Miracle-dz/UniFAR-FTUs huggingface dataset:
The Determinants of Public Education Expenditures: Longer-Run Evidence from the States.

rTTie prominence of public education finance reform in public policy A circles over the last 25 years has motivated a growing literature in which researchers attempt to assess the consequences of specific education finance reforms. In order to ascertain the longer-run consequences associated with an education finance reform, however, one must also be able to control for factors associated with other longrun changes that may be occurring simultaneously, such as changes in income, students and demographics. The objective of this paper is to determine how long-run changes in these factors affect resources devoted to K-12 education. We do this by examining the time series evidence for a panel data set for the U.S. states over the period 1 9701990.
Machine learning method with compensation distance technique for gear fault detection

In this paper, a condition monitoring and fault diagnosis method for rotating machineries using machine learning technologies including artificial neural network (ANNs) and support vector machine (SVMs) is described. The vibration signal is acquired from gearbox used in local power generation industry for analysis of potential defects. Wavelet packet transforms (WPT) and time domains statistical are used to extraction features, and the compensation distance evaluation technique (CDET) is applied to select optimal feature via sensitivities ranking. A comparative experiment study of the efficiency of ANN and SVM in predication of failure is carried out. The results reveal that the proposed feature selection and machine learning algorithms could be effectively used automatic diagnosis of gear faults.
Physical Science Teachers’ Insights into Pedagogical and Content Knowledge, andthe Nature of Support of an ACE In-service Programme

This study was designed to gain insights into physical science teacherstudents’ pedagogical knowledge, understanding of science concepts and the nature of support that they received during an in-service developmental programme. Understanding the experiences and learning gained during the programme can inform the design of future courses. The study is located within an interpretive paradigm that employs a qualitative approach to data collection and analysis. Participants (n = 156) were selected by convenience sampling from teachers on an in-service Advanced Certificate in Education programme. Data were collected towards the end of the proramme through a questionnaire administered to 211 in-service teachers and eight teachers were interviewed. The study identified three critical aspects gained during the programme: content knowledge, teaching strategies and confidence. The findings also reinforce the need for institutions of higher learning to design programmes that provide the appropriate level of support: material, emotional or both for all teachers on in-service programmes.
Application of Kernel Method on Face Feature Extraction

Principle component analysis (PCA) and linear discriminant analysis(LDA) are effective methods for feature extraction, and they were successfully utilized for face recognition. But face image data distribution in practice is highly complex because of illumination, facial expression and pose variations. So it is necessary to extract nonlinear features for face recognition. Both PCA and LDA are performed by only using the second-order statistics among image pixels, and not sensitive to high order statistics in the data. In this paper, the kernel trick is used firstly to project the original samples into an implicit space called feature space by nonlinear kernel mapping, then the high order relations are extracted in high dimensional space. The experiment shows that the nonlinear feature extraction methods are effective and outperform the traditional methods.
Automatic Image Annotation Using GHSOM

Due to the improvement of digital image technology and increasing amount of digital image data, the issue of automatic image annotation technology and applications becomes more and more important. In order to retrieval image efficiently, it is important to extract and represent the semantics of images. Traditional image semantics extraction and representation schemes were commonly divided into two categories, namely visual features and text annotations. However, visual feature scheme are difficult to extract and are often semantically inconsistent. On the other hand, the image semantics can be well represented by text annotations. It is also easier to retrieve images according to their annotations. The problem is annotating images are always time-consuming and requiring lots of human effort. In this thesis, we try to develop an automatic image annotation method. We adopted the Growing Hierarchical Self-Organizing Map (GHSOM) to help us discover the concealed relations between image data and annotation data, and annotate image according to such relations. We first applied GHSOM to cluster and generate hierarchies for images and annotations individually. We then proposed a hierarchical mapping method to discover the relations between image clusters and annotation clusters. New images can be annotated according to such relations. We conducted experiments using the proposed method on an image dataset and obtained promising result, which showed that our method could be plausible.
Genome-Scale NAD(H/+) Availability Patterns as a Differentiating Feature between Saccharomyces cerevisiae and Scheffersomyces stipitis in Relation to Fermentative Metabolism

Scheffersomyces stipitis is a yeast able to ferment pentoses to ethanol, unlike Saccharomyces cerevisiae, it does not present the so-called overflow phenomenon. Metabolic features characterizing the presence or not of this phenomenon have not been fully elucidated. This work proposes that genome-scale metabolic response to variations in NAD(H/+) availability characterizes fermentative behavior in both yeasts. Thus, differentiating features in S. stipitis and S. cerevisiae were determined analyzing growth sensitivity response to changes in available reducing capacity in relation to ethanol production capacity and overall metabolic flux span. Using genome-scale constraint-based metabolic models, phenotypic phase planes and shadow price analyses, an excess of available reducing capacity for growth was found in S. cerevisiae at every metabolic phenotype where growth is limited by oxygen uptake, while in S. stipitis this was observed only for a subset of those phenotypes. Moreover, by using flux variability analysis, an increased metabolic flux span was found in S. cerevisiae at growth limited by oxygen uptake, while in S. stipitis flux span was invariant. Therefore, each yeast can be characterized by a significantly different metabolic response and flux span when growth is limited by oxygen uptake, both features suggesting a higher metabolic flexibility in S. cerevisiae. By applying an optimization-based approach on the genome-scale models, three single reaction deletions were found to generate in S. stipitis the reducing capacity availability pattern found in S. cerevisiae, two of them correspond to reactions involved in the overflow phenomenon. These results show a close relationship between the growth sensitivity response given by the metabolic network and fermentative behavior.
Turbo recognition: a statistical approach to layout analysis

Turbo recognition (TR) is a communication theory approach to the analysis of rectangular layouts, in the spirit of Document Image Decoding. The TR algorithm, inspired by turbo decoding, is based on a generative model of image production, in which two grammars are used simultaneously to describe structure in orthogonal (horizontal and vertical directions. This enables TR to strictly embody non-local constraints that cannot be taken into account by local statistical methods. This basis in finite state grammars also allows TR to be quickly retargetable to new domains. We illustrate some of the capabilities of TR with two examples involving realistic images. While TR, like turbo decoding, is not guaranteed to recover the statistically optimal solution, we present an experiment that demonstrates its ability to produce optimal or near-optimal results on a simple yet nontrivial example, the recovery of a filled rectangle in the midst of noise. Unlike methods such as stochastic context free grammars and exhaustive search, which are often intractable beyond small images, turbo recognition scales linearly with image size, suggesting TR as an efficient yet near-optimal approach to statistical layout analysis.
Fast and accurate multigrid solution of Poissons equation using diagonally oriented grids

We solve Poisson's equation using new multigrid algorithms that converge rapidly. The novel feature of the 2D and 3D algorithms are the use of extra diagonal grids in the multigrid hierarchy for a much richer and effective communication between the levels of the multigrid. Numerical experiments solving Poisson's equation in the unit square and unit cube show simple versions of the proposed algorithms are up to twice as fast as correspondingly simple multigrid iterations on a conventional hierarchy of grids.
Empirical Software Linguistics: An Investigation of Code Reviews, Recommendations and Faults

Communication is fundamental to human nature and underlies many of its successes as a species. In recent decades, the adoption of increasingly abstract software languages has supported many advances in computer science and software engineering. Although in many regards distinct from natural language, software language has proven surprisingly similar to it as well and has been studied successfully using natural language models. Recent studies have investigated this "naturalness" property of software in relation to a variety of applications including code completion, fault detection, and language migration. In this thesis, based on three research papers, we investigate three main aspects of software naturalness. Firstly, we investigate the relation between perceived (un)naturalness of source code (according to the statistical model) and the reaction to such code by software developers. In open-source projects, we find that those contributions which contain code that (statistically speaking) fits in less well are also subject to more scrutiny from reviewers and are rejected more often. Secondly, we investigate an application of highly predictable code: code completion. Previous work had evaluated the performance of language models in this application in isolation; we compare the language model approach to a commonly used code completion engine. We find that it compares favorably, achieving substantially higher accuracy scores. In particular, a combination of the two approaches yielded the best results. Finally, we investigate instances of highly unpredictable code in order to automatically detect faults. We find that buggy lines of code are substantially less predictable, becoming more predictable after a bug is fixed. Our bug detection approach yields performance comparable to popular static bug finders, such as FindBugs and PMD. Our results further confirm that statistical (ir)regularity of source code from a natural language perspectives reflects real-world phenomena.
Appearance modeling under geometric context

We propose a unified framework based on a general definition of geometric transform (GeT) for modeling appearance. GeT represents the appearance by applying designed functionals over certain geometric sets. We show that image warping, Radon transform, trace transform, etc. are special cases of our definition. Moreover, three different types of GeTs are designed to handle deformation, articulation and occlusion and applied to fingerprinting the appearance inside a contour. They include the contour-driven GeT, the feature curve based GeT and selecting functionals to model the appearance inside the convex hull of the contour. A multi-resolution representation that combines both shape and appearance information is also proposed. We apply our approach to image synthesis and object recognition. The proposed approach produces promising results when applied to fingerprinting the appearance of human and body parts despite the challenges due to articulated motion and deformations
Stem Cells and Gene Therapy for Cartilage Repair

Cartilage defects represent a common problem in orthopaedic practice. Predisposing factors include traumas, inflammatory conditions, and biomechanics alterations. Conservative management of cartilage defects often fails, and patients with this lesions may need surgical intervention. Several treatment strategies have been proposed, although only surgery has been proved to be predictably effective. Usually, in focal cartilage defects without a stable fibrocartilaginous repair tissue formed, surgeons try to promote a natural fibrocartilaginous response by using marrow stimulating techniques, such as microfracture, abrasion arthroplasty, and Pridie drilling, with the aim of reducing swelling and pain and improving joint function of the patients. These procedures have demonstrated to be clinically useful and are usually considered as first-line treatment for focal cartilage defects. However, fibrocartilage presents inferior mechanical and biochemical properties compared to normal hyaline articular cartilage, characterized by poor organization, significant amounts of collagen type I, and an increased susceptibility to injury, which ultimately leads to premature osteoarthritis (OA). Therefore, the aim of future therapeutic strategies for articular cartilage regeneration is to obtain a hyaline-like cartilage repair tissue by transplantation of tissues or cells. Further studies are required to clarify the role of gene therapy and mesenchimal stem cells for management of cartilage lesions.
A Distributed Data-Gathering Protocol Using AUV in Underwater Sensor Networks

In this paper, we propose a distributed data-gathering scheme using an autonomous underwater vehicle (AUV) working as a mobile sink to gather data from a randomly distributed underwater sensor network where sensor nodes are clustered around several cluster headers. Unlike conventional data-gathering schemes where the AUV visits either every node or every cluster header, the proposed scheme allows the AUV to visit some selected nodes named path-nodes in a way that reduces the overall transmission power of the sensor nodes. Monte Carlo simulations are performed to investigate the performance of the proposed scheme compared with several preexisting techniques employing the AUV in terms of total amount of energy consumption, standard deviation of each node’s energy consumption, latency to gather data at a sink, and controlling overhead. Simulation results show that the proposed scheme not only reduces the total energy consumption but also distributes the energy consumption more uniformly over the network, thereby increasing the lifetime of the network.
Stimulus intensity affects the latency but not the amplitude of the N2pc

The N2pc component of the event-related potential (ERP) is an index of visual–spatial attention. It is not clear whether the N2pc reflects pure top-down attentional activity or an interaction of top-down activity with bottom-up sensory activity. Here, we manipulated stimulus intensity of the items composing the target display. Although the amplitude of the P1 component increased monotonically with increasing stimulus intensity, the amplitude of the N2pc did not vary with stimulus intensity. Instead, the onset latency of the N2pc was delayed for weaker stimuli, suggesting that the strength of the selection cue (target color) influenced the moment at which attention was deployed. The results reveal one way in which early sensory ERP amplitude differences are converted into later latency differences.
Is the aerobic power a delimitating factor for performance on canoe slalom? An analysis of Olympic Slovak canoe slalom medalists and non-Olympics since Beijing 2008 to Rio 2016

Information regarding the aerobic power on canoe slalom performance is scares. Moreover, the comparison of maximum oxygen uptake (VO2max) via specific and non-specific ergometer for slalom kayakers may improve training prescription and controlling over Olympic cycles. Lastly, it is still unknown to what extent the VO2max delimitate the high performance in this sport. To test this perspective, a highly qualified sample is desired. In overall statistics, Slovakian athletes gathered 14 Olympic medals over the last sixteen years. Therefore, the main aim of this study is to compare the aerobic power of Olympic medallists and Non-Olympic Slovakian kayakers via specific and non-specific evaluations from Beijing 2008 to Rio 2016 Summer Olympic Games. Forty-two male canoe slalom athletes from Slovak national team were evaluated between the years 2006 and 2016. Slovakian athletes were tested for specific (i.e. paddling ergometer) and non-specific (i.e. treadmill) incremental protocols for VO2max determination. Over the last three Summer Olympic Games, the VO2max of Slovakian Olympic medallists was consistently lower than most of the Slovakian team. Moreover, disregarding the medallist characteristic or the moment, Slovakian kayakers presented higher VO2max on treadmill (57.7±6.8 mL.kg-1.min-1) when compared to paddle ergometer (46.9±6.5 mL.kg-1.min-1) (p=0.000; ES=1.6). Based on the collected data over the last ten years, we suggest that although aerobic power may play a relevant and indirect role on performance of slalom kayakers, does not delimitate the high performance in this sport.
Iterative power control to mitigate the interference between D2D and CDMA uplink services

To cope with data communication explosion, the alternative, “D2D communication” which enables mobile devices to communicate each other without base station is studied. Even though D2D communication has a lot of benefits (infra-less communication providing service at a lower cost and etc), the interference induced by D2D links is inevitable problem where D2D and cellular links share same radio channels. On the assumption that the D2D link uses the same uplink cellular channel in a CDMA system, this paper proposes a heuristic and iterative power control algorithm which approaches to the optimal solution maximizing the total uplink cellular throughput.
A Cross-Layer Routing Protocol for Mobile Ad Hoc Networks Based on Minimum Interference Duration

Cross-layer design of routing protocol for mobile ad hoc networks (MANETs) has become a hot research topic in recent years. By integrating physical layer, MAC layer and network layer together for cross-layer design, a cross-layer routing protocol called MIR is proposed in this paper. Via predicting the duration of the interference imposed by the neighbors at every hop along the route, a new routing metric is presented which guarantees that the established routes will not break frequently while having the minimum interference. Simulation results show that MIR can significantly improve the network performance.
Agency Costs, Risk Management, and Capital Structure

The joint determination of capital structure and investment risk is examined. Optimal capital structure reflects both the tax advantages of debt less default costs (Modigliani and Miller (1958, 1963)), and the agency costs resulting from asset substitution (Jensen and Meckling (1976)). Agency costs restrict leverage and debt maturity and increase yield spreads, but their importance is small for the range of environments considered. Risk management is also examined. Hedging permits greater leverage. Even when a firm cannot precommit to hedging, it will still do so. Surprisingly, hedging benefits often are greater when agency costs are low. Copyright The American Finance Association 1998.
Cell Broadband Engine processor: Design and implementation

The Cell Broadband Engine™ (Cell/B.E.) processor was developed by Sony, Toshiba, and IBM engineers to deliver a high-speed, high-performance, multicore processor that brings supercomputer performance via a custom system-on-a-chip (SoC) implementation. To achieve its goals, the Cell/B.E. processor uses an innovative architecture, new circuit design styles, and hierarchical integration and verification techniques. The Cell/B.E. processor design point was also targeted at high-volume manufacturing. To meet high-volume manufacturing requirements, the chip was designed so that it could be completely tested in less than 26 seconds. In addition to the above items, the Cell/B.E. processor was designed with the "triple design constraints" of maximizing performance while minimizing area and power consumed. The initial application was targeted at real-time systems that require high-speed data movement for both on-chip and off-chip transfers. This application also required very high speed compute and real-time response processes.
Proxy Contests in an Era of Increasing Shareholder Power: Forget Issuer Proxy Access and Focus on E-Proxy

The current debate over shareholder access to the issuer's proxy for the purpose of making director nomination is both overstated in its importance and misses the serious issue in question. The Securities Exchange Commission's new e-proxy rules, which permit reliance on proxy materials posted on a website, should substantially reduce the production and distribution cost differences between a meaningful contest waged via issuer proxy access and a freestanding proxy solicitation. The serious question relates to the appropriate disclosure required of a shareholder nominator no matter which avenue is used. Institutional investors and other shareholder activists should focus their energies on working through the mechanics of waging short-slate proxy contests using e-proxy solicitations. Activist institutions need to prepare the disclosure package required under the existing proxy rules. Such disclosure may be tested (and refined) through litigation, but a standardized package should emerge relatively quickly that the institution could use in proxy contests without a control motive. Institutional investors need to become facile with the e-proxy model (including coordinating a practice for opting-in to web-access) and should appreciate the extent to which proxy advisory services will do much of the actual solicitation work. If institutions are unwilling to make the relatively modest investment to master the mechanics of e-proxy contest, both in their initiation as well as voting in support of them, then their role in corporate governance will necessarily be limited.
Modele de constructie a portofoliilor de instrumente financiare

The construction of any portfolio has as a first step identifying financial instruments which are to be traded, as well as the moment opportune for entering the stock market (the optimal timing of trading). This is the stage in which it's made the market analysis with the aim of collecting necessary information on the process of making the investment decision.
Music emotion recognition with adaptive aggregation of Gaussian process regressors

This paper describes a novel method for estimating the emotions elicited by a piece of music from its acoustic signals. Previous research in this field has centered on finding effective acoustic features and regression methods to relate features to emotions. The state-of-the-art method is based on a multi-stage regression, which aggregates the results from different regressors trained with training data. However, after training, the aggregation happens in a fixed way and cannot be adapted to acoustic signals with different musical properties. We propose a method that adapts the aggregation by taking into account new acoustic signal inputs. Since we cannot know the emotions elicited by new inputs beforehand, we need a way of adapting the aggregation weights. We do so by exploiting the deviation observed in the training data using Gaussian process regressions. We confirmed with an experiment comparing different aggregation approaches that our adaptive aggregation is effective in improving recognition accuracy.