scholarly journals Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization

2021 ◽  
Author(s):  
Hongchang Gao ◽  
Hanzi Xu ◽  
Slobodan Vucetic
Keyword(s):  
Large Scale ◽  
Distributed Data ◽  
Sample Complexity ◽  
Learning Problem ◽  
Computational Overhead ◽  
Important Challenge ◽  
Submodular Maximization ◽  
Published Result ◽  
Theoretical Results ◽  
Better Than

Continuous DR-submodular maximization is an important machine learning problem, which covers numerous popular applications. With the emergence of large-scale distributed data, developing efficient algorithms for the continuous DR-submodular maximization, such as the decentralized Frank-Wolfe method, became an important challenge. However, existing decentralized Frank-Wolfe methods for this kind of problem have the sample complexity of $\mathcal{O}(1/\epsilon^3)$, incurring a large computational overhead. In this paper, we propose two novel sample efficient decentralized Frank-Wolfe methods to address this challenge. Our theoretical results demonstrate that the sample complexity of the two proposed methods is $\mathcal{O}(1/\epsilon^2)$, which is better than $\mathcal{O}(1/\epsilon^3)$ of the existing methods. As far as we know, this is the first published result achieving such a favorable sample complexity. Extensive experimental results confirm the effectiveness of the proposed methods.

PENGEMBANGAN MEDIA BIG BOOK UNTUK MENINGKATKAN HASIL BELAJAR PECAHAN SENILAI SISWA SD

2018 ◽  
Vol 16 (1) ◽  
pp. 67-76
Author(s):  
Disyacitta Neolia Firdana ◽  
Trimurtini Trimurtini
Keyword(s):  
Learning Outcomes ◽  
Large Scale ◽  
Fourth Grade ◽  
Learning Activities ◽  
Equivalent Fractions ◽  
Fourth Grade Students ◽  
Teacher Needs ◽  
The Media ◽  
Test Result ◽  
Better Than

This research aimed to determine the properness and effectiveness of the big book media on learning equivalent fractions of fourth grade students. The method of research is Research and Development  (R&D). This study was conducted in fourth grade of SDN Karanganyar 02 Kota Semarang. Data sources from media validation, material validation, learning outcomes, and teacher and students responses on developed media. Pre-experimental research design with one group pretest-posttest design. Big book developed consist of equivalent fractions material, students learning activities sheets with rectangle and circle shape pictures, and questions about equivalent fractions. Big book was developed based on students and teacher needs. This big book fulfill the media validity of 3,75 with very good criteria and scored 3 by material experts with good criteria. In large-scale trial, the result of students posttest have learning outcomes completness 82,14%. The result of N-gain calculation with result 0,55 indicates the criterion “medium”. The t-test result 9,6320 > 2,0484 which means the average of posttest outcomes is better than the average of pretest outcomes. Based on that data, this study has produced big book media which proper and effective as a media of learning equivalent fractions of fourth grade elementary school.

scholarly journals Assessment of Numerical Methods for Plunging Breaking Wave Predictions

2021 ◽  
Vol 9 (3) ◽  
pp. 264
Author(s):  
Shanti Bhushan ◽  
Oumnia El Fajri ◽  
Graham Hubbard ◽  
Bradley Chambers ◽  
Christopher Kees
Keyword(s):  
Solitary Wave ◽  
Wave Breaking ◽  
Large Scale ◽  
Turbulence Models ◽  
Navier Stokes ◽  
Wave Crest ◽  
Breaking Wave ◽  
Rans Turbulence Models ◽  
Run Up ◽  
Better Than

This study evaluates the capability of Navier–Stokes solvers in predicting forward and backward plunging breaking, including assessment of the effect of grid resolution, turbulence model, and VoF, CLSVoF interface models on predictions. For this purpose, 2D simulations are performed for four test cases: dam break, solitary wave run up on a slope, flow over a submerged bump, and solitary wave over a submerged rectangular obstacle. Plunging wave breaking involves high wave crest, plunger formation, and splash up, followed by second plunger, and chaotic water motions. Coarser grids reasonably predict the wave breaking features, but finer grids are required for accurate prediction of the splash up events. However, instabilities are triggered at the air–water interface (primarily for the air flow) on very fine grids, which induces surface peel-off or kinks and roll-up of the plunger tips. Reynolds averaged Navier–Stokes (RANS) turbulence models result in high eddy-viscosity in the air–water region which decays the fluid momentum and adversely affects the predictions. Both VoF and CLSVoF methods predict the large-scale plunging breaking characteristics well; however, they vary in the prediction of the finer details. The CLSVoF solver predicts the splash-up event and secondary plunger better than the VoF solver; however, the latter predicts the plunger shape better than the former for the solitary wave run-up on a slope case.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

scholarly journals CHORD: Distributed Data-sharing via Hybrid ROS 1 and 2 for Multi-robot Exploration of Large-scale Complex Environments

2021 ◽  
pp. 1-1
Author(s):  
Muhammad Fadhil Ginting ◽  
Kyohei Otsu ◽  
Jeffrey Edlund ◽  
Jay Gao ◽  
Ali-akbar Agha-mohammadi
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Distributed Data ◽  
Complex Environments ◽  
Multi Robot

scholarly journals SPAN512: A new mid-latitude pulsar survey with the Nançay Radio Telescope

2012 ◽  
Vol 8 (S291) ◽  
pp. 375-377 ◽  
Author(s):  
Gregory Desvignes ◽  
Ismaël Cognard ◽  
David Champion ◽  
Patrick Lazarus ◽  
Patrice Lespagnol ◽  
...  
Keyword(s):  
Radio Telescope ◽  
Large Scale ◽  
High Time ◽  
Frequency Resolution ◽  
Integration Time ◽  
Sensitivity Limit ◽  
Long Period ◽  
Large Bandwidth ◽  
L Band ◽  
Better Than

AbstractWe present an ongoing survey with the Nançay Radio Telescope at L-band. The targeted area is 74° ≲ l < 150° and 3.5° < |b| < 5°. This survey is characterized by a long integration time (18 min), large bandwidth (512 MHz) and high time and frequency resolution (64 μs and 0.5 MHz) giving a nominal sensitivity limit of 0.055 mJy for long period pulsars. This is about 2 times better than the mid-latitude HTRU survey, and is designed to be complementary with current large scale surveys. This survey will be more sensitive to transients (RRATs, intermittent pulsars), distant and faint millisecond pulsars as well as scintillating sources (or any other kind of radio faint sources) than all previous short-integration surveys.

scholarly journals The Management Strategy of Metadata in Large-scale Network Storage System

2018 ◽  
Vol 228 ◽  
pp. 01011
Author(s):  
Haifeng Zhong ◽  
Jianying Xiong
Keyword(s):  
Large Scale ◽  
Storage System ◽  
Hash Table ◽  
Distributed Hash Table ◽  
Distributed Data ◽  
Mass Storage ◽  
Large Scale Network ◽  
Network Storage System ◽  
Mass Storage System ◽  
Scale Network

The wan Internet storage system based on Distributed Hash Table uses fully distributed data and metadata management, and constructs an extensible and efficient mass storage system for the application based on Internet. However, such systems work in highly dynamic environments, and the frequent entry and exit of nodes will lead to huge communication costs. Therefore, this paper proposes a new hierarchical metadata routing management mechanism based on DHT, which makes full use of the node stabilization point to reduce the maintenance overhead of the overlay. Analysis shows that the algorithm can effectively improve efficiency and enhance stability.

scholarly journals A networked voting rule for democratic representation

2018 ◽  
Vol 5 (3) ◽  
pp. 172265 ◽  
Author(s):  
Alexis R. Hernández ◽  
Carlos Gracia-Lázaro ◽  
Edgardo Brigatti ◽  
Yamir Moreno
Keyword(s):  
Large Scale ◽  
Selection Process ◽  
General Relation ◽  
Community Size ◽  
Rule Based ◽  
Voting Rule ◽  
Large Populations ◽  
Negative Effect ◽  
High Connectivity ◽  
Better Than

We introduce a general framework for exploring the problem of selecting a committee of representatives with the aim of studying a networked voting rule based on a decentralized large-scale platform, which can assure a strong accountability of the elected. The results of our simulations suggest that this algorithm-based approach is able to obtain a high representativeness for relatively small committees, performing even better than a classical voting rule based on a closed list of candidates. We show that a general relation between committee size and representatives exists in the form of an inverse square root law and that the normalized committee size approximately scales with the inverse of the community size, allowing the scalability to very large populations. These findings are not strongly influenced by the different networks used to describe the individuals’ interactions, except for the presence of few individuals with very high connectivity which can have a marginal negative effect in the committee selection process.

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