Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Poverty Rapidly Using Statistical Imputations

Poverty is an indicator of paramount importance for gauging the socioeconomic wellbeing of a population. Especially during or after a shock, poverty estimates are invaluable for assessing the severity of the impact and for identifying which parts of the population were most affected. The measurement of consumption, however, has traditionally been very time consuming. A household consumption questionnaire usually includes more than 200 items, including both food and nonfood items, often requiring more than two hours to administer. This paper proposes a new methodology that combines an innovative questionnaire design with standard imputation techniques. It substantially shortens the time required to administer a household consumption questionnaire to less than 60 minutes by imputing deliberately absent consumption values for items that are not explicitly asked. The proposed methodology makes it possible to derive poverty estimates without compromising the credibility of the resulting estimate, and it performs considerably better than alternative approaches based on reduced consumption aggregates and cross-survey imputations. This new methodology is particularly useful in fragile states given the significant risks associated with lengthy interviews. It can also be useful to reduce enumerator and respondent fatigue, or to mitigate the problem of high non-response rates.
Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning

ABSTRACT Stacking fault energy (SFE) is of the most critical microstructure attribute for controlling the deformation mechanism and optimizing mechanical properties of austenitic steels, while there are no accurate and straightforward computational tools for modeling it. In this work, we applied both thermodynamic modeling and machine learning to predict the stacking fault energy (SFE) for more than 300 austenitic steels. The comparison indicates a high need of improving low-temperature CALPHAD (CALculation of PHAse Diagrams) databases and interfacial energy prediction to enhance thermodynamic model reliability. The ensembled machine learning algorithms provide a more reliable prediction compared with thermodynamic and empirical models. Based on the statistical analysis of experimental results, only Ni and Fe have a moderate monotonic influence on SFE, while many other elements exhibit a complex effect that their influence on SFE may change with the alloy composition. Graphical Abstract
Estimation of the Copy Number of Exogenous Gene in Transgenic Rice by Real-time Fluorescence Quantitative PCR

In transgenic plants, the number of transgene copy is an important factor that can greatly influence the level of expression and genetic stability of the target gene. Estimating the transgene copy number becomes a significant step in transgenic research. High-throughput, fast and sensitive SYBR GreenⅠreal-time fluorescence quantitative PCR was used to estimate the copy number of nicotianamine synthase gene (NAS1) of barley in transgenic rice, and the sucrose phosphate synthase gene (SPS) in rice was used as endogenous reference gene. With a serial of dilutions, the standard curves of the threshold cycle (Ct) relative to the log of each initial template copy of NAS1 and SPS gene were obtained, and the correlation coefficients were 0.99976 and 0.99571, respectively. The transgenic copy number was obtained by comparing the initial template copy of NAS1 with that of SPS. Among the eight putative transgenic lines, one was non-transgenic plant, one had one copy number, three had two copies, and the other had three, four and seven copies, respectively. At the same time, the negative control had zero copy. The method of estimating copy number of exogenous gene by real-time fluorescence quantitative PCR was characterized as speediness, convenience and nicety, which can satisfy the demands of elite line selection in transgenic breeding.
AutoCP: Automated Pipelines for Accurate Prediction Intervals

Successful application of machine learning models to real-world prediction problems, e.g. financial forecasting and personalized medicine, has proved to be challenging, because such settings require limiting and quantifying the uncertainty in the model predictions, i.e. providing valid and accurate prediction intervals. Conformal Prediction is a distribution-free approach to construct valid prediction intervals in finite samples. However, the prediction intervals constructed by Conformal Prediction are often (because of over-fitting, inappropriate measures of nonconformity, or other issues) overly conservative and hence inadequate for the application(s) at hand. This paper proposes an AutoML framework called Automatic Machine Learning for Conformal Prediction (AutoCP). Unlike the familiar AutoML frameworks that attempt to select the best prediction model, AutoCP constructs prediction intervals that achieve the user-specified target coverage rate while optimizing the interval length to be accurate and less conservative. We tested AutoCP on a variety of datasets and found that it significantly outperforms benchmark algorithms.
Organizational ambidexterity and resilience: empirical evidence from uncertain transition economic context

Purpose Despite extensive interests in the effect of organizational ambidexterity on firm performance, there has been limited research on how ambidexterity may promote organizational resilience. Thus, grounded on the dynamic capabilities theory, this paper investigates the influence of ambidexterity on organizational resilience. Methodology This study follows a quantitative method. Using a multi-item survey instrument, a total of 323 questionnaires were distributed among 80 different organizations in Kazakhstan. The collected data were analysed using Spearman's rank correlation coefficient, in order to determine the relationship existing between the variables. FindingsThe proposed link between organizational ambidexterity and resilience was found. There was a moderate, positive monotonic correlation between ambidexterity and resilience. Conclusion – Organizations operating in a turbulent and dynamic than ever before environment should adopt exploitative and explorative strategies in order to remain resilient.
On the EEG footprint of image saliency

This article investigates the possibility to classify images according to their subjective interest by looking at recorded EEG signals. We classify subjects’ EEG recordings while they are shown images in a rapid serial visualization (RSVP) task. Our protocol is essentially derived from the well-known ‘oddball paradigm’. Our exerimental methodology focusses on maximum usability of the device. We show promising results obtained with a simple ‘wearable’ 4-node setup; we evaluate the accuracy that can be obtained under such experimental conditions, and quantify the eﬀect of image-display speed in this context
Initial Centroid Optimization in K-Means Algorithm for Educational Data Mining

Senior High School is a formal educational institution at the level of secondary education that aims to develop students’ potential in various disciplines. In the process of development, students are directed to certain interests. In the traditional method, the process of selecting interests is carried out by the teacher which turns out to be difficult, especially in identifying and finding information that is useful to determine student areas of interest. To overcome these problems, the Educational Data Mining (EDM) approach is used. K-Means is one method that can be used in EDM. However, the K-Means method has a disadvantage in choosing initial centroids, because it is slow and less accurate. This study proposed 3 methods for conducting initial centroid selection. Evaluation results obtained in the study using 245 student data showed that the initial centroid using average calculations on data that had been normalized using min-max normalization obtained better results.
ALLOZYME DIVERSITY IN AN O P H ELE S QUADRI MAC U LATU S ( SENSU STRICTO ) POPULATIONS IN NORTTTPESTBNN ARKANSAS

ABSTRACT A comparison of electrophoretically detectable isozyme differences in 6 population s of Anopheles quadrimaclrlalas (sensu stricto) from northeastern Arkansas was undertaken to te;t the hypothesis that microgeographic variation in habitat types was promoting significant withinand between-popuiation genetrc diversity' Genetic heterogeneity within populations was substantial, with ail of the enzyme loci examined having 2-'7 alleles and average.levels of polymorphisms per population between 54.5 and 7i3Eo. Heterozygot". muoe up an average over all loci of between 2O.6 and 24.8Va of the individuals examined. Only weak ",rld". r. " *u. found for,gametic disequilibrium between pairs of loci. Neither F-statistic nor genetic distance analysis ,ugg"rtad interpopulation divergence. The F$ value averaged over loci was 0.190. All Nei distances for pair-wise population comparisons were greater than 0.010, which was much lower than published values fiorn comparisons between populations belonging to different species of the complex. Diverge'nce was not significantly correlated to either geographic distance or habitat type. Examination of ihe results ;uggests that little genetic divergence has occurred between populations of An. quadrimaculatus in northeastern? rkansas, possi6ly because of the dispersal ability and low level of discrimination between oviposition sites exhibited by this species.
Responses of Bighorn Sheep and Mule Deer to Fire and Rain in the San Gabriel Mountains, California

We used retrospective analyses to evaluate relationships between fire history, precipitation, and productivity in bighorn sheep (Ovis canadensis nelsoni) and mule deer (Odocoileus hemionus californicus) in southern California’s San Gabriel Mountains. The number of bighorn sheep increased 5 times faster after fires on chaparral-covered ranges than the number of sheep on unburned chaparral ranges. When individual time periods were considered during 1976-2006, bighorn sheep population estimates were positively associated (P <0.01) with the amount to winter-spring range burned during all years except 1989-1995. As vegetation matured and habitat suitability declined, the density of female sheep increased, and recruitment declined. Mule deer recruitment rates were positively associated (P <0.1) with the amount of winter-spring range burned during 1976-1989, but not in later years. During 1985-2004, there was a linear relationship (r = 0.58, P = 0.004) between mule deer recruitment rates and precipitation during pregnancy, whereas bighorn sheep recruitment rates were not associated (P > 0.05) with precipitation during pregnancy. During years with less than normal precipitation (1989-1990 and 1999-2004) mule deer recruitment rates were approximately 50% less than recruitment rates during all years with at least normal precipitation, which was also reflected in lower mule deer abundance. We hypothesize that a lack of wildfires combined with drought reduced mule deer availability, and mountain lion (Felis concolor) predation was responsible for the population decline in bighorn sheep during 1989-1995.
Welfare Dynamics in the Arab World: Synthetic Analysis Using Objective and Subjective Data

This paper provides a study of welfare dynamics for countries in the MENA region that combines both objective and subjective welfare measures. Absent actual panel data, we employ recently developed statistical techniques to construct synthetic panels using repeated cross sections from household surveys and the Gallup Poll data, which span the Arab Spring period. Our findings suggest that analysis of welfare dynamics using household surveys’ expenditure data do not always align with that based on subjective wellbeing data, which points to the need to combine both type of data for richer analysis. We find that before the Arab Spring, upward mobility for objective welfare was reasonably strong for Palestine, Tunisia, and Syria, while downward mobility is larger for Yemen, Egypt, and Jordan. Life satisfaction was falling, however, in most countries during the Arab Spring transitions. Only Morocco shows positive dynamics with the size of the unhappy population declining by more than a quarter. JEL: C15, D31, I31, O10, O57
An Approach to Online Parameter Estimation in Nonlinear Dynamical Systems

Given a dynamical system ẏ = f(q∗, y, t) in a (possibly infinite dimensional) Hilbert space Y , we consider the problem of estimating the unknown parameter q∗, element of another (possibly infinite dimensional) Hilbert space X, simultaneously to the evolution of the physical state y from observations of the latter. Even if the dynamical system is linear, this inverse problem typically involves a nonlinear parameter-to-output-map. To our knowledge, theory based online estimation techniques were only analyzed in the context of linear and finite dimensional problems y(t) = A(t)q∗. We present an online parameter estimator for nonlinear dynamical problems based on the minimization of a prediction error and utilize Lyapunov theory for its analysis. Numerical results are presented.
Improving Evidence Detection using Warrants as External Knowledge

Evidence detection [12] is an important sub-task of argument mining and finds an application in a wide range of natural language comprehension tasks like identifying support and attack relation [3], essay evaluation [19] and identifying entailment between sentences[2]. Given a claim and a set of candidate premises (or evidences), the evidence detection task identifies the correct link between the given claim and a candidate premise. Suppose the following example:
Industry-Level Scale Economies: from Micro-Estimation to Macro-Implications∗

What are the macroeconomic impacts of trade and industrial policy? A long tradition in economic analysis indicates that our answer to such questions depend upon (i) the industry-level degree of comparative advantage, which is reflected in the trade elasticity, and (ii) the industry-level degree of scale economies, which is reflected in the scale elasticity. In this paper we propose an empirical methodology to jointly recover these two key elasticities from firm-level trade data. Our key finding is that scale economies are weaker than assumed in standard theories, and vary considerably across industries. We use our estimates to shed new light on cross-national income differences, the gains from trade, and the empirical relevance of classic trade and industrial policy prescriptions.
A PRIORI AND A POSTERIORI ERROR ANALYSIS OF AN AUGMENTED MIXED FINITE ELEMENT METHOD FOR INCOMPRESSIBLE ELASTICITY

Abstract. In this paper we extend recent results on the a priori and a posteriori error analysis of an augmented mixed finite element method for the linear elasticity problem, to the case of incompressible materials. Similarly as before, the present approach is based on the introduction of the Galerkin least-squares type terms arising from the constitutive and equilibrium equations, and from the relations defining the pressure in terms of the stress tensor and the rotation in terms of the displacement, all them multiplied by stabilization parameters. We show that these parameters can be suitably chosen so that the resulting augmented variational formulation is defined by a strongly coercive bilinear form, whence the associated Galerkin scheme becomes well posed for any choice of finite element subspaces. Next, we derive a reliable and efficient residual-based a posteriori error estimator for the augmented mixed finite element scheme. Finally, several numerical results confirming the theoretical properties of this estimator, and illustrating the capability of the corresponding adaptive algorithm to localize the singularities and the large stress regions of the solution, are also reported.
Prediction and Visual Intelligence Platform for Detection of Irregularities and Abnormal Behaviour (short paper)

Nowadays, (cyber)criminals demonstrate an ever more increasing resolve to exploit new technology so to achieve their unlawful purposes. Therefore, Law Enforcement Agencies (LEAs) should accommodate an approach that sur-pass the existing limits in policing practices. In this light, the authors introduce an innovative platform that provides near real-time advanced social behavior analytics using irregularities detection based on historical patterns
Finite Element Method Simulations of Composite Materials

A composite material consists of a mixture of a base material called the matrix, with grains of other materials called the inclusions. These materials are very common in engineering applications like the construction industry. Composite materials have also been used for the production of capacitors with different military applications, yet the role of the composite in the efficiency of such device is not well understood. Modeling composite materials is difficult due to the random nature of such mixtures. However if some controls are established during the production process, it is reasonable to assume homogeneity and isotropy. Models for composite materials have been proposed in the literature, for example by Hill (1972), and can be either random or periodic. The main goal of the present work is to study numerical methods based on the finite element method to determine the mechanical properties of periodic composite materials. We present some preliminary computations for a model of a composite material based on the equations of nonlinear elasticity. The constitutive equations that we used are those usually employed to model rubbery materials. We study a composite of two materials: a matrix with inclusions of a given (nonlinear) material, and determine numerically how the stress distribution depends on the different constitutive and geometric parameters.
Learning-to-Suggest: Product Recommendation via Several Questions

In this paper, we propose a learning-to-suggest (LTS) framework for product recommendation through chatbots via asking the user several questions. We focus on two scenarios: general gift recommendation and specific keyword-based recommendation. For general gift recommendation, the user is supposed to know for whom to buy (such as family members) but do not know what to buy. Then, the chatbot’s task is to construct a giftreceiver profile through asking several questions which are related to gender and hobbies. This is more alike “mind-reading” based recommendation. For specific keyword-based recommendation, the use knows what to buy (such as shoes) or what to book (such as hotel rooms) but need detailed selection from candidate categories, materials and attributes of the target candidate products related to those keywords. Then, the chatbot’s task is to construct a product profile beforehand and ask several questions related to hierarchical category, material or attribute information (illustrated in Figure 1). Traditional search engine or collaborative filtering based methods are not well suitable for these scenarios. The first is that search engines will always need keywords for keyword-product matching. The second is that collaborative filtering will make a recommendation based on other/same people’s buying history. However, we prefer a user-friendly way of first talking to the user and then making a suggestion based on asking questions related to mind-reading or product categories/attributes. Note that there are gaps of information richness of between the chatbot and the users. The users are supposed to be unfamiliar with the details of the products and they need a user-friendly way to obtain these information. These motivated our LTS framework for U: Help me find a gift A: Sure, who are you shopping for? 1.family; 2.friend; 3.coworker U: 2 A: what do your friend do frequently? 1.playing outside; 2.reading; 3.TV watching U: any shoes? A: Sure, 1.sports; 2.casual; 3. U: 1 A: is it for 1.pure running; 2.basketball; 3. A: Hi there, how can I help you? 1.hotel, 2.hospital, 3.restaurant, 4.train, 5.attraction U: 1 A: Okay, do you have a specific area you want to stay in? 1.centre, 2.east, 3.west, 4.south, 5.north U: 1 A: Which price range do you prefer? 1.economic, 2.business, 3.luxury U: 1
IMPACT OF PUBLIC WELLNESS, COMPETITIVENESS AND GOVERNMENT EFFECTIVENESS ON QUALITY OF EDUCATION: A PANEL DATA ANALYSIS OF ASIAN COUNTRIES

The present study attempts to examine the impact of Public wellness, competitiveness and government effectiveness along with Literacy rate and Population growth on quality of education. For this research, the panel data has been collected over the period of 25 years for 10 Asian countries from World Bank and Global Economy data base. The data is specific to employed variables. Different statistical test was applied on the valid data like Heteroskedasticity, Autocorrelation, Cross-section dependence and Multicollinearity, and the estimation of static and dynamic GMM. The results show that, public wellness, Government effectiveness, literacy rate and population growth significantly impact the quality of education in Asian Countries. Whereas, the insignificant impact of competitiveness was evidenced from outcomes of the research. Also, the study embraces several theoretical, practical and policy making implications for the government to improve the quality of education and devise the policies for its efficiency and the policies for enhancement of wellness in its citizens. Lastly, various limitations have been given along with the recommendations for in-depth findings by the future researchers.
Extricating from GroundTruth: An Unpaired Learning Based Evaluation Metric for Image Captioning

Recently, instead of pursuing high performance on classical evaluation metrics, the research focus of image captioning has shifted to generating sentences which are more vivid and stylized than human-written ones. However, there are still no applicable metrics which can judge how close the generated captions are to the human-written ones. In this paper, we propose a novel learning-based evaluation metric, namely Unpaired Image Captioning Evaluation (UICE), which can be trained to distinguish between human-written and generated captions. Unlike existing metrics, our UICE consists of two parts: the semantic alignment module measuring the semantic distance between extracted image features and caption meanings, and the syntactic discriminating module syntactically judging how human-like the candidate caption is. The semantic alignment module is implemented by mapping the image features and the word embedding into a unified tensor space. And the syntactic discriminating module is designed to be learning-based, and thereby can be trained to be stylized by users’ own, fed with additional personalized corpus during the training process. Extensive experiments indicate that our metric can correctly judge the grammatical correctness of generated captions and the semantic consistency between captions and corresponding images.