Empirical Characteristics Research Articles

Sensor and Feature Level Fusion of Thermal Image and ECG Signals in Recognizing Human Emotions

Recent studies on recognition of various emotion labels concentrated on speech signals, text, visual images and anatomical variables. The proposed system combines the features of ECG which are extracted using empirical mode decomposition and features of thermal images which are extracted from Gray Level Co-occurrence Matrix (GLCM) viz energy, contrast, homogeneity and correlation. ECG is acquired from AD8232 module and thermal images from FLUKE TiS20. Data of ECG and thermal images are acquired simultaneously from a subject and database consists of data from 40 subjects in age group of 20 years to 40 years from Hassan, Karnataka, India. Here different labels of emotions have been classified based on K-nearest neighbor decision rule. This system yielded highest accuracy for disgust and lowest for anger using ECG and highest accuracy for disgust and surprise and least for sad.

Stationary solution of semi-discrete traffic flow model on a ring road with slopes

The traffic bottleneck effects are investigated on a ring road with up- and down-slopes to the semi-discrete model under the Lagrange coordinate. The kinetic wave theory is applied to discuss all types of stationary solutions for a small relaxation time. Moreover, the analytical formulas of the queuing lengths before bottlenecks and the critical densities of the saturated flux are derived. The stability of the stationary solution on every road section under different mass increments is discussed. The relation between the slope gradient and saturated flux platform is also obtained. The numerical simulation reproduces many complex nonlinear phenomena, such as the steady-state flow, oscillatory congested traffic and saturated flux platform. This study helps to explain the empirical features on inhomogeneous roads in the real-world traffic.

Earth’s gravity ― the reason of earthquakes

In the paper, an attempt has been made to describe seismic processes by means of classical mechanics. Dynamic parameters such as energy, impulse, and forces that determine the movement of masses are estimated. An earthquake model is regarded with consideration of the Earth gravity field influence on distribution of masses composing the Earth crust. The Earth crust is considered as a complex of geological bodies which is ordered by the planets gravity field. Due to isostasy the difference in density of the equal matter volumes leads to formation of mountain structures specific for the surface. When a base of such structures collapses a rock block shifts down and acquires mechanical impulse. Authors assume that this acquired mechanical impulse determines the earthquake’s magnitude. Impulse propagates in medium as a seismic wave. Front of the wave generates pressure effected on an environment that is not active yet. It determines macro-processes of the earthquake measured as earthquake intensity. Authors consider mass (weight) of a rocks block, magnitude (mechanical impulse acquired during an impact), seismic emission, which is a distribution of a momentum and a pressure of wave front (product of impulse by time), as parameters of a seismic process. To estimate dynamic parameters of earthquake processes, such as shifted mass, mechanical impulse, pressure of wave front, a nomogram is presented. Magnitude of a seismic event has dimension of a mechanical impulse, seismic intensity (earthquake intensity) has a dimension of pressure. Nomogram consists of several parts. The first one, allows analyzing the source of the emission. It shows the relation between emitted momentum, mass of a shifted body and energy. The next part of the nomogram represents momentum density per unit of area of the wavefront, which decreases in proportion to the square of a distance from the source. It characterizes the spherical propagation of seismic radiation in an isotropic medium. The third part of the nomogram helps to estimate the value of an average seismic pressure of the wavefront. The last part of the nomogram allows rating the empirical characteristics of a seismic event. A relation between a scale of seismic pressure and standardized scale of seismic intensity MSK-64 is established. As an example a Spitaka earthquake, December 7 th , 1988, was analyzed.

Growing (un)equal: fiscal policy and income inequality in China and BRIC+

This article investigates the empirical characteristics of income inequality in China and other large emerging market economies over the period 1980–2013, with a focus on the redistributive contribution of fiscal policy. Using instrumental variable techniques to deal with potential endogeneity, we find evidence supporting the hypothesis of the existence of a Kuznets curve – an inverted U-shaped relationship between income inequality and economic development – in China and the panel of BRIC + countries. In the case of China, the empirical results indicate that government spending and taxation have opposing effects on income inequality. While government spending appears to have a worsening impact, taxation improves income distribution. Even though the redistributive effect of fiscal policy in China appears to be stronger than what we identify in the BRIC + panel, it is not large enough to compensate for the adverse impact of other influential factors.

A Study on the Empirical Space Characteristics of Children's Museum

A Study on the Empirical Space Characteristics of Children's Museum

The Impact of Spatial Normalization Strategies on the Temporal Features of the Resting-State Functional MRI: Spatial Normalization Before rs-fMRI Features Calculation May Reduce the Reliability.

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies frequently applied the spatial normalization on fMRI time series before the calculation of temporal features (here referred to as “Prenorm”). We hypothesized that calculating the rs-fMRI features, for example, functional connectivity (FC), regional homogeneity (ReHo), or amplitude of low-frequency fluctuation (ALFF) in individual space, before the spatial normalization (referred to as “Postnorm”) can be an improvement to avoid artifacts and increase the results’ reliability. We utilized two datasets: (1) simulated images where temporal signal-to-noise ratio (tSNR) is kept a constant and (2) an empirical fMRI dataset with 50 healthy young subjects. For simulated images, the tSNR is constant as generated in individual space but increased after Prenorm and intersubject variability of tSNR was induced. In contrast, tSNR was kept constant after Postnorm. Consistently, for empirical images, higher tSNR, ReHo, and FC (default mode network, seed in precuneus) and lower ALFF were found after Prenorm compared to those of Postnorm. Coefficient of variability of tSNR and ALFF was higher after Prenorm compared to those of Postnorm. Moreover, the significant correlation was found between simulated tSNR after Prenorm and empirical tSNR, ALFF, and ReHo after Prenorm, indicating algorithmic variation in empirical rs-fMRI features. Furthermore, comparing to Prenorm, ALFF and ReHo showed higher intraclass correlation coefficients between two serial scans after Postnorm. Our results indicated that Prenorm may induce algorithmic intersubject variability on tSNR and reduce its reliability, which also significantly affected ALFF and ReHo. We suggest using Postnorm instead of Prenorm for future rs-fMRI studies using ALFF/ReHo.

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative hippocampal sclerosis (HS) can hamper early diagnosis and surgical intervention for patients in clinical practice, resulting in disease progression. Our aim was to automatically detect and evaluate the structural alterations of HS. Eighty patients with pharmacoresistant epilepsy and histologically proven HS and 80 healthy controls were included in the study. Two automated classifiers relying on clinically empirical and radiomics features were developed to detect HS. Cross-validation was implemented on all participants, and specificity was assessed in the 80 controls. The performance, robustness, and clinical utility of the model were also evaluated. Structural analysis was performed to investigate the morphological abnormalities of HS. The computational model based on clinical empirical features showed excellent performance, with an area under the curve (AUC) of 0.981 in the primary cohort and 0.993 in the validation cohort. One of the features, gray-white matter boundary blurring in the temporal pole, exhibited the highest weight in model performance. Another model based on radiomics features also showed satisfactory performance, with AUC of 0.997 in the primary cohort and 0.978 in the validation cohort. In particular, the model improved the detection rate of MRI-negative HS to 96.0%. The novel feature of cortical folding complexity of the temporal pole not only played a crucial role in the classifier but also had significant correlation with disease duration. Machine learning with quantitative clinical and radiomics features is shown to improve HS detection. HS-related structural alterations were similar in the MRI-positive and MRI-negative HS patient groups, indicating that misdiagnosis originates mainly from empirical interpretation. The cortical folding complexity of the temporal pole is a potentially valuable feature for exploring the nature of HS.

A dynamic conditional regime-switching GARCH CAPM for energy and financial markets

This paper develops a methodology for estimating a time-varying conditional version of the CAPM with regime changes in conditional variance dynamics. Our research goal is related to documenting the power of the beta when it is estimated dynamically. The empirical performance is tested across a sample of 81 financial, energy, and other commodity markets for the period August 1999–January 2018. The conditional regime-switching GARCH CAPM, with time-varying betas explaining both bull and bear markets, outperforms the unconditional (static) CAPM. Among stocks, there are significant time variations in betas across our models and regimes. This empirical feature is even more pronounced in the USA, the UK, Germany, France, China, and Malaysia. Among energy and other commodities, we find similar variations in the market price of risk. The direction of the relation with market returns for Crude Oil, Gold, Copper, Tin, Rubber, Aluminum, and Platinum is the same across our nested models. This result also holds for aggregate markets indices. Secondly, we provide a ranking by mean filtered volatility series where Natural Gas stands out at a high level. Average pricing errors are inferior in the case of the conditional model, and for Crude Oil. Lastly, we demonstrate that the regime-switching model delivers better estimates of one-day-ahead Value-at-Risk than its non-switching counterpart. Our results shed light on the supremacy of the market factor alone associated with time variation in risk premia across the energy and financial markets.

The Value-Momentum Correlation: An Investment Explanation

This paper shows that both value and momentum premia arise in a q-theoretic framework that considers optimal corporate policies under uncertain financing conditions. Book-to-market and past performance predict future returns because they serve as indicators of firm financing position. The book-to-market ratio increases with financing constraints, with high book-to-market firms investing less and demanding higher risk premia compared to unconstrained, low book-to-market firms. The value premium increases in bad times because value firms become even more constrained, while ample cash reserves allow growth firms to fare much better. Momentum effects appear among the most financially constrained firms. For these firms, financing constraints imply large price swings following cash-flow shocks and valuable equity market timing options imply positive autocorrelation in returns. Absent equity market timing during bad times, momentum disappears. The model is able to explain many empirical features of value and momentum premia, such as: (i) the procyclicality of the momentum premium, (ii) the countercyclicality of the value premium (iii) the negative correlation between the two, and (iv) severe losses to momentums strategies during market rebounds. Several new testable hypotheses arise regarding the fundamentals of value and momentum stocks. The empirical evidence is largely supportive.

Quantitative Methods for the Comparative Analysis of Cities in History

Comparative studies of cities throughout history are one of the greatest sources of insight into the nature of change in human societies. This paper discusses strategies to anchor these comparisons on well-defined, quantitative and empirical characteristics of cities, derived from theory and observable in the archeological and historical records. We show how quantitative comparisons based on a few simple variables across settlements allow us to analyze how different places and peoples dealt with general problems of any society. These include demographic change, the organization of built spaces, the intensity and size of socioeconomic networks and the processes underlying technological change and economic growth. Because the historical record contains a much more varied and more independent set of experiences than contemporary urbanization, it has a unique power of illuminating present puzzles of human development and testing emergent urban theory.

Roller Bearing Fault Diagnosis Based on Empirical Mode Decomposition and Targeting Feature Selection

At present, the feature extraction of frequency signal based on empirical mode decomposition (EMD) has been widely studied and applied in fault diagnosis of rolling bearings. However, there are still some shortcomings in fault diagnosis based on EMD. Therefore, a fault diagnosis method based on the combination of EMD and target feature selection (TFS) is proposed in this paper. The method firstly analyzes the fault signal through EMD and extracts the fault features. Then, it removes the redundant features and optimizes the feature subsets by using TFS. TFS selects the most effective feature for each target sample space through filtering evaluation criteria and heuristic search strategy, thereby effectively improving the accuracy and efficiency of fault diagnosis.

Оцінка тягово-швидкісних характеристик військової автомобільної техніки за умов руху бездоріжжям методами імітаційного моделювання

Проведено аналіз відомих досліджень і методик комп’ютерного моделювання руху та оцінки прохідності повноприводних автомобілів в умовах бездоріжжя. Відомі теоретичні дослідження базуються на отриманих емпіричних характеристиках різних типів опорної поверхні за різних ступенях насиченості ґрунту вологою – модуля деформації. У країнах НАТО аналогічні дослідження та оцінка прохідності ВАТ базуються на оцінці несних властивостей опорної поверхні на основі емпіричного визначення т. зв. конусного індексу VCI (Vehicle Cone Index). Враховуючи методичні відмінності в оцінці прохідності радянською і західною науковими школами, акцентовано увагу на необхідності оцінки даного аспекту за допомогою комп’ютерного моделювання. Опрацьовано алгоритм оцінки прохідності – моделювання руху у середовищі імітаційного моделювання динамічних систем Simulink програмного комплексу MATLAB.

SOVEREIGN DEFAULT, TRADE, AND TERMS OF TRADE

Sovereign defaults are associated with income and trade reductions and terms-of-trade deterioration. This paper develops a two-country model to study the interactions between income, trade, terms of trade, and foreign-debt default risk and default events. Such default risk and events are costly because they adversely affect the demand for a borrower country’s intermediate goods exports and its income. Consequently, trade flows change due to the income loss and consumption home bias. The defaulter’s terms of trade also deteriorate endogenously, which accelerates its income and trade losses. The model produces procyclical imports, exports, terms of trade, and other empirical features of emerging countries’ business cycles and default episodes.

Semi-supervised learning with summary statistics

Nowadays, the extensive collection and analyzing of data is stimulating widespread privacy concerns, and therefore is increasing tensions between the potential sources of data and researchers. A privacy-friendly learning framework can help to ease the tensions, and to free up more data for research. We propose a new algorithm, LESS (Learning with Empirical feature-based Summary statistics from Semi-supervised data), which uses only summary statistics instead of raw data for regression learning. The selection of empirical features serves as a trade-off between prediction precision and the protection of privacy. We show that LESS achieves the minimax optimal rate of convergence in terms of the size of the labeled sample. LESS extends naturally to the applications where data are separately held by different sources. Compared with the existing literature on distributed learning, LESS removes the restriction of minimum sample size on single data sources.

On the Spatial Evolution of Traffic State Transitions: Empirical Observations and General Features

Features of transitions between stationary traffic states for both congestion onset and dissipation were evaluated through empirical observations at two freeway sites. Results reveal that transition durations vary significantly between sites and that they can be rather long, with observed average transition durations of 42–45 min in one site. It was also observed that transition durations remain relatively constant within a queue, while transition rates vary as the transition zone travels upstream of the bottleneck. In addition, results also suggest that complex site geometry, particularly the presence of multiple lane reductions, has an effect on the spatial evolution of transition rates, while single merging and diverging maneuvers do not seem to affect the mechanism of transitions. Finally, it was observed that the presence of non-stationary traffic periods could have a considerable effect on the estimation of key traffic variables in locations where long transition durations are observed.

Using metapopulation theory for practical conservation of mangrove endemic birds.

As a landscape becomes increasingly fragmented through habitat loss, the individual patches become smaller and more isolated and thus less likely to sustain a local population. Metapopulation theory is appropriate for analyzing fragmented landscapes because it combines empirical landscape features with species‐specific information to produce direct information on population extinction risks. This approach contrasts with descriptions of habitat fragments, which provide only indirect information on risk. Combining a spatially explicit metapopulation model with empirical data on endemic species’ ranges and maps of habitat cover, we calculated the metapopulation capacity—a measure of a landscape's ability to sustain a metapopulation. Mangroves provide an ideal model landscape because they are of conservation concern and their patch boundaries are easily delineated. For 2000–20015, we calculated global metapopulation capacity for 99 metapopulations of 32 different bird species endemic to mangroves. Northern Australia and Southeast Asia had the highest richness of mangrove endemic birds. The Caribbean, Pacific coast of Central America, Madagascar, Borneo, and isolated patches in Southeast Asia in Myanmar and Malaysia had the highest metapopulation losses. Regions with the highest loss of habitat area were not necessarily those with the highest loss of metapopulation capacity. Often, it was not a matter of how much, but how the habitat was lost. Our method can be used by managers to evaluate and prioritize a landscape for metapopulation persistence.

The Empirical Analysis of Bitcoin Market in the General Equilibrium Framework

This paper firstly pursues the fundamental price of Bitcoin in the general equilibrium framework and its empirical characteristics. Our theoretical model predicts that (i) the Bitcoin price and the total hash rate are determined simultaneously in the long-run and (ii) the hash rate of Bitcoin Granger-causes Bitcoin prices in the short-run. Using a cointegration framework, our empirical analysis provides consistent results with these theoretical predictions. Our empirical results suggest that the Bitcoin market has been under a fundamental value instead of speculative bubbles.

Multi-modal facial expression feature based on deep-neural networks

Emotion recognition based on facial expression is a challenging research topic and has attracted a great deal of attention in the past few years. This paper presents a novel method, utilizing multi-modal strategy to extract emotion features from facial expression images. The basic idea is to combine the low-level empirical feature and the high-level self-learning feature into a multi-modal feature. The 2-dimensional coordinate of facial key points are extracted as low-level empirical feature and the high-level self-learning feature are extracted by the Convolutional Neural Networks (CNNs). To reduce the number of free parameters of CNNs, small filters are utilized for all convolutional layers. Owing to multiple small filters are equivalent of a large filter, which can reduce the number of parameters to learn effectively. And label-preserving transformation is used to enlarge the dataset artificially, in order to address the over-fitting and data imbalance of deep neural networks. Then, two kinds of modal features are fused linearly to form the facial expression feature. Extensive experiments are evaluated on the extended Cohn–Kanade (CK+) Dataset. For comparison, three kinds of feature vectors are adopted: low-level facial key point feature vector, high-level self-learning feature vector and multi-modal feature vector. The experiment results show that the multi-modal strategy can achieve encouraging recognition results compared to the single modal strategy.

The past, present and uncertain future of community energy in Denmark: Critically reviewing and conceptualising citizen ownership

Important progress has been made in the understanding of citizen ownership. However, confusion persists about distinct characteristics of different models and particularly about the concept of community energy. Moreover, quantitative understanding of the contribution of citizen ownership to energy transitions is limited. This study advances the knowledge of citizen ownership by describing the empirical characteristics of citizen ownership of wind turbines and district heating systems in Denmark in the period of 1975-2016. The methods comprise statistical analysis, literature review and contact to experts in order to identify, quantify, describe and categorise Danish citizen ownership models. The results show the significant importance of citizen ownership to investment in and implementation of decentralised sustainable energy technologies. Modifications of institutional incentives have led to multiple and diverse citizen ownership models, whose main distinctive characteristics are related to geographical scope, type of profits and distribution of benefits. This observation is valid also for other countries, which makes the citizen ownership categories developed in this study relevant for international research on the topic.

Unit Root Test with High-Frequency Data

Deviations of asset prices from the random walk dynamic imply the predictability of asset returns and thus have important implications for portfolio construction and risk management. This paper proposes a real-time monitoring device for such deviations using intraday high-frequency data. The proposed procedure is based on unit root tests with in-fill asymptotics but extended to take the empirical features of high-frequency financial data (particularly jumps) into consideration. We derive the limiting distribution of the test statistic under both the null hypothesis of a random walk with jumps and the alternative of mean reversion/explosiveness with jumps. The limiting results show that ignoring the presence of jumps could potentially lead to severe size distortions of both the left-sided (against mean reversion) and right-sided (against explosive) unit root tests. The simulation results reveal the satisfactory performance of the test even with data from a relatively short time span (i.e., one quarter). As an illustration, we apply the procedure to the Nasdaq composite index at the 10-minute frequency from January 2, 1996 to December 8, 2017. We find strong evidence of explosiveness dynamics in asset prices during the dot-com bubble and the subprime mortgage crisis periods and mean reversion in the late 2015 and early 2016.