Asynchronous Distributed ADMM for Learning with Large-Scale and High-Dimensional Sparse Data Set

The user's interaction with the retrieval engines, while seeking a particular image (or set of images) in large-scale databases, defines better his request. This interaction is essentially provided by a relevance feedback step. In fact, the semantic gap is increasing in a remarkable way due to the application of approximate nearest neighbor (ANN) algorithms aiming at resolving the curse of dimensionality. Therefore, an additional step of relevance feedback is necessary in order to get closer to the user's expectations in the next few retrieval iterations. In this context, this paper details a classification of the different relevance feedback techniques related to region-based image retrieval applications. Moreover, a technique of relevance feedback based on re-weighting regions of the query-image by selecting a set of negative examples is elaborated. Furthermore, the general context to carry out this technique which is the large-scale heterogeneous image collections indexing and retrieval is presented. In fact, the main contribution of the proposed work is affording efficient results with the minimum number of relevance feedback iterations for high dimensional image databases. Experiments and assessments are carried out within an RBIR system for "Wang" data set in order to prove the effectiveness of the proposed approaches.

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Digitalization of Seagoing Vessels Under High Dimensional Data Driven Models

Volume 7A: Ocean Engineering ◽

10.1115/omae2017-61011 ◽

2017 ◽

Cited By ~ 1

Author(s):

Lokukaluge P. Perera ◽

Brage Mo

Keyword(s):

Domain Knowledge ◽

Large Scale ◽

Dimensional Space ◽

Data Driven ◽

High Dimensional ◽

Data Handling ◽

Shipping Industry ◽

Data Set ◽

Data Clusters ◽

Ship Performance

Modern ships are supported by internet of things (IoT) to collect ship performance and navigation information. That should be utilized towards digitalization of the shipping industry. However, such information collection systems are always associated with large-scale data sets, so called Big Data, where various industrial challenges are encountered during the respective data handling processes. This study proposes a data handling framework with data driven models (i.e. digital models) to cope with the shipping industrial challenges as the main contribution, where conventional mathematical models may fail. The proposed data driven models are developed in a high dimensional space, where the respective ship performance and navigation parameters of a selected vessel are separated as several data clusters. Hence, this study identifies the distribution of the respective data clusters and the structure of each data cluster in relation to ship performance and navigation conditions. An appropriate structure into the data set of ship performance and navigation parameters is assigned by this method as the main contribution. However, the domain knowledge (i.e. vessel operational and navigation conditions) is also included in this situation to derive a meaningful data structure.

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A Fast Clustering Algorithm for Large-scale and High Dimensional Data

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2009.00859 ◽

2009 ◽

Vol 35 (7) ◽

pp. 859-866

Author(s):

Ming LIU ◽

Xiao-Long WANG ◽

Yuan-Chao LIU

Keyword(s):

Large Scale ◽

Clustering Algorithm ◽

High Dimensional Data ◽

High Dimensional

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ProGen:Provenance database generator for large-scale data set

Journal of Computer Applications ◽

10.3724/sp.j.1087.2008.02737 ◽

2009 ◽

Vol 28 (11) ◽

pp. 2737-2740

Author(s):

Xiao ZHANG ◽

Shan WANG ◽

Na LIAN

Keyword(s):

Large Scale ◽

Data Set ◽

Large Scale Data ◽

Scale Data

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Integrative Data Analysis from a Unifying Research Synthesis Perspective

10.1093/oso/9780190676001.003.0020 ◽

2018 ◽

Author(s):

Eun-Young Mun ◽

Anne E. Ray

Keyword(s):

Data Analysis ◽

Large Scale ◽

Research Synthesis ◽

Alcohol Intervention ◽

Data Set ◽

Integrative Data Analysis ◽

Level Data ◽

Model Complex ◽

Wide Range ◽

Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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Financial distress determinants among SMEs: empirical evidence from Sweden

Journal of Economic Studies ◽

10.1108/jes-01-2019-0030 ◽

2020 ◽

Vol 47 (3) ◽

pp. 547-560 ◽

Cited By ~ 1

Author(s):

Darush Yazdanfar ◽

Peter Öhman

Keyword(s):

Financial Crisis ◽

Financial Distress ◽

Large Scale ◽

Global Financial Crisis ◽

Binary Logistic Regression ◽

Data Availability ◽

Cross Sectional ◽

Data Set ◽

Content Type ◽

The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

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Relationship between large-scale ionospheric field-aligned currents and electron/ion precipitations: DMSP observations

Earth Planets and Space ◽

10.1186/s40623-020-01286-z ◽

2020 ◽

Vol 72 (1) ◽

Author(s):

Chao Xiong ◽

Claudia Stolle ◽

Patrick Alken ◽

Jan Rauberg

Keyword(s):

Time Distribution ◽

Large Scale ◽

Particle Flux ◽

Particle Energy ◽

Lower Latitude ◽

Particle Precipitation ◽

Data Set ◽

The Mean ◽

Two Parameters ◽

Meteorological Satellite

Abstract In this study, we have derived field-aligned currents (FACs) from magnetometers onboard the Defense Meteorological Satellite Project (DMSP) satellites. The magnetic latitude versus local time distribution of FACs from DMSP shows comparable dependences with previous findings on the intensity and orientation of interplanetary magnetic field (IMF) By and Bz components, which confirms the reliability of DMSP FAC data set. With simultaneous measurements of precipitating particles from DMSP, we further investigate the relation between large-scale FACs and precipitating particles. Our result shows that precipitation electron and ion fluxes both increase in magnitude and extend to lower latitude for enhanced southward IMF Bz, which is similar to the behavior of FACs. Under weak northward and southward Bz conditions, the locations of the R2 current maxima, at both dusk and dawn sides and in both hemispheres, are found to be close to the maxima of the particle energy fluxes; while for the same IMF conditions, R1 currents are displaced further to the respective particle flux peaks. Largest displacement (about 3.5°) is found between the downward R1 current and ion flux peak at the dawn side. Our results suggest that there exists systematic differences in locations of electron/ion precipitation and large-scale upward/downward FACs. As outlined by the statistical mean of these two parameters, the FAC peaks enclose the particle energy flux peaks in an auroral band at both dusk and dawn sides. Our comparisons also found that particle precipitation at dawn and dusk and in both hemispheres maximizes near the mean R2 current peaks. The particle precipitation flux maxima closer to the R1 current peaks are lower in magnitude. This is opposite to the known feature that R1 currents are on average stronger than R2 currents.

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Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

Publications of the Astronomical Society of Australia ◽

10.1017/pasa.2020.46 ◽

2020 ◽

Vol 37 ◽

Author(s):

Lior Shamir

Keyword(s):

Large Scale ◽

Spiral Galaxies ◽

Hubble Space Telescope ◽

Gravitational Interaction ◽

Large Data ◽

Sloan Digital Sky Survey ◽

Data Sets ◽

Dipole Axis ◽

Data Set ◽

The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

IEEE Transactions on Computational Social Systems ◽

10.1109/tcss.2021.3051189 ◽

2021 ◽

pp. 1-13

Author(s):

Usman Naseem ◽

Imran Razzak ◽

Matloob Khushi ◽

Peter W. Eklund ◽

Jinman Kim

Keyword(s):

Sentiment Analysis ◽

Large Scale ◽

Data Set ◽

Twitter Data

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