Miracle-dz/UniFAR-FTUs huggingface dataset:
Australia Department of Econometrics and Business Statistics Cobb-douglas Utility – Eventually! Cobb-douglas Utility − Eventually!

Consider the following two opinions, both of which can be found in the literature of consumer demand systems: (a) As the real income of a consumer becomes indefinitely large, re-mixing the consumption bundle becomes irrelevant: having chosen the ultimately satisfying budget shares at any given set of relative prices, the superlatively wealthy continue to allocate additional income in the same proportions. With very large and increasing per capita income, ultimately the utility function becomes indistinguishable from Cobb-Douglas. (b) Consumer demand systems in which the income elasticities monotonically approach one (from above, in the case of luxuries; from below, in the case of necessities) are unsatisfactory both theoretically and empirically. For instance, a necessity with a low (< 1) income elasticity may very well become less elastic with further increases in income. The issue is important for CGE modelers because explicit direct additivity (as in the linear expenditure system [LES]) is often the modeler's default choice: this leaves us firmly in the world of (a). Hanoch's implicit direct additivity exhibits very flexible Engel properties. Rimmer and Powell's AIDADS system belongs to this class. Within such a system it is possible to satisfy the motivations underlying both (a) and (b), as illustrated by the Engel (income) elasticities for a 3-commodity AIDADS system shown below in Figure 1.Whilst the system eventually converges to Cobb-Douglas, some income elasticities can be effectively zero at any imaginable actual income level. Inferiority can also be accommodated over a range of incomes. This paper discusses the above in more detail, strengthening the case for implicit direct additivity. An experimental calibration of a database to the AIDADS system is illustrated with modifications made to the ORANI-G teaching model. A technical appendix establishes the effectively global regularity of AIDADS.
Tensor Magnetic Reluctivity Properties of Soft Magnetic Composite Materials

In order to comprehensively analyze the magnetic field distribution in rotating flux machines, a three dimensional (3-D) magnetic properties testing system fitting for cubic specimen has been constructed and modeled. This paper emphatically studies the tensor magnetic reluctivity properties based on measurement of soft magnetic composite (SMC) materials in 3-D magnetization conditions. By means of the experiential data, 3-D reluctivity tensor is calculated and analyzed. The characteristics of the tensor matrix diagonal and off-diagonal elements are analyzed in different conditions considering the maximum flux density, orientation, and axis ratio of the elliptical magnetic flux density B locus.
Kernel- based Chinese recognition with ontology

since the Chinese websites have increased in the explosive Internet era, making efficient information retrieval systems has become one of the major endeavors, especially in fields of Chinese recognition. In this paper, the authors study the integration of subsequence kernel function based on ontology. Using the Vector Space Model (VSM) to create subsequence kernels, the kernel methodology described here not only overcomes the VSM ignoring any semantic relation between words, but also results both in functional similarity and in sequence/words similarity by gap-weighted subsequences kernels, and the most important is that semantic character is also taken into account, which is very useful for Chinese recognition on internet. Experiments show that the method has more exact retrieval results, and its cost is under the accepted tolerance.
Sentinel-1A Product Geolocation Accuracy: Commissioning Phase Results

Sentinel-1A (S1A) is an Earth observation satellite carrying a state-of-the-art Synthetic Aperture Radar (SAR) imaging instrument. It was launched by the European Space Agency (ESA) on 3 April 2014. With the end of the in-orbit commissioning phase having been completed at the end of September 2014, S1A data products are already consistently providing highly accurate geolocation. StripMap (SM) mode products were acquired regularly and tested for geolocation accuracy and consistency during dedicated corner reflector (CR) campaigns. At the completion of this phase, small geometric inconsistencies had been understood and mitigated, with the high quality of the final product geolocation estimates reflecting the mission’s success thus far. This paper describes the measurement campaign, the methods used during geolocation estimation, and presents best estimates of the product Absolute Location Error (ALE) available at the beginning of S1A’s operational phase.
Optimization of the Use of Residential Lighting with Neural Network

In this document is presented the implementation of the programming schedules as a method of lighting control, to perform a total saving and a personalized saving using neural networks. With the acquisition of a series of data about the operation of five lightings located in different parts of a specific house, it was designed a neural network to illuminate it and was implemented this design to the remaining. These neural networks were trained with input vectors; hour of the day, day of the week, holiday Monday's and their respective objective vectors "total saving and personalized saving" with the purpose of evaluating the performance of the neural networks in the optimization of methods for saving electric energy in residential lighting.
Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic datasets under different levels of noise in order to test its performance and the reported results are then compared to that of a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered with the objective of finding all existing relations between atmospheric pollution and climatological conditions.
Complainant emotional expressions and perceived credibility: Exploring the role of perceivers’ facial mimicry and empathy

Purpose. This research investigated the roles of perceivers’ facial mimicry and empathy in the emotional victim effect (EVE) – the finding that complainants tend to appear more credible when exhibiting (vs. not exhibiting) negative emotional displays during their statements. Because facial mimicry plays a key role in empathic responding, it was hypothesized that inhibiting and facilitating perceivers’ mimicry would attenuate and amplify the EVE, respectively.
Reducing the Social Gradient in Uptake of the NHS Colorectal Cancer Screening Programme Using a Narrative-Based Information Leaflet: A Cluster-Randomised Trial

Objective. To test the effectiveness of adding a narrative leaflet to the current information material delivered by the NHS English colorectal cancer (CRC) screening programme on reducing socioeconomic inequalities in uptake. Participants. 150,417 adults (59–74 years) routinely invited to complete the guaiac Faecal Occult Blood test (gFOBt) in March 2013. Design. A cluster randomised controlled trial (ISRCTN74121020) to compare uptake between two arms. The control arm received the standard NHS CRC screening information material (SI) and the intervention arm received the standard information plus a supplementary narrative leaflet, which had previously been shown to increase screening intentions (SI + N). Between group comparisons were made for uptake overall and across socioeconomic status (SES). Results. Uptake was 57.7% and did not differ significantly between the two trial arms (SI: 58.5%; SI + N: 56.7%; odds ratio = 0.93; 95% confidence interval: 0.81–1.06; p = 0.27). There was no interaction between group and SES quintile (p = 0.44). Conclusions. Adding a narrative leaflet to existing information materials does not reduce the SES gradient in uptake. Despite the benefits of using a pragmatic trial design, the need to add to, rather than replace, existing information may have limited the true value of an evidence-based intervention on behaviour.
Exploratory Factor Analysis Student Anxiety Questionnaire on Statistics

Abstract This study aimed to analyze the dimensions of capabilities contained in the questionnaire on statistics student anxiety. This study is a quantitative descriptive exploratory study. The data were derived from responses 123 students of the Faculty of Education and Language Arts Teachers 'Training College PGRI Bojonegoro on students' anxiety questionnaire statistics. Analysis of data to identify the dimensions is done by applying confirmatory factor analysis on the anxiety questionnaire responses of students to statistics. Exploratory factor analysis performed with SPSS version 22. The results showed that the items in the questionnaire anxiety of students to statistical analysis, all of which contain the main dimensions when analyzed using graph method, the variance can be explained.
3-D Motion and Structure Estimation Using Inertial Sensors and Computer Vision for Augmented Reality

A new method for registration in augmented reality (AR) was developed that simultaneously tracks the position, orientation, and motion of the user’s head, as well as estimating the three-dimensional (3-D) structure of the scene. The method fuses data from headmounted cameras and head-mounted inertial sensors. Two Extended Kalman Filters (EKF) are used; one of which estimates the motion of the user’s head and the other that estimates the 3-D locations of points in the scene. A recursive loop is used between the two EKFs. The algorithm was tested using a combination of synthetic and real data, and in general was found to perform well. A further test showed that a system using two cameras performed much better than a system using a single camera, although improving the accuracy of the inertial sensors can partially compensate for the loss of one camera. The method is suitable for use in completely unstructured and unprepared environments. Unlike previous work in this area, this method requires no a priori knowledge about the scene, and can work in environments where the objects of interest are close to the user. Index terms : Augmented reality, pose estimation, registration, Kalman filter, structure from motion, computer vision, inertial sensors Draft of a paper submitted to Presence: Teleoperators and Virtual Environments, November 2000
Chunk concatenation evolves with practice and sleep-related enhancement consolidation in a complex arm movement sequence

Abstract This paper addresses the notion of chunk concatenation being associated with sleep-related enhancement consolidation of motor sequence memory, thereby essentially contributing to improvements in sequence execution speed. To this end, element movement times of a multi-joint arm movement sequence incorporated in a recent study by Malangré et al. (2014) were reanalyzed. As sequence elements differed with respect to movement distance, element movement times had to be purged from differences solely due to varying trajectory lengths. This was done by dividing each element movement time per subject and trial block by the respective “reference movement time” collected from subjects who had extensively practiced each sequence element in isolation. Any differences in these “relative element movement times” were supposed to reflect element-specific “production costs” imposed solely by the sequence context. Across all subjects non-idiosyncratic, lasting sequence segmentation was shown, and four possible concatenation points (i.e. transition points between successive chunks) within the original arm movement sequence were identified. Based on theoretical suppositions derived from previous work with the discrete sequence production task and the dual processor model (Abrahamse et al., 2013), significantly larger improvements in transition speed occurring at these four concatenation points as compared to the five fastest transition positions within the sequence (associated with mere element execution) were assumed to indicate increased chunk concatenation. As a result, chunk concatenation was shown to proceed during acquisition with physical practice, and, most importantly, to significantly progress some more during retention following a night of sleep, but not during a waking interval.
Chaos Particle Swarm Optimization Algorithm for Estimating Solute Transport Parameters of Streams from Tracer Experiment Data

In this paper, we combined chaotic search into standard particle swarm search into one and proposed a new algorithm named as chaos particle swarm optimization algorithm(CPSO). The CPSO algorithm may speed the search process, and improve the ability of seeking the global optimal solution and convergence. And the CPSO algorithm was applied to analysis of one-dimensional tracing test date of river stream with tracer injected instantaneously, and further to optimization of function for estimating the water quality parameters of river stream. The results show that the proposed algorithm is superior to the standard particle swarm optimization one in the speed of search and convergence.
Risk Estimation Without Using Stein's Lemma - Application to Image Denoising

We address the problem of image denoising in additive white noise without placing restrictive assumptions on its statistical distribution. In the recent literature, specific noise distributions have been considered and correspondingly, optimal denoising techniques have been developed. One of the successful approaches for denoising relies on the notion of unbiased risk estimation, which enables one to obtain a useful substitute for the mean-square error. For the case of additive white Gaussian noise contamination, the risk estimation procedure relies on Stein's lemma. Sophisticated wavelet-based denoising techniques, which are essentially nonlinear, have been developed with the help of the lemma. We show that, for linear, shift-invariant denoisers, it is possible to obtain unbiased risk estimates of the mean-square error without using Stein's lemma. An interesting consequence of this development is that the unbiased risk estimator becomes agnostic to the statistical distribution of the noise. As a proof of principle, we show how the new methodology can be used to optimize the parameters of a simple Gaussian smoother. By locally adapting the parameters of the Gaussian smoother, we obtain a shift-variant smoother, which has a denoising performance (quantified by the improvement in peak signal-to-noise ratio (PSNR)) that is competitive to far more sophisticated methods reported in the literature. The proposed solution exhibits considerable parallelism, which we exploit in a Graphics Processing Unit (GPU) implementation.
Metrics Based Accuracy Perpetuation for Bioinformatics Sequence Analysis Tools

Bioinformatics refers computational biology that merges with other fields of sciences like applied mathematics, statistics, chemistry for solving biological problems. The research areas in this field include sequence analysis, protein-protein interaction, gene expression analysis which leads it path in drug discovery, disease identification, and evolutionary relationships. At present the bioinformatics tools are increasingly important to allow real-time decision making for solving biological problems. Performance of a tool can be evaluated by means of feature, functionality comparison or by means of evaluating metrics. Metrics based evaluation leads to measure the qualities that qualify the characteristics of the product. In this work, some of the sequences analysis tools are considered for evaluating the accuracy of mining the required information's with respect to the key characteristics like functional analysis, structural analysis, and similarity analysis. The selected bioinformatics sequence analysis tools that have been analyzed here have been evaluated by means of features, functionalities comparison and by means of evaluating metrics which has been represented in this paper. The performance evaluation is made with the use of selected metrics such as accuracy, sensitivity etc… with detailed result analysis. This result analyzed from the observation made on sequence analysis tools provides a standard guideline for developing and maintaining better systems with enhanced qualities.
Effect of Ketamine on Inflammatory and Immune Responses After Short-Duration Surgery in Obese Patients

In non-obese patients ketamine decreases inflammatory responses and prevents overexpression of immune re- sponses. Its effect in obese patients is unknown. This prospective, blinded, randomized controlled trial was designed to determine the effect of ketamine on cytokines and immune cell responses after short-duration surgery in obese patients. Thirty-six patients received either ketamine 0.15 mg/kg IV prior to induction of general anesthesia, or an equal volume of normal saline. Cytokine concentrations and immune cell responses were determined pre-operatively and at 4, 24, and 48 h after operation. Interleukin (IL)-6 production was significantly greater in the control group (126.0 ± 18.8 ng/ml, mean ± SEM, n = 19) than in the ketamine group (57.9 ± 8.4 ng/ml) at 4 h. At other time periods IL-6 and tumor necrosis factor � increased and IL-2, lymphocyte proliferation, and natural killer cell cytotoxity decreased compared to pre-operative values in the control group but not in the ketamine group. We conclude that effects of ketamine on inflammatory and immune re- sponses after short-duration surgery in obese patients are similar to those previously reported in non-obese patients.
The effects of muscle quality and continuous cycling on motor unit behavior of the vastus lateralis

Introduction: Recent advancements in surface electromyographic signal decomposition and the inclusion of ultrasonography have presented the opportunity for a deeper understanding of motor unit behavior. However, the influence of sex and muscle composition on motor unit recruitment patterns has yet to be investigated. In addition, little is known regarding the neuromuscular adaptions from endurance training on motor unit behavior during submaximal tasks and long duration activities. Methods: Three distinct experiments were conducted to expand the current literature on motor unit behavior. The first project investigated the influence of sex and muscle quality on motor unit action potential sizes for 10 males and 10 females. The second project longitudinally investigated the effects of 10-weeks of continuous cycling on motor unit firing rate behavior and recruitment patterns for 23 sedentary individuals. The third project longitudinally investigated the effects of 5-weeks of continuous cycling on motor unit firing rate behavior and recruitment patterns during consecutive, long duration contractions for 25 sedentary individuals. Conclusions: For project one, the greater muscle quality and motor unit action potential sizes exhibited by males suggests greater muscle fiber sizes of the higherthreshold motor units. For project two, participants displayed increases in maximal aerobic capacity and decreases in maximal strength. The decreases in maximal strength were associated with alterations in motor unit firing rate patterns and increases in muscle activation to match pretraining absolute torques. For project three, participants displayed increases in maximal aerobic capacity and decreases in maximal strength. Following 5 weeks of training, there were time and repetition dependent change in motor unit firing rate and likely recruitment patterns to compensate for losses in strength.
S TOCHASTIC S EARCHING ON THE L INE AND ITS A PPLICATIONS TO P ARAMETER L EARNING IN N ONLINEAR O PTIMIZATION

We consider the problem of a learning mechanism (for example, a robot) locating a point on a line when it is interacting with an random environment which essentially informs it, possibly erroneously, which way it should move. In this paper we present a novel scheme by which the point can be learnt using some recently devised learning principles. The heart of the strategy involves discretizing the space and performing a controlled random walk on this space. The scheme is shown to be ε-optimal and to converge with probability 1. Although the problem is solved in its generality, its application in non-linear optimization has also been suggested. Typically, an optimization process involves working one's way toward the maximum (minimum) using the local information that is available. However, the crucial issue in these strategies is that of determining the parameter to be used in the optimization itself. If the parameter is too small the convergence is sluggish. On the other hand, if the parameter is too large, the system could erroneously converge or even oscillate. Our strategy can be used to determine the best parameter to be used in the optimization.