Multiple metrics informed projections of future precipitation in China

2021 ◽  
Author(s):  
Lei Wang ◽  
Yun Qian ◽  
L. Ruby Leung ◽  
Xiaodong Chen ◽  
Chandan Sarangi ◽  
...  
Keyword(s):  
Multiple Metrics

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

Combining Multiple Metrics to Control BSP Process Rescheduling in Response to Resource and Application Dynamics

2011 ◽  
Author(s):  
Rodrigo da Rosa Righi ◽  
Lucas Graebin ◽  
Rafael Bohrer Avila ◽  
Philippe Olivier Alexadre Navaux ◽  
Laercio Lima Pilla
Keyword(s):  
Multiple Metrics

The Value of Using Multiple Metrics to Evaluate PCB Exposure

2017 ◽  
Vol 74 (3) ◽  
pp. 361-371 ◽  
Author(s):  
Megan C. Archer ◽  
Amanda D. Harwood ◽  
Samuel A. Nutile ◽  
Kara E. Huff Hartz ◽  
Marc A. Mills ◽  
...  
Keyword(s):  
Multiple Metrics ◽  
Pcb Exposure

scholarly journals Measures of speech rhythm and the role of corpus-based word frequency: a multifactorial comparison of Spanish(-English) speakers

2011 ◽  
Vol 11 (2) ◽  
pp. 1 ◽  
Author(s):  
Michael J. Harris ◽  
Stefan Th. Gries
Keyword(s):  
Word Frequency ◽  
Binary Logistic Regression ◽  
Spanish Speakers ◽  
English Speakers ◽  
Vowel Duration ◽  
Mexican Spanish ◽  
Speech Rhythm ◽  
Multiple Metrics ◽  
Main Effects ◽  
The Impact

In this study, we address various measures that have been employed to distinguish between syllable and stress- timed languages. This study differs from all previous ones by (i) exploring and comparing multiple metrics within a quantitative and multifactorial perspective and by (ii) also documenting the impact of corpus-based word frequency. We begin with the basic distinctions of speech rhythms, dealing with the differences between syllable-timed languages and stress-timed languages and several methods that have been used to attempt to distinguish between the two. We then describe how these metrics were used in the current study comparing the speech rhythms of Mexican Spanish speakers and bilingual English/Spanish speakers (speakers born to Mexican parents in California). More specifically, we evaluate how well various metrics of vowel duration variability as well as the so far understudied factor of corpus-based frequency allow to classify speakers as monolingual or bilingual. A binary logistic regression identifies several main effects and interactions. Most importantly, our results call the utility of a particular rhythm metric, the PVI, into question and indicate that corpus data in the form of lemma frequencies interact with two metrics of durational variability, suggesting that durational variability metrics should ideally be studied in conjunction with corpus-based frequency data.

Multiple metrics provide context for the distribution of a highly mobile fish predator, the blue catfish

2018 ◽  
Vol 28 (1) ◽  
pp. 141-155 ◽  
Author(s):  
Kayla M. Gerber ◽  
Martha E. Mather ◽  
Joseph M. Smith ◽  
Zachary J. Peterson
Keyword(s):  
Blue Catfish ◽  
Fish Predator ◽  
Multiple Metrics

scholarly journals A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 92 ◽  
Author(s):  
Mingda Wang ◽  
Guangmin Hu
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Loss Functions ◽  
Neural Structure ◽  
Text Information ◽  
Novel Method ◽  
Structural Controllability ◽  
Graph Neural Networks ◽  
Multiple Metrics

Twitter sentiment analysis is an effective tool for various Twitter-based analysis tasks. However, there is still no neural-network-based research which takes both the tweet-text information and user-connection information into account. To this end, we propose the Attentional-graph Neural Network based Twitter Sentiment Analyzer (AGN-TSA), a Twitter sentiment analyzer based on attentional-graph neural networks. AGN-TSA fuses the tweet-text information and the user-connection information through a three-layered neural structure, which includes a word-embedding layer, a user-embedding layer and an attentional graph network layer. For the training of AGN-TSA, dedicated loss functions are designed for the structural controllability of AGN-TSA network. Experiments based on real-world dataset concerning the 2016 presidential election of America exhibit that AGN-TSA is superior under multiple metrics over several prevailing methods, with a performance boost of over 5%. The empirical settings of parameters are given based on extensive rotation experiments.

scholarly journals Evaluating the Evidential Value of Empirically Supported Psychological Treatments (ESTs): A Meta-Scientific Review

2019 ◽  
Author(s):  
John Kitchener Sakaluk ◽  
Alexander Williams ◽  
Robyn Kilshaw ◽  
Kathleen T. Rhyner
Keyword(s):  
Statistical Power ◽  
Empirical Support ◽  
Therapeutic Interventions ◽  
Scientific Review ◽  
Evidential Value ◽  
Empirically Supported ◽  
Research Findings ◽  
Almost All ◽  
Multiple Metrics ◽  
Statistical Criteria

Empirically supported treatments (or therapies; ESTs) are the gold standard in therapeutic interventions for psychopathology. Based on a set of methodological and statistical criteria, the APA has assigned particular treatment-diagnosis combinations EST status and has further rated their empirical support as Strong, Modest, and/or Controversial. Emerging concerns about the replicability of research findings in clinical psychology highlight the need to critically examine the evidential value of EST research. We therefore conducted a meta-scientific review of the EST literature, using clinical trials reported in an existing online APA database of ESTs, and a set of novel evidential value metrics (i.e., rates of misreported statistics, statistical power, R-Index, and Bayes Factors). Our analyses indicated that power and replicability estimates were concerningly low across almost all ESTs, and individually, some ESTs scored poorly across multiple metrics, with Strong ESTs failing to continuously outperform their Modest counterparts. Lastly, we found evidence of improvements over time in statistical power within the EST literature, but not for the strength of evidence of EST efficacy. We describe the implications of our findings for practicing psychotherapists and offer recommendations for improving the evidential value of EST research moving forward.

Quality of Service for Multimedia and Real-Time Services

2010 ◽  
Vol 5 (4) ◽  
pp. 1-22
Author(s):  
F. W. Albalas ◽  
B. A. Abu-Alhaija ◽  
A. Awajan ◽  
A. Awajan ◽  
Khalid Al-Begain
Keyword(s):  
Decision Making ◽  
Quality Of Service ◽  
Real Time ◽  
Qos Routing ◽  
Routing Algorithms ◽  
Complete Problem ◽  
Filtering Technique ◽  
Np Complete ◽  
Multiple Metrics

New web technologies have encouraged the deployment of various network applications that are rich with multimedia and real-time services. These services demand stringent requirements are defined through Quality of Service (QoS) parameters such as delay, jitter, loss, etc. To guarantee the delivery of these services QoS routing algorithms that deal with multiple metrics are needed. Unfortunately, QoS routing with multiple metrics is considered an NP-complete problem that cannot be solved by a simple algorithm. This paper proposes three source based QoS routing algorithms that find the optimal path from the service provider to the user that best satisfies the QoS requirements for a particular service. The three algorithms use the same filtering technique to prune all the paths that do not meet the requirements which solves the complexity of NP-complete problem. Next, each of the three algorithms integrates a different Multiple Criteria Decision Making method to select one of the paths that have resulted from the route filtering technique. The three decision making methods used are the Analytic Hierarchy Process (AHP), Multi-Attribute Utility Theory (MAUT), and Kepner-Tregoe KT. Results show that the algorithms find a path using multiple constraints with a high ability to handle multimedia and real-time applications.

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