Empirical Distribution Of Data Research Articles

A diffusion probabilistic model for traditional Chinese landscape painting super-resolution

AbstractTraditional Chinese landscape painting is prone to low-resolution image issues during the digital protection process. To reconstruct high-quality images from low-resolution landscape paintings, we propose a novel Chinese landscape painting generation diffusion probabilistic model (CLDiff), which is similar to the Langevin dynamic process, and realizes the transformation of the Gaussian distribution into the empirical data distribution through multiple iterative refinement steps. The proposed CLDiff can provide ink texture clear super-resolution predictions by gradually transforming the pure Gaussian noise into a super-resolution landscape painting condition on a low-resolution input through a parameterized Markov Chain. Moreover, by introducing an attention module with an energy function into the U-Net architecture, we turn the denoising diffusion probabilistic model into a powerful generator. Experimental results show that CLDiff achieves better visual results and highly competitive performance in traditional Chinese Landscape painting super-resolution tasks.

Critical Controlling for the Network Security and Privacy Based on Blockchain Technology: A Fuzzy DEMATEL Approach

The Internet of Things (IoT) has been considered in various fields in the last decade. With the increasing number of IoT devices in the community, secure, accessible, and reliable infrastructure for processing and storing computed data has become necessary. Since traditional security protocols are unsuitable for IoT devices, IoT implementation is fraught with privacy and security challenges. Thus, blockchain technology has become an effective solution to the problems of IoT security. Blockchain is an empirical data distribution and storage model involving point-to-point transmission, consensus mechanism, asymmetric encryption, smart contract, and other computer technologies. Security and privacy are becoming increasingly important in using the IoT. Therefore, this study provides a comprehensive framework for classifying security criteria based on blockchain technology. Another goal of the present study is to identify causal relationship factors for the security issue using the Fuzzy Decision-Making Trial-and-Evaluation Laboratory (FDEMATEL) approach. In order to deal with uncertainty in human judgment, fuzzy logic is considered an effective tool. The present study’s results show the proposed approach’s efficiency. Authentication (CR6), intrusion detection (CR4), and availability (CR5) were also introduced as the most effective and essential criteria, respectively.

Benford's Law for economic data reliability: The case of tourism flows in Sicily

This paper proposes a data science approach based on Benford's Law to analyse tourist flows – being tourism a relevant economic sector in Sicily. In particular, we are interested in detecting irregular patterns in the numerical data that may represent manipulations, inaccuracies or biases in the self-reported data from tourism organisations. The analysis is carried out by using monthly data for arrivals and overnight stays in hotels, B&Bs, and complementary accommodations in the seven provinces of the island from January 2016 to December 2019.We perform the analysis by employing several statistical tests and through a visual inspection of the difference between the empirical distributions and the theoretical Benford's.Conformity to Benford's distribution is mostly confirmed for the total number of overnight stays and the data considered on a yearly basis. On the contrary, we found evident deviations from Benford's Law in the empirical distribution of data broken down by nationality of tourist and accommodation type. Some comments on possible motivations for such deviations are also advanced, even though a detailed exploration of them deserves a devoted study.

Distributionally robust learning-to-rank under the Wasserstein metric.

Despite their satisfactory performance, most existing listwise Learning-To-Rank (LTR) models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, distributional data shift, and malicious adversaries who wish to degrade the algorithm's performance. It has been shown that Distributionally Robust Optimization (DRO) is resilient against various types of noise and perturbations. To fill this gap, we introduce a new listwise LTR model called Distributionally Robust Multi-output Regression Ranking (DRMRR). Different from existing methods, the scoring function of DRMRR was designed as a multivariate mapping from a feature vector to a vector of deviation scores, which captures local context information and cross-document interactions. In this way, we are able to incorporate the LTR metrics into our model. DRMRR uses a Wasserstein DRO framework to minimize a multi-output loss function under the most adverse distributions in the neighborhood of the empirical data distribution defined by a Wasserstein ball. We present a compact and computationally solvable reformulation of the min-max formulation of DRMRR. Our experiments were conducted on two real-world applications: medical document retrieval and drug response prediction, showing that DRMRR notably outperforms state-of-the-art LTR models. We also conducted an extensive analysis to examine the resilience of DRMRR against various types of noise: Gaussian noise, adversarial perturbations, and label poisoning. Accordingly, DRMRR is not only able to achieve significantly better performance than other baselines, but it can maintain a relatively stable performance as more noise is added to the data.

Confirmatory Factor Analyses in Psychological Test Adaptation and Development

Confirmatory Factor Analyses in Psychological Test Adaptation and Development

데이터 애널리틱스를 활용한 컨테이너 적재 문제 해결

Purpose This paper proposes a data analytics approach that effective solves a container loading problem. The problem is to decide how much products must be loaded on a container that is delivered by a third party logistics (3PL) providers. The main difficulty in the problem is due to government regulation on the total gross weight. It regulates the maximum gross weight but 3PL’s trucks have random weights. Methods This paper develops an efficient data analytics method that efficiently incorporates the randomness of truck weight using an empirical distribution of historical data. Results Using detailed shipping data from a real company, this paper provides numerical results that reveal that the proposed analytics method provides a significant cost reduction. Conclusion Considering the fact that many manufacturing companies are subject to a similar logistics practice, the data analytics method studied in this paper can be applied in diverse industries.

Influence of Maintenance and Repair of Axle Boxes on the Risks of Their Failure

Purpose. The main purpose of the study is to identify the interconnection between the risks of failure of axle boxes of freight cars and the success of performing a complex task of maintenance and repair. To do this, one should identify the sequence of calculating the values of successful completion of the complex task of maintenance and repair of freight cars, make practical calculations using the example of car repair companies of Prydniprovska Railway regional branch and identify the dependencies of failure risks of the freight cars and the process of successful completion of the task. Methodology. The paper constructs the interconnections of the probabilities of performing and not performing a complex task of maintenance and repair of freight cars. In addition, the calculation sequence of static probability estimates of successful performing and not performing of the set task, static estimates of the average deviation time and the main time of successful performing, as well as its intensity is presented. Findings. The obtained statistical data of the effective implementation of the complex task of maintenance and repair of freight cars at Prydniprovska Railway regional branch and the results of their empirical distribution throughout a period of 90 days, showed that the sample average is 24.1078 cars, the sample variance is 3.28 cars, the value of the standard deviation is 1.81 cars, and the coefficient of variation is 0.075 cars. The influence of diagnostics on the failure risks of axle boxes of freight cars during operation after fulfilling the task of maintenance and repair is also established. Originality. Based on statistical processing of experimental data, for the first time an empirical distribution of statistical data of successful completion of a complex task of maintenance and repair of freight cars was performed, which allows calculating the reliability indicators of axle boxes after maintenance and repair of freight cars. For the first time, the dependence of axle boxes diagnostics on the risk of failure of performing the task of maintenance and repair of freight cars is received, which will allow reducing the risk of failure if the task is successfully completed. Practical value. The use of diagnostics of axle boxes of freight cars during maintenance and repair can reduce the risk of failure by 2… 4.5 times.

Оценка причин прекращения деятельности компаний в период пандемии в 2021 году

The relevance of studying the problem of entrepreneurs leaving their business has increased significantly in recent years due to the fact that under the influence of the consequences of the COVID-19 pandemic, this process has significantly accelerated. Therefore, one of the possible reasons for the termination of entrepreneurial activity starting in 2020 is the negative consequences of the pandemic, along with other possible positive and negative options for leaving the business. The purpose of the study was to assess the opinion of entrepreneurs who have ceased their activities about the reasons for this according to the results of surveys conducted in a number of countries in 2021. The objectives of the study were: formation of a database of initial data reflecting the opinions of retired entrepreneurs about the reasons for this; development of mathematical models describing the distribution of empirical data across different countries; evaluation of the values of indicators characterizing the exit from business for positive reasons, as well as negative reasons related and unrelated to the pandemic. The initial data were the results of a survey of entrepreneurs in 47 countries during the work on the Global Entrepreneurship Monitoring project. An assessment of four indicators characterizing the positive and negative reasons for the termination of their activities by entrepreneurs was carried out. Empirical data modeling based on the development of normal distribution density functions was used to evaluate the indicators. It is proved that the majority (58.1%) of all entrepreneurs who stopped their activities did so for negative reasons, but not related to the COVID-19 pandemic. It is proved that almost a quarter (23.7%) of all entrepreneurs who stopped their business were forced to do so due to the negative consequences of the pandemic. It is shown that only one in six entrepreneurs who stopped their activities had positive reasons for leaving their business in 2021. The obtained results make a significant contribution to the existing knowledge about the reasons for the termination of entrepreneurial activity in modern national economies.

ATAC-Based Car-Following Model for Level 3 Autonomous Driving Considering Driver’s Acceptance

To date, commercial fully autonomous driving is not realized, while level 3 is the next step in the development of autonomous driving. At level 3, the vehicle is driving under the control of the machine, but when feature requests, human driver must take over control. Therefore, autonomous driving control should consider not only efficiency and safety but also human driver’s acceptance. This paper develops a car-following (CF) model as a longitudinal control strategy for level 3 autonomous driving based on the automating entropy adjustment on Tsallis actor-critic (ATAC) algorithm. 1641 pairs of CF trajectories extracted from the Next Generation Simulation (NGSIM) data are applied to train the reinforcement learning (RL) agent. Based on the empirical data distributions, we use time margin, time gap, and jerk to construct the reward function and testify the proposed CF model’s merits. Simulation results show that the proposed model can enable vehicles to drive safely, efficiently, and comfortably. The proposed model has good stability, and the generated driving behaviors are more acceptable for drivers. This work sheds light on developing a better autonomous driving system from the perspective of human factors.

Decision thresholding on fMRI activation maps using the Hilbert–Huang transform

Objective. Functional magnetic resonance imaging (fMRI) requires thresholds by which to identify brain regions with significant activation, particularly for experiments involving real-life paradigms. One conventional non-parametric approach to generating surrogate data involves decomposition of the original fMRI time series using the Fourier transform, after which the phase is randomized without altering the magnitude of individual frequency components. However, it has been reported that spontaneous brain signals could be non-stationary, which, if true, could lead to false-positive results. Approach. This paper introduces a randomization procedure based on the Hilbert–Huang transform by which to account for non-stationarity in fMRI time series derived from two fMRI datasets (stationary or non-stationary). The significance of individual voxels was determined by comparing the distribution of empirical data versus a surrogate distribution. Main results. In a comparison with conventional phase-randomization and wavelet-based permutation methods, the proposed method proved highly effective in generating activation maps indicating essential brain regions, while filtering out noise in the white matter. Significance. This work demonstrated the importance of considering the non-stationary nature of fMRI time series when selecting resampling methods by which to probe brain activity or identify functional networks in real-life fMRI experiments. We propose a statistical testing method to deal with the non-stationarity of continuous brain signals.

Continuous Probability Distributions generated by the PIPE Algorithm.

We investigate the use of the Probabilistic Incremental Programming Evolution (PIPE) algorithm as a tool to construct continuous cumulative distribution functions to model given data sets. The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. These candidates are generated by following a set of probability rules. The set of rules is then evolved over a number of iterations to generate better candidates regarding some optimality criteria. This approach rivals that of generated distribution, obtained by adding parameters to existing probability distributions. There are two main advantages for this method. The first is that it is possible to explicitly control the complexity of the candidate functions, by specifying which mathematical functions and operators can be used and how lengthy the mathematical expression of the candidate can be. The second advantage is that this approach deals with model selection and estimation at the same time. The overall performance in both simulated and real data was very satisfying. For the real data applications, the PIPE algorithm obtained better likelihoods for the data when compared to existing models, but with remarkably simpler mathematical expressions.

Regret in the Newsvendor Model with Demand and Yield Randomness

We study the fundamental stochastic newsvendor model that considers both demand and yield randomness. It is usually difficult in practice to describe precisely the joint demand and yield distribution, although partial statistical information and empirical data about this ambiguous distribution are often accessible. We combat the issue of distributional ambiguity by taking a data‐driven distributionally robust optimization approach to hedge against all distributions that are sufficiently close to a uniform and discrete distribution of empirical data, where closeness is measured by the type‐∞ Wasserstein distance. We adopt the minimax regret decision criterion to assess the optimal order quantity that minimizes the worst‐case regret. Several properties about the minimax regret model, including optimality condition, regret bound, and the worst‐case distribution, are presented. The optimal order quantity can be determined via an efficient golden section search. We extend the analysis to the Hurwicz criterion model, which generalizes the popular albeit pessimistic maximin model (maximizing the worst‐case expected profit) and its (less noticeable) more optimistic counterpart—the maximax model (maximizing the best‐case expected profit). Finally, we present numerical comparisons of our data‐driven minimax regret model with data‐driven models based on the Hurwicz criterion and with a minimax regret model based on partial statistical information on moments.

Aspiration risk in relation to Glasgow Coma Scale score and clinical parameters in patients with severe acute alcohol intoxication: a single-centre, retrospective study

ObjectivesIn alcohol intoxicated patients, the decision for or against airway protection can be challenging and is often based on the Glasgow Coma Scale (GCS). Primary aim of this study was...

Modeling Medicare’s Oncology Care Model bundles at more clinically granular levels: Evaluating the impact on provider performance.

e18854 Background: Aggregating different subtypes of cancers into bundles is an important methodology in oncology payment reform as an alternative to fee for service. However, expected resource utilization can vary significantly across cancer subtypes. We evaluated the impact that modeling Chronic Leukemia into a more clinically granular two part framework of chronic myelogenous leukemia (CML) and chronic lymphocytic leukemia would have on OCM results and the risk that the distribution of clinical subtypes at a practice would influence overall performance in the bundle. Methods: OCM episodes of chronic leukemia initiating between July 2016 and June 2019 were subdivided on the basis of individual ICD-10 coded diagnoses on cancer-related E&M visits. From a total of 4,658 episodes, we randomly sampled with replacement 3,500 episodes from 16 practices using empirical data distributions. Data models and mappings were developed based on clinical knowledge and exploratory data analyses to subdivide the OCM bundle of Chronic Leukemias into CLL and CML. Total cost of care and episode target prices were calculated through implementation of the OCM methodology. The distributional consistencies of episode target, cost, cost above target, and percent above target for the two diseases were evaluated by two-sample Kolmogorov-Smirnov (KS) tests. Results: The CML and CLL subtypes modeled from the aggregate OCM bundle demonstrated significantly different cost distributions relative to each other. As anticipated, treatments used in each subtype varied significantly marking different patterns of expected resource utilization. In our model, CLL episodes were on average 13.7% over target. Average CLL episode costs were $52.2K vs. an average target of $47.6K with 54% of episodes running over target. In contrast, CML episodes were 6.1% under target. Average CML episode costs were $45.2K vs. an average target of $50.3K with 43% of episodes running over target. Conclusions: Value based payment models in oncology such as OCM can be improved by modeling cancer bundles in more clinically granular ways that better reflect expected resource utilization for appropriate, standard of care. Insufficient clinical granularity in bundle construction can lead to provider performance being influenced by the distribution of patient subtypes at the practice. This can lead to inappropriate shifts of risk from payers to providers in value based models. Aggregate vs. subtype episode costs (mean, 5th, and 95th percentiles).[Table: see text]

Evaluating differential cost growth across individual cancers: Insights from Oncology Care Model data.

e18853 Background: Analyzing cost growth in oncology often faces significant challenges, including fragmentation of data for infused vs. oral agents and ascertaining the cost of inpatient care. How differential cost growth at the individual cancer level contributes to aggregate cost growth in oncology is often opaque. We leverage the integrated claims data provided by Medicare for its Oncology Care Model (OCM) pilot to analyze variations across individual cancers with respect to cost growth. Methods: OCM’s innovative methodology creates a natural experiment where costs are compared against a counterfactual comprised of Medicare patients seen in non-OCM practices. Leveraging this differential counterfactual provides an opportunity to gain insight into cost growth for individual cancers compared to oncology as a whole. Specifically, cost growth is measured each Performance Period (PP) with respect to the Baseline Period, from 2012 to 2015. We analytically decomposed and remodeled key quantitative factors in OCM associated with cost dynamics in oncology, including the Trend Factor (TF), which represents non-OCM cost growth. From 124,896 episodes, we sampled with replacement 19,191 episodes from 17 practices between PP1 and PP6 using empirical data distributions. We assumed neutral novel therapy and experience adjustments and then compared the overall TF to the remodeled cancer-level TF, reaggregated from the individual episodes. Results: Reallocating the TF reveals that cost growth among cancer types is highly variable in the broader Medicare population. Cost growth at the individual cancer level varied from +99.3% to -14.3%. Of the 21 OCM cancers, 18 have TFs greater than zero, indicating cost growth in the non-OCM Medicare population since the Baseline Period. Four have a TF greater than 50%. Three cancer types show decreasing costs relative to the baseline period: intestinal cancer, MDS, and CNS tumor. Conclusions: Significant variation exists across individual cancer subtypes in terms of cost growth. Aggregate analyses of cancer at large have insufficient specificity to rationalize payment mechanisms. Payment reform efforts within cancer care should directly address dynamics at the individual cancer or cancer subtype level in order to provide more valid considerations for expected resource utilization, including in future payment policies. [Table: see text]

Half of global methane emissions come from highly variable aquatic ecosystem sources

Atmospheric methane is a potent greenhouse gas that plays a major role in controlling the Earth’s climate. The causes of the renewed increase of methane concentration since 2007 are uncertain given the multiple sources and complex biogeochemistry. Here, we present a metadata analysis of methane fluxes from all major natural, impacted and human-made aquatic ecosystems. Our revised bottom-up global aquatic methane emissions combine diffusive, ebullitive and/or plant-mediated fluxes from 15 aquatic ecosystems. We emphasize the high variability of methane fluxes within and between aquatic ecosystems and a positively skewed distribution of empirical data, making global estimates sensitive to statistical assumptions and sampling design. We find aquatic ecosystems contribute (median) 41% or (mean) 53% of total global methane emissions from anthropogenic and natural sources. We show that methane emissions increase from natural to impacted aquatic ecosystems and from coastal to freshwater ecosystems. We argue that aquatic emissions will probably increase due to urbanization, eutrophication and positive climate feedbacks and suggest changes in land-use management as potential mitigation strategies to reduce aquatic methane emissions.

Investments in Capital Asset at the Regions of Russia in 2019

The purpose of the study. It is known that the further development of the Russian regions requires significant investment in all areas of activity. Therefore, the problem of assessing the existing indicators of investment activity, characteristic of each of the regions, is put forward as an urgent one. At the same time, given the wide variety of Russian regions, a comparative analysis of absolute investment volumes is not appropriate. In this regard, we suggest using a comparison of specific indicators for the analysis. The purpose of our study is to assess the levels of specific investment in capital asset per capita in all regions of our country.Materials and methods. The study used the methodological approach proposed by the author, based on the consideration of specific indicators describing investment activity in the regions of Russia. The study included four stages. As initial information, we considered the official statistics provided on the ROSSTAT website, which characterize investments in the regions, as well as the number of their population in 2019. The study conducted a cluster analysis, as well as economic and mathematical modeling of the distribution of the considered indicators by the regions of the country.Results. The cluster analysis allowed us to identify five clusters that unite the regions of Russia with similar values of specific investments per inhabitant of the corresponding region. The first cluster includes four regions, the second cluster - five regions, the third cluster - thirteen regions, the fourth cluster - twenty-six regions and the fifth cluster - thirty-four regions. The cluster analysis showed that in nine regions in 2019, there was a high level of investment, due to the tasks of their strategic development to solve federal problems. For 73 regions there were relatively low values of specific investment, the distribution of empirical data was modeled using the normal distribution function.Conclusion. The scientific novelty of the study is related to the cluster analysis and the study of the distribution of specific investments by region. Regions with high and low values of specific investments in capital asset were identified. It is proved that the values of specific investments have a significant differentiation across the regions of the country. The results of our work have a certain theoretical and practical significance for the government, regional and local authorities. The methodological approach to assessing the level of investment presented in the article can be used in further research.

Dynamics of Bitcoin trading on the Binance cryptocurrency exchange

Currently, there are a great number of platform-projects and frameworks based on blockchain technology. Consequently, it is necessary to define the most relevant blockchain platforms and analyze them taking into consideration a variety of features. Also, there is a need to investigate the logistics growth and the price of Bitcoin on the Binance cryptocurrency exchange. The authors have examined modern technologies used by manufacturing companies in the field of fintech in the context of the 2019-2024 period. The results show that sensors and automatic identification take the leading position both at present and in 2024. Aartificial intelligence and blockchain are also in demand by manufacturers today, however in the nearest future their ranking positions will increase sixfold from 10% to 60%. In the current paper the authors review the largest companies that effectively use blockchain technology in their businesses. The conducted survey shows that 18% of companies use blockchain technology based on Bitcoin. The authors have analysed a number of Bitcoin transactions for the period from January 2017 to February 2021 and concluded that the COVID-19 pandemic has had a favourable effect on the indicator data. A maximum number of transactions equal to 10.15 million was carried out in July 2020. Using the method of the Ordinary Least Squares (OLS) and statistical estimation methods the authors have revealed an underestimation of the equilibrium state of the empirical distribution of price data and the volume of daily trading of Bitcoin on the Binance cryptocurrency exchange through the channel of the right-hand confidence interval. The blockchain technology based on Bitcoin has positively reacted to the macroeconomic factors such as the COVID-19 pandemics and further growth in Bitcoin transactions is expected. With the help of economic modelling, the authors have defined the predictable volume and the price of Bitcoin on the Binance cryptocurrency exchange.

On a jump-diffusion process driven by the asymmetric Laplace distribution for stock price models

In this paper, a generalized jump-diffusion process driven by the Asymmetric Laplace (AL) Distribution for stock price modeling was proposed. The probability density function was derived for the dynamics of the log-returns when the random process of the jump amplitude obeys the AL distribution. Based on the derived density function, a Lévy-Khintchine formula for the process was obtained, which proved useful for the computation of moments of the process. Hence, the Asymmetric Laplace jump diffusion model can be useful for modelling of stock price processes with empirical features like discontinuous paths, asymmetry and high peaks found in the empirical distribution of most financial data.

Comparison of Online Learning Effectiveness in the Ele During Covid-19 in Malaysia and Indonesia

The Covid-19 pandemic has an impact on education. It has changed the learning system, from conventional interaction into digital interaction. The lecturers and students in Malaysia and Indonesia are required to master online learning to conduct the teaching and learning processes. However, not many lecturers apply it maximally. This study aims to find out the comparison of online learning effectiveness in English Language Education (ELE) in Malaysia and in Indonesia. This quantitative research used a questionnaire survey to collect data. The data were analyzed descriptively with tabulations and distributions of empirical data. The result reveals that 75% of Malaysian lecturers and 83% of Indonesian lecturers prefer to use synchronous learning; 25% of Malaysian lecturers and 17% of Indonesian lecturers prefer to use asynchronous learning; 65% of Malaysian students and 71% of Indonesian students prefer to use synchronous learning; 35% of Malaysian students and 29% of Indonesian students prefer to use asynchronous learning. For synchronous learning, 58% of the total respondents in Malaysia like virtual meeting as an online learning tool. In Indonesia, 59% of the total respondents like phone call as an online learning tool. For asynchronous learning, 54% of the total respondents in Malaysia and Indonesia like blog as an online learning tool. Online learning has good effects on the English learning skills, especially listening. Academics in both countries agree that online learning is beneficial in the ELE learning and teaching process. It can be generally concluded that the use of online learning and teaching is effective in ELE.