Miracle-dz/UniFAR-FTUs huggingface dataset:
Title of Document : ASSOCIATIONS BETWEEN PARENTING AND CHILD BEHAVIOR PROBLEMS AMONG LATINO MOTHERS AND CHILDREN

Title of Document: ASSOCIATIONS BETWEEN PARENTING AND CHILD BEHAVIOR PROBLEMS AMONG LATINO MOTHERS AND CHILDREN Yamalis Diaz, M.A., 2005 Directed By: Assistant Professor Andrea M. Chronis, Department of Psychology The present study examines the ability of global parenting styles and specific parenting practices to predict attention and behavior problems in Latino children. Sociodemographic variables and acculturation were considered in all analyses to examine, and account for, their influences. 107 Latino mothers with a child between 6 and 12 years old completed demographic, parenting, and child behavior measures. Hierarchical linear regression analyses were conducted in order to predict child behavior from sociodemographic variables, acculturation, and parenting. All three parenting styles, and most practices, predicted reported behavior problems. Level of acculturation also consistently predicted child behavior problems. The present study adds to the growing body of literature demonstrating some differences in the associations between these styles and child behavior problems compared to what has been found in the general literature. In addition, it highlights the importance of considering level of acculturation when examining these associations within this population. ASSOCIATIONS BETWEEN PARENTING AND CHILD BEHAVIOR PROBLEMS AMONG LATINO MOTHERS AND CHILDREN
A New Analytic Approximation Technique for Options in a Regime-Switching Market

Both deterministic and stochastic volatility models have been used to price and hedge European options. Observation of real market data suggests that volatility, while stochastic, is well modelled to alternate between just two states. We consider a set of coupled pricing partial differential equations (PDE) similar to existing regime-switching equations, with the inclusion of a market price of volatility risk term which arises from the typical contract independence assumption. The resulting PDE system has not yet been solved in closed form. Instead, we use the Cauchy-Kowalevski Theorem to obtain an approximate solution in terms of known Black-Scholes formulae. The error in this approximate solution can be quantified in the backward error sense by solving a related root-finding problem. The results obtained from our method are compared to those obtained by implementing a Crank-Nicolson numerical scheme.
HL7 healthcare information management using aspect-oriented programming

Given the heterogeneity of healthcare software systems, data from each system is often incompatible inhibiting interoperability. To enable the sharing and exchange of healthcare information interoperability standards must be adhered to. Health Level Seven (HL7) is the international standards organisation that promotes and enforces the standardisation of electronic healthcare information to facilitate its exchange and management. Incorporating HL7 functionality into existing applications requires significant modification and intrusive extensions. Using Aspect-Oriented Programming (AOP), we can introduce HL7 functionality into existing applications without the requirement for refactoring or modification. HL7 data formatting affects multiple parts of an application and hence is a “crosscut-ting concern”. These concerns which entwine with base functionality introduce complexity and reduce modularity. A second benefit of AOP is its advanced modularisation capabilities which are capable of modularising “crosscut-ting concerns”. We illustrate the benefits of using AOP in HL7 by example and measure the effects of the approach on healthcare applications.
Learning Parse Decisions from Examples with Rich Context

We present a knowledge and context-based system for parsing natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morpholgical, syntactical, semantical and other aspects of a given parse state.
Obesity Has an Interactive Effect with Genetic Variation in the Activating Transcription Factor 6 Gene on the Risk of Pre-Diabetes in Individuals of Chinese Han Descent

Endoplasmic reticulum (ER) stress is one of the contributing factors to the development of β-cell failure in type 2 diabetes. ER stress response through ATF6 has been shown to play an important role in insulin resistance and pancreatic β-cell function. We investigated whether genetic polymorphisms in ATF6 were associated with the risk of pre-diabetes in a Chinese Han population, and whether they had a synergistic effect with obesity. Our samples included 828 individuals who were diagnosed as pre-diabetic, and 620 controls. The minor allele A at rs2340721 was associated with increased risk for pre-diabetes(p = 0.013), and this association was still significant after adjusting for gender, age, body mass index (BMI), and waist-hip ratio(p′ = 0.011). BMI, treated as a continuous variable, and rs2340721 had an interactive effect on pre-diabetic risk(p for interaction = 0.003, β = 0.106). Carriers of GG at rs7522210 were also at a higher risk compared to non-carriers (OR = 1.390, 95%CI:1.206–1.818, p = 0.013, adjusted OR′ = 1.516, 95%CI:1.101–2.006, p′ = 0.006). GG homozygotes had increased fasting blood glucose (FBG) levels(GG vs CX: 5.6±0.52 vs 5.5±0.57 mmol/L, p = 0.016), lower insulin levels (0,30,120 minutes after glucose load) (p<0.05), and reduced areas under the insulin curve than non-carriers(GG vs CX:67.3(44.2–102.3) vs 73.1(49.4–111.4), p = 0.014). rs10918270 was associated with FBG, and rs4657103 with 2 hour glucose levels after a 75 g glucose load. We also identified a haplotype of TTAG composed of rs4657103, rs2134697, rs2340721, and rs12079579, which was associated with pre-diabetes. The genetic variation in ATF6 is associated with pre-diabetes and has interactive effects with BMI on pre-diabetes in the Chinese Han population.
WHAT DRIVES RACIAL SEGREGATION ? EVIDENCE FROM THE SAN FRANCISCO BAY AREA USING MICRO-CENSUS DATA

Residential segregation on the basis of race is widespread and has important welfare consequences. This paper sheds new light on the forces that drive observed segregation patterns. Making use of restricted micro-Census data from the San Francisco Bay Area and a transparent new measurement framework, it assesses the extent to which the correlation of race with other household characteristics, such as income, education and immigration status, can explain a significant portion of observed racial segregation. Our analysis shows that individual household characteristics can explain a considerable fraction of segregation by race. In doing so, it draws attention to the possibility that race may not itself be a fundamental driving force, at least for some racial groups. We find that non-race household characteristics together explain almost 95 percent of segregation for Hispanic households, over 50 percent for Asian households, and approximately 30 percent for White and Black households. Our analysis also indicates that different factors drive the segregation of different races. Language explains a substantial proportion more than 30 percent of Asian and Hispanic segregation, education explains a further 20 percent of Hispanic segregation, while income is the most important non-race household characteristic for Black households, explaining around 10 percent of Black segregation. We would like to thank Fernando Ferreira (University of California – Berkeley) for outstanding research assistance as well as Jackie Chou and Pedro Cerdan for help assembling the data used in the paper. We would also like to thank Jon Sonstelie, Chris Udry, Junfu Zhang and seminar participants at Johns Hopkins, UCLA, and Yale for helpful comments. We are grateful to the California Census Research Data Center for providing access to the data and to Ritch Milby in particular. All output included in this paper has been subject to thorough disclosure analysis by Census Bureau officials and meets stringent standards to safeguard confidentiality. Financial support from the Public Policy Institute of California is gratefully acknowledged. Please send correspondence to any of the authors patrick.bayer@yale.edu, mcmillan@chass.utoronto. ca, or rueben@ppic.org.
Evaluation of the Impact that Teacher Targeted Bullying has on Individual Safety Perceptions and Stress

This study investigated teacher targeted bullying in primary schools to examine whether students serve as an unsafe work stressor for teachers, because teacher targeted bullying is on the rise. The study looked at teacher targeted bullying as an antecedent of stress and the mechanisms, such as mediators and moderators, which affected this bullying-stress relationship. 113 primary school teachers completed a self-report questionnaire which measured bullying, self-efficacy, safety climate perceptions, social support and stress. The results provided evidence that social support from others outside work moderated the low level bullying-stress, and severe bullying-stress relationships. Most importantly, unsafe job perceptions mediated the relationship between low level bullying and stress. The results of this research imply that student bullying can influence teachers to perceive their job as unsafe and lead to stress. These findings can be used to develop effective strategies to not only prevent and manage bullying, but create safer schools for teachers and pupils.
Investigating Time-Efficient Methods to Price Compound Options in the Heston Model

The primary purpose of this paper is to provide an in-depth analysis of a number of structurally different methods to numerically evaluate European compound option prices under Heston’s stochastic volatility dynamics. Therefore, we first outline several approaches that can be used to price these type of options in the Heston model: a modified sparse grid method, a fractional fast Fourier transform technique, a (semi-)analytical valuation formula using the Green’s function of logarithmic spot and volatility and a Monte Carlo simulation. Then we compare the methods on a theoretical basis and report on their numerical properties with respect to computational times and accuracy. One key element of our analysis is that the analyzed methods are extended to incorporate piecewise time-dependent model parameters, which allows for a more realistic compound option pricing.
Running head: PROBABILISTIC CLUSTERING THEORY OF VSTM 1 A Probabilistic Clustering Theory of the Organization of Visual Short-Term Memory

Experimental evidence suggests that the content of a memory for even a simple display encoded in visual short-term memory (VSTM) can be very complex. VSTM uses organizational processes that make the representation of an item dependent on the feature values of all displayed items as well as on these items’ representations. Here, we develop a Probabilistic Clustering Theory (PCT) for modeling the organization of VSTM for simple displays. PCT states that VSTM represents a set of items in terms of a probability distribution over all possible clusterings or partitions of those items. Because PCT considers multiple possible partitions, it can represent an item at multiple granularities or scales simultaneously. Moreover, using standard probabilistic inference, it automatically determines the appropriate partitions for the particular set of items at hand, and the probabilities or weights that should be allocated to each partition. A consequence of these properties is that PCT accounts for experimental data that have previously motivated hierarchical models of VSTM, thereby providing an appealing alternative to hierarchical models with pre-specified, fixed structures. We explore both an exact implementation of PCT based on Dirichlet process mixture models, and approximate implementations based on Bayesian finite mixture models. We show that a previously proposed two-level hierarchical model can be seen as a special case of PCT with a single cluster. We show how a wide range of previously reported results on the organization of VSTM can be understood in terms of PCT. In particular, we find that, consistent with empirical evidence, PCT predicts biases in estimates of the feature values of individual items, and also predicts a novel form of dependence between estimates of the feature values of different items. We qualitatively confirm this last prediction in three novel experiments designed to directly measure biases and dependencies in subjects’ estimates. PROBABILISTIC CLUSTERING THEORY OF VSTM 3 A Probabilistic Clustering Theory of the Organization of Visual Short-Term Memory
A novel designated receiver based convertible multi-signcryption scheme

This paper presents a convertible multi-signcryption scheme based on the security of three computationally hard problems, namely computationally Diffie-Helmann problem (CDH), Discrete Logarithmic Problem (DLP) and Integer Factorization problem. The uniqueness of this scheme is, a message is signcrypted by more than two signcrypters and only a designated receiver can verify the signcrypted text. The proposed scheme is convertible as it can easily produce the ordinary signature without the co-operation from any one of the signcrypters. The scheme has been proved to be secure against active attacks, such as, chosen ciphertext attack and forgery attack. In case of a later dispute on repudiation, the recipient has the ability to convert the authenticated signcrypted text into an ordinary one for convincing anyone of the signer's dishonesty. This scheme is more efficient for multi-party applications since the size of the generated signcrypted text is independent of the number of total participating signcrypters. In addition, the computational costs for the verifier will not increase even after increasing the number of signcrypters. This scheme is secure enough to be applicable in practical areas where information security is desirable, such as, press releases, e-commerce applications and digital rights management.
Nonlinear tracking of underactuated surface vessels

A control law for tracking of underactuated surface vessels is presented. Optimal trajectories for surface vessels are given as combinations of arcs of circles and line segments. It is shown how optimal trajectories can be made smooth by a technique called splines, and how these trajectories can be stabilized by backstepping. The control law depends on the forward velocity of the surface vessel always being positive, which is not a major limitation for tracking purposes. It is also shown how this work can be part of a hierarchical hybrid system for control of surface vessels.
An improved deep learning-based approach for sentiment mining

The sentiment mining approaches can typically be divided into lexicon and machine learning approaches. Recently there are an increasing number of approaches which combine both to improve the performance when used separately. However, this still lacks contextual understanding which led to the introduction of deep learning approaches which allows for semantic compositionality over a sentiment treebank. This paper enhances the deep learning approach with semantic lexicon so that scores can be computed in-stead merely nominal classification. Besides, neutral classification is also improved. Results suggest that the approach outperforms its original.
A Simulation Framework for Distributed Super-Peer Topology Construction Using Network Coordinates

Scalability constitutes a property of particular interest when investigating peer-to-peer overlay behavior. Recent advances that improve scalability include super-peer infrastructures and network coordinates. To this end, we introduce a lightweight simulation framework that focuses on peer-to-peer topology construction supported by network coordinates and optionally, super-peers. One of the design goals has been a straightforward way to re-use simulation code in practical applications. We illustrate the utility of our simulation framework by means of a comprehensive application case study that compares different network coordinates generation mechanisms in the context of a distributed super-peer topology construction effort.
A new high-speed SAR ADC architecture

The design plan and HSPICE measurement of a high acquisition speed for a sample of 8-bit CMOS differential successive approximation register (SAR) Analog-to-digital converter (ADC) are presented. The operation of the conventional main switch-capacitor array is divided into two switch-capacitor arrays. Such that, one switched-capacitor array is used to define the four most-significant bits, while the other switched-capacitor array is used to refine the digital output by measuring the least four significant bits. Thus, the critical time constant, which is usually realized in a single conversion cycle, is divided into four conversion cycles (i.e. the time required to quantize the four most-significant bits). Thereby, our proposed ADC operates at 200MHz conversion cycle for a sample rate of 25 MS/sec. with power consumption of 3.7mW. The design is based on 0. 25µm CMOS TSMC technology and comprises three comparator circuits, sign-comparator and two switched-capacitor arrays' comparators.
Sorted label classifier chains for learning images with multi-label

In the real world, images always have several visual objects instead of only one, which makes it difficult for conventional object recognition methods to deal with them. In this paper, we present a topologically sorted classifier chain method for learning images with multi-label. We first provide a means of generating a topo-logically sorted label chain ordering by employing a topological sort algorithm and then apply the chain ordering to the classifier chain model proposed by [1] to classify multi-label images. Our method can capture the correlations between labels very effectively due to the sorted label chain ordering and the advantages brought by classifier chain method. We evaluate the proposed method on Corel dataset and demonstrate the micro and macro F1 measures superior to the state-of-the-art methods.
3D Reconstruction through Measure Based Image Selection

3D reconstruction is one of the research focus of computer vision and widely applied in various fields. The main steps of 3D reconstruction include image acquisition, image selection, feature point extraction and matching, calculation of camera parameters and 3D coordinates of the scene, and production of dense 3D scene models. Of all the steps, the image selection step is necessary and important. In this paper, a new 3D reconstruction algorithm consisting of four main steps is proposed and its main contribution is that the image selection step uses a new effective method. The new image selection method first uses structure-from-motion (SFM) algorithms to calculate the position and attitude of each camera, and then calculates the contributed value to 3D reconstruction of each image, finally selects images according to the contributed value of each image and their effects on contributed values of other images. Experimental results show that the 3D reconstruction algorithm proposed in this paper can reconstruct target scenes very well.
The Effect of a Social Experiment in Education

The impact of compulsory schooling laws as well as the abolition of early selection by ability reamin important issues in the educational debate. These issues were the focus of a major education reform in Sweden which was implemented in the 60s. The reform was preceded by a "social experiment" ub which only a proportion of municipalities received the new school system. We use survey data linked with tax records covering 10% of one of the cohorts, who were educated during the experimental period, to evaluate the impact of the reform on educational attainment and earnings. We find significant increases in the educational attainment of individuals from poorer backgrounds. In addition we estimate the returns to education for those affected by the reform. By exploiting the differential impact of the reform by county we are able, in some cases, to distinguish its direct effect on earnings from the effect it had by increasing the quantity of education. We find that the main source of increased earnings came from increasing educational attainment.
Leave-one-out Word Alignment without Garbage Collector Effects

Expectation-maximization algorithms, such as those implemented in GIZA++ pervade the field of unsupervised word alignment. However, these algorithms have a problem of over-fitting, leading to “garbage collector effects,” where rare words tend to be erroneously aligned to untranslated words. This paper proposes a leave-one-out expectationmaximization algorithm for unsupervised word alignment to address this problem. The proposed method excludes information derived from the alignment of a sentence pair from the alignment models used to align it. This prevents erroneous alignments within a sentence pair from supporting themselves. Experimental results on Chinese-English and Japanese-English corpora show that the F1, precision and recall of alignment were consistently increased by 5.0% ‐ 17.2%, and BLEU scores of end-to-end translation were raised by 0.03 ‐ 1.30. The proposed method also outperformed l0-normalized GIZA++ and Kneser-Ney smoothed GIZA++.
ARNE - A tool for Namend Entity Recognition from Arabic Text

In this paper, we study the problem of finding named entities in the Arabic text. For this task we present the development of our pipeline software for Arabic named entity recognition (ARNE), which includes tokenization, morphological analysis, Buckwalter transliteration, part of speech tagging and named entity recognition of person, location and organisation named entities. In our first attempt to recognize named entites, we have used a simple, fast and language independent gazetteer lookup approach. In our second attempt, we have used the morphological analysis provided by our pipeline to remove affixes and observed hence an improvement in our performance. The pipeline presented in this paper, can be used in future as a basis for a named entity recognition system that recognized named entites not only using gazetteers, but also making use of morphological information and part of speech tagging.
Cypriot Intangible Cultural Heritage : Digitizing Folk Dances

Cyprus has a long and rich history of dance tradition which unfortunately, year after year, tends to be forgotten; thus, it is our duty to help documenting and disseminating our dance heritage to the younger generations. In this work, we aim to preserve the Cypriot folk dance heritage, creating a state-of-the-art publicly accessible digital archive of folk dances. Our dance library, apart from the rare video materials that are commonly used to document dance performances, utilises three dimensional motion capture technologies to record and archive high quality motion data of expert dancers.
Some Theoretical Aspects of Base Control

This paper focuses on the implications of using the monetary base or bank reserves as an instrument to control a monetary aggregate. Following analysis of a series of theoretical models of increasing complexity, it is concluded that in a system with either institutional or structural lags base control may entail very sharp and possibly undamped oscillations of short-term interest rates. The shorter the time period over which the authorities choose to bring the monetary aggregate back to its target, the more volatile will be the movements of interest rates. Furthermore, there is an asymmetry in the U.S. institutional structure such that rigid implementation of the base control system will under certain circumstances lead to a decline in short-term interest rates to very low levels. The final section of the paper is devoted to the examination of the Canadian institutional structure emphasizing the differences between the U.S. and Canadian systems.