Miracle-dz/UniFAR-FTUs huggingface dataset:
Best Practice Recommendation for Forecasting Counts

The existing techniques of forecasting a future count either treat the time series of counts as a Gaussian time series or use a random effects based dynamic Poisson model. The normality based approach may not yield valid forecasting, whereas the random effects based model usually generates a complex correlation structure for the time series of counts which may be impractical to use for forecasting. Moreover, when the time series contains moderately large or large counts, the later random effects based models are known to be inefficient in forecasting a future count, based on such a time series of large counts. In this report, we propose an observation driven non-stationary correlation model to fit a time series of counts with possible overdispersion. Analogoues to the Gaussian time series techniques, we develop forecasting functions to forecast future counts. The proposed forecasting approach is simpler than the existing approaches and it is shown through a simulation study that this approach provides satisfactory forecasting for a future count, irrespective of the cases whether time series contains small or large counts. Thus, the proposed approach may be recommended as the best practical approach for forecasting a count. The forecasting methodology is illustrated by analyzing a U.S. time series of polio counts. Some key words: Consistency; Efficiency; Latent process driven longitudinal correlation; Observations driven longitudinal autocorrelation; Overdispersion; Regression effects; Forecasting.
Finite Element Analysis of The Influence of Edge Roundness on The Stress and Temperature Fields Induced by High Speed Machining

High speed machining (HSM) may produces parts with substantially higher fatigue strength; increased subsurface micro-hardness and plastic deformation, mostly due to the ploughing of the round cutting tool edge associated with induced stresses, and can have far more superior surface properties than surfaces generated by grinding and polishing. Cutting edge roundness may induce stress and temperature fields on the machined subsurface and influence the finished surface properties as well as tool life. In this paper, a Finite Element Method (FEM) modeling approach with Arbitrary Lagrangian Eulerian (ALE) fully coupled thermal-stress analysis is employed. In order to realistically simulate high speed machining using edge design tools, an FEM model for orthogonal cutting is designed and solution techniques such as adaptive meshing, and explicit dynamics are performed. Work material flow around the round edge-cutting tool is successfully simulated without implementing a chip separation criterion and without the use of a remeshing scheme. FEM simulations for machining-induced stresses by round edge cutting tools are performed for high speed machining of AISI AISI 4340 steel. Once FEM simulations are complete for different edge radii and depth of cuts, tool is unloaded and stresses are relieved. Stress fields are compared with experimentally measured residual stresses obtain from literature. The results indicate that the round edge design tools influence the stress and temperature fields greatly.
Tonal Index in digital recognition of lung auscultation

In the paper the Tonal Index (TI) as a feature to wheezes detection in lung auscultation is presented. The lungs auscultation is a non invasive test in asthma diagnose. On the basis of such tests, the medical doctors can precise evaluate the stage of the disease. The TI is compared with the other features taken from literature: Kurtosis, Frequency Ratio, Spectral Peaks Entropy and Spectral Flatness. The modified Frequency Ratio called Energy Ratio (ER) is presented as well. The recognition process was taken on artificial and real data.
Pursuit tracks chase: exploring the role of eye movements in the detection of chasing

We explore the role of eye movements in a chase detection task. Unlike the previous studies, which focused on overall performance as indicated by response speed and chase detection accuracy, we decompose the search process into gaze events such as smooth eye movements and use a data-driven approach to separately describe these gaze events. We measured eye movements of four human subjects engaged in a chase detection task displayed on a computer screen. The subjects were asked to detect two chasing rings among twelve other randomly moving rings. Using principal component analysis and support vector machines, we looked at the template and classification images that describe various stages of the detection process. We showed that the subjects mostly search for pairs of rings that move one after another in the same direction with a distance of 3.5–3.8 degrees. To find such pairs, the subjects first looked for regions with a high ring density and then pursued the rings in this region. Most of these groups consisted of two rings. Three subjects preferred to pursue the pair as a single object, while the remaining subject pursued the group by alternating the gaze between the two individual rings. In the discussion, we argue that subjects do not compare the movement of the pursued pair to a singular preformed template that describes a chasing motion. Rather, subjects bring certain hypotheses about what motion may qualify as chase and then, through feedback, they learn to look for a motion pattern that maximizes their performance.
The Global Diffusion of Regulatory Capitalism

This article analyzes the rise and diffusion of the new order of regulatory capitalism. It offers an analytical and historical analysis of relations between capitalism and regulation and suggests that change in the governance of capitalist economy is best captured by reference to (1) a new division of labor between state and society (e.g., privatization), (2) an increase in delegation, (3) proliferation of new technologies of regulation, (4) formalization of interinstitutional and intrainstitutional arrangements of regulation, and (5) growth in the influence of experts in general, and of international networks of experts in particular. Regulation, though not necessarily directly by the state, seems to be on the increase despite efforts to redraw the boundaries between state and society.
Transient aggregation of nascent thyroglobulin in the endoplasmic reticulum: relationship to the molecular chaperone, BiP

Because of its unusual length, nascent thyroglobulin (Tg) requires a long time after translocation into the endoplasmic reticulum (ER) to assume its mature tertiary structure. Thus, Tg is an ideal molecule for the study of protein folding and export from the ER, and is an excellent potential substrate for molecular chaperones. During the first 15 min after biosynthesis, Tg is found in transient aggregates with and without interchain disulfide bonds, which precede the formation of free monomers (and ultimately dimers) within the ER. By immunoprecipitation, newly synthesized Tg was associated with the binding protein (BiP); association was maximal at the earliest chase times. Much of the Tg released from BiP by the addition of Mg-ATP was found in aggregates containing interchain disulfide bonds; other BiP- associated Tg represented non-covalent aggregates and unfolded free monomers. Importantly, the immediate precursor to Tg dimer was a compact monomer which did not associate with BiP. The average stoichiometry of BiP/Tg interaction involved nearly 10 BiP molecules per Tg molecule. Cycloheximide was used to reduced the ER concentration of Tg relative to chaperones, with subsequent removal of the drug in order to rapidly restore Tg synthesis. After this treatment, nascent Tg aggregates were no longer detectable. The data suggest a model of folding of exportable proteins in which nascent polypeptides immediately upon translocation into the ER interact with BiP. Early interaction with BiP may help in presenting nascent polypeptides to other helper molecules that catalyze folding, thereby preventing aggregation or driving aggregate dissolution in the ER.
Analgesic Techniques in Hip and Knee Arthroplasty: From the Daily Practice to Evidence-Based Medicine

Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are major orthopedic surgery models, addressing mainly ageing populations with multiple comorbidities and treatments, ASA II–IV, which may complicate the perioperative period. Therefore effective management of postoperative pain should allow rapid mobilization of the patient with shortening of hospitalization and social reintegration. In our review we propose an evaluation of the main analgesics models used today in the postoperative period. Their comparative analysis shows the benefits and side effects of each of these methods and guides us to how to use evidence-based medicine in our daily practice.
Giant folliculosebaceous cystic hamartoma of the upper extremity

Background: Folliculosebaceous cystic hamartoma (FSCH) is a rare cutaneous hamartoma consisting of dilated folliculosebaceous units invested in mesenchymal elements. These lesions have a striking predilection for the central face and scalp of adults. The vast majority of lesions present as 0.5–1.5‐cm papules or exophytic nodules. A single case of giant FSCH has been reported on the upper back.
Preparing Critically Conscious Dual-Language Teachers: Recognizing and Interrupting Dominant Ideologies

Due to the preK–12 student growth, the exponential growth of dual-language programs, and the gentrification of dual-language education, the need for critically conscious dual-language teachers is undeniably vital today. Institutions of higher education, in particular colleges of education, find themselves at the center of a transformative era, in both theory and practice, with respect to dual-language teacher preparation. Currently, dual-language teacher educators’ conversations revolve around the challenge in identifying, naming, and confronting pressing issues related to the preparation of dual-language teachers and their readiness to meet the demands created by the proliferation of dual-language schools across the nation. This article centers on the fundamental matters of recognizing and interrupting dominant ideologies and hegemonic practices that have made their way into dual-language classrooms by explicitly addressing how a teacher’s ideology, sociocultural/linguistic funds of identity, and pedagogy announce or denounce dual-language teachers’ critical consciousness that leads to the creation of equitable teaching and learning spaces.
The Effectiveness of Indirect Written Corrective Feedback as Perceived By Teachers and Students of a Public University in Vietnam

The current study investigates the effectiveness of indirect written corrective feedback (WCF) on five different aspects of writing (grammar, language use, mechanic use, content and organization) as perceived by teachers and English-major sophomores in a Vietnamese public university. Specifically, it provides an insight into the effectiveness of this feedback pattern as perceived by the teachers and their students. To fulfill the stated aims, this research utilizes two main instruments, namely questionnaire and in-depth interview. The data from the questionnaire was analysed using statistical procedures. Meanwhile, the data from the interviews was processed using qualitative analysis. With regards to the findings, teachers and students agree that given feedback suits students’ understandability, but somewhat exceeds their self-correction ability. Teachers and students’ perceptions match on the efficacy of indirect WCF for the treatment of grammatical errors and its inefficacy for the betterment of content. Both parties also remain neutral about the correction efficiency of this feedback pattern regarding lexical and mechanical errors. Finally, organization is the aspect on which the perceptions between two sides mismatch the most. Teachers and students also share several reasons in accounting for their perceptions. The findings implicate that changes should be made to feedback-giving practices of teachers and feedback-handling practices of students to enhance the effectiveness of indirect WCF.
Effects of a rare sugar, D-allulose, coingested with fat on postprandialglycemia and lipidemia in young women.

Aim: Postprandial hyperlipidemia is a major risk factor for coronary heart disease. Our previous study revealed that ingesting fructose with fat cream delays and exacerbates postprandial lipidemia. A rare sugar, D-allulose, has recently attracted attention as an alternative zero-calorie sweetener to natural sugars. In this study, we investigated the effect of ingesting D-allulose with fat cream on postprandial glycemia and lipidemia in young women. Methods: Eleven young Japanese women with apolipoprotein E3/3 phenotype were enrolled. They underwent 4 test trials: fat cream (0.35 g/kg as fat; F trial), fat cream with D-allulose (0.5 g/kg; FA trial), fat cream with fructose (0.5 g/kg; FFr trial), or fat cream with sucrose (0.5 g/kg; FS trial). Blood samples were taken before (0) and at 0.5, 1, 2, 4, and 6 h after ingestion. Results: In the FA trial, serum glucose and fructose concentrations at 0.5 and1 h were significantly lower than those in the FFr and FS trials. In the FA trial, the serum glucose concentration did not increase during the experiment. Serum triglyceride and remnant-like particle-triglyceride concentrations peaked at 2 h in the F trial, and at 4 h in the FA, FFr, and FS trials. Conclusion: When ingested with fat, D-allulose showed almost no glycemic response in contrast to fructose or sucrose. However, ingesting D-allulose with fat may delay postprandial lipidemia similar to these sugars.
Usage-based statistical testing of web applications

The large growth in the number of large Web applications currently developed brings concerns related to their quality, and more specifically their testing and reliability. Web application testing is still in its infancy and relies mostly upon traditional software coverage testing processes, which are highly impractical. Recent studies have looked at usage-based statistical models for testing Web applications. One of these studies, conducted by Kallepalli and Tian [8], used Unified Markov Models (UMMs), built from Web server access logs, as basis for test case selection. In addition, server error logs were also used to measure a Web application's reliability, and consequently to investigate the effectiveness of UMMs as a suitable testing mechanism. This paper describes two experiments that replicated Kallepalli and Tian's work. Our results showed that, in contrast to [8], multiple set UMMs were needed for trustworthy test case generation. In addition, our reliability assessment corroborated results from [8], confirming that UMMs seem to be a suitable testing mechanism to use to test Web applications.
When the Money Stops: Fluctuations in Financial Remittances and Incumbent Approval in Central Eastern Europe, the Caucasus and Central Asia

Fluctuations in the volume and the value of financial remittances received from abroad affect the livelihood of households in developing economies across the world. Yet, political scientists have little to say about how changes in remittances, as opposed to the receipt of remittance payments alone, affect recipients’ political attitudes. Relying on a unique four-wave panel study of Kyrgyz citizens between 2010–2013 and a cross-sectional sample of 28 countries in Central Eastern Europe, the Caucasus and Central Asia, we show that when people experience a decrease (increase) in remittances, they become less (more) satisfied about their household economic situation and misattribute responsibility to the incumbent at home. Our findings advance the literature on the political consequences of remittance payments and suggest that far from exclusively being an international risk-sharing mechanism for developing countries, remittances can also drive fluctuations in incumbent approval and compromise rudimentary accountability mechanisms in the developing world.
Modelling local deep convolutional neural network features to improve fine-grained image classification

We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have considerably improved the performance of object recognition. However, to date there has been limited work using these deep CNNs as local feature extractors. This partly stems from CNNs having internal representations which are high dimensional, thereby making such representations difficult to model using stochastic models. To overcome this issue, we propose to reduce the dimensionality of one of the internal fully connected layers, in conjunction with layer-restricted retraining to avoid retraining the entire network. The distribution of low-dimensional features obtained from the modified layer is then modelled using a Gaussian mixture model. Comparative experiments show that considerable performance improvements can be achieved on the challenging Fish and UEC FOOD-100 datasets.
Scintillation in scalar-tensor theories of gravity

We study the scintillation produced by time-varying gravitational fields within scalar-tensor theories of gravity. The problem is treated in the geometrical optics approximation for a very distant light source emitting quasi plane monochromatic electromagnetic waves. We obtain a general formula giving the time dependence of the photon flux received by a freely falling observer. In the weak-field approximation, we show that the contribution to the scintillation effect due to the focusing of the light beam by a gravitational wave is of first order in the amplitude of the scalar perturbation. Thus scalar-tensor theories contrast with general relativity, which predicts that the only first-order effect is due to the spectral shift. Moreover, we find that the scintillation effects caused by the scalar field have a local character: they depend only on the value of the perturbation at the observer. This effect provides in principle a mean to detect the presence of a long range scalar field in the Universe, but its smallness constitutes a tremendous challenge for detection.
A New Family of Error Distributions for Bayesian Quantile Regression

We propose a new family of error distributions for model-based quantile regression, which is constructed through a structured mixture of normal distributions. The construction enables fixing specific percentiles of the distribution while, at the same time, allowing for varying mode, skewness and tail behavior. It thus overcomes the severe limitation of the asymmetric Laplace distribution -- the most commonly used error model for parametric quantile regression -- for which the skewness of the error density is fully specified when a particular percentile is fixed. We develop a Bayesian formulation for the proposed quantile regression model, including conditional lasso regularized quantile regression based on a hierarchical Laplace prior for the regression coefficients, and a Tobit quantile regression model. Posterior inference is implemented via Markov Chain Monte Carlo methods. The flexibility of the new model relative to the asymmetric Laplace distribution is studied through relevant model properties, and through a simulation experiment to compare the two error distributions in regularized quantile regression. Moreover, model performance in linear quantile regression, regularized quantile regression, and Tobit quantile regression is illustrated with data examples that have been previously considered in the literature.
Optimal flight parameters for unmanned aerial vehicles collecting spatial information for estimating large-scale waste generation

ABSTRACT Large-scale waste and debris from disasters and building construction can cause secondary damage and environmental pollution. Satellite images and the Global Positioning System are being used to calculate the amounts of such waste. However, efficiency in terms of accuracy and cost remains problematic. To solve this problem, this study used an unmanned aerial vehicle (UAV) to build three-dimensional (3D) spatial information for large-scale waste generation. Appropriate parameters were selected to quickly and accurately estimate the amount of waste. Fifty-six scenarios were distinguished according to parameters such as flight altitude, image overlap, and number of ground control points (GCPs). Images were then registered by implementing the SIFT (Scale Invariant Feature Transform) and SfM (Structure from Motion) algorithms to build 3D spatial information and estimate the amount of waste. The accuracy was generally high when flight altitude and image overlap were high, and the number of GCPs was specific. Scenario A, with the lowest flight altitude and overlap, did not yield any registrable images. The results of the flight altitude showed a different tendency from those of existing studies. This could be attributable to the difference in the distance covered on the ground by an image in the width direction, which depended on the characteristics of study sites. Among all the scenarios, D-7 had the highest accuracy, with a root-mean-square error of about 0.08 m. In this scenario, the amount of waste was estimated to be approximately 767,571.3 m3. The top 10 scenarios in terms of the accuracy of x, y, and z locations displayed similar tendencies with regard to the amount of waste. In contrast, the bottom 15 scenarios showed a variety of results. When the accuracy and flight time of each scenario were considered, scenarios H-3, H-4, and H-5 achieved the highest accuracy in a short time. Thus, these scenarios were the most effective and applicable for estimating the amount of waste. Consequently, parameters such as flight altitude, image overlap, and GCPs need to be considered when estimating large-scale waste associated with a difference in elevation using a UAV.
Depression, Stressful Life Events, and the Impact of Variation in the Serotonin Transporter: Findings from the National Longitudinal Study of Adolescent to Adult Health (Add Health)

Background The low transcriptionally efficient short-allele of the 5HTTLPR serotonin transporter polymorphism has been implicated to moderate the relationship between the experience of stressful life events (SLEs) and depression. Despite numerous attempts at replicating this observation, results remain inconclusive. Methods We examined this relationship in young-adult Non-Hispanic white males and females between the ages of 22 and 26 (n = 4724) participating in the National Longitudinal Study of Adolescent to Adult Health (Add Health) with follow-up information every six years since 1995. Results Linear and logistic regression models, corrected for multiple testing, indicated that carriers of one or more of the S-alleles were more sensitive to stress than those with two L-alleles and at a higher risk for depression. This relationship behaved in a dose-response manner such that the risk for depression was greatest among those who reported experiencing higher numbers of SLEs. In post-hoc analyses we were not able to replicate an interaction effect for suicide ideation but did find suggestive evidence that the effects of SLEs and 5HTTLPR on suicide ideation differed for males and females. There were no effects of childhood maltreatment. Discussion Our results provide partial support for the original hypothesis that 5-HTTLPR genotype interacts with the experience of stressful life events in the etiology of depression during young adulthood. However, even with this large sample, and a carefully constructed a priori analysis plan, the results were still not definitive. For the purposes of replication, characterizing the 5HTTLPR in other large data sets with extensive environmental and depression measures is needed.