Processes as Patterns of Occurrence

2018 ◽  
Author(s):  
Antony Galton
Keyword(s):  
Natural Language ◽  
Spatial Domain ◽  
Superordinate Category ◽  
Temporal Domain

This chapter explores the idea that processes may be understood as patterns of occurrence, whose individual realizations may take on the character of states or events, depending on the perspective from which they are considered. In this way the ontological relations between states, processes, and events are clarified by effectively defusing the question as to whether processes should be classed as subordinate to events, or vice versa, or whether they are both specializations of some broader superordinate category. A key distinction is made between open and closed patterns, initially in the spatial domain and then in the temporal domain, where new light is thrown on why the term ‘process’ has come to be used in strikingly different ways by different authors. Finally, the account of processes as patterns is put to use in providing a fruitful framework within which to investigate aspectual phenomena in natural language.

Aspect and space

2017 ◽  
Author(s):  
Lucas Champollion
Keyword(s):  
Spatial Domain ◽  
The Other ◽  
Alternative View ◽  
Temporal Domain ◽  
Spatial Aspect ◽  
Temporal Aspect ◽  
The Way

This chapter models the relation between temporal aspect (run for an hour vs. *run all the way to the store for an hour) and spatial aspect (meander for a mile vs. *end for a mile) previously discussed by Gawron (2009). The chapter shows that for-adverbials impose analogous conditions on the spatial domain and on the temporal domain, and that an event may satisfy stratified reference with respect to one of the domains without satisfying it with respect to the other one as well. This provides the means to extend the telic-atelic opposition to the spatial domain. The chapter argues in some detail that stratified reference is in this respect empirically superior to an alternative view of telicity based on divisive reference (Krifka 1998).

A Time-Parallel CFD Approach for Unsteady Combustor Flow Analysis and Design Optimization

2012 ◽  
Author(s):  
Moresh J. Wankhede ◽  
Neil W. Bressloff ◽  
Andy J. Keane
Keyword(s):  
Flow Field ◽  
Domain Decomposition ◽  
Design Optimization ◽  
Cfd Simulation ◽  
Simulation Method ◽  
Spatial Domain ◽  
Parallel Simulation ◽  
Analysis And Design ◽  
Temporal Domain ◽  
Parallel Cfd

Computational fluid dynamics (CFD) simulations to predict and visualize the reacting flow dynamics inside a combustor require fine resolution over the spatial and temporal domain, making them computationally very expensive. The traditional time-serial approach for setting up a parallel combustor CFD simulation is to divide the spatial domain between computing nodes and treat the temporal domain sequentially. However, it is well known that spatial domain decomposition techniques are not very efficient especially when the spatial dimension (or mesh count) of the problem is small and a large number of nodes are used, as the communication costs due to data parallelism becomes significant per iteration. Hence, temporal domain decomposition has some attraction for unsteady simulations, particularly on relatively coarse spatial meshes. The purpose of this study is two-fold: (i), to develop a time-parallel CFD simulation method and apply it to solve the transient reactive flow-field in a combustor using an unsteady Reynolds-averaged Navier Stokes (URANS) formulation in the commercial CFD code FLUENT™ and (ii) to investigate its benefits relative to a time-serial approach and its potential use for combustor design optimization. The results show that the time-parallel simulation method correctly captures the unsteady combustor flow evolution but, with the applied time-parallel formulation, a clear speed-up advantage, in terms of wall-clock time, is not obtained relative to the time-serial approach. However, it is clear that the time-parallel simulation method provides multiple stages of transient combustor flow-field solution data whilst converging towards a final converged state. The availability of this resulting data could be used to seed multiple levels of fidelity within the framework of a multi-fidelity co-Kriging based design optimization strategy. Also, only a single simulation would need to be setup from which multiple fidelities are available.

scholarly journals Explicit control of step timing during split-belt walking reveals interdependent recalibration of movements in space and time

2019 ◽  
Author(s):  
Marcela Gonzalez-Rubio ◽  
Nicolas F. Velasquez ◽  
Gelsy Torres-Oviedo
Keyword(s):  
Motor Adaptation ◽  
Spatial Domain ◽  
Hierarchical Organization ◽  
The Other ◽  
Spatial Control ◽  
Limb Position ◽  
Locomotor Pattern ◽  
Temporal Domain ◽  
Temporal Features ◽  
Spatio Temporal

ABSTRACTSplit-belt treadmills that move the legs at different speeds are thought to update internal representations of the environment, such that this novel condition generates a new locomotor pattern with distinct spatio-temporal features compared to those of regular walking. It is unclear the degree to which such recalibration of movements in the spatial and temporal domains is interdependent. In this study, we explicitly altered subjects’ limb motion in either space or time during split-belt walking to determine its impact on the adaptation of the other domain. Interestingly, we observed that motor adaptation in the spatial domain was susceptible to altering the temporal domain, whereas motor adaptation in the temporal domain was resilient to modifying the spatial domain. This nonreciprocal relation suggests a hierarchical organization such that the control of timing in locomotion has an effect on the control of limb position. This is of translational interest because clinical populations often have a greater deficit in one domain compared to the other. Our results suggest that explicit changes to temporal deficits cannot occur without modifying the spatial control of the limb.

scholarly journals Spatio-Temporal Pattern Representation from AI Inspired Brain Model in Spiking Neural Network

2021 ◽  
Vol 12 (5) ◽  
pp. 6618-6631
Keyword(s):  
Motor Neurons ◽  
Visual Stimuli ◽  
Spatial Domain ◽  
Brain Network ◽  
The Body ◽  
Domain Modeling ◽  
Population Activity ◽  
Temporal Domain ◽  
Spatio Temporal ◽  
The Brain

Neuronal population activity in the brain is the combined response of information in the spatial domain and dynamics in the temporal domain. Modeling such Spatio-temporal mechanisms is a complex process because of the complexity of the brain and the limitations of the hardware. In this paper, we demonstrate how information processing principles adapted from the brain can be used to create a brain-inspired artificial intelligence (AI) model and represent Spatio-temporal patterns. The same is demonstrated by designing the tiny brain using spiking neural networks, where activated neuronal populations represent information in the spatial domain and transmitting signals represent dynamics in the temporal domain. Spatially located sensory neurons excited by input visual stimuli further activate motor neurons to trigger a motor response that causes behavior modification of the robotic agent. Initially, an isolated brain network is simulated to understand the excitation part from sensory to motor neurons while plotting waveform between membrane potential and time. The response of the network to stimulate robot body movements is also plotted to demonstrate representation. The simulation shows how the response of particular visual stimuli modifies behavior and helps us understand the body and brain synchronization. The perceived environment and resultant behavior response allow us to study body interaction with the environment.

scholarly journals In-Pixel Temperature Sensors with an Accuracy of ±0.25 °C, a 3σ Variation of ±0.7 °C in the Spatial Domain and a 3σ Variation of ±1 °C in the Temporal Domain

Micromachines ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 665 ◽  
Author(s):  
Accel Abarca ◽  
Albert Theuwissen
Keyword(s):  
Temperature Sensor ◽  
Image Sensor ◽  
Temperature Sensors ◽  
Spatial Domain ◽  
Dynamic Element Matching ◽  
Bipolar Junction Transistor ◽  
Correlated Double Sampling ◽  
Temporal Domain ◽  
Junction Transistor ◽  
Improved Accuracy

This article presents in-pixel (of a CMOS image sensor (CIS)) temperature sensors with improved accuracy in the spatial and the temporal domain. The goal of the temperature sensors is to be used to compensate for dark (current) fixed pattern noise (FPN) during the exposure of the CIS. The temperature sensors are based on substrate parasitic bipolar junction transistor (BJT) and on the nMOS source follower of the pixel. The accuracy of these temperature sensors has been improved in the analog domain by using dynamic element matching (DEM), a temperature independent bias current based on a bandgap reference (BGR) with a temperature independent resistor, correlated double sampling (CDS), and a full BGR bias of the gain amplifier. The accuracy of the bipolar based temperature sensor has been improved to a level of ±0.25 °C, a 3σ variation of ±0.7 °C in the spatial domain, and a 3σ variation of ±1 °C in the temporal domain. In the case of the nMOS based temperature sensor, an accuracy of ±0.45 °C, 3σ variation of ±0.95 °C in the spatial domain, and ±1.4 °C in the temporal domain have been acquired. The temperature range is between −40 °C and 100 °C.

Error Concealment Scheme for Intra Frames Based on Spatial and Temporal Interpolation

2013 ◽  
Vol 411-414 ◽  
pp. 1368-1371
Author(s):  
Geng Wei ◽  
Ruo Ying Wang ◽  
Yu Dong Zhang
Keyword(s):  
Error Concealment ◽  
Video Quality ◽  
Motion Vector ◽  
Spatial Domain ◽  
Weighted Interpolation ◽  
Domain Motion ◽  
Temporal Domain ◽  
Temporal Interpolation ◽  
Simulation Results

The lost of a block in an intra frame will damage the video quality extremely. In this paper, an efficient concealment for intra frame is proposed based on the spatial and temporal domain interpolation. For spatial domain concealment, the weighted interpolation (WI) and texture prediction (TP) are adopted according to the blocks surrounding the damaged macroblock (MB). If the blocks surrounding the damaged MB are all available, the TP method will be used. Otherwise the WI method will be used. For temporal domain, motion vector copy (MVC) is adopted for the interpolation. Simulation results have shown that the proposed method can significantly improve the quality of the video.

scholarly journals Micro-Expression Recognition Enhanced by Macro-Expression from Spatial-Temporal Domain

2021 ◽  
Author(s):  
Bin Xia ◽  
Shangfei Wang
Keyword(s):  
High Performance ◽  
Spatial Domain ◽  
Behavior Patterns ◽  
Expression Recognition ◽  
Expression Data ◽  
Baseline Model ◽  
Temporal Domain ◽  
Temporal Features ◽  
Facial Behavior ◽  
Micro Expression

Facial micro-expression recognition has attracted much attention due to its objectiveness to reveal the true emotion of a person. However, the limited micro-expression datasets have posed a great challenge to train a high performance micro-expression classifier. Since micro-expression and macro-expression share some similarities in both spatial and temporal facial behavior patterns, we propose a macro-to-micro transformation framework for micro-expression recognition. Specifically, we first pretrain two-stream baseline model from micro-expression data and macro-expression data respectively, named MiNet and MaNet. Then, we introduce two auxiliary tasks to align the spatial and temporal features learned from micro-expression data and macro-expression data. In spatial domain, we introduce a domain discriminator to align the features of MiNet and MaNet. In temporal domain, we introduce relation classifier to predict the correct relation for temporal features from MaNet and MiNet. Finally, we propose contrastive loss to encourage the MiNet to give closely aligned features to all entries from the same class in each instance. Experiments on three benchmark databases demonstrate the superiority of the proposed method.

Cognitive Components of Ingroup Projection

2014 ◽  
Vol 45 (1) ◽  
pp. 15-30 ◽  
Author(s):  
Maya Machunsky ◽  
Thorsten Meiser
Keyword(s):  
Mental Representation ◽  
Cognitive Representation ◽  
Social Categories ◽  
Projection Model ◽  
Minimal Group ◽  
Model Experiments ◽  
Superordinate Category ◽  
Cognitive Components ◽  
Per Se

This research investigated whether relative ingroup prototypicality (i.e., the tendency to perceive one’s own ingroup as more prototypical of a superordinate category than the outgroup) can result from a prototype-based versus exemplar-based mental representation of social categories, rather than from ingroup membership per se as previously suggested by the ingroup projection model. Experiments 1 and 2 showed that a prototype-based group was perceived as more prototypical of a superordinate category than an exemplar-based group supporting the hypothesis that an intergroup context is not necessary for biased prototypicality judgments. Experiment 3 introduced an intergroup context in a minimal-group-like paradigm. The findings demonstrated that both the kind of cognitive representation and motivational processes contribute to biased prototypicality judgments in intergroup settings.

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