scholarly journals Predicting the Times of Retweeting in Microblogs

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Li Kuang ◽  
Xiang Tang ◽  
Kehua Guo
Keyword(s):  
Time Series ◽  
Time Distribution ◽  
Distribution Curve ◽  
Information Propagation ◽  
Web Portals ◽  
Traditional Media ◽  
Two Phase ◽  
Two Phases ◽  
The Times ◽  
The Given

Recently, microblog services accelerate the information propagation among peoples, leaving the traditional media like newspaper, TV, forum, blogs, and web portals far behind. Various messages are spread quickly and widely by retweeting in microblogs. In this paper, we take Sina microblog as an example, aiming to predict the possible number of retweets of an original tweet in one month according to the time series distribution of its topnretweets. In order to address the problem, we propose the concept of a tweet’s lifecycle, which is mainly decided by three factors, namely, the response time, the importance of content, and the interval time distribution, and then the given time series distribution curve of its topnretweets is fitted by a two-phase function, so as to predict the number of its retweets in one month. The phases in the function are divided by the lifecycle of the original tweet and different functions are used in the two phases. Experiment results show that our solution can address the problem of predicting the times of retweeting in microblogs with a satisfying precision.

Which Elasticity Tensors are Realizable?

1995 ◽  
Vol 117 (4) ◽  
pp. 483-493 ◽  
Author(s):  
Graeme W. Milton ◽  
Andrej V. Cherkaev
Keyword(s):  
Building Blocks ◽  
Elasticity Tensor ◽  
Isotropic Phase ◽  
Order Tensor ◽  
Two Phase ◽  
Effective Elasticity ◽  
Elasticity Tensors ◽  
Two Phases ◽  
The Given ◽  
Fourth Order Tensor

It is shown that any given positive definite fourth order tensor satisfying the usual symmetries of elasticity tensors can be realized as the effective elasticity tensor of a two-phase composite comprised of a sufficiently compliant isotropic phase and a sufficiently rigid isotropic phase configured in an suitable microstructure. The building blocks for constructing this composite are what we call extremal materials. These are composites of the two phases which are extremely stiff to a set of arbitrary given stresses and, at the same time, are extremely compliant to any orthogonal stress. An appropriately chosen subset of the extremal materials are layered together to form the composite with elasticity tensor matching the given tensor.

scholarly journals A Two-Phase Approach for Semi-Supervised Feature Selection

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 215
Author(s):  
Amit Saxena ◽  
Shreya Pare ◽  
Mahendra Singh Meena ◽  
Deepak Gupta ◽  
Akshansh Gupta ◽  
...  
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Second Phase ◽  
Two Phase ◽  
Phase Approach ◽  
Whole Process ◽  
Novel Approach ◽  
Two Phases ◽  
Number Of Classes ◽  
The Given

This paper proposes a novel approach for selecting a subset of features in semi-supervised datasets where only some of the patterns are labeled. The whole process is completed in two phases. In the first phase, i.e., Phase-I, the whole dataset is divided into two parts: The first part, which contains labeled patterns, and the second part, which contains unlabeled patterns. In the first part, a small number of features are identified using well-known maximum relevance (from first part) and minimum redundancy (whole dataset) based feature selection approaches using the correlation coefficient. The subset of features from the identified set of features, which produces a high classification accuracy using any supervised classifier from labeled patterns, is selected for later processing. In the second phase, i.e., Phase-II, the patterns belonging to the first and second part are clustered separately into the available number of classes of the dataset. In the clusters of the first part, take the majority of patterns belonging to a cluster as the class for that cluster, which is given already. Form the pairs of cluster centroids made in the first and second part. The centroid of the second part nearest to a centroid of the first part will be paired. As the class of the first centroid is known, the same class can be assigned to the centroid of the cluster of the second part, which is unknown. The actual class of the patterns if known for the second part of the dataset can be used to test the classification accuracy of patterns in the second part. The proposed two-phase approach performs well in terms of classification accuracy and number of features selected on the given benchmarked datasets.

Some Statistical Properties of Short-Duration Rainfall

1984 ◽  
Vol 16 (8-9) ◽  
pp. 93-100
Author(s):  
D M Hershfield
Keyword(s):  
Short Duration ◽  
Time Distribution ◽  
Distribution Curve ◽  
Frequency Factor ◽  
Design Criteria ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Small Areas ◽  
Drainage Design ◽  
Time Distribution Curve

Storm data and climatological quantities from both dense raingage networks and individual stations are used to elucidate some of the important problems in developing drainage design criteria for small areas. Examples are presented displaying the variability of rainfall rates for very short durations of time over very small areas. An “average” time distribution curve is presented along with relationships between rainfall amounts for durations from 2- to 60-min. One example outlines a procedure for estimating and comparing six quantities from series of annual maximum rainfalls for several short durations. The quantities include a frequency factor, 100-yr value, the probable maximum rainfall, and the observed world maximum rainfalls.

SPECIFICITY OF IMAGE CONSTRUCTION OF POLITICAL PARTIES IN MODERN MEDIADISCURSIVE PRACTICE: EVOLUTION OF CONVERGENCE AND ANTINOMY

2020 ◽  
Vol 15 (6) ◽  
pp. 71-87
Author(s):  
A.V. TOLOCHKO ◽  
◽  
V.A. MATVIENKO ◽  
Keyword(s):  
Political Parties ◽  
Modern World ◽  
Political Organizations ◽  
Traditional Media ◽  
Image Construction ◽  
Effective Strategies ◽  
Discourse Practices ◽  
Media Space ◽  
The Media ◽  
The Given

The purpose of the study is to analyze the specifics of constructing the image of political parties in modern world media discourse practices. Achievement of this goal by the authors of this article determines the formulation of a number of important tasks, for the solution of which, first of all, the study of the image-making of political organizations is carried out, which is of interest to representatives of various worldview and social groups. The article examines the algorithm for constructing the image of parties and party coalitions, identifies the factors influencing their modifications. The authors determine the most effective strategies and tactics that influence the formation of a positive image during the electoral process, conduct a detailed analysis of the communication tools that generate a highly effective image of political parties using both traditional media practitioners and modern Internet agrigers. The work analyzes the main and auxiliary resources that have a multifaceted impact on the collaboration of party forces and their leaders with the media in the process of creating the given images, and broadcasting information to the target audience. As a result, a conclusion is made about the discursiveness of the image-making technologies existing in the media space, the presence of convergences and antinomies in them.

scholarly journals Sensitivity Analysis of Sentinel-1 Backscatter to Oil Palm Plantations at Pluriannual Scale: A Case Study in Gabon, Africa

2021 ◽  
Vol 13 (11) ◽  
pp. 2075
Author(s):  
J. David Ballester-Berman ◽  
Maria Rastoll-Gimenez
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Oil Palm ◽  
Time Interval ◽  
Potential Candidate ◽  
Initial Growth ◽  
Natural Forests ◽  
Wet Season ◽  
Radar Signatures ◽  
The Times

The present paper focuses on a sensitivity analysis of Sentinel-1 backscattering signatures from oil palm canopies cultivated in Gabon, Africa. We employed one Sentinel-1 image per year during the 2015–2021 period creating two separated time series for both the wet and dry seasons. The first images were almost simultaneously acquired to the initial growth stage of oil palm plants. The VH and VV backscattering signatures were analysed in terms of their corresponding statistics for each date and compared to the ones corresponding to tropical forests. The times series for the wet season showed that, in a time interval of 2–3 years after oil palm plantation, the VV/VH ratio in oil palm parcels increases above the one for forests. Backscattering and VV/VH ratio time series for the dry season exhibit similar patterns as for the wet season but with a more stable behaviour. The separability of oil palm and forest classes was also quantitatively addressed by means of the Jeffries–Matusita distance, which seems to point to the C-band VV/VH ratio as a potential candidate for discrimination between oil palms and natural forests, although further analysis must still be carried out. In addition, issues related to the effect of the number of samples in this particular scenario were also analysed. Overall, the outcomes presented here can contribute to the understanding of the radar signatures from this scenario and to potentially improve the accuracy of mapping techniques for this type of ecosystems by using remote sensing. Nevertheless, further research is still to be done as no classification method was performed due to the lack of the required geocoded reference map. In particular, a statistical assessment of the radar signatures should be carried out to statistically characterise the observed trends.

A Lightweight Formalism for Reference Lifetimes and Borrowing in Rust

2021 ◽  
Vol 43 (1) ◽  
pp. 1-73
Author(s):  
David J. Pearce
Keyword(s):  
Memory Management ◽  
Programming Model ◽  
Type System ◽  
Control Flow ◽  
Two Phase ◽  
Type Checking ◽  
Strong Focus ◽  
Reference Implementation ◽  
Two Phases ◽  
Complex Features

Rust is a relatively new programming language that has gained significant traction since its v1.0 release in 2015. Rust aims to be a systems language that competes with C/C++. A claimed advantage of Rust is a strong focus on memory safety without garbage collection. This is primarily achieved through two concepts, namely, reference lifetimes and borrowing . Both of these are well-known ideas stemming from the literature on region-based memory management and linearity / uniqueness . Rust brings both of these ideas together to form a coherent programming model. Furthermore, Rust has a strong focus on stack-allocated data and, like C/C++ but unlike Java, permits references to local variables. Type checking in Rust can be viewed as a two-phase process: First, a traditional type checker operates in a flow-insensitive fashion; second, a borrow checker enforces an ownership invariant using a flow-sensitive analysis. In this article, we present a lightweight formalism that captures these two phases using a flow-sensitive type system that enforces “ type and borrow safety .” In particular, programs that are type and borrow safe will not attempt to dereference dangling pointers. Our calculus core captures many aspects of Rust, including copy- and move-semantics, mutable borrowing, reborrowing, partial moves, and lifetimes. In particular, it remains sufficiently lightweight to be easily digested and understood and, we argue, still captures the salient aspects of reference lifetimes and borrowing. Furthermore, extensions to the core can easily add more complex features (e.g., control-flow, tuples, method invocation). We provide a soundness proof to verify our key claims of the calculus. We also provide a reference implementation in Java with which we have model checked our calculus using over 500B input programs. We have also fuzz tested the Rust compiler using our calculus against 2B programs and, to date, found one confirmed compiler bug and several other possible issues.

scholarly journals XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  
Keyword(s):  
Image Classification ◽  
Second Phase ◽  
Two Phase ◽  
X Ray ◽  
The Neural Network ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Two Phases ◽  
Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

scholarly journals Effects of Salt Tracer Volume and Concentration on Residence Time Distribution Curves for Characterization of Liquid Steel Behavior in Metallurgical Tundish

Metals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 430
Author(s):  
Changyou Ding ◽  
Hong Lei ◽  
Hong Niu ◽  
Han Zhang ◽  
Bin Yang ◽  
...  
Keyword(s):  
Residence Time ◽  
Time Distribution ◽  
Residence Time Distribution ◽  
Mass Concentration ◽  
Distribution Curve ◽  
Navier Stokes ◽  
Navier Stokes Equation ◽  
Residence Time Distribution Curve ◽  
Tracer Solution ◽  
Time Distribution Curve

The residence time distribution (RTD) curve is widely applied to describe the fluid flow in a tundish, different tracer mass concentrations and different tracer volumes give different residence time distribution curves for the same flow field. Thus, it is necessary to have a deep insight into the effects of the mass concentration and the volume of tracer solution on the residence time distribution curve. In order to describe the interaction between the tracer and the fluid, solute buoyancy is considered in the Navier–Stokes equation. Numerical results show that, with the increase of the mass concentration and the volume of the tracer, the shape of the residence time distribution curve changes from single flat peak to single sharp peak and then to double peaks. This change comes from the stratified flow of the tracer. Furthermore, the velocity difference number is introduced to demonstrate the importance of the density difference between the tracer and the fluid.

Kinematic and Dynamic Parameters of a Liquid-Solid Pipe Flow Using DPIV∕Accelerometry

2007 ◽  
Vol 129 (11) ◽  
pp. 1415-1421 ◽  
Author(s):  
Joseph Borowsky ◽  
Timothy Wei
Keyword(s):  
Pipe Flow ◽  
Solid Phase ◽  
Fluid Phase ◽  
Central Moment ◽  
Steady Flows ◽  
Dynamic Parameters ◽  
Two Phase ◽  
Turbulence Structures ◽  
Two Phases ◽  
Flat Profile

An experimental investigation of a two-phase pipe flow was undertaken to study kinematic and dynamic parameters of the fluid and solid phases. To accomplish this, a two-color digital particle image velocimetry and accelerometry (DPIV∕DPIA) methodology was used to measure velocity and acceleration fields of the fluid phase and solid phase simultaneously. The simultaneous, two-color DPIV∕DPIA measurements provided information on the changing characteristics of two-phase flow kinematic and dynamic quantities. Analysis of kinematic terms indicated that turbulence was suppressed due to the presence of the solid phase. Dynamic considerations focused on the second and third central moments of temporal acceleration for both phases. For the condition studied, the distribution across the tube of the second central moment of acceleration indicated a higher value for the solid phase than the fluid phase; both phases had increased values near the wall. The third central moment statistic of acceleration showed a variation between the two phases with the fluid phase having an oscillatory-type profile across the tube and the solid phase having a fairly flat profile. The differences in second and third central moment profiles between the two phases are attributed to the inertia of each particle type and its response to turbulence structures. Analysis of acceleration statistics provides another approach to characterize flow fields and gives some insight into the flow structures, even for steady flows.

scholarly journals Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Main Idea ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
The Times ◽  
Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

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