Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy of three‐charge black holes on a circle

We study phases of five‐dimensional three‐charge black holes with a circle in their transverse space. In particular, when the black hole is localized on the circle we compute the corrections to the metric and corresponding thermodynamics in the limit of small mass. When taking the near‐extremal limit, this gives the corrections to the finite entropy of the extremal three‐charge black hole as a function of the energy above extremality. For the partial extremal limit with two charges sent to infinity and one finite we show that the first correction to the entropy is in agreement with the microscopic entropy by taking into account that the number of branes shift as a consequence of the interactions across the transverse circle.
The International Reserves Glut: Is it for Real?

Monthly data from January 1985 to December 2004 are used to investigate reserves management in ten Asiatic and Latin American countries. Idiosyncratic explanatory variables enter cointegration relationships based on a stochastic buffer stock model, where a reserve variability measure is obtained via conditional variance approaches. International factors influence the cointegration residuals (representing the excess demands for reserves), which tend to co-move within and across geographical areas. Principal components analysis is implemented then to associate their common drivers with the US fed fund effective interest rate and real effective exchange rate. This two-step approach sheds light on some controversial aspects of reserves and exchange rate management in emerging markets such as “fear of floating” and mercantilist behavior. Our results suggest, contrary to common belief, that the size of recent excess reserves holdings is probably overstated.
The Politics of School/Community Relations

Frank Lutz and Carol Merz draw on the Dissatisfaction Theory of American Democracy, and show how that political theory can be practically applied in local school districts in order to build and maintain a strong school/community relationship. Five important case studies are presented, including the famous Robertsdale case. Chapters cover such areas as: conflict and change in modern school governance; the history of the American public schools and of the American community; superintendent/school board relationships; putting dissatisfaction to work; and applying dissatisfaction to the study of site politics and public relations.
TraceMatch: a computer vision technique for user input by tracing of animated controls

Recent works have explored the concept of movement correlation interfaces, in which moving objects can be selected by matching the movement of the input device to that of the desired object. Previous techniques relied on a single modality (e.g. gaze or mid-air gestures) and specific hardware to issue commands. TraceMatch is a computer vision technique that enables input by movement correlation while abstracting from any particular input modality. The technique relies only on a conventional webcam to enable users to produce matching gestures with any given body parts, even whilst holding objects. We describe an implementation of the technique for acquisition of orbiting targets, evaluate algorithm performance for different target sizes and frequencies, and demonstrate use of the technique for remote control of graphical as well as physical objects with different body parts.
Analysis based on chunk size and threads for file transfer using Message Transmission Optimization Mechanism (MTOM)

Web Services are used for communication between different applications over the Internet. So for sending any type of information different encryption techniques are used. But sending any type of information may not be suitable for all applications. To overcome this information in binary form can be sent as binary form is suitable to all domains. For sending binary data different techniques are used. One of the W3C recommendations is MTOM (Message Transmission Optimization Mechanism). The binary data is transferred without breaking XML Infoset. This paper provides architecture and analysis based on chunk size and number of thread for sending binary files as attachment using MTOM and the results are calculated depending upon the chunk sizes and threads. DIME (Direct Internet Message Encapsulation) also works on similar principle but the binary content is sent as an attachment outside the SOAP Envelope of the XML message. This makes your message secure but not the attachment. Using MTOM, web services automatically handles encoding of the data in web service message. The architecture described in this paper gives approximately 20-25% faster result with MTOM as compared to DIME.
The impact of financial development, economic growth and energy consumption on environmental degradation

Purpose – The purpose of this paper is to investigate the impact of financial development, economic growth and energy consumption on environment degradation for Indian economy by using the time series data for the period 1971-2011. Design/methodology/approach – The stationary properties of the variables are checked by ADF, DF-GLS, PP and Ng-Perron unit root tests. The long-run relationship is examined by implementing the Autoregressive Distributed Lag bounds testing approach to co-integration and error correction method (ECM) is applied to examine the short-run dynamics. The direction of the causality is checked by VECM framework and variance decomposition is used to predict exogenous shocks of the variables. Findings – The empirical evidence confirms the existence of long-run relationship among the variables. Financial development appears to increase environmental degradation in India. The main contributors to environmental degradation are: economic growth, energy consumption financial development and ur.
Learning From Demonstrations via Structured Prediction

Demonstrations from a teacher are invaluable to any student trying to learn a given behavior. Used correctly, demonstrations can speed up both human and machine learning by orders of magnitude. An important question, then, is how best to extract the knowledge encoded by the teacher in these demonstrations. In this paper, we present a method of learning from demonstrations that leverages some of the structured prediction techniques currently under investigation in the literature. We report encouraging results in Wargus, a real-time strategy game.
Individual Assessment: As Practiced in Industry and Consulting

Contents: E.A. Fleishman, J.N. Cleveland, Series Foreword. Preface. Part I: The Practice of Individual Assessment. Introduction to Individual Assessment. Job Modeling. Establishing the Assessment Protocol. Conducting the Assessment. Integration and Interpretation. Feedback, Reporting, and Program Evaluation. Part II: Additional Issues Affecting Practice. Legal, Professional, and Ethical Issues. Final Thoughts and Prognostications. Appendices: Primer of Measurement Terms and Concepts. Management and Executive Model. Supervisory Model. Administrative Model.
Dynamic velocity and yaw-rate control of the 6 WD skid-steering mobile robot RobuROC 6 using sliding mode technique

A robust dynamic feedback controller is designed and implemented, based on the dynamic model of the six-wheel skid-steering RobuROC6 robot, performing high speed turns. The control inputs are respectively the linear velocity and the yaw angle. The main object of this paper is to elaborate a sliding mode controller, proved to be robust enough to ignore the knowledge of the forces within the wheel-soil interaction, in the presence of sliding phenomena and ground level fluctuations. Finally, a 3D simulation is performed with an accurate physical engine to evaluate the efficiency of this designed control law.
Discovery of Diagnosis Pattern of Coronary Heart Disease with Qi Deficiency Syndrome by the T-Test-Based Adaboost Algorithm

Coronary heart disease (CHD) is still the leading cause of death for adults worldwide. Traditional Chinese medicine (TCM) has a history of 1000 years fighting against the disease and provides a complementary and alternative treatment to it. Syndrome is the core of TCM diagnosis and it is traditionally diagnosed based on macroscopic symptoms as well as tongue and pulse recognitions of patients. Establishment of the diagnosis method in the microcosmic level is an urgent and major problem in TCM. The aim of this study was to establish characteristic diagnosis pattern for CHD with Qi deficiency syndrome (QDS). Thirty-four biological parameters were detected in 52 patients having unstable angina (UA) with or without QDS. Then, we presented a novel data mining method, t-test-based Adaboost algorithm, to establish highest prediction accuracy with the least number of biological parameters for UA with QDS. We gained a pattern composed of five biological parameters that distinguishes UA with QDS patients from non-QDS patients. The diagnosis accuracy of the patterns could reach 84.5% based on a 3-fold cross validation technique. Moreover, we included 85 UA cases collected from hospitals located in the north and south of China to further verify the association between the pattern and QDS. The classification accuracy is 83.5%, which keeps consistent with the accuracy obtained by the cross-validation technique. The association between a symptom and the five biological parameters was established by the data mining method and it reached an accuracy of ∼80%. These results showed that the t-test-based Adaboost algorithm might be a powerful technique for diagnosing syndrome in TCM in the context of CHD.
Protective Role of Morin, a Flavonoid, against High Glucose Induced Oxidative Stress Mediated Apoptosis in Primary Rat Hepatocytes

Apoptosis is an early event of liver damage in diabetes and oxidative stress has been linked to accelerate the apoptosis in hepatocytes. Therefore, the compounds that can scavenge ROS may confer regulatory effects on high-glucose induced apoptosis. In the present study, primary rat hepatocytes were exposed to high concentration (40 mM) of glucose. At this concentration decreased cell viability and enhanced ROS generation was observed. Depleted antioxidant status of hepatocytes under high glucose stress was also observed as evident from transcriptional level and activities of antioxidant enzymes. Further, mitochondrial depolarisation was accompanied by the loss of mitochondrial integrity and altered expression of Bax and Bcl-2. Increased translocation of apoptotic proteins like AIF (Apoptosis inducing factor) & Endo-G (endonuclease-G) from its resident place mitochondria to nucleus was also observed. Cyt-c residing in the inter-membrane space of mitochondria also translocated to cytoplasm. These apoptotic proteins initiated caspase activation, DNA fragmentation, chromatin condensation, increased apoptotic DNA content in glucose treated hepatocytes, suggesting mitochondria mediated apoptotic mode of cell death. Morin, a dietary flavonoid from Psidium guajava was effective in increasing the cell viability and decreasing the ROS level. It maintained mitochondrial integrity, inhibited release of apoptotic proteins from mitochondria, prevented DNA fragmentation, chromatin condensation and hypodiploid DNA upon exposure to high glucose. This study confirms the capacity of dietary flavonoid Morin in regulating apoptosis induced by high glucose via mitochondrial mediated pathway through intervention of oxidative stress.
An overview of GOOD

GOOD is an acronym, standing for Graph-Oriented Object Database. GOOD is being developed as a joint research effort of Indiana University and the University of Antwerp. The main thrust behind the project is to indicate general concepts that are fundamental to any graph-oriented database user-interface. GOOD does not restrict its attention to well-considered topics such as ad-hoc query facilities, but wants to cover the full spectrum of database manipulations. The idea of graph-pattern matching as a uniform object manipulation primitive offers a uniform framework in which this can be accomplished.
Anonymous Password Based Authenticated Key Exchange with Sub-Linear Communication

In this paper we propose a new anonymous password-based authenticated key exchange protocol. The communication cost of our protocol is sub-linear O (√N), which improves a previous one of O (√N) cost, where N is the number of users in the system. We show that the session key is secure against an active adversary in the random oracle model and identity anonymity is secure against a semi-honest adversary in the standard model.
Low Damage SiNX Surface Passivation using Remote ICP-CVD for AlGaN / GaN HEMTs

1. Introduction The surface trap of AlGaN/GaN high electron mobility transistors (HEMTs) can cause current collapse phenomenon which is the most serious limiting factor of the device's output power at high frequency operation. The surface passivation is a key step to reduce surface state effects and the quality of surface passivation film is very important [1]. Silicon nitride (SiNX) is widely used for Al-GaN/GaN HEMTs passivation layer and is generally deposited by plasma enhanced chemical vapor deposition (PECVD) method. The RF power of in-ductively coupled plasma (ICP) source can easily crack N2 whose bonding energy is relatively strong. In ICP-CVD system, N2 gas can be used as a reactor instead of NH3 gas which is used generally at PECVD. Since nitrogen ion is supplied without hydrogen to deposit SiNX, the hydrogen concentration of SiNX film can be decreased [2]. The semiconductor surface and 2-dimensional electron gas (DEG) of AlGaN/GaN heterostructure can be damaged by plasma during deposition. Because such plasma damage can degrade the surface pas-sivation effects [3], 'remote-mode PECVD', in which the film deposition region is away from plasma generating region, is an attractive method [4] for AlGaN/GaN HEMT's passivation [5]. In this paper, the low plasma damage high quality SiNX surface passivation method was developed using remote ICP-CVD and it was applied to AlGaN/GaN HEMTs. And DC, RF, pulsed I-V and output power characteristics are measured to confirm surface passivation effects by the remote ICP-CVD.
On the high-density expansion for Euclidean random matrices

Diagrammatic techniques to compute perturbatively the spectral properties of Euclidean random matrices (ERM) in the high-density regime are introduced and discussed in detail. Such techniques are developed in two alternative and very different formulations of the mathematical problem and are shown to give identical results up to second order in the perturbative expansion. One method, based on writing the so-called resolvent function as a Taylor series, allows us to group the diagrams into a small number of topological classes, providing a simple way to determine the infrared (small momenta) behaviour of the theory up to third order, which is of interest for the comparison with experiments. The other method, which reformulates the problem as a field theory, can instead be used to study the infrared behaviour at any perturbative order.
The L 2 F Language Recognition System for Albayzin 2012 Evaluation

This document presents a description of INESC-ID’s Spoken Language Systems Laboratory (LF) systems submitted to the Albayzin 2012 Language Recognition evaluation. The submitted systems differ on the number of sub-systems selected for fusion and the back-end configuration. The basic set of sub-systems considered are four conventional phonotactic sub-systems based on n-gram modelling of phoneme sequences, four additional phonotactic sub-systems based on SVM discriminative modelling of expected phone counts extracted from lattices, and an i-vector based sub-system with linear generative classifiers. Similarly to the LF Albayzin 2010 system, individual language models for clean and noisy conditions have been trained for each target language of the Plenty of Training condition. The LF primary system exploits Gaussian back-ends for each sub-system and linear logistic regression fusion of k sub-systems, selected automatically following a non-exhaustive fast greedy search method to find the best (sub-optimal) combination. This search process and the determination of the back-end parameters is performed per evaluation condition. Additionally, three contrastive systems have been developed. Language detection results have been submitted for all the evaluation conditions for every system.
Model-driven development with optimization of non-functional constraints in sensor network

Current methods for sensor network programming lead developers to cope with not only high-level concerns such as application logic, quality of service, adaptability, reliability, but also low-level mechanism like ad-hoc routing and communication, resource management, data filtering, and aggregation. This makes the development of software expensive and error-prone, even for expert programmers. Model-driven development (MDD) allows designers to model their systems at different abstraction levels and thus reduces the complexity of the development task. However MDD does not help system architects to solve the problem of optimizing the trade-off between different constraints such as memory usage, latency, and power consumption in the application design process. This paper proposes an approach to address this issue by combining MDD with Evolutionary Algorithms (EA). In our approach various metamodels of the system to be developed are generated and evolved, the optimal model is selected in terms of evaluating the trade-off between different constraint criteria and performance value.
A Comparison of Constructive Induction with Diierent Types of New Attribute

This paper studies the eeects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, M-of-N, and X-of-N representations). To reduce eeects of other factors such as tree learning methods, new attribute search strategies, search starting points, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With diierent option settings, it can construct four diierent types of new attribute, but all other factors are xed. The study reveals that conjunctive and disjunctive representations have very similar performance in terms of prediction accuracy and theory complexity on a variety of concepts, even on DNF and CNF concepts that are usually thought to be suited only to one of the two kinds of representation. In addition, the study demonstrates that the stronger representation power of M-of-N than conjunction and disjunction and the stronger representation power of X-of-N than these three types of new attribute can be reeected in the performance of decision tree learning in terms of higher prediction accuracy and lower theory complexity.
An Examination of Faculty Satisfaction at Two-Year Higher Education Institutions

Part-time faculty members represent the majority of faculty at public two-year postsecondary institutions. Utilizing part-time faculty enables two-year institutions to control their instructional costs and maintain scheduling flexibility. However, part-time faculty are diverse in regards to their employment preference, some prefer part-time employment while others would prefer a full-time position. Since retaining and attracting qualified and experienced part-time faculty members is essential, it is imperative that their satisfaction be understood. This study uses the 2004 National Study of Postsecondary Faculty (NSOPF: 04) to study faculty satisfaction. Faculty was disaggregated according to employment preference into full-time, involuntary part-time, and voluntary part-time in order to study the structure of satisfaction for each group and the factors that influence the satisfaction for each group. The factors studied were perception of equity, partial inclusion, demographic differences and academic discipline. I found that the structure of faculty satisfaction and the influence of variables on faculty satisfaction differ among full-time, involuntary part-time, and voluntary part-time faculty.
Interpretable aesthetic features for affective image classification

Images can not only display contents themselves, but also convey emotions, e.g., excitement, sadness. Affective image classification is useful and hot in many fields such as computer vision and multimedia. Current researches usually consider the relationship model between images and emotions as a black box. They extract the traditional discursive visual features such as SIFT and wavelet textures, and use them directly upon various classification algorithms. However, these visual features are not interpretable, and people cannot know why such a set of features induce a particular emotion. And due to the highly subjective nature of images, the classification accuracies on these visual features are not satisfactory for a long time. We propose the interpretable aesthetic features to describe images inspired by art theories, which are intuitive, discriminative and easily understandable. Affective image classification based on these features can achieve higher accuracy, compared with the state-of-the-art. Specifically, the features can also intuitively explain why an image tends to convey a certain emotion. We also develop an emotion guided image gallery to demonstrate the proposed feature collection.
Efficient Topology Discovery for Multihop Wireless Sensor Networks

The overhead of periodic routing updates in proactive routing protocols has been always the main impediment in the way of using this kind of protocols in wireless ad hoc networks specially sensor networks. In this paper, we introduce a new topology discovery protocol based on the link state approach that uses node clustering and exploits bi-directional links in order to reduce this overhead. In this protocol, only clusterheads generate routing update packets as opposed to ordinary link state protocols where all nodes do so. In addition, clusterheads remove the redundancy associated with the double inclusion of bidirectional links in these messages. Simulations show over 50% reduction in the control overhead of topology discovery compared to a regular link state routing protocol.
Automatic Classification of Unstructured Blog Text

Automatic classification of blog entries is generally treated as a semi-supervised machine learning task, in which the blog entries are automatically assigned to one of a set of pre-defined classes based on the features extracted from their textual content. This paper attempts automatic classification of unstructured blog entries by following pre-processing steps like tokenization, stop-word elimination and stemming; statistical techniques for feature set extraction, and feature set enhancement using semantic resources followed by modeling using two alternative machine learning models—the na? ve Bayesian model and the artificial neural network model. Empirical evaluations indicate that this multi-step classification approach has resulted in good overall classification accuracy over unstructured blog text datasets with both machine learning model alternatives. However, the na? ve Bayesian classification model clearly out-performs the ANN based classification model when a smaller feature-set is available which is usually the case when a blog topic is recent and the number of training datasets available is restricted.
Syntax and interpretation

This paper describes a model of syntactical analysis. It shows first how a context-free grammar formalism can be modified in order to reduce the number of rules required by a natural language, and second, how this formalism can be extended to the handling of some non-context-free phenomena. The description of the algorithm is also given. The process of discontinuous constituents is performed by transformations.