Multi-Model Selection and Analysis for COVID-19

In the face of an increasing number of COVID-19 infections, one of the most crucial and challenging problems is to pick out the most reasonable and reliable models. Based on the COVID-19 data of four typical cities/provinces in China, integer-order and fractional SIR, SEIR, SEIR-Q, SEIR-QD, and SEIR-AHQ models are systematically analyzed by the AICc, BIC, RMSE, and R means. Through extensive simulation and comprehensive comparison, we show that the fractional models perform much better than the corresponding integer-order models in representing the epidemiological information contained in the real data. It is further revealed that the inflection point plays a vital role in the prediction. Moreover, the basic reproduction numbers R0 of all models are highly dependent on the contact rate.

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COMPARATIVE STUDY ON WAVELET FILTER AND THRESHOLDING SELECTION FOR GPS/INS DATA FUSION

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691310003572 ◽

2010 ◽

Vol 08 (03) ◽

pp. 457-473 ◽

Cited By ~ 10

Author(s):

AHMED M. HASAN ◽

KHAIRULMIZAM SAMSUDIN ◽

ABDUL RAHMAN RAMLI ◽

RAJA SYAMSUL AZMIR

Keyword(s):

Error Estimation ◽

Autonomous Vehicle ◽

Real Data ◽

Wavelet Filter ◽

Wavelet Filters ◽

Global Positioning ◽

Comprehensive Comparison ◽

Selection Algorithms ◽

Better Than

Navigation and guidance of an autonomous vehicle require determination of the position and velocity of the vehicle. Therefore, fusing the Inertial Navigation System (INS) and Global Positioning System (GPS) is important. Various methods have been applied to smooth and predict the INS and GPS errors. Recently, wavelet de-noising methodologies have been applied to improve the accuracy and reliability of the GPS/INS system. In this work, analysis of real data to identify the optimal wavelet filter for each GPS and INS component for high quality error estimation is presented. A comprehensive comparison of various wavelet thresholding selections with different level of decomposition is conducted to study the effect on GPS/INS error estimation while maintaining the original features of the signal. Results show that while some wavelet filters and thresholding selection algorithms perform better than others on each of the GPS and INS components, no specific parameter selection perform uniformly better than others.

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Real Time Gender and Age Prediction using Deep Learning Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2906.049620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 797-801

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Real Time ◽

Vital Role ◽

Learning Techniques ◽

Gender And Age ◽

The Face ◽

And Gender ◽

Gender Detection ◽

Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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Evaluation for estimating of the PDF and the CDF of Generalized Inverted Exponential Distribution with Application in Industry

Advances in Mathematics: Scientific Journal ◽

10.37418/amsj.9.1.39 ◽

2020 ◽

pp. 507-522

Author(s):

Parisa Torkaman

Keyword(s):

Least Squares ◽

Exponential Distribution ◽

Mean Squared Error ◽

Weighted Least Squares ◽

Real Data ◽

Minimum Variance ◽

Cumulative Distribution ◽

Estimation Methods ◽

Data Set ◽

Better Than

The generalized inverted exponential distribution is introduced as a lifetime model with good statistical properties. This paper, the estimation of the probability density function and the cumulative distribution function of with five different estimation methods: uniformly minimum variance unbiased(UMVU), maximum likelihood(ML), least squares(LS), weighted least squares (WLS) and percentile(PC) estimators are considered. The performance of these estimation procedures, based on the mean squared error (MSE) by numerical simulations are compared. Simulation studies express that the UMVU estimator performs better than others and when the sample size is large enough the ML and UMVU estimators are almost equivalent and efficient than LS, WLS and PC. Finally, the result using a real data set are analyzed.

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COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

10.2196/preprints.23997 ◽

2020 ◽

Author(s):

Eduardo Atem De Carvalho ◽

Rogerio Atem De Carvalho

Keyword(s):

New York ◽

Reproduction Number ◽

Moving Average ◽

Real Data ◽

Time Dependent ◽

Initial Value ◽

Health Authorities ◽

Reproduction Numbers ◽

Effect Estimation ◽

The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

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The Alternatives to War

10.1093/oso/9780198755203.001.0001 ◽

2018 ◽

Cited By ~ 1

Author(s):

James Pattison

Keyword(s):

Just War ◽

Just War Theory ◽

Economic Sanctions ◽

Responsibility To Protect ◽

Military Action ◽

Comprehensive Account ◽

The Face ◽

Normative Issues ◽

Better Than

If states are not to go to war, what should they do instead? In The Alternatives to War: From Sanctions to Non-violence, James Pattison considers the case for the alternatives to military action to address mass atrocities and aggression. He covers the normative issues raised by measures ranging from comprehensive economic sanctions, diplomacy, and positive incentives, to criminal prosecutions, non-violent resistance, accepting refugees, and arming rebels. For instance, given the indiscriminateness of many sanctions regimes, are sanctions any better than war? Should states avoid ‘megaphone diplomacy’ and adopt more subtle measures? What, if anything, can non-violent methods such as civilian defence and civilian peacekeeping do in the face of a ruthless opponent? Is it a serious concern that positive incentives can appear to reward aggressors? Overall, Pattison provides a comprehensive account of the ethics of the alternatives to war. In doing so, he argues that the case for war is weaker and the case for many of the alternatives is stronger than commonly thought. The upshot is that, when reacting to mass atrocities and aggression, states are generally required to pursue the alternatives to war rather than military action. Pattison concludes that this has significant implications for pacifism, Just War Theory, and the responsibility to protect doctrine.

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Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽

10.3390/s21134618 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4618

Author(s):

Francisco Oliveira ◽

Miguel Luís ◽

Susana Sargento

Keyword(s):

Machine Learning ◽

Cellular Networks ◽

Real Data ◽

Emerging Technology ◽

Machine Learning Algorithms ◽

Base Stations ◽

Aerial Vehicle ◽

Positioning Algorithm ◽

The Military ◽

Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

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Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig

Animals ◽

10.3390/ani11020442 ◽

2021 ◽

Vol 11 (2) ◽

pp. 442

Author(s):

Meiqing Wang ◽

Ali Youssef ◽

Mona Larsen ◽

Jean-Loup Rault ◽

Daniel Berckmans ◽

...

Keyword(s):

Heart Rate ◽

Estimation Error ◽

Well Being ◽

Absolute Error ◽

Mean Value ◽

Video Data ◽

Window Functions ◽

The Face ◽

Short Time ◽

Better Than

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

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A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

Remote Sensing ◽

10.3390/rs13020164 ◽

2021 ◽

Vol 13 (2) ◽

pp. 164

Author(s):

Chuyao Luo ◽

Xutao Li ◽

Yongliang Wen ◽

Yunming Ye ◽

Xiaofeng Zhang

Keyword(s):

Short Term Memory ◽

Weather Forecast ◽

Vital Role ◽

Data Sets ◽

Short Term ◽

Learning Techniques ◽

Radar Echo ◽

Hidden States ◽

Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

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Stereotypes in the face of reality: Intergroup contact inconsistent with group stereotypes changes attitudes more than stereotype-consistent contact

Group Processes & Intergroup Relations ◽

10.1177/1368430220946816 ◽

2020 ◽

pp. 136843022094681

Author(s):

Tibor Zingora ◽

Loris Vezzali ◽

Sylvie Graf

Keyword(s):

Intergroup Contact ◽

The Face ◽

Positive Contact ◽

Better Than

In a longitudinal two-wave study we examined the effects of positive and negative intergroup contact on outgroup attitudes in participants who perceived positive, negative, or ambivalent group stereotypes. We focused on stereotype-consistent contact, occurring when the valence of participants’ contact matches the valence of the perceived group stereotype (e.g., negative–negative), and on stereotype-inconsistent contact, occurring when the valence of contact contradicts the valence of the group stereotype (e.g., positive–negative). In relations of the Czech majority ( N = 890) with two distinctly stereotyped minorities, the Roma and the Vietnamese, stereotype-inconsistent contact predicted changes in attitudes better than stereotype-consistent contact. In the case of negatively stereotyped groups, positive intergroup contact is a viable way to improve attitudes. For positively stereotyped groups, negative contact can worsen attitudes, while positive contact does not have any attitude-improving effect. Interventions aimed at improving outgroup attitudes need to be applied with caution, considering the valence of group stereotypes.

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Monitoring Persistence Change in Heavy-Tailed Observations

Symmetry ◽

10.3390/sym13060936 ◽

2021 ◽

Vol 13 (6) ◽

pp. 936

Author(s):

Dan Wang

Keyword(s):

Kernel Method ◽

Alternative Hypothesis ◽

Null Distribution ◽

Real Data ◽

Ratio Test ◽

Finite Sample ◽

Test Statistic ◽

Bootstrap Approximation ◽

Heavy Tailed ◽

Better Than

In this paper, a ratio test based on bootstrap approximation is proposed to detect the persistence change in heavy-tailed observations. This paper focuses on the symmetry testing problems of I(1)-to-I(0) and I(0)-to-I(1). On the basis of residual CUSUM, the test statistic is constructed in a ratio form. I prove the null distribution of the test statistic. The consistency under alternative hypothesis is also discussed. However, the null distribution of the test statistic contains an unknown tail index. To address this challenge, I present a bootstrap approximation method for determining the rejection region of this test. Simulation studies of artificial data are conducted to assess the finite sample performance, which shows that our method is better than the kernel method in all listed cases. The analysis of real data also demonstrates the excellent performance of this method.

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