PARALLEL RANGE MINIMA ON COARSE GRAINED MULTICOMPUTERS

1999 ◽  
Vol 10 (04) ◽  
pp. 375-389
Author(s):  
H. MONGELLI ◽  
S. W. SONG
Keyword(s):  
Common Ancestor ◽  
Basic Problem ◽  
Coarse Grained ◽  
Time Range ◽  
Communication Overhead ◽  
Constant Number ◽  
Real Numbers ◽  
Graph Problems ◽  
Large N ◽  
Lowest Common Ancestor

Given an array of n real numbers A=(a0, a1, …, an-1), define MIN(i,j)= min {ai,…,aj}. The range minima problem consists of preprocessing array A such that queries MIN(i,j), for any 0≤i≤n-1 can be answered in constant time. Range minima is a basic problem that appears in many other important graph problems such as lowest common ancestor, Euler tour, etc. In this work we present a parallel algorithm under the CGM model (coarse grained multicomputer), that solves the range minima problem in O(n/p) time and constant number of communication rounds. The communication overhead involves the transmission of p numbers (independent of n). We show promising experimental results with speedup curves approximating the optimal for large n.

scholarly journals Quantum information probes of charge fractionalization in large-N gauge theories

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Brandon S. DiNunno ◽  
Niko Jokela ◽  
Juan F. Pedraza ◽  
Arttu Pönni
Keyword(s):  
Degrees Of Freedom ◽  
Gauge Theories ◽  
Entanglement Entropy ◽  
Coarse Grained ◽  
Strongly Coupled ◽  
Information Theoretic ◽  
Large N ◽  
Wide Range ◽  
Charge Fractionalization ◽  
Electric Flux

Abstract We study in detail various information theoretic quantities with the intent of distinguishing between different charged sectors in fractionalized states of large-N gauge theories. For concreteness, we focus on a simple holographic (2 + 1)-dimensional strongly coupled electron fluid whose charged states organize themselves into fractionalized and coherent patterns at sufficiently low temperatures. However, we expect that our results are quite generic and applicable to a wide range of systems, including non-holographic. The probes we consider include the entanglement entropy, mutual information, entanglement of purification and the butterfly velocity. The latter turns out to be particularly useful, given the universal connection between momentum and charge diffusion in the vicinity of a black hole horizon. The RT surfaces used to compute the above quantities, though, are largely insensitive to the electric flux in the bulk. To address this deficiency, we propose a generalized entanglement functional that is motivated through the Iyer-Wald formalism, applied to a gravity theory coupled to a U(1) gauge field. We argue that this functional gives rise to a coarse grained measure of entanglement in the boundary theory which is obtained by tracing over (part) of the fractionalized and cohesive charge degrees of freedom. Based on the above, we construct a candidate for an entropic c-function that accounts for the existence of bulk charges. We explore some of its general properties and their significance, and discuss how it can be used to efficiently account for charged degrees of freedom across different energy scales.

scholarly journals A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Shanshan Yu ◽  
Jicheng Zhang ◽  
Ju Liu ◽  
Xiaoqing Zhang ◽  
Yafeng Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Denial Of Service ◽  
Attack Detection ◽  
Coarse Grained ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Fine Grained ◽  
Ddos Attack ◽  
Network Status ◽  
Ddos Attack Detection

AbstractIn order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

scholarly journals On the Distribution of Depths in Increasing Trees

10.37236/409 ◽  
2010 ◽  
Vol 17 (1) ◽  
Author(s):  
Markus Kuba ◽  
Stephan Wagner
Keyword(s):  
Common Ancestor ◽  
Short Note ◽  
Bijective Proof ◽  
Lowest Common Ancestor ◽  
Recursive Trees

By a theorem of Dobrow and Smythe, the depth of the $k$th node in very simple families of increasing trees (which includes, among others, binary increasing trees, recursive trees and plane ordered recursive trees) follows the same distribution as the number of edges of the form $j-(j+1)$ with $j < k$. In this short note, we present a simple bijective proof of this fact, which also shows that the result actually holds within a wider class of increasing trees. We also discuss some related results that follow from the bijection as well as a possible generalization. Finally, we use another similar bijection to determine the distribution of the depth of the lowest common ancestor of two nodes.

scholarly journals A scalable approach to computing representative lowest common ancestor in directed acyclic graphs

2013 ◽  
Vol 513 ◽  
pp. 25-37 ◽  
Author(s):  
Santanu Kumar Dash ◽  
Sven-Bodo Scholz ◽  
Stephan Herhut ◽  
Bruce Christianson
Keyword(s):  
Common Ancestor ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs ◽  
Lowest Common Ancestor

The lowest common ancestor problem on a tree with an unfixed root

1999 ◽  
Vol 119 (1-2) ◽  
pp. 125-130 ◽  
Author(s):  
Biing-Feng Wang ◽  
Jiunn-Nan Tsai ◽  
Yuan-Cheng Chuang
Keyword(s):  
Common Ancestor ◽  
Lowest Common Ancestor

scholarly journals Pangolin homology associated with 2019-nCoV

2020 ◽  
Author(s):  
Tao Zhang ◽  
Qunfu Wu ◽  
Zhigang Zhang
Keyword(s):  
Amino Acid ◽  
Intermediate Host ◽  
Common Ancestor ◽  
Whole Genome ◽  
Amino Acid Residues ◽  
Pathogenic Potential ◽  
Lowest Common Ancestor ◽  
Amino Acid Mutations ◽  
Genome Level ◽  
Novel Coronavirus

AbstractTo explore potential intermediate host of a novel coronavirus is vital to rapidly control continuous COVID-19 spread. We found genomic and evolutionary evidences of the occurrence of 2019-nCoV-like coronavirus (named as Pangolin-CoV) from dead Malayan Pangolins. Pangolin-CoV is 91.02% and 90.55% identical at the whole genome level to 2019-nCoV and BatCoV RaTG13, respectively. Pangolin-CoV is the lowest common ancestor of 2019-nCoV and RaTG13. The S1 protein of Pangolin-CoV is much more closely related to 2019-nCoV than RaTG13. Five key amino-acid residues involved in the interaction with human ACE2 are completely consistent between Pangolin-CoV and 2019-nCoV but four amino-acid mutations occur in RaTG13. It indicates Pangolin-CoV has similar pathogenic potential to 2019-nCoV, and would be helpful to trace the origin and probable intermediate host of 2019-nCoV.

A CLUSTERED WIRELESS SENSOR NETWORK BASED AIR POLLUTION MONITORING SYSTEM WITH SWARM INTELLIGENCE BASED DATA AGGREGATION

2016 ◽  
Vol 78 (7) ◽  
Author(s):  
Murali, S. ◽  
Jaisankar, N.
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Monitoring System ◽  
Data Aggregation ◽  
Hybrid Genetic Algorithm ◽  
Sensor Nodes ◽  
Pollution Monitoring ◽  
Coarse Grained ◽  
Communication Overhead ◽  
Air Pollution Monitoring

Air pollution has significant influence on the concentration of constituents in the atmosphere leading to effects like global warming and acid rains. The disproportion of the constituents in the air or atmosphere is monitored using the air pollution monitoring system. The classical air pollution monitoring system uses the costly instruments for monitoring the environment at fixed locations. Most of the traditional monitoring system is coarse- grained and costlier during the real time implementation. Some system have problems such as communication overhead, time and power consuming. The efficient clustering based data aggregation is proposed in this paper for reducing the communication overhead and efficiently monitoring the environment. The sensor nodes in the networks are grouped into clusters and the cluster head is selected using the optimization algorithm such as firefly algorithm. The data aggregation using the hybrid genetic algorithm is also proposed in this paper for efficient data transmission by reducing the communication overhead. The simulation results shows that the performance of the proposed methodology is better than the existing one and the proposed system collects the reliable source of real time fine-grain pollution data.

Sign in / Sign up

Export Citation Format

Share Document