Decoupling spatial and temporal patterns in short-term beach shoreline response to wave climate

Geomorphology ◽  
2011 ◽  
Vol 128 (3-4) ◽  
pp. 199-208 ◽  
Author(s):  
A. Alvarez-Ellacuria ◽  
A. Orfila ◽  
L. Gómez-Pujol ◽  
G. Simarro ◽  
N. Obregon
Keyword(s):  
Temporal Patterns ◽  
Wave Climate ◽  
Spatial And Temporal Patterns ◽  
Short Term

scholarly journals Capturing Spatial and Temporal Patterns for Facial Landmark Tracking through Adversarial Learning

2019 ◽  
Author(s):  
Shi Yin ◽  
Shangfei Wang ◽  
Guozhu Peng ◽  
Xiaoping Chen ◽  
Bowen Pan
Keyword(s):  
Short Term Memory ◽  
Temporal Patterns ◽  
Ground Truth ◽  
Spatial And Temporal Patterns ◽  
Short Term ◽  
Adversarial Learning ◽  
Facial Landmark ◽  
Landmark Tracking ◽  
Memory Network ◽  
Long Short Term Memory

The spatial and temporal patterns inherent in facial feature points are crucial for facial landmark tracking, but have not been thoroughly explored yet. In this paper, we propose a novel deep adversarial framework to explore the shape and temporal dependencies from both appearance level and target label level. The proposed deep adversarial framework consists of a deep landmark tracker and a discriminator. The deep landmark tracker is composed of a stacked Hourglass network as well as a convolutional neural network and a long short-term memory network, and thus implicitly capture spatial and temporal patterns from facial appearance for facial landmark tracking. The discriminator is adopted to distinguish the tracked facial landmarks from ground truth ones. It explicitly models shape and temporal dependencies existing in ground truth facial landmarks through another convolutional neural network and another long short-term memory network. The deep landmark tracker and the discriminator compete with each other. Through adversarial learning, the proposed deep adversarial landmark tracking approach leverages inherent spatial and temporal patterns to facilitate facial landmark tracking from both appearance level and target label level. Experimental results on two benchmark databases demonstrate the superiority of the proposed approach to state-of-the-art work.

SPATIAL AND TEMPORAL PATTERNS OF GROUNDWATER ELEVATION AND SALINITY ON ASSATEAGUE ISLAND

2016 ◽  
Author(s):  
Ruth Coffey ◽  
◽  
Hannah Sprinkle ◽  
Eric Sherry ◽  
Brian Sturgis ◽  
...  
Keyword(s):  
Temporal Patterns ◽  
Spatial And Temporal Patterns ◽  
Assateague Island

THE NEW RADIOCARBON DATASET FOR TRILITH MONUMENTS OF SOUTHEASTERN ARABIA

Radiocarbon ◽  
2020 ◽  
pp. 1-11
Author(s):  
R Garba ◽  
P Demján ◽  
I Svetlik ◽  
D Dreslerová
Keyword(s):  
Iron Age ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Coastal Plains ◽  
Spatial And Temporal Patterns ◽  
Two Phase ◽  
Stone Monuments ◽  
South Central ◽  
Geographical Expansion ◽  
Spatio Temporal

ABSTRACT Triliths are megalithic monuments scattered across the coastal plains of southern and southeastern Arabia. They consist of aligned standing stones with a parallel row of large hearths and form a space, the meaning of which is undoubtedly significant but nonetheless still unknown. This paper presents a new radiocarbon (14C) dataset acquired during the two field seasons 2018–2019 of the TSMO (Trilith Stone Monuments of Oman) project which investigated the spatial and temporal patterns of the triliths. The excavation and sampling of trilith hearths across Oman yielded a dataset of 30 new 14C dates, extending the use of trilith monuments to as early as the Iron Age III period (600–300 BC). The earlier dates are linked to two-phase trilith sites in south-central Oman. The three 14C pairs collected from the two-phase trilith sites indicated gaps between the trilith construction phases from 35 to 475 years (2 σ). The preliminary spatio-temporal analysis shows the geographical expansion of populations using trilith monuments during the 5th to 1st century BC and a later pull back in the 1st and 2nd century AD. The new 14C dataset for trilith sites will help towards a better understanding of Iron Age communities in southeastern Arabia.

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