Travel Destination Recommendation Based on Probabilistic Spatio-temporal Inference

2016 ◽  
pp. 94-100 ◽  
Author(s):  
Chang Choi ◽  
Junho Choi ◽  
Htet Myet Lynn ◽  
Pankoo Kim
Keyword(s):  
Travel Destination ◽  
Temporal Inference ◽  
Spatio Temporal

Spatio-temporal Inference

2014 ◽  
pp. 293-308
Author(s):  
Ben Goertzel ◽  
Cassio Pennachin ◽  
Nil Geisweiller
Keyword(s):  
Temporal Inference ◽  
Spatio Temporal

Probabilistic spatio-temporal inference for motion event understanding

2013 ◽  
Vol 122 ◽  
pp. 24-32 ◽  
Author(s):  
Chang Choi ◽  
Junho Choi ◽  
Eunji Lee ◽  
Ilsun You ◽  
Pankoo Kim
Keyword(s):  
Motion Event ◽  
Temporal Inference ◽  
Spatio Temporal

Spatio-Temporal Inference Transformer Network for Video Inpainting

2021 ◽  
Author(s):  
Gajanan Tudavekar ◽  
Santosh S. Saraf ◽  
Sanjay R. Patil
Keyword(s):  
Deep Learning ◽  
Qualitative Evaluation ◽  
Learning Approaches ◽  
Video Inpainting ◽  
Video Frames ◽  
Temporal Inference ◽  
Spatio Temporal ◽  
Temporal Dimensions ◽  
Better Than ◽  
Attention Weight

Video inpainting aims to complete in a visually pleasing way the missing regions in video frames. Video inpainting is an exciting task due to the variety of motions across different frames. The existing methods usually use attention models to inpaint videos by seeking the damaged content from other frames. Nevertheless, these methods suffer due to irregular attention weight from spatio-temporal dimensions, thus giving rise to artifacts in the inpainted video. To overcome the above problem, Spatio-Temporal Inference Transformer Network (STITN) has been proposed. The STITN aligns the frames to be inpainted and concurrently inpaints all the frames, and a spatio-temporal adversarial loss function improves the STITN. Our method performs considerably better than the existing deep learning approaches in quantitative and qualitative evaluation.

scholarly journals MODELING A SPATIO-TEMPORAL INDIVIDUAL TRAVEL BEHAVIOR USING GEOTAGGED SOCIAL NETWORK DATA: A CASE STUDY OF GREATER CINCINNATI

2017 ◽  
Vol IV-4/W2 ◽  
pp. 207-214
Author(s):  
M. Saeedimoghaddam ◽  
C. Kim
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Predictive Accuracy ◽  
Demand Management ◽  
Large Data ◽  
Data Sources ◽  
Mobile Phone Data ◽  
Travel Demand Management ◽  
Travel Destination ◽  
Spatio Temporal

Understanding individual travel behavior is vital in travel demand management as well as in urban and transportation planning. New data sources including mobile phone data and location-based social media (LBSM) data allow us to understand mobility behavior on an unprecedented level of details. Recent studies of trip purpose prediction tend to use machine learning (ML) methods, since they generally produce high levels of predictive accuracy. Few studies used LSBM as a large data source to extend its potential in predicting individual travel destination using ML techniques. In the presented research, we created a spatio-temporal probabilistic model based on an ensemble ML framework named “Random Forests” utilizing the travel extracted from geotagged Tweets in 419 census tracts of Greater Cincinnati area for predicting the tract ID of an individual’s travel destination at any time using the information of its origin. We evaluated the model accuracy using the travels extracted from the Tweets themselves as well as the travels from household travel survey. The Tweets and survey based travels that start from same tract in the south western parts of the study area is more likely to select same destination compare to the other parts. Also, both Tweets and survey based travels were affected by the attraction points in the downtown of Cincinnati and the tracts in the north eastern part of the area. Finally, both evaluations show that the model predictions are acceptable, but it cannot predict destination using inputs from other data sources as precise as the Tweets based data.

Sparse Spatio-temporal Inference of Electromagnetic Brain Sources

2010 ◽  
pp. 157-164 ◽  
Author(s):  
Carsten Stahlhut ◽  
Hagai T. Attias ◽  
David Wipf ◽  
Lars K. Hansen ◽  
Srikantan S. Nagarajan
Keyword(s):  
Temporal Inference ◽  
Spatio Temporal

E3 ubiquitin ligases

2005 ◽  
Vol 41 ◽  
pp. 15-30 ◽  
Author(s):  
Helen C. Ardley ◽  
Philip A. Robinson
Keyword(s):  
Protein Complexes ◽  
Loss Of Function ◽  
Direct Role ◽  
Cellular Processes ◽  
Substrate Protein ◽  
Protein Ubiquitination ◽  
C Terminus ◽  
Full Complement ◽  
Spatio Temporal ◽  
Eukaryotic Organisms

The selectivity of the ubiquitin–26 S proteasome system (UPS) for a particular substrate protein relies on the interaction between a ubiquitin-conjugating enzyme (E2, of which a cell contains relatively few) and a ubiquitin–protein ligase (E3, of which there are possibly hundreds). Post-translational modifications of the protein substrate, such as phosphorylation or hydroxylation, are often required prior to its selection. In this way, the precise spatio-temporal targeting and degradation of a given substrate can be achieved. The E3s are a large, diverse group of proteins, characterized by one of several defining motifs. These include a HECT (homologous to E6-associated protein C-terminus), RING (really interesting new gene) or U-box (a modified RING motif without the full complement of Zn2+-binding ligands) domain. Whereas HECT E3s have a direct role in catalysis during ubiquitination, RING and U-box E3s facilitate protein ubiquitination. These latter two E3 types act as adaptor-like molecules. They bring an E2 and a substrate into sufficiently close proximity to promote the substrate's ubiquitination. Although many RING-type E3s, such as MDM2 (murine double minute clone 2 oncoprotein) and c-Cbl, can apparently act alone, others are found as components of much larger multi-protein complexes, such as the anaphase-promoting complex. Taken together, these multifaceted properties and interactions enable E3s to provide a powerful, and specific, mechanism for protein clearance within all cells of eukaryotic organisms. The importance of E3s is highlighted by the number of normal cellular processes they regulate, and the number of diseases associated with their loss of function or inappropriate targeting.

Elucidating cyclic AMP signaling in subcellular domains with optogenetic tools and fluorescent biosensors

2019 ◽  
Vol 47 (6) ◽  
pp. 1733-1747 ◽  
Author(s):  
Christina Klausen ◽  
Fabian Kaiser ◽  
Birthe Stüven ◽  
Jan N. Hansen ◽  
Dagmar Wachten
Keyword(s):  
Cyclic Amp ◽  
Adenosine Monophosphate ◽  
Adenylyl Cyclases ◽  
Temporal Precision ◽  
Fluorescent Biosensors ◽  
Subcellular Domains ◽  
Spatio Temporal ◽  
Hydrolysis Of ◽  
Shed Light ◽  
Cyclic Amp Signaling

The second messenger 3′,5′-cyclic nucleoside adenosine monophosphate (cAMP) plays a key role in signal transduction across prokaryotes and eukaryotes. Cyclic AMP signaling is compartmentalized into microdomains to fulfil specific functions. To define the function of cAMP within these microdomains, signaling needs to be analyzed with spatio-temporal precision. To this end, optogenetic approaches and genetically encoded fluorescent biosensors are particularly well suited. Synthesis and hydrolysis of cAMP can be directly manipulated by photoactivated adenylyl cyclases (PACs) and light-regulated phosphodiesterases (PDEs), respectively. In addition, many biosensors have been designed to spatially and temporarily resolve cAMP dynamics in the cell. This review provides an overview about optogenetic tools and biosensors to shed light on the subcellular organization of cAMP signaling.

Sign in / Sign up

Export Citation Format

Share Document