Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal variability of infiltration rates under contrasting slope conditions in southeast Spain

Infiltration is the key process in the rainfall-runoff relationship. Little is known, however, about the seasonal and spatial variability, which is important for the behaviour of the slope surface geomorphological processes. The infiltration rates for contrasting slopes in southeast Spain have been measured by means of simulated rainfall and ponding. A northand a south-facing slope were selected to analyze effects of aspect. The results show that aspect as well as slope position and vegetation cover determine the steady state infiltration rates. Moreover, seasonal changes play an important role in the soil hydrology. During summer the infiltration rates are high, no runoff is observed on vegetation covered soils. On the bare surfaces, runoff was only 10% of the rainfall. During the wet season, especially in autumn, the infiltration rates are lower, and runoff coefficients equal to 0.3. For the vegetated surfaces, runoff is negligible. Infiltration measurements by means of ponding and simulated rainfall are both suitable to study the infiltration process. In the present study, the infiltration rates measured by cylinder infiltrometer were 8 times greater than by rainfall simulation at 55 mm h 1.
C V ] 1 M ay 2 01 7 Mind the Class Weight Bias : Weighted MaximumMean Discrepancy for Unsupervised Domain Adaptation

In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a discrepancy metric between the distributions of source and target domains. However, existing MMD-based domain adaptation methods generally ignore the changes of class prior distributions, i.e., class weight bias across domains. This remains an open problem but ubiquitous for domain adaptation, which can be caused by changes in sample selection criteria and application scenarios. We show that MMD cannot account for class weight bias and results in degraded domain adaptation performance. To address this issue, a weighted MMD model is proposed in this paper. Specifically, we introduce class-specific auxiliary weights into the original MMD for exploiting the class prior probability on source and target domains, whose challenge lies in the fact that the class label in target domain is unavailable. To account for it, our proposed weighted MMD model is defined by introducing an auxiliary weight for each class in the source domain, and a classification EM algorithm is suggested by alternating between assigning the pseudo-labels, estimating auxiliary weights and updating model parameters. Extensive experiments demonstrate the superiority of our weighted MMD over conventional MMD for domain adaptation.
The impact of foreign direct investment ( FDI ) on technological progress and productivity growth – – A case study of Chinese industries

This study adopts the principle of Malmquist productivity growth index calculation to analyze the Chinese inward foreign direct investment (IFDI) and outward foreign direct investment (OFDI) for the past 12 years and their technological spillovers to the Chinese economy. Especially, it provides a comparison among Chinese different industries and sectors. To this end, the study uses the two-stage (parametric and nonparametric) optimization approach to estimate the elasticity of China’s IFDI and OFDI that contribute to the development of domestic technological progress and productivity growth. Besides, in order to appropriately evaluate the effect of technological spillovers, this study also utilizes the component of technological progress of Malmquist productivity growth index to measure total factor productivity (TFP) instead of Solow residual. Finally, this study provides a comparative analysis of the effect of technological spillover across country’s different industries and sectors from both the inward foreign direct investment (IFDI) and the outward foreign direct investment (OFDI). As a result, it concludes that China has attracted a significant amount of high technologies through foreign direct investment (FDI), which plays a great role in promoting the development of China’s economy in recent decades. On the other side, a large-scale of outward foreign direct investment (OFDI) of Chinese enterprises is still in its infancy. Thus, the reverse technological spillover of OFDI to China is not obvious at a current stage or even has a negative impact.
Evaluating multimedia courseware: A study on the effectiveness of hotel English

Recent focus on the promotion of Taiwan tourism has resulted in the increase of ESP Tourism courses. With the benefits of using CALL, this study will focus on evaluating the effectiveness of the courseware “English for Hotel Staff”. This single group pre/post-test quasi-experimental study will focus on the usage of the courseware. The courseware is composed of ten units covering a particular area pertinent to a hotel’s daily business. This study will be able to help bring into light the actual effectiveness of courseware related pedagogical advances.
UserMap – an Exploitation of User-Specified XML-to-Relational Mapping Requirements and Related Problems ( Technical Report )

As the XML has become a standard for data representation, it is inevitable to propose and implement techniques for efficient managing of XML data. A natural alternative is to exploit features of (object-)relational database systems, i.e. to rely on their long theoretical and practical history. The main concern of such techniques is the choice of an appropriate XML-to-relational mapping strategy. In this paper we focus on enhancing of user-driven techniques which leave the mapping decisions in hands of users who specify their requirements using schema annotations. We describe our prototype implementation called UserMap which is able to exploit the annotations more deeply searching the user-specified “hints” in the rest of the schema and applies an adaptive method on the remaining schema fragments. Using a sample set of supported fixed mapping methods we discuss problems related to query evaluation for storage strategies generated by the system, in particular correction of the candidate set of annotations and related query translation. And finally, we describe the architecture of the whole system.
Value at Risk and Expected Shortfall : A Forecast Combination Approach

The recent nancial crisis that started in 2007 provides a challenge for improving or proposing new methods in risk management. We study two measures of market risk, Value at Risk and Expected Shortfall and show that various forecast combination methods provide accurate forecasts. These methods address the model uncertainty due to alternative volatility model speci cations and estimators, distributions of standardized returns and risk management methods. Our model space includes models from three alternative methods, parametric, ltered historical simulation and extreme value theory. We compare the out of sample performance of individual models and forecast combination methods using international stock market indices. Our empirical results show that individual models su¤er from the problem of model risk and that forecast combinations can provide more accurate predictions of risk. Keywords: Expected shortfall, forecast combinations, value at risk, volatility JEL Classi cations: C53, C52, C22 P.O. Box 537, CY 1678, Nicosia, Cyprus, e-mail: elena.andreou@ucy.ac. cy yAcknowledgements: We would like to thank the participants of the Computational and Financial Econometrics (CFE09) conference for helpful comments. We would like to thank Valentina Corradi for important remarks. We are also greatful to the Cyprus Research Promotion Foundation and the School of Economics and Management of the University of Cyprus for nancial support. The rst author also acknowledges support of the European Research Council under the European Community FP7/2007-2013 ERC grant 209116. zCorresponding author: P.O. Box 537, CY 1678, Nicosia, Cyprus, e-mail:c.kourouyiannis@ucy.ac. cy xP.O. Box 537, CY 1678, Nicosia, Cyprus, e-mail: andros@ucy.ac. cy.
A Visionary Approach to Listening: Determining The Role Of Vision In Auditory Scene Analysis

To recognize and understand the auditory environment, the listener must first separate sounds that arise from different sources and capture each event. This process is known as auditory scene analysis. The aim of this thesis is to investigate whether and how visual information can influence auditory scene analysis. The thesis consists of four chapters. Firstly, I reviewed the literature to give a clear framework about the impact of visual information on the analysis of complex acoustic environments. In chapter II, I examined psychophysically whether temporal coherence between auditory and visual stimuli was sufficient to promote auditory stream segregation in a mixture. I have found that listeners were better able to report brief deviants in an amplitude modulated target stream when a visual stimulus changed in size in a temporally coherent manner than when the visual stream was coherent with the non-target auditory stream. This work demonstrates that temporal coherence between auditory and visual features can influence the way people analyse an auditory scene. In chapter III, the integration of auditory and visual features in auditory cortex was examined by recording neuronal responses in awake and anaesthetised ferret auditory cortex in response to the modified stimuli used in Chapter II. I demonstrated that temporal coherence between auditory and visual stimuli enhances the neural representation of a sound and influences which sound a neuron represents in a sound mixture. Visual stimuli elicited reliable changes in the phase of the local field potential which provides mechanistic insight into this finding. Together these findings provide evidence that early cross modal integration underlies the behavioural effects in chapter II. Finally, in chapter IV, I investigated whether training can influence the ability of listeners to utilize visual cues for auditory stream analysis and showed that this ability improved by training listeners to detect auditory-visual temporal coherence.
DETECTION IN NATURAL IMAGES AND VIDEO USING MAXIMALLY STABLE EXTREMAL REGIONS AND TEXT CANDIDATE CLASSIFICATION

The performance of text detection and recognition in colour images is analyzed and compared to solve the technical challenges. Maximally stable Extremal Regions (MSERs) are extracted as character candidates by a fast and effective pruning algorithm. Text candidates are formed by grouping the character candidates using single link clustering algorithm. Here, clustering threshold and distance weights are known automatically by self training distance metric learning algorithm. Character classifier is used to estimate the probability of text candidates corresponding to non text ones. Text classifier is to identify the texts by eliminating text candidates having high non result reveals the better performance of the proposed work.
Knowledge Management System in Rental Company with Socialization , Externalization , Combination , Internalization Method Case Study : PT Surya Sudeco

Knowledge in an organization is an important thing to anticipate competition with business opponents. At this time, PT Surya Sudeco already have a way to transfer knowledge. However, to manage organizational knowledge, several problems found. First, no distinction found between obsolete forms and new forms that should be used in a corporate environment that will cause a repetitive job such as reprinting and collecting signatures. Second, urgent tasks could be delayed, due to employees’ permission / leave. Third, the occurrence of knowledge walkout. Fourth, centralized storage of knowledge-based assets is not found. Fifth, an occurrence of repeating mistakes, which should be avoided. The authors propose a prototype of knowledge management system (KMS) with nonaka model, socialization, externalization, combination, internalization (SECI) to convert knowledge from tacit to explicit and the other way around. The prototype method is used for this software development process. Sample collection techniques, questionnaires and interviews will be used for collecting data. This research has been tested by using user acceptance test (UAT), and the test results get a percentage of 82.5% which means the system is acceptable and as expected.
Polynomial Approximation of Signals Corrupted By Noise

The polynomial approximation is a very useful procedure for the optimization of the design of electronic equipment. This procedure is more complex when the function to be processed is perturbed by noise. Exploiting the connection between wavelets and polynomials and the well known abilities of the Discrete Wavelet Transform to denoise, in this paper is proposed a new method for the accurate polynomial approximation of a function, despite the noise that perturbs it. This method can be used to enhance the estimation of any polynomial function perturbed by noise. Examples, containing applications of the proposed method, are also presented. The good simulation results obtained recommend the use of the proposed method in any difficult estimation problem. Index Terms — polynomial approximation, Discrete Wavelet Transform, denoising, estimation, instantaneous frequency.
A Functional Approach for the Automatic Parallelization of Non-Associative Reductions via Semiring Algebra

Recent developments in computer architecture have yielded to a surge in the number of parallel devices, such as general purpose GPUs and application specific accelerators, making parallel programming more common than ever before.However, parallel programming poses many challenges to the programmer, as implementing correct solution requires expert knowledge of the target platform, in addition to advanced programming skills. A popular solution to this problem is the adoption of a pattern-based programming model, wherein the code is written using high-level transformations. While automatic parallelisation for some of these transformations is easily performed, this is not the case for others. In particular, the automatic parallelisation of Reduction is commonly supported only for a restricted class of well known associative operators: outside of these cases, the programmer must either resort to handwritten ad-hoc implementations or settle for using a sequential implementation. This document proposes a method for the automatic parallelisation of Reductions which, by taking advantage of properties of purely functional programs and the algebraic notion of a semiring, is capable of handling cases in which the reduction operator is not necessarily associative, but instead satisfies a much weaker set of properties. It then presents a series of additions to the Lift compiler implementing said methods, and finally proceeds to evaluate the performance of optimised Lift programs, demonstrating that the automatically parallelised code can outperform equivalent handwritten implementations, without requiring any explicit intervention from the programmer.
Banks and Corporate Sector in Russia - the Evolution and Current State of Relations in a Corporate Governance Context

The main goal of the paper is to analyse the evolution and the current state of banks’ role in corporate governance in Russia. The article is partly a review as it includes the analysis of results obtained from studies conducted by the main Russian research centres, and was additionally supplemented with a statistical data analysis. Banks played a significant role in the corporate sector development in the 1990s. Their strong position was undermined as a result of the financial crisis of 1998. In the first decade of the 21st century, the role of banks in the functioning of the corporate sector was restricted due to ownership redistribution, the development of corporate control market as well as the strengthening of the state’s position in the economy. Recent years, especially due to the difficult situation of the Russian economy since 2014, indicates an increase in the significance of the banking system.
Differentiation of Induced Pluripotent Stem Cells ( iPSCs ) into Retinal Ganglion Cells ( RGCs ) in Culture

Glaucoma is a common neurodegenerative disease that can cause blindness and occurs worldwide. Currently, lowering intraocular pressure is the only therapy available to protect retinal ganglion cells (RGCs). However, this therapy does not prevent RGC death in all patients. Therefore, new therapeutic approaches for glaucoma are urgently required, and neuroprotection of RGCs is a focus for many researchers. Optineurin (OPTN) is one of the normal tension glaucoma (NTG) relative genes, while mutant OPTN can form a characteristic aggregation, causing RGC death. Hence, elucidation of the mechanism of OPTN aggregation might provide a clue to help understand RGC death. To examine whether non-mutant OPTN could also aggregate, we pharmacologically induced some glaucoma-related stresses, such as endoplasmic reticulum (ER) stress, glutamate toxicity, activation of TNF-α signaling, mitochondrial dysfunction, and autophagic flux impairment. Our results showed that ER stress, TNF-α signaling, and autophagic flux are involved in OPTN aggregation. Furthermore, our data indicated that increased ER stress, activation of TNF-α signaling, and impaired autophagic flux induce OPTN aggregation, suggesting that OPTN aggregation might be an important therapeutic target not only for familial NTG with mutated OPTN but also for patients with glaucoma more generally.
Direct vs Indirect Aggression Tactics as a Function of Domin-Specific Self-Esteem

Although much theory and research has focused on the causes of aggression in response to provocation, including the role of domain-specific self-esteem, most studies either fail to distinguish between aggression tactics or focus exclusively on direct aggression. Our research is designed to address these deficiencies and examine the degree to which different domains of self-esteem lead people to preferentially employ indirect rather than direct aggression tactics. In an online correlational study, we found that the degree to which participants favored a indirect over direct aggression in response to provocation was (1) greater among women than men; (2) inversely correlated with measures of competitive self­ esteem (self-perceived superiority, mate value, and dominance); but (3) uncorrelated with social-inclusion or global self-esteem. In a second online study, an experimental manipulation of mate value had the predicted effect on women, but not on men; in addition, correlational analyses replicated the main findings from Study 1. Study 3, which was identical to Study 2, except for the use of an undergraduate sample, produced mixed results that replicated som e but not all of the results of Study 2. We conclude that particular domains of self­ esteem may calibrate one’s aggression tactic preference, but perhaps differently in men and women. TABLE OF CONTENTS Acknowledgements ii List of Tables iii Chapter
Technological Forecasting & Social Change R & D investments and high-tech fi rms ' stock return volatility

Article history: Received 30 September 2013 Accepted 30 September 2013 Available online 30 October 2013 The empirical evidence suggests that firms in high-tech industries exhibit high stock return volatility. In this paper, we conceive of the R&D investment intensity as a possible explanation for the stock volatility behavior in these industries. We suggest that R&D activities generate information asymmetry about the prospects of the firm and make its stock riskier. Relying on Panel data models, we investigate this relationship for French high-tech firms. We find out a strong positive relationship between stock return volatility and R&D investment intensity. This finding suggests that R&D intensive firms should implement an efficient information disclosure policy to reduce information asymmetry and to avoid excessive stock return volatility. © 2013 Elsevier Inc. All rights reserved.
Making global value chains more inclusive in the MED region: The role of MNE-SME linkages

This background note has been prepared for the regional workshop “Business linkages in the MED region: Policies and tools”, taking place on 17-18 April 2018 in Beirut, Lebanon. This workshop is part of the EU-OECD Programme on Promoting Investment in the Mediterranean launched in October 2016, which aims at supporting the implementation of sound investment policies and effective institutions in the Southern Mediterranean region (MED region).
Framework for Face Recognition Evaluation using Synthetic Data

Making datasets for evaluation of face recognition algorithms has always been costly and relatively complex. However it is a necessary evil in the path to improving face detection. Pose, lighting, expression are some of the common variables analyzed to show there effect on recognition algorithms. Effects of weather and other atmospheric distortions caused by being outdoors are area’s of research growing in popularity. However they are extremely hard to study without significant amounts of data. Gathering data at distance and/or outside increases dataset collecting complexity even more. We propose the use of synthetic data as a solution to help fill the void in data in these newer research area’s. This thesis will describe a robust framework of how to create and use synthetic models for the purpose of evaluation of face recognition algorithms and systems. We show that synthetic models provide a valid alternative to using real people and also allow for data to be collected in area’s that otherwise is not possible. Examples of how to validate, display, and capture models outdoors at distance is also presented within the thesis.
Optimal Power Flow by a Primal-Dual Interior Point Method

One of the most important requirements in power system operation, control and planning in energy management system (EMS) of modern power system control centers is optimal power flow (OPF). Optimal power flow, which is characterized as a difficult optimization problem, involves the optimization of an objective function that can take various forms, for example, minimization of total production cost, and minimization of total loss in transmission networks, subject to a set of physical and operating constraints such as generation and load balance, bus voltage limits, power flow equations, and active and reactive power limits. This paper presents a method to solve an optimal power flow problem with the objective of the minimization of total production cost based on a primal-dual interior point theory. The method has been implemented in a computer program which can effectively solve a non-linear objective function with linear constrains and has a good convergence characteristic. The efficiency of the methodology applied to the optimal power flow problem is demonstrated by the IEEE 14-, 30-, 57and 118-bus test systems. Keywords— IEEE test systems, KKT conditions , optimal-dual interior point method, optimal power flow