Miracle-dz/UniFAR-FTUs huggingface dataset:
On the political sustainability of redistributive social insurance systems

We consider social insurance schemes with a two-part benefit formula: a flat (constant) term and a variable term which is proportional to individuals' contributions. The factor of proportionality defines the type of social insurance. We adopt a two-stage political economy approach. At the first, constitutional stage, the type of social insurance is chosen "behind the veil of ignorance", according to the Rawlsian or the utilitarian criterion. At this stage, private insurance can also be prohibited or allowed. At the second stage, tax rate and benefit level are chosen by majority voting. Three main results emerge. First, it may be appropriate to adopt a system which is less redistributive than otherwise optimal, in order to ensure political support for an adequate level of coverage in the second stage. Second, supplementary private insurance may increase the welfare of the poor, even if it is effectively bought only by the rich. Third, the case for prohibiting (supplementary) private insurance may become stronger when the efficiency of private insurance markets increases.
An Energy Efficient Routing Protocol in Wireless Sensor Networks

Wireless sensor network consisting of a large number of small sensors is effective for gathering various data in a variety of environments. Since each sensor operates on battery, energy efficient data transfer is indispensable to maximize the lifetime of the network. In this paper we propose a new data transfer protocol employed in large-scale wireless sensor network. Here only one designated cluster-head sends the data received from other cluster-heads to the base station during one round of communication. Also, we adopt a method that replaces weak cluster-head using a proxy node. Comparison with the existing schemes such as LEACH and PEACH through computer simulation reveals that the proposed scheme significantly improves the network lifetime for practical operational conditions.
A robust linear receiver for multi-access space-time block-coded MIMO systems based on probability-constrained optimization

Traditional multiuser receiver algorithms developed for multiple-input multiple-output (MIMO) wireless communication systems are based on the assumption that the channel state information (CSI) is precisely known at the receiver. However, in practical situations the exact CSI is never available. We address the problem of robustness of multi-access space-time block-coded MIMO systems against imperfect CSI. We propose a new linear receiver which guarantees the robustness against CSI errors with a certain selected probability. The proposed receiver has a form of a probability-constrained optimization problem, which can be simplified to a convex nonlinear programming (NLP) problem, based on the observation that the CSI mismatch is Gaussian. The later problem can be efficiently solved using standard optimization methods. Numerical simulations demonstrate the robustness of the proposed receiver against CSI errors.
Head plane estimation improves the accuracy of pedestrian tracking in dense crowds

Human detection and tracking in high density crowds is an unsolved problem. Standard preprocessing techniques such as background modeling fail when most of the scene is in motion. Because of high levels of occlusion, dense features, and shadows, object detectors tend to produce large numbers of false detections. We introduce a new method based on 3D head plane estimation that reduces these false detections while preserving high detection rates. Our algorithm learns the head plane from observations of human heads, without any a priori extrinsic camera calibration information. In an experimental evaluation, we show that the head plane estimation technique dramatically improves the performance of a pedestrian tracker for dense crowds based on a Viola and Jones AdaBoost cascade classifier for head detection, a particle filter for tracking, and color histograms for appearance modeling.
Evaluation of the ANN Based Nonlinear System Models in the MSE and CRLB Senses

The System Identification problem looks for a suitably parameterized model, representing a given process. The parameters of the model are adjusted to optimize a performance function based on error between the given process output and identified process output. The linear system identification field is well established with many classical approaches whereas most of those methods cannot be applied for nonlinear systems. The problem becomes tougher if the system is completely unknown with only the output time series is available. It has been reported that the capability of Artificial Neural Network to approximate all linear and nonlinear input-output maps makes it predominantly suitable for the identification of nonlinear systems, where only the output time series is available. [1][2][4][5]. The work reported here is an attempt to implement few of the well known algorithms in the context of modeling of nonlinear systems, and to make a performance comparison to establish the relative merits and demerits. Keywords—Multilayer neural networks, Radial Basis Functions, Clustering algorithm, Back Propagation training, Extended Kalman filtering, Mean Square Error, Nonlinear Modeling, Cramer Rao Lower Bound.
Fuzzy Opinion Networks: A Mathematical Framework for the Evolution of Opinions and Their Uncertainties Across Social Networks

We propose a new mathematical framework for the evolution and propagation of opinions, called fuzzy opinion network, which is the connection of a number of Gaussian nodes, possibly through some weighted average, time delay, or logic operators, where a Gaussian node is a Gaussian fuzzy set with the center and the standard deviation being the node inputs and the fuzzy set itself being the node output. In this framework, an opinion is modeled as a Gaussian fuzzy set with the center representing the opinion itself and the standard deviation characterizing the uncertainty about the opinion. We study the basic connections of fuzzy opinion networks, including basic center, basic standard deviation (sdv), basic center-sdv, chain-in-center, and chain-in-sdv connections, and we analyze a number of dynamic connections to show how opinions and their uncertainties propagate and evolve across different network structures and scenarios. We explain what insights we might gain from these mathematical results about the formation and evolution of human opinions.
Standby Consumption in Households Analyzed With a Practice Theory Approach

This article focuses on the energy consumption of households and the question of how daily routines can be changed in a more sustainable direction. It discusses different theoretical approaches with which to understand consumer behavior and introduces practice theory that emphasizes sociotechnical structures as the basis for analyzing stability of consumer practices and opportunities for change. Through analysis of ten in‐depth interviews with families participating in a project aimed at reducing standby consumption, it is shown how technological configurations, everyday life routines, knowledge, and motivation constitute the practice and also structure the possibilities for change. The article concludes by contending that a conception of human behavior that is both less rational and less individualistic is needed to understand stability and change of households’ energy consumption behavior.
Examination of Quality in a Community Family Communication Course.

Adult education is under-researched and when quality is researched it is based on summative assessment, having implications for both policy and practice. Courses which deal with parenting often measure progress in this manner, missing out on the experience of learners. The present study uses conventional content analysis to examine the positive experiences of participants as an indicator of quality. The principal ﬁnding of this study was that the group process was critical to the learning, and that being listened to was the main beneﬁt noted by participants, though this is not often used as a signiﬁer of quality.
A Multidisciplinary Approach to High Throughput Nuclear Magnetic Resonance Spectroscopy

Nuclear Magnetic Resonance (NMR) is a non-contact, powerful structure-elucidation technique for biochemical analysis. NMR spectroscopy is used extensively in a variety of life science applications including drug discovery. However, existing NMR technology is limited in that it cannot run a large number of experiments simultaneously in one unit. Recent advances in micro-fabrication technologies have attracted the attention of researchers to overcome these limitations and significantly accelerate the drug discovery process by developing the next generation of high-throughput NMR spectrometers using Complementary Metal Oxide Semiconductor (CMOS). In this paper, we examine this paradigm shift and explore new design strategies for the development of the next generation of high-throughput NMR spectrometers using CMOS technology. A CMOS NMR system consists of an array of high sensitivity micro-coils integrated with interfacing radio-frequency circuits on the same chip. Herein, we first discuss the key challenges and recent advances in the field of CMOS NMR technology, and then a new design strategy is put forward for the design and implementation of highly sensitive and high-throughput CMOS NMR spectrometers. We thereafter discuss the functionality and applicability of the proposed techniques by demonstrating the results. For microelectronic researchers starting to work in the field of CMOS NMR technology, this paper serves as a tutorial with comprehensive review of state-of-the-art technologies and their performance levels. Based on these levels, the CMOS NMR approach offers unique advantages for high resolution, time-sensitive and high-throughput bimolecular analysis required in a variety of life science applications including drug discovery.
An improved constant-time algorithm for computing the Radon and Hough transforms on a reconfigurable mesh

The Hough transform is an important problem in image processing and computer vision. An efficient algorithm for computing the Hough transform has been proposed on a reconfigurable array by Kao et al. (1995). For a problem with an /spl radic/N/spl times//spl radic/N image and an n/spl times/n parameter space, the algorithm runs in a constant time on a three-dimensional (3-D) n/spl times/n/spl times/N reconfigurable mesh where the data bus is N/sup 1/c/-bit wide. To our best knowledge, this is the most efficient constant-time algorithm for computing the Hough transform on a reconfigurable mesh. In this paper, an improved Hough transform algorithm on a reconfigurable mesh is proposed. For the same problem, our algorithm runs in constant time on a 3-D n*n/spl times/n/spl times//spl radic/n/spl radic/n reconfigurable mesh, where the data bus is only log N-bit wide. In most practical situations, n=O(/spl radic/N). Hence, our algorithm requires much less VLSI area to accomplish the same task. In addition, our algorithm can compute the Radon transform (a generalized Hough transform) in O(1) time on the same model, whereas the algorithm in the above paper cannot be adapted to computing Radon transform easily.
Omni-Kernel: An Operating System Architecture for Pervasive Monitoring and Scheduling

The <italic>omni-kernel</italic> architecture is designed around pervasive monitoring and scheduling. Motivated by new requirements in virtualized environments, this architecture ensures that all resource consumption is measured, that resource consumption resulting from a scheduling decision is attributable to an activity, and that scheduling decisions are fine-grained. <italic>Vortex</italic>, implemented for multi-core x86-64 platforms, instantiates the omni-kernel architecture, providing a wide range of operating system functionality and abstractions. With Vortex, we experimentally demonstrated the efficacy of the omni-kernel architecture to provide accurate scheduler control over resource allocation despite competing workloads. Experiments involving Apache, MySQL, and Hadoop quantify the cost of pervasive monitoring and scheduling in Vortex to be below <inline-formula><tex-math>$6$</tex-math> <alternatives> <inline-graphic xlink:type="simple" xlink:href="kvalnes-ieq1-2362540.gif"/></alternatives></inline-formula> percent of <sc> cpu</sc> consumption.
ECS 289 F-Network Theory and Applications Communities for A Better Yelp

Yelp.com, has become a cornerstone of the internet we know today, yet it lacks the basic features of personalization. In this paper, we implement a new method for calculating business ratings that is based on community detection. We place users into communities of like-minded people, and see if they provide a better predictor of what a user will rate a business. It turns out though, that counter to our original thoughts, the consensus of those in your community are no better predictors than the consensus of the rest of the population and in fact, can be worse because of the small sample size.
Folding and misfolding of designed proteinlike chains with mutations

We study the impact of mutations (changes in amino acid sequence) on the thermodynamics of simple proteinlike heteropolymers consisting of N monomers, representing the amino acid sequence. The sequence is designed to fold into its native conformation on a cubic lattice. It is found that quite a large fraction, between one-half and one-third of the substitutions, which we call “cold errors,” make important contributions to the dynamics of the folding process, increasing folding times typically by a factor of 2, the altered chain still folding into the native structure. Few mutations (“hot errors”), have quite dramatic effects, leading to protein misfolding. Our analysis reveals that mutations affect primarily the energetics of the native conformation and to a much lesser extent the ensemble of unfolded conformations, corroborating the utility of the “energy gap” concept for the analysis of folding properties of proteinlike heteropolymers.
Contextualizing child-centeredness: Lessons from an American Samoan Head Start

Child-centeredness is a pedagogical approach common in US early childhood education, one that advocates young children should direct their own learning and excercise individual choice in activitites. This approach is reflected in national US Head Start policy. Using multivocal, video-cued, and traditional ethnographic methods, this study presents an analysis of interview data collected from three focus groups with American Samoan teachers to argue that the child-centered approach in newly adopted performance standards may not actually be child-centered, particuarly when ignoring the knowledge base and cultural expectations for children in culturally diverse communities. Analyzed through post-colonial theory, which recognizes the erasure of indigenous approaches to educating young children, we focus on Samoan teachers’ understanding of child-centeredness. Results indicate Samoan teachers had drastically different understandings of child-centeredness, instead pointing to optimal pedagogy as collaborative, community-oriented, and structured, and stressing the value of learning from each other. In forgrounding the voice of Samoan educators, we complicate the existing and pervasive binary positioning of child-centered and teacher-directed instruction in early childhood curriculums, to offer another alternative, an expanded notion of child-centeredness that is contextually bound and locally determined.
Using semi-supervised learning in multi-label classification problems

In traditional classification problems (single-label), patterns are associated with a single label from the set of disjoint labels. When an example can simultaneously belong to more than one label, we call it a multi-label classification problem. In relation to the learning strategy, the majority of classification methods requires a large number of training instances to be able to generalize the mapping function, making predictions with high accuracy. However, it is usually difficult to find a number of instances labeled which is sufficient to induce an accurate classification model. This problem is enhanced in the multi-label context, since the number of possible combinations in the label attributes increases considerably. In order to smooth out this problem, the idea of semi-supervised learning has emerged. It combines labeled and unlabeled data during the training phase. Some semi-supervised methods have been proposed for single-label classification methods. However, very little effort has been done in the context of multi-label classification. This paper proposes three semi-supervised methods for the multi-label classification. In order to validate the feasibility of these methods, an empirical analysis will be conducted, aiming to evaluate the performance of such methods in different tasks and using different evaluation metrics.
Subcutaneous Fascial Bands—A Qualitative and Morphometric Analysis

Background Although fascial bands within the subcutaneous (SQ) layer are commonly seen in ultrasound images, little is known about their functional role, much less their structural characteristics. This study's objective is to describe the morphological features of SQ fascial bands and to systematically evaluate the bands using image analyses tools and morphometric measures. Methods In 28 healthy volunteers, ultrasound images were obtained at three body locations: the lateral aspect of the upper arm, medial aspect of the thigh and posterior aspect of lower leg. Using image analytical techniques, the total SQ band area, fascial band number, fascial band thickness, and SQ zone (layer) thickness were determined. In addition, the SQ spatial coherence was calculated based on the eigenvalues associated with the largest and smallest eigenvectors of the images. Results Fascial bands at these sites were contiguous with the dermis and the epimysium forming an interconnected network within the subcutaneous tissue. Subcutaneous blood vessels were also frequently encased by these fascial bands. The total SQ fascial band area was greater at the thigh and calf compared to the arm and was unrelated to SQ layer (zone) thickness. The thigh was associated with highest average number of fascial bands while calf was associated with the greatest average fascial band thickness. Across body regions, greater SQ zone thickness was associated with thinner fascial bands. SQ coherence was significantly associated with SQ zone thickness and body location (calf with statistically greater coherence compared to arm). Conclusion Fascial bands are structural bridges that mechanically link the skin, subcutaneous layer, and deeper muscle layers. This cohesive network also encases subcutaneous vessels and may indirectly mediate blood flow. The quantity and morphological characteristics of the SQ fascial band may reflect the composite mechanical forces experienced by the body part.
Gender Differences in Social Mastery Motivation and Its Relationships to Vocabulary Knowledge, Behavioral Self-Regulation, and Socioemotional Skills

ABSTRACT The present study investigated gender differences in social mastery motivation, vocabulary knowledge, behavioral self-regulation, and socioemotional skills and examined the relationships among this knowledge and these skills by gender. Participants were 134 Chinese children (68 boys, M age = 3.80; 66 girls, M age = 3.89) and their parents recruited through local kindergartens’ parent groups. The children were administered measures of social mastery motivation, vocabulary knowledge, behavioral self-regulation, and nonverbal intelligence. Parents reported their education level and children’s socioemotional skills. Research Findings: Results revealed that boys exhibited more social mastery interactions than girls, and girls showed better behavioral self-regulation and socioemotional skills than boys. Girls with higher social mastery interaction frequency demonstrated better vocabulary knowledge and socioemotional skills, whereas boys with higher social mastery interaction frequency showed lower behavioral self-regulation. Boys, who showed more positive affect during social mastery interactions, tended to have better expressive vocabulary, which facilitated their behavioral self-regulation. Practice or Policy: Findings highlight social mastery motivation as a potential factor that facilitates children’s early development, but it may contribute to boys and girls in different ways.
ASSESSING EMPIRICAL SOFTWARE DATA WITH MLP NEURAL NETWORKS

Software measurements provide developers and software managers with information on various aspects of software systems, such as effectiveness, functionality, maintainability, or the effort and cost needed to develop a software system. Based on collected data, models capturing some aspects of software development process can be constructed. A good model should allow software professionals to not only evaluate current or completed projects but also predict future projects with an acceptable degree of accuracy. Artificial neural networks employ a parallel distributed processing paradigm for learning of system and data behavior. Some network models, such as multilayer perceptrons, can be used to build models with universal approximation capabilities. This paper describes an application in which neural networks are used to capture the behavior of several sets of software development related data. The goal of the experiment is to gain an insight into the modeling of software data, and to evaluate the quality of available data sets and some existing conventional models.
Repeat Cell Therapy for Refractory Angina: Déjà vu All Over Again?

Because the mortality for ischemic heart disease has improved, an increasing number of patients exhaust their revascularization options and are left with refractory angina.1–3 Previous studies suggested that up to 15% of patients undergoing catheterization are suboptimal candidates for further revascularization.4 This number may be growing because a recent analysis from the Duke Databank for Cardiovascular Disease found that 25% of patients had Canadian Cardiovascular Society (CCS) class II-IV angina and coronary disease not amenable to further revascularization.5 Contemporary data suggest that mortality in these complex patients is decreasing, despite the significant burden of coronary artery disease, whereas rehospitalization rates and resource utilization remain high, reflecting the poor quality of life and functional status in these patients.1,3,6 See Article by Mann et al Limited treatment options are available for patients with refractory angina beyond pharmacological treatment and revascularization. This unmet clinical need has stimulated the investigation of novel treatment options, including advanced techniques to treat chronic total occlusions, neuromodulation, reduction of the coronary sinus, shockwave therapy, and stem cell therapy.1,3 Because the primary problem for these patients is inadequate myocardial perfusion, stem cell therapy using angiogenic progenitors represents a theoretically attractive approach. Initial trials for this specific indication have arguably been the most positive in the field of stem cell therapy. Three recent meta-analysis of patients with refractory angina with or without left ventricular dysfunction reach similar and consistent conclusions that this treatment may result in improvements in angina, quality of life, exercise capacity, left ventricular function, and even mortality.7–9 Still no definitive Phase 3 trial has been completed, and a large number of questions remain unanswered, including the ideal cell type and method of delivery. In this issue of Circulation: Cardiovascular Interventions , …