Workload-Aware Partitioning for Maintaining Temporal Consistency upon Multiprocessor Platforms

2011 ◽  
Author(s):  
Jianjun Li ◽  
Jian-Jia Chen ◽  
Ming Xiong ◽  
Guohui Li
Keyword(s):  
Temporal Consistency ◽  
Multiprocessor Platforms

Temporal Consistency Maintenance Upon Partitioned Multiprocessor Platforms

2016 ◽  
Vol 65 (5) ◽  
pp. 1632-1645 ◽  
Author(s):  
Jianjun Li ◽  
Jian-Jia Chen ◽  
Ming Xiong ◽  
Guohui Li ◽  
Wei Wei
Keyword(s):  
Temporal Consistency ◽  
Consistency Maintenance ◽  
Multiprocessor Platforms

scholarly journals iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2

2021 ◽  
Vol 13 (4) ◽  
pp. 654
Author(s):  
Erwin Wolters ◽  
Carolien Toté ◽  
Sindy Sterckx ◽  
Stefan Adriaensen ◽  
Claire Henocq ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Negative Bias ◽  
Surface Reflectance ◽  
Temporal Consistency ◽  
Correction Algorithm ◽  
Land Surfaces ◽  
Relative Differences ◽  
Level 2

To validate the iCOR atmospheric correction algorithm applied to the Sentinel-3 Ocean and Land Color Instrument (OLCI), Top-of-Atmosphere (TOA) observations over land, globally retrieved Aerosol Optical Thickness (AOT), Top-of-Canopy (TOC) reflectance, and Vegetation Indices (VIs) were intercompared with (i) AERONET AOT and AERONET-based TOC reflectance simulations, (ii) RadCalNet surface reflectance observations, and (iii) SYN Level 2 (L2) AOT, TOC reflectance, and VIs. The results reveal that, overall, iCOR’s statistical and temporal consistency is high. iCOR AOT retrievals overestimate relative to AERONET, but less than SYN L2. iCOR and SYN L2 TOC reflectances exhibit a negative bias of ~−0.01 and −0.02, respectively, in the Blue bands compared to the simulations. This diminishes for RED and NIR, except for a +0.02 bias for SYN L2 in the NIR. The intercomparison with RadCalNet shows relative differences < ±6%, except for bands Oa02 (Blue) and Oa21 (NIR), which is likely related to the reported OLCI “excess of brightness”. The intercomparison between iCOR and SYN L2 showed R2 = 0.80–0.93 and R2 = 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR’s higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations.

Reasoning over temporal knowledge graph with temporal consistency constraints

2021 ◽  
pp. 1-10
Author(s):  
Xiaojun Chen ◽  
Shengbin Jia ◽  
Ling Ding ◽  
Yang Xiang
Keyword(s):  
Short Term Memory ◽  
Knowledge Graph ◽  
Temporal Consistency ◽  
Short Term ◽  
Basic Model ◽  
Consistency Constraints ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Relation Prediction ◽  
Time Information

Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson, spouse Of, Ryan Reynolds) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransRILP, which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming. Specifically, we utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransRILP can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on Hits@10.

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