Miracle-dz/UniFAR-FTUs huggingface dataset:
Absent and Problematic Men: Demographic Accounts of Male Reproductive Roles

Both men and women are important actors in bringing children into life, yet demographic studies on reproduction have tended to focus on women alone. The aims of this article are: 1) to describe why men have attracted limited interest as subjects of such research; 2) to evaluate existing research on men's roles in developing countries; and 3) to suggest directions for future research on male reproductive roles. Men, once neglected, are now included in research on fertility but from a narrow, overly problem-oriented perspective. A review of the literature, however, raises questions about the adequacy of a problem oriented approach. The authors argue that demography should focus on men not only as women's partners, but also as individuals with distinct reproductive histories. In situations, now increasingly common, where the links between marriage and childbearing erode, the differences in men's and women's reproductive experiences and the costs and benefits of parenting will become more salient for future research. Copyright 2000 by The Population Council, Inc.
Macroeconometric model of the Thai economy

Thailand is an agricultural country in Southeast Asia. She always faces the same problems as other developing countries, such as high growth rate of population, low income per capita, unequal income distribution, and low productivity of labor. Thus, government attempts to solve these problems through the national development planning. The first national development planning was adopted in 1960. It did not achieve as expected due to lack of good statistical data and trained manpower. The Ministry of Agriculture and Cooperatives was assigned to make a plan for agriculture. In 1973, there was a joint project between the Ministry of Agriculture and Cooperatives (through its division of agricultural economics), Iowa State University, and United States Overseas Mission/Thailand. The objective of this project was to apply agricultural economic research in supporting development of Thailand's Fourth Five-Year Development Plan for the period 1977-1981. National linear programming and other analyses were constructed except a macroeconometric model. Until 1974, it was felt that the macroeconometric should be included in order to obtain a complete picture of Thai economy. Therefore, the Thai macroeconometric model was constructed. When the model was built, there were some difficulties in terms of econometrics and computer programming. Thus, the equations in the model were estimated by Ordinary Least Squares. Two Stage Least Squares could not be applied directly. These problems led to further study in this dissertation. The objective of this research is to construct a model which can describe the Thai economy in a more extensive and disaggregate manner. ;The estimated model in this study consists of 91 equations which contain 68 behavioral equations and 23 identities. The model contains 89 endogenous variables and 107 predetermined variables. The estimation of the model was done by the method of two stage principal component (2SPC). Short time series data from 1963 to 1978 were used in the estimation of coefficients in all sectors except in the compensation of employee equation where the data used range from 1967 to 1978. The model simulation uses the Gauss-Seidel algorithm procedure. ;In the model, there remain weaknesses, especially in the investment sector, income distribution sector, and the balance of payment equation. Therefore, the model should be revised. It is hoped that these weaknesses can be reduced in the future.
The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public’s perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit “big data. ”
Mergeable persistent data structures

Irmin is an OCaml library to design purely functional data structures that can be persisted on disk and be merged and synchronised efficiently. In this paper, we focus on the "merge" aspect of the library and present two data structures built on top of Irmin: (i) queues and (ii) ropes that extend the corresponding purely functional data structures with a 3-way merge operation. We provide early theoretical and practical complexity results for these new data structures. Irmin is available as open-source code as part of the MirageOS project.
Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot® lactation model

Genetic selection programs have driven development of most lactation models, to estimate the magnitude of animals’ productive capacity from sampled milk production data. There has been less attention to management and research applications, where it may also be important to quantify the shape of lactation curves, and predict future daily milk production for incomplete lactations since residuals between predicted and actual daily production can be used to quantify the response to an intervention. A model may decrease the confounding effects of lactation stage, parity, breed, and possibly other factors depending on how the model is constructed and used, thus increasing the power of statistical analyses. Models with a mechanistic derivation may allow direct inference about biology from fitted production data. The MilkBot® lactation model is derived from abstract suppositions about growth of udder capacity. This permits inference about shape of the lactation curve directly from parameter values, but not direct conclusions about physiology. Individual parameters relate to the overall scale of the lactation, the ramp, or rate of growth around parturition, decay describing the senescence of productive capacity (inversely related to persistence), and the relatively insignificant time offset between calving and the physiological start of milk secretion. A proprietary algorithm was used to fit monthly test data from two parity groups in 21 randomly selected herds, and results displayed in box-and-whisker charts and Z-test tables. Fitted curves are constrained by the MilkBot® equation to a single peak that blends into an exponential decline in late lactation. This is seen as an abstraction of productive capacity, with actual daily production higher or lower due to random error plus short-term environmental effects. The four MilkBot® parameters, and metrics calculated directly from them including fitting error, peak milk and cumulative production, can be used to describe and compare individual lactations or groups of lactations. There is considerable intra-herd and inter-herd variability in scale, ramp, decay, RMSE, peak milk, and cumulative production, suggesting that management and environment have significant influence on both shape and magnitude of normal lactation curves.
ICAGENT: Balancing between reactivity and deliberation

The aim of this paper is to present a framework for developing intelligent agents that act in dynamic and unpredictable environments by balancing between deliberation and reaction. Acting reactively, an agent executes routine tasks without considering its options and thinking about alternatives. Acting deliberatively, an agent deals with complex goals that need careful, although not detailed planning and reconciles its desires and its intentions. This paper emphasizes on the utility of plan management techniques, as well as on the integrated treatment of agents’ mental structures during reaction and deliberation. The framework is thoroughly explained and the behaviour of an agent in a specific environment is examined.
The Relationship of Early Class Start Times on Sleepiness and Driving Behaviors in an Emerging Adult Population

THE RELATIONSHIP OF EARLY CLASS START TIMES ON SLEEPINESS AND DRIVING BEHAVIORS IN AN EMERGING ADULT POPULATION Jessica L. Fry Old Dominion University, 2017 Director: Dr. Bryan E. Porter Teenage driver sleepiness is a recent concern for preventing motor vehicle fatalities. Early school start times limit the amount of sleep teenage high school students acquire during the week and have been related to increased crash risk. The current study extends this finding to teenage and emerging adult college students. The author examined the link between sleepiness and teenage driving behaviors, including the relationship between school start times and sleepiness. In all, 536 participants were recruited to participate in an online survey assessing driving and sleep behaviors. Correlations and path analysis found that sleepiness fully mediated the relationship between early class start times and driving behaviors. Surprisingly only daytime sleepiness (as measured by the ESS), not sleep quality (as measured by the PSQI), mediated this relationship. Furthermore, both driving errors and driving violations were related outcomes to class start time as mediated by sleepiness, with hypotheses only expecting errors to be so. Reasons for violations being predicted when unexpected are discussed. Overall, this study adds to a growing literature supporting the influence of later class start times, and indicates that college students may be at similar driving risk as high school teenagers with early class start
Semantic subtyping for imperative object-oriented languages

Semantic subtyping is an approach for defining sound and complete procedures to decide subtyping for expressive types, including union and intersection types; although it has been exploited especially in functional languages for XML based programming, recently it has been partially investigated in the context of object-oriented languages, and a sound and complete subtyping algorithm has been proposed for record types, but restricted to immutable fields, with union and recursive types interpreted coinductively to support cyclic objects. In this work we address the problem of studying semantic subtyping for imperative object-oriented languages, where fields can be mutable; in particular, we add read/write field annotations to record types, and, besides union, we consider intersection types as well, while maintaining coinductive interpretation of recursive types. In this way, we get a richer notion of type with a flexible subtyping relation, able to express a variety of type invariants useful for enforcing static guarantees for mutable objects. The addition of these features radically changes the defi- nition of subtyping, and, hence, the corresponding decision procedure, and surprisingly invalidates some subtyping laws that hold in the functional setting. We propose an intuitive model where mutable record val- ues contain type information to specify the values that can be correctly stored in fields. Such a model, and the correspond- ing subtyping rules, require particular care to avoid circularity between coinductive judgments and their negations which, by duality, have to be interpreted inductively. A sound and complete subtyping algorithm is provided, together with a prototype implementation.
YOUTH AND ADULTS STUDENTS INTERPRETING BAR AND LINE GRAPHS

The objective of this study was to investigate the influence of schooling on the performance of Youth and Adults (EJA) students in solving activities of construction and interpretation of bar and line charts. The participants were 30 students divided into three groups: 10 students in the early years of elementary school, 10 students in the final years of elementary school and 10 high school students. The results indicated no significant differences in the performance presented by participants according to the segments of education to which they belonged. There were significant differences in performance on the basis of the activities of interpretation of the bar charts, but not between the bar and line charts.
Empirical Evaluation of an SVM-based Software Reliability Model

Support Vector Machines (SVMs) are known as some of the best learning models for pattern recognition, and an SVM can be used as a software reliability model to predict fault-prone modules from complexity metrics. We experimentally evaluated the prediction performance of an SVM model, comparing it with commonlyused conventional models including linear discriminant analysis, logistic regression, a classification tree, and a neural network. The results revealed that the SVM model exhibited showed the best performance among all the models tested.
Interoception in insula subregions as a possible state marker for depression—an exploratory fMRI study investigating healthy, depressed and remitted participants

Background: Interoceptive awareness (iA), the awareness of stimuli originating inside the body, plays an important role in human emotions and psychopathology. The insula is particularly involved in neural processes underlying iA. However, iA-related neural activity in the insula during the acute state of major depressive disorder (MDD) and in remission from depression has not been explored. Methods: A well-established fMRI paradigm for studying (iA; heartbeat counting) and exteroceptive awareness (eA; tone counting) was used. Study participants formed three independent groups: patients suffering from MDD, patients in remission from MDD or healthy controls. Task-induced neural activity in three functional subdivisions of the insula was compared between these groups. Results: Depressed participants showed neural hypo-responses during iA in anterior insula regions, as compared to both healthy and remitted participants. The right dorsal anterior insula showed the strongest response to iA across all participant groups. In depressed participants there was no differentiation between different stimuli types in this region (i.e., between iA, eA and noTask). Healthy and remitted participants in contrast showed clear activity differences. Conclusions: This is the first study comparing iA and eA-related activity in the insula in depressed participants to that in healthy and remitted individuals. The preliminary results suggest that these groups differ in there being hypo-responses across insula regions in the depressed participants, whilst non-psychiatric participants and patients in remission from MDD show the same neural activity during iA in insula subregions implying a possible state marker for MDD. The lack of activity differences between different stimulus types in the depressed group may account for their symptoms of altered external and internal focus.
Promoting Growth, Maintaining Progressivity, and Dealing with the Fiscal Crisis

Trade-offs between economic efficiency, growth, and distributional equity permeate economics, including discussions of tax policy and tax reform. Computable general equilibrium (CGE) modeling is one tool that is often used to estimate the magnitudes of the variables that determine the efficiency, growth, and equity properties of alternative tax reforms. In this article, we report the results of simulations of a CGE model that examines the economic and distributional effects of the enactment in the United States of a temporary value-added tax used to reduce the level of the national debt. The results suggest that such a reform is generally moderately progressive both for cohorts alive at the time of reform and for future generations, at least within the context of lifetime measures of tax burden, and that current middle-aged and elderly generations must bear a burden to confer a gain, relative to the status quo, on younger and future generations.
Efficient nn-rasud search space reduction in a large vocabulary speech recognition system

In very large vocabulary speech recognition systems using the hypothesis-veriiicali~)np aradigm, the vcrification stage is usually the most time consumiilg. State ofihi. art systems combine fixed six hypothesized search spaces with odvanccd pmninp techniques. In this paper we propose R novel strategy to dynamically calculate thc hypotliesired search spacc, rising neural neiamorks as the estimation module and designing the inpui feature set wiih a cnrcfiil gredy-based selectioii approach. The main achievement lias been a statistically signilicaiit rclniivc dccrrase in error rate of 33.53%, while gelling a relative decrease in average computational demands of up to 14.40%.
A pr 2 00 0 Cosmic String Current Stability

The stability of fermionic charge carriers on cosmic strings is considered. We show that neutral fermion currents in cosmic strings are always chiral or time-like, in contrast to the case of bosonic currents. The spectrum of bound states on an abelian SO(10) string is determined both before and after the electroweak phase transition. We determine the mass acquired by the zero mode at this transition. A range of charge carrier scattering processes are considered and corresponding decay rates calculated. Couplings between the carriers and the electroweak sector generate scattering from the plasma which can thermalise some currents. If the zero mode is isolated from the electroweak sector, then it survives. Current–current scattering is considered, but found to be unimportant in realistic settings where the string density is low. DAMTP/99-168 SWAT/229 S.C. Davis@swansea.ac.uk w.perkins@swansea.ac.uk A.C. Davis@damtp.cam. ac.uk
Performance Analysis of Cloud Centers under Burst Arrivals and Total Rejection Policy

Quality of service, QoS, has a great impact on wider adoption of cloud computing. Maintaining the QoS at an acceptable level for cloud users requires an accurate and well adapted performance analysis approach. In this paper, we describe a new approximate analytical model for performance evaluation of cloud server farms under burst arrivals and solve it to obtain important performance indicators such as mean request response time, blocking probability, probability of immediate service and probability distribution of number of tasks in the system. This model allows cloud operators to tune the parameters such as the number of servers and/or burst size, on one side, and the values of blocking probability and probability that a task request will obtain immediate service, on the other.
Curvature and topology in smectic-A liquid crystals

Considerations of rotational invariance in one-dimensionally modulated systems such as smectics-A, necessitate nonlinearities in the free energy. The presence of these nonlinearities is critical for determining the layer configurations around defects. We generalize our recent construction for finding exact minima of an approximate nonlinear free energy to the full, rotationally invariant smectic free energy. Our construction exhibits the detailed connection between mean curvature, Gaussian curvature and layer spacing. For layers without Gaussian curvature, we reduce the Euler–Lagrange equation to an equation governing the evolution of a surface. As an example, we determine the layer profile and free energy of an edge dislocation.
Wavelet Transform for Visibility Analysis in Fog Situations

In this article a new technique for the estimation of the distance of visibility in fog conditions is presented. It is based on a psychovisual model and on contrast estimation with wavelet transform. For this application, specific B–spline wavelets are introduced in order to have a good space and frequency localization and to enable a fast implementation. Using images stemming from situations on motorways, this technique is compared to a direct local contrast calculation.
Capacity limit of visual short-term memory in human posterior parietal cortex

approach using intersexual dynamics. Annu. Rev. Ecol. Syst. 28, 571–592 (1997). 28. Giraldeau, L. A. & Caraco, T. Social Foraging Theory (Princeton Univ. Press, Princeton, 2000). 29. Sober, E. & Wilson, D. S. Unto Others (Harvard Univ. Press, Cambridge, Massachusetts, 1998). 30. Boyd, R. & Richerson, P. J. Group selection among alternative evolutionarily stable strategies. J. Theor. Biol. 145, 331–342 (1990).
Embedding EfS in teacher education : an introductory guide to using the systems change model

[Extract] Sustainability is on the global agenda and education has been internationally identified as a key strategy for bringing about the necessary shift towards sustainability. Contemporary and future socio-ecological challenges set an overarching context for teacher education by predicating the kind of attitudes, understandings and competencies that will enable our future teachers to adequately prepare the citizens of tomorrow for what is predicted to be a volatile and uncertain future. Unfortunately, in practice, there is little evidence of this agenda in teacher education. This may be because of a lack of understanding of sustainability, why it is important to teacher educators, or how to engage with these ideas. We see this guide as one way in which to begin to address these issues.
Motion Planning Based on Learning From Demonstration for Multiple-Segment Flexible Soft Robots Actuated by Electroactive Polymers

Multiple-segment flexible and soft robotic arms composed by ionic polymer--metal composite (IPMC) flexible actuators exhibit compliance but suffer from the difficulty of path planning due to their redundant degrees of freedom, although they are promising in complex tasks such as crossing body cavities to grasp objects. We propose a learning from demonstration method to plan the motion paths of IPMC-based manipulators, by statistics machine-learning algorithms. To encode demonstrated trajectories and estimate suitable paths for the manipulators to reproduce the task, models are built based on Gaussian mixture model and Gaussian mixture regression, respectively. The forward and inverse kinematic models of IPMC-based soft robotic arm are derived for the motion control. A flexible and soft robotic manipulator is implemented with six IPMC segments, and it verifies the learned paths by successfully completing a representative task of navigating through a narrow keyhole.
Evaluating Natural Language Processors in the Clinical Domain

Abstract Evaluating natural language processing (NLP) systems in the clinical domain is a difficult task which is important for advancement of the field. A number of NLP systems have been reported that extract information from free-text clinical reports, but not many of the systems have been evaluated. Those that were evaluated noted good performance measures but the results were often weakened by ineffective evaluation methods. In this paper we describe a set of criteria aimed at improving the quality of NLP evaluation studies. We present an overview of NLP evaluations in the clinical domain and also discuss the Message Understanding Conferences (MUC) [1-41. Although these conferences constitute a series of NLP evaluation studies performed outside of the clinical domain, some of the results are relevant within medicine. In addition, we discuss a number of factors which contribute to the complexity that is inherent in the task of evaluating natural language systems.