Miracle-dz/UniFAR-FTUs huggingface dataset:
A Generalized Class of Stationary Frame-Current Controllers for Grid-Connected AC–DC Converters

Within power systems, high-power pulsewidth-modulated ac-dc converters are used in flexible ac transmission systems controllers and for interfacing renewable energy sources to the grid. These converters traditionally employed PI controllers designed in the synchronous dq-frame with decoupling of d and q axes. Recently, stationary -frame proportional-resonant (PR) controllers have been proposed. Though both types of control are suitable for the regulation of three-phase converters, the PR controller displays steady-state and dynamic behavior that differs significantly from that of decoupled dq-frame controllers. This paper derives a stationary frame controller that is the exact equivalent of the commonly used synchronous frame controller with L decoupling. The new stationary frame “PRX2” controller consists of a proportional (P), a resonant (R), and two cross-coupling components. The PRX2 controller offers identical transient and steady-state performance and has the same frequency response as the decoupled synchronous frame PI controller. Unlike other stationary frame controllers containing resonant components, the PRX2 controller is unique because it contains a cross-coupling feedback component, which accounts for the behavior of the L decoupling branches present in synchronous frame controllers. It is shown that ignoring this decoupling component greatly increases the controller's sensitivity to frequency variation. Numerous stationary frame controllers, including the common PR controller, may be derived from the general PRX2 controller.
A Formal Look at Dependency Grammars and Phrase-Structure Grammars, with Special Consideration of Word-Order Phenomena

The central role of the lexicon in Meaning-Text Theory (MTT) and other dependency-based linguistic theories cannot be replicated in linguistic theories based on context-free grammars (CFGs). We describe Tree Adjoining Grammar (TAG) as a system that arises naturally in the process of lexicalizing CFGs. A TAG grammar can therefore be compared directly to an Meaning-Text Model (MTM). We illustrate this point by discussing the computational complexity of certain non-projective constructions, and suggest a way of incorporating locality of word-order definitions into the Surface-Syntactic Component of MTT.
Estimating the Selectivity of XML Path Expressions for Internet Scale Applications

Data on the Internet is increasingly presented in XML format. This enables novel applications that pose queries over “all the XML data on the Internet. ” Queries over XML data use path expressions to navigate through the structure of the data, and optimizing these queries requires estimating the selectivity of these path expressions. In this paper, we propose two techniques for estimating the selectivity of simple XML path expressions over complex large-scale XML data as would be handled by Internet-scale applications: path trees and Markov tables. Both techniques work by summarizing the structure of the XML data in a small amount of memory and using this summary for selectivity estimation. We experimentally demonstrate the accuracy of our proposed techniques, and explore the different situations that would favor one technique over the other. We also demonstrate that our proposed techniques are more accurate than the best previously known alternative.
Making Robots Proactive through Equilibrium Maintenance

In order to be proactive, robots should be capable of generating and selecting their own goals, and pursuing activities towards their achievement. Goal reasoning has focused on the former set of cognitive abilities, and automated planning on the latter. Despite the existence of robots that possess both capabilities, we lack a general understanding of how to combine goal generation and goal achievement. This paper introduces the notion of equilibrium maintenance as a contribution to this understanding. We provide formal evidence that equilibrium maintenance is conducive to proactive robots, and demonstrate our approach in a closed loop with a real robot in a smart home.
V4 neural network model for visual saliency and discriminative local representation of shapes

Visual area V4 lies in the middle of the ventral visual pathway in the primate brain. It is an intermediate stage in the visual processing for object discrimination. It plays an important role in the neural mechanism of visual attention and shape recognition. V4 neurons exhibit selectivity for salient features of contour conformation. In this paper, we propose a novel model of V4 neurons based on a multilayer neural network inspired by recent studies on V4. Its low-level layers consist of computational units simulating simple cells and complex cells in the primary visual cortex. These layers extract preliminary visual features including edges and orientations. The V4 computational units calculate the entropy of the extracted features as a measure of visual saliency. The salient features are then selected and encoded with a layer of Restricted Boltzmann Machine to generate an intermediate representation of object shapes. The model was evaluated in shape distinction, handwritten digits classification, feature detection, and feature matching experiments. The results demonstrate that this model generates discriminative local representation of object shapes. It provides clues to understand the high level representation of visual stimuli in the brain.
Chinese and North American college students' attitudes toward seeking professional psychological help : gender and ethnic comparisons

Through a multicultural perspective the counseling field in North America is adjusting itself to meet the demands of cultural diversity and a global context. The methods and systems of North American counseling have also begun to influence the mental health systems of other countries. As we change our own system and as other countries adopt and adapt similar systems, the attitudes that people have towards seeking help from professionals may guide outreach education, and also help clinicians understand the social, cognitive, and emotional process diverse clients go through when seeking help. This study examined differences in attitudes toward seeking help from mental health professionals for North American college students in California and Chinese college students in China. The focus of the study was on gender and cultural differences between groups. Results replicate consistent findings for gender group differences men being less likely than women to endorse positive help seeking attitudes. This study further adds to a new body of literature on cross national differences between Chinese and American college students, Chinese being less likely than American’s to endorse positive attitudes towards seeking help from professionals for mental health issues. Furthermore, both gender and national differences were consistent on overall attitude toward seeking help from mental health professionals as well as the specific attitudes of psychological openness, help-seeking propensity, and indifference to stigma with Chinese national group and male gender group endorsing less positive attitudes. The
Trigeminal Rhizotomy Performed with Modern Image-guided Linac: Case Report

Introduction: There are substantive articles published within the last ten years describing the use of linear accelerator (Linac) based treatment of trigeminal neuralgia (TN). In this paper, we report a single case of a patient with classic TN successfully treated by radiosurgical rhizotomy using a modern image-guided linear accelerator with a high-definition multileaf collimator and an optically guided targeting system. The measurement of absolute dose prior to treatment greatly facilitated our confidence with radiosurgical lesioning. Materials and Methods: This 54-year-old patient had a six-year history of lancinating pain involving the lower third of the right face which dramatically intensified in December of 2012, shortly after beginning Vincristine. A treatment plan was generated using a nine-field dynamic arc technique. A target volume was placed at 2-3 mm from the root entry zone measuring 6 x 3 x 3 mm. The PTV dose was prescribed to 60 Gy to the 80% isodose line. Initial verification of absolute dose using Gafchromic film resulted in an increase of the planned MU's by 7.5%. Remeasurement improved the absolute dose to within ±2 % of the calculated dose. Results: The patient was evaluated two weeks and three months following treatment. Her trigeminal pain was 80% resolved after two weeks and 100% at the three-month evaluation. She required none of her chronic pre-treatment medications and demonstrated no clinically detected sensory deficits. Conclusions: Linear accelerator-based radiosurgery utilizing intrafraction motion management and image guidance offers an acceptable alternative for the radiation treatment of trigeminal neuralgia patients if absolute doses are measured prior to treatment.
CAS-ICT at TREC 2005 Robust Track: Using Query Expansion and RankFusion to Improve Effectiveness and Robustness of Ad Hoc Information Retrieval

Our participation in this year's robust track aims to: (1) test how to improve the effectiveness of IR (according to MAP) using different retrieval methods with different local analysis-based query expansion methods; (2) test how to improve the retrieval robustness (according to gMAP) using RankFusion, a novel fusion technique proposed in our experiments. Our results show that although query expansion can improve the effectiveness of IR significantly, it hurts the robustness of IR seriously. However, with appropriate parameters setting, using RankFusion for merging multiple retrieval results can improve the robustness significantly while not harming the average precision too much or even increasing it in some cases.
Evaluating a Cube-Based User Interface for Exploring Music Collections

This paper studies the idea and feasibility of using a cube metaphor as a graphical user interface for exploring large music collections. In the implemented prototype, new music recommendations and related information are generated on the different faces of the cube based on the selected genre. In a user study with 40 participants, the cube was seen as an interesting and playful new concept that has great potential as a user interface. The prototype was considered to be inspiring and fun, and the participants appreciated to see extra information related to the currently playing track/artist. In the longer-term use, the prototype would have benefited from, e.g., text-based search functionality. Several other interesting ideas for the future development of the concept were also received.
A multiobjective approach for protein structure prediction using a steady-state genetic algorithm with phenotypic crowding

In this paper a multiobjective steady-state genetic algorithm with phenotypic crowding is proposed to the (free-modeling) protein structure prediction problem. In this algorithm, the parental replacement is carried out using a criterion associated with the individuals structural similarity, providing a better exploration of highly multimodal energy landscape and the concurrent identification of multiple minima. Classical force field terms, hydrogen bond potentials and a hydrophobic com-pactation term were selected to compose different objectives. Two multiobjective approaches containing two and three objectives were evaluated in predictions performed for a set of 45 proteins. Their results were compared with the ones obtained using a standard single-objective approach. Our results show that the multiobjective approach developed in this work proved to be quite promising in dealing with the PSP problem. In this sense, the algorithm with three objectives showed a better exploration of the search space and obtained significant improvements in the prediction of models for larger proteins and also for structures containing β-sheet secondary structures.
Bayesian Parameter Estimation and Variable Selection for Quantile Regression A thesis submitted for the degree of Doctor of Philosophy

The principal goal of this work is to provide efficient algorithms for implementing the Bayesian approach to quantile regression. There are two major obstacles to overcome in order to achieve this. Firstly, it is necessary to specify a suitable likelihood given that the frequentist approach generally avoids such specifications. Secondly, sampling methods are usually required as analytical expressions for posterior summaries are generally unavailable in closed form regardless of the prior used. The asymmetric Laplace (AL) likelihood is a popular choice and has a direct link to the frequentist procedure of minimising a weighted absolute value loss function that is known to yield the conditional quantile estimates. For any given prior, the Metropolis Hastings algorithm is always available to sample the posterior distribution. However, it requires the specification of a suitable proposal density, limiting it’s potential to be used more widely in applications. It is shown that the Bayesian quantile regression model with the AL likelihood can be converted into a normal regression model conditional on latent parameters. This makes it possible to use a Gibbs sampler on the augmented parameter space and thus avoids the need to choose proposal densities. Using this approach of introducing latent variables allows more complex Bayesian quantile regression models to be treated in much the same way. This is illustrated with examples varying from using robust priors and non parametric regression using splines to allowing model uncertainty in parameter estimation. This work is applied to comparing various measures of smoking and which measure is most suited to predicting low birthweight infants. This thesis also offers a short tutorial on the R functions that are used to produce the analysis. Declaration I declare that this thesis was composed by myself and that the work contained therein is my own, except where explicitly stated otherwise in the text.
Development of Student Assessment Survey for Northeastern University's University College Liberal Arts Program.

This practicum report describes the development of a student assessment survey to evaluate whether the liberal arts programs at Northeastern University (Massachusetts) were meeting the needs of the adult student population. Emphasis was on determining appropriate content and format design for a student assessment survey. A student outcomes survey was designed and was pilot tested with seven graduates of the liberal arts programs. Validation indicated the measure was useful in assessing the outcomes of graduates. The measure allowed respondents to evaluate the program's effectiveness in developing respondents' comprehension, accessibility, retrieval, and communication skills and appeared to be relevant to the educational experience and career choice of the adult student. It was also concluded that the mail survey was the best format. Appendices include names of individuals involved in the study, a list of survey objectives, and the survey instrument itself. (Contains 28 references.) (DB) ******************************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ********************************************************************************
Exploring the computing literature using temporal graph visualization

We present a system for the visualization of computing literature with an emphasis on collaboration patterns, interactions between related research specialties and the evolution of these characteristics through time. Our computing literature visualization system, has four major components: A mapping of bibliographical data to relational schema coupled with an RDBMS to store the relational data, an interactive GUI that allows queries and the dynamic construction of graphs, a temporal graph layout algorithm, and an interactive visualization tool. We use a novel technique for visualization of large graphs that evolve through time. Given a dynamic graph, the layout algorithm produces two-dimensional representations of each timeslice, while preserving the mental map of the graph from one slice to the next. A combined view, with all the timeslices can also be viewed and explored. For our analysis we use data from the Association of Computing Machinery's Digital Library of Scientific Literature which contains more than one hundred thousand research papers and authors. Our system can be found online at http://tgrip.cs. arizona.edu.
A new Content-Based Image Retrieval methodology based on Image Registration algorithms processed in Grid Computing

The Content-Based Image Retrieval (CBIR) has received great attention in the medical community because it is capable of retrieving similar images that have known pathologies. This work proposes a new methodology based on higher processing provided by the Grid Computing (GC) technology to achieve the CBIR using Registration algorithms. We evaluate the accuracy in retrieving images of the same plane, weighted and acquisition sequence related to a reference image. This works uses a heterogeneous image database and uses two anatomic regions (breast and head) as reference. The IR procedure uses Mean Square Metric (MSM) and Cross Correlation (CC). Both metrics showed a higher efficiency, MSM obtained 83% (breast) and 94% (head) precision average, the CC showed 81% (breast) and 98% (head). The higher computational cost related to the Image Registration were amortized by the GC, showing a potential low cost solution for secure data interchanging and integrating multiple hospitals and clinics.
Building chromosome-wide LD maps

SUMMARY BMapBuilder builds maps of pairwise linkage disequilibrium (LD) in either two or three dimensions. The optimized resolution allows for graphical display of LD for single nucleotide polymorphisms (SNPs) in a whole chromosome. AVAILABILITY The program is coded in Java, which runs on all relevant operating systems, including Windows, Mac and Unix/Linux, and is available from http://bios.ugr. es/BMapBuilder.
Impact Evaluation Methods in Public Finance

Recent years have seen a large expansion in the use of rigorous impact evaluation techniques. Increasingly, public administrations are collaborating with academic economists and other quantitative social scientists to apply such rigorous methods to the study of public finance. These developments allow for more reliable measurements of the effects of different policy options on the behavioral responses of citizens, firm owners, or public officials. They can help decision makers in tax administrations, public procurement offices, and other public agencies design programs informed by well-founded evidence. This article provides an introductory overview of the most frequently used impact evaluation methods. It is aimed at facilitating communication and collaboration between practitioners and academics by introducing key vocabulary and concepts used in rigorous impact evaluation methods, starting with randomized controlled trials and comparing them with other methods ranging from simple pre–post analysis to difference-in-differences, matching estimations, and regression discontinuity designs. DOI: https://doi.org/10.1177/1091142115614392 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-136558 Akzeptierte Version Originally published at: Pomeranz, Dina (2017). Impact evaluation methods in public economics. Public Finance Review, 45(1):10-43. DOI: https://doi.org/10.1177/1091142115614392
Media Abstraction Framework for the Pervasive Middleware PalCom

Pervasive middleware systems are important for enabling the configuration and coordination of the services provided by devices in pervasive environments. PalCom is an example of such a system that aims to enable interaction among pervasive services over heterogeneous networks. In this paper we discuss the need for providing networking-media independent messaging among pervasive devices, and identify the problem of designing a media abstraction framework for a pervasive middleware. We identify a number of design principles and features that need to be satisfied by such a framework. The most important of these principles is the separation of networking protocol aspects into abstraction representations, which are exposed in a unified format to upper layers of PalCom, and communication mechanisms which are hidden by media-specific implementations of our proposed media-abstraction framework for PalCom. Also, we explain our implementation and evaluation of that framework.
The design and analysis of microarray experiments using pooled samples for the study of quantitative traits

Microarrays can simultaneously measure the expression levels of many genes and are widely applied to study complex biological problems at the genetic level. To contain costs, instead of obtaining a microarray on each individual, mRNA from several subjects can be first pooled and then measured with a single array. mRNA pooling is also necessary when there is not enough mRNA from each subject. Several studies have investigated the impact of pooling mRNA on inferences about gene expression, but have typically modeled the process of pooling as if it occurred in some transformed scale. This assumption is unrealistic. We propose modeling the gene expression levels in a pool as a weighted average of mRNA expression of all individuals in the pool on the original measurement scale, where the weights correspond to individual sample contributions to the pool. Based on these improved statistical models, we develop the appropriate F statistics to test for differentially expressed genes. We present formulae to calculate the power of various statistical tests under different strategies for pooling mRNA and compare resulting power estimates to those that would be obtained by following the approach proposed by Kendziorski et al. (2003). We find that the Kendziorski estimate
Overtting Explained: a Case Study 1 Introduction

Over tting is a widely observed pathology of induction algorithms. Over tted models contain unnecessary structure that re ects nothing more than random variation in the data sample used to construct the model. Such models are less e cient to store and use than their correctly-sized counterparts. Using these models requires the collection of unnecessary data. Portions of over tted models are wrong and mislead users. Finally, over tting can reduce the accuracy of induced models on new data [8]. For induction algorithms that build decision trees [1, 7, 10], is a common approach to correct over tting. Pruning methods take an induced tree, examine individual subtrees, and remove those subtrees deemed to be unnecessary. Pruning methods primarily di er in the criterion used to judge subtrees. Many criteria have been proposed, including statistical signi cance tests [10], corrected error estimates [7], and minimum description length calculations [9]. In this paper, we bring together three threads of our research on over tting and decision tree pruning. First, we show that several common methods for pruning decision trees still allow over tting to occur. Second, we explain over tting in terms of statistical decision making with incorrect reference distributions. Third, we present a method that adjusts for using incorrect reference distributions, and we present an experiment that evaluates that method. These topics hold important implications for intelligent data analysis. Our analysis of over tting indicates that many existing techniques for building decision trees fail to consider the statistical implications of examining many possible subtrees. We show how a simple statistical adjustment can allow such systems to reason correctly in this speci c situation.