Today's Large-Scale Science depends on Network Data Movement based on Twenty Five Years of Technology Development

2013 ◽  
pp. 41-65
Author(s):  
W.E. Johnston ◽  
E. Dart ◽  
B. Tierney
Keyword(s):  
Large Scale ◽  
Technology Development ◽  
Network Data ◽  
Data Movement

Some applications of electron beam microanalysis techniques in VLSI process evaluation

1983 ◽  
Vol 41 ◽  
pp. 160-161
Author(s):  
Simon Thomas
Keyword(s):  
Integrated Circuits ◽  
Process Evaluation ◽  
Large Scale ◽  
Technology Development ◽  
Analytical Techniques ◽  
Successful Implementation ◽  
Analysis Of Failures ◽  
Characterization Of Materials ◽  
Circuits Vlsi

Trends in the technology development of very large scale integrated circuits (VLSI) have been in the direction of higher density of components with smaller dimensions. The scaling down of device dimensions has been not only laterally but also in depth. Such efforts in miniaturization bring with them new developments in materials and processing. Successful implementation of these efforts is, to a large extent, dependent on the proper understanding of the material properties, process technologies and reliability issues, through adequate analytical studies. The analytical instrumentation technology has, fortunately, kept pace with the basic requirements of devices with lateral dimensions in the micron/ submicron range and depths of the order of nonometers. Often, newer analytical techniques have emerged or the more conventional techniques have been adapted to meet the more stringent requirements. As such, a variety of analytical techniques are available today to aid an analyst in the efforts of VLSI process evaluation. Generally such analytical efforts are divided into the characterization of materials, evaluation of processing steps and the analysis of failures.

Incremental Community Detection on Large Complex Attributed Network

2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Zhe Chen ◽  
Aixin Sun ◽  
Xiaokui Xiao
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Network Data ◽  
Topological Information ◽  
Community Membership ◽  
Attributed Network ◽  
Benchmark Datasets ◽  
Modularity Maximization ◽  
Large Scale Networks

Community detection on network data is a fundamental task, and has many applications in industry. Network data in industry can be very large, with incomplete and complex attributes, and more importantly, growing. This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains users with transactions. The results demonstrate inc-AGGMMR’s effectiveness and practicability.

Proposal for the generation of network data for pedestrians in South Korea using large-scale maps

2015 ◽  
Vol 19 (5) ◽  
pp. 1483-1491 ◽  
Author(s):  
Jiyoung Kim ◽  
Yong Huh ◽  
Jung Ok Kim ◽  
Jae Bin Lee
Keyword(s):  
South Korea ◽  
Large Scale ◽  
Network Data

Prospects for Cooperation Between Russia and the Republic of Korea in the Fields of Big Data, Network and Artificial Intelligence for the Development of the Digital Economy

2021 ◽  
Vol 65 (8) ◽  
pp. 51-60
Author(s):  
Yujeong Kim
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Large Scale ◽  
Technology Development ◽  
Technological Development ◽  
Digital Economy ◽  
Republic Of Korea ◽  
Data Network ◽  
Leading Position ◽  
The Government

Today, each country has interest in digital economy and has established and implemented policies aimed at digital technology development and digital transformation for the transition to the digital economy. In particular, interest in digital technologies such as big data, 5G, and artificial intelligence, which are recognized as important factors in the digital economy, has been increasing recently, and it is a time when the role of the government for technological development and international cooperation becomes important. In addition to the overall digital economic policy, the Russian and Korean governments are also trying to improve their international competitiveness and take a leading position in the new economic order by establishing related technical and industrial policies. Moreover, Republic of Korea often refers to data, network and artificial intelligence as D∙N∙A, and has established policies in each of these areas in 2019. Russia is also establishing and implementing policies in the same field in 2019. Therefore, it is timely to find ways to expand cooperation between Russia and Republic of Korea. In particular, the years of 2020and 2021marks the 30th anniversary of diplomatic relations between the two countries, and not only large-scale events and exchange programs have prepared, but the relationship is deepening as part of the continued foreign policy of both countries – Russia’s Eastern Policy and New Northern Policy of Republic of Korea. Therefore, this paper compares and analyzes the policies of the two countries in big data, 5G, and artificial intelligence to seek long-term sustainable cooperation in the digital economy.

scholarly journals Artificial Intelligence and the Transformation of Humans, Law and Technology Interactions in Judicial Proceedings

2020 ◽  
Vol 2 (1) ◽  
pp. 4-18 ◽  
Author(s):  
Francesco Contini
Keyword(s):  
Artificial Intelligence ◽  
Large Scale ◽  
Technology Development ◽  
Actor Network Theory ◽  
Digital Environment ◽  
Law And Technology ◽  
Administration Of Justice ◽  
Case Comparison ◽  
Judicial Proceedings ◽  
Online Forms

The paper connects the potentially disruptive effects of Artificial Intelligence (AI) deployment in the administration of justice to the pre-existing trajectories and consequences of court technology development. The theoretical framework combines Luhmann’s theory of technology with actor–network theory to analyse how the new digital environment affects judicial agency. Then, it explores law and technology dynamics to map out the conditions that make legal the use of technologies in judicial proceedings. The framework is applied to analyse ‘traditional’ digital technologies (simple online forms and large-scale e-justice platforms) and AI-based systems (speech-to-text and recidivism assessment). The case comparison shows similarities and dynamics triggered by AI and traditional technologies, as well as a radical difference. While system developers and owners remain accountable before the law for the functioning of traditional systems, with AI, such accountability is transferred to users. Judges—users in general—remain accountable for the consequences of their actions supported or suggested by systems that are opaque and autonomous. This contingency, if not adequately faced with new forms of accountability, restricts the areas in which AI can be used without hampering judicial integrity.

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