrophy. Warm, dry years with high salinity had lower degrees of summer autotrophy.

scholarly journals Infants in Control: Prospective Motor Control and Executive Functions in Action Development

2018 ◽  
Author(s):  
Janna M. Gottwald
Keyword(s):  
Motor Control ◽  
Cognitive Control ◽  
Executive Functions ◽  
Visual Information ◽  
Motion Tracking ◽  
Planning Process ◽  
Action Planning ◽  
Sources Of Information ◽  
Action Sequences ◽  
High Level

This thesis assesses the link between action and cognition early in development. Thus the notion of an embodied cognition is investigated by tying together two levels of action control in the context of reaching in infancy: prospective motor control and executive functions. The ability to plan our actions is the inevitable foundation of reaching our goals. Thus actions can be stratified on different levels of control. There is the relatively low level of prospective motor control and the comparatively high level of cognitive control. Prospective motor control is concerned with goal-directed actions on the level of single movements and movement combinations of our body and ensures purposeful, coordinated movements, such as reaching for a cup of coffee. Cognitive control, in the context of this thesis more precisely referred to as executive functions, deals with goal-directed actions on the level of whole actions and action combinations and facilitates directedness towards mid- and long-term goals, such as finishing a doctoral thesis. Whereas prospective motor control and executive functions are well studied in adulthood, the early development of both is not sufficiently understood.This thesis comprises three empirical motion-tracking studies that shed light on prospective motor control and executive functions in infancy. Study I investigated the prospective motor control of current actions by having 14-month-olds lift objects of varying weights. In doing so, multi-cue integration was addressed by comparing the use of visual and non-visual information to non-visual information only. Study II examined the prospective motor control of future actions in action sequences by investigating reach-to-place actions in 14-month-olds. Thus the extent to which Fitts’ law can explain movement duration in infancy was addressed. Study III lifted prospective motor control to a higher that is cognitive level, by investigating it relative to executive functions in 18-months-olds.Main results were that 14-month-olds are able to prospectively control their manual actions based on object weight. In this action planning process, infants use different sources of information. Beyond this ability to prospectively control their current action, 14-month-olds also take future actions into account and plan their actions based on the difficulty of the subsequentaction in action sequences. In 18-month-olds, prospective motor control in manual actions, such as reaching, is related to early executive functions, as demonstrated for behavioral prohibition and working memory. These findings are consistent with the idea that executive functions derive from prospective motor control. I suggest that executive functions could be grounded in the development of motor control. In other words, early executive functions should be seen as embodied.

scholarly journals Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction

2021 ◽  
Vol 13 (16) ◽  
pp. 3338
Author(s):  
Xiao Xiao ◽  
Zhiling Jin ◽  
Yilong Hui ◽  
Yueshen Xu ◽  
Wei Shao
Keyword(s):  
Smart Cities ◽  
Convolutional Networks ◽  
Temporal Correlations ◽  
Spatial Features ◽  
Temporal Features ◽  
Prediction Scheme ◽  
Temporal Graph ◽  
Real World Datasets ◽  
The Internet Of Things

With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called distAtt to measure the similarity of parking duration distributions. Through the distAtt mechanism, we add the long-term spatial–temporal correlations to our spatial–temporal convolutional block, and thus, we can capture complex hybrid spatial–temporal correlations to achieve a higher accuracy of parking availability prediction. Based on real-world datasets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the parking occupancy rate.

scholarly journals Analyzing Space–Time Coherence in Precipitation Seasonality across Different European Climates

2020 ◽  
Vol 12 (1) ◽  
pp. 171
Author(s):  
Maria Lanfredi ◽  
Rosa Coluzzi ◽  
Vito Imbrenda ◽  
Maria Macchiato ◽  
Tiziana Simoniello
Keyword(s):  
Annual Cycle ◽  
Progressive Deformation ◽  
Precipitation Regime ◽  
Equilibrium Conditions ◽  
Spatial Properties ◽  
Spatial Features ◽  
Precipitation Seasonality ◽  
Temporal And Spatial ◽  
Average Annual Precipitation ◽  
Simulated Scenario

Seasonality is a fundamental feature of environmental systems which critically depend on the climate annual cycle. The regularity of the precipitation regime, in particular, is a basic factor to sustain equilibrium conditions. An incomplete or biased understanding of precipitation seasonality, in terms of temporal and spatial properties, could severely limit our ability to respond to climate risk, especially in areas with limited water resources or fragile ecosystems. Here, we analyze precipitation data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) at 0.050 resolution to study the spatial features of the precipitation seasonality across different climate zones in Central-Southern Europe during the period 1981–2018. A cluster analysis of the average annual precipitation cycle shows that seasonality under the current climate can be synthesized in the form of a progressive deformation process of the annual cycle, which starts from the northernmost areas with maximum values in summer and ends in the south, where maximum values are recorded in winter. Our analysis is useful to detect local season-dependent changes, enhancing our understanding of the geography of climate change. As an example of application to this issue, we discuss the seasonality analysis in a simulated scenario based on IPCC projections.

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