scholarly journals La dénotation spatiale des noms d’événements

2011 ◽  
Vol 34 (1) ◽  
pp. 138-155 ◽  
Author(s):  
Richard Huyghe
Keyword(s):  
Direct Relation ◽  
Spatial Extension ◽  
Temporal Properties ◽  
Spatial Features ◽  
Temporal Features ◽  
Il Y A ◽  
Spatial Entities ◽  
The Church ◽  
Temporal Specification

This paper deals with the spatial features of event-denoting nouns [EvNs], which are often overlooked in the linguistic literature on space. EvNs can refer to spatial entities, as they can be used as trajectors in localization sentences (Il y a une cérémonie dans l’église ‘There is a ceremony in the church’). Still, EvNs differ in several ways from nouns denoting prototypical spatial entities. They do not combine with complements denoting spatial extension (*une cérémonie de deux hectares ‘a four acres’ ceremony’), and they are associated with specific nouns and verbs of location (le lieu / *la place de la cérémonie ‘the location / the place of the ceremony’, Une cérémonie a lieu / *se trouve dans l’église ‘A ceremony takes place / is in the church’). It is assumed that the peculiarity of the spatial denotation of EvNs is due to their direct relation to time. The dependence between the spatial and temporal properties of EvNs shows when these nouns are used as landmarks (Pierre se rend à la cérémonie ‘Peter goes to the ceremony’). First, spatial eventive landmarks bear a temporal specification. Second, the temporal features of events determine their ability to be used as spatial landmarks.

scholarly journals A spatio-temporal specification language and its completeness & decidability

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Tengfei Li ◽  
Jing Liu ◽  
Haiying Sun ◽  
Xiang Chen ◽  
Lipeng Zhang ◽  
...  
Keyword(s):  
Intelligent Transportation System ◽  
Cyber Physical Systems ◽  
Specification Language ◽  
Finite Model ◽  
Critical Systems ◽  
Physical Systems ◽  
Spatial Objects ◽  
Temporal Properties ◽  
Spatio Temporal ◽  
Temporal Specification

AbstractIn the past few years, significant progress has been made on spatio-temporal cyber-physical systems in achieving spatio-temporal properties on several long-standing tasks. With the broader specification of spatio-temporal properties on various applications, the concerns over their spatio-temporal logics have been raised in public, especially after the widely reported safety-critical systems involving self-driving cars, intelligent transportation system, image processing. In this paper, we present a spatio-temporal specification language, STSL PC, by combining Signal Temporal Logic (STL) with a spatial logic S4 u, to characterize spatio-temporal dynamic behaviors of cyber-physical systems. This language is highly expressive: it allows the description of quantitative signals, by expressing spatio-temporal traces over real valued signals in dense time, and Boolean signals, by constraining values of spatial objects across threshold predicates. STSL PC combines the power of temporal modalities and spatial operators, and enjoys important properties such as finite model property. We provide a Hilbert-style axiomatization for the proposed STSL PC and prove the soundness and completeness by the spatio-temporal extension of maximal consistent set and canonical model. Further, we demonstrate the decidability of STSL PC and analyze the complexity of STSL PC. Besides, we generalize STSL to the evolution of spatial objects over time, called STSL OC, and provide the proof of its axiomatization system and decidability.

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.

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 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.

Modeling Spatial Evolution

2018 ◽  
pp. 235-259 ◽  
Author(s):  
G. Jayanthi ◽  
V. Uma
Keyword(s):  
Temporal Relations ◽  
Spatial Evolution ◽  
Two Dimensional ◽  
Formal Representation ◽  
Effect Analysis ◽  
Spatial Features ◽  
Spatio Temporal ◽  
Point Line ◽  
Spatial Entities ◽  
Interval Logic

Geographic features in the real world are represented by spatial entities such as point, line, and area in two-dimensional surfaces. These features tend to evolve in time, thereby characterizing change in their physical identity, evolution into new species, thus describing geomorphological change of geographic features. These phenomena can be formalized using spatio-temporal relations. Formal representation of changing geographic (spatial) features is the interest of this chapter. Formal methods for representing the event and process that causes geomorphological change are presented. The formalization of geographic entities that are temporally and spatially related in a two-dimensional plane using the interval logic and spatial logic would facilitate the understanding of how modeling of space-time using spatio-temporal relations represents spatial evolution over time. Representation of temporal dynamism can be accomplished using various models. Modeling using spatio-temporal graph is more apt as it contributes to the cause-effect analysis.

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.

scholarly journals Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yu Li ◽  
Hongfei Cao ◽  
Carla M. Allen ◽  
Xin Wang ◽  
Sanda Erdelez ◽  
...  
Keyword(s):  
Visual Attention ◽  
Computational Method ◽  
Human Cognition ◽  
Visual Knowledge ◽  
Spatial Features ◽  
Temporal Features ◽  
Visual Tasks ◽  
Visual Understanding ◽  
Reasoning Patterns ◽  
Expertise Level

AbstractVisual reasoning is critical in many complex visual tasks in medicine such as radiology or pathology. It is challenging to explicitly explain reasoning processes due to the dynamic nature of real-time human cognition. A deeper understanding of such reasoning processes is necessary for improving diagnostic accuracy and computational tools. Most computational analysis methods for visual attention utilize black-box algorithms which lack explainability and are therefore limited in understanding the visual reasoning processes. In this paper, we propose a computational method to quantify and dissect visual reasoning. The method characterizes spatial and temporal features and identifies common and contrast visual reasoning patterns to extract significant gaze activities. The visual reasoning patterns are explainable and can be compared among different groups to discover strategy differences. Experiments with radiographers of varied levels of expertise on 10 levels of visual tasks were conducted. Our empirical observations show that the method can capture the temporal and spatial features of human visual attention and distinguish expertise level. The extracted patterns are further examined and interpreted to showcase key differences between expertise levels in the visual reasoning processes. By revealing task-related reasoning processes, this method demonstrates potential for explaining human visual understanding.

scholarly journals Divergent acoustic properties of gelada and baboon vocalizations and their implications for the evolution of human speech

2017 ◽  
Vol 2 (1) ◽  
pp. 20-36 ◽  
Author(s):  
Morgan L. Gustison ◽  
Thore J. Bergman
Keyword(s):  
Acoustic Properties ◽  
Cycle Duration ◽  
Speech Rhythm ◽  
Call Type ◽  
Human Speech ◽  
Temporal Properties ◽  
Temporal Features ◽  
Vowel Space ◽  
Speech Evolution ◽  
Number Of Cycles

Abstract Human speech has many complex spectral and temporal features traditionally thought to be absent in the vocalizations of other primates. Recent explorations of the vocal capabilities of non-human primates are challenging this view. Here, we continue this trend by exploring the spectro-temporal properties of gelada (Theropithecus gelada) vocalizations. First, we made cross-species comparisons of geladas, chacma baboons, and human vowel space area. We found that adult male and female gelada exhaled grunts–a call type shared with baboons—have formant profiles that overlap more with human vowel space than do baboon grunts. These gelada grunts also contained more modulation of fundamental and formant frequencies than did baboon grunts. Second, we compared formant profiles and modulation of exhaled grunts to the derived call types (those not shared with baboons) produced by gelada males. These derived calls contained divergent formant profiles, and a subset of them, notably wobbles and vocalized yawns, were more modulated than grunts. Third, we investigated the rhythmic patterns of wobbles, a call type shown previously to contain cycles that match the 3–8 Hz tempo of speech. We use a larger dataset to show that the wobble rhythm overlaps more with speech rhythm than previously thought. We also found that variation in cycle duration depends on the production modality; specifically, exhaled wobbles were produced at a slower tempo than inhaled wobbles. Moreover, the variability in cycle duration within wobbles aligns with a linguistic property known as ‘Menzerath’s law’ in that there was a negative association between cycle duration and wobble size (i.e. the number of cycles). Taken together, our results add to growing evidence that non-human primates are anatomically capable of producing modulated sounds. Our results also support and expand on current hypotheses of speech evolution, including the ‘neural hypothesis’ and the ‘bimodal speech rhythm hypothesis’.

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