Miracle-dz/UniFAR-FTUs huggingface dataset:
Threshold Effects of Budgetary Policy on Economic Growth in Cemac

CEMAC countries, with an aim of coordinating their budget policies, established within their union some rules of economic policy which seem to be restrictive for an effective budget policy and thus sustainable in the long run. Indeed, in the economic theory, several arguments were advanced to justify the effectiveness of budget policy with a view to stabilization. For some, expansionist budget policies can have favorable effects on the economic activity, while for others, this kind of policy does not have any effect and can even be harmful for the activity; it would be then in this case more adapted to follow a contractionnist policy. Nevertheless, these two effects can coexist on the activity, thus putting forward the nonlinear character of budget policy on the activity. The objective of this study is thus to appreciate the nature of the relation between budget policy and the activity in CEMAC by taking of account the existence of possible nonlinear effects of the budget policy.For that, we initially test the stationnarity of the debt on GDP ratio of the countries in the zone over the period 1970-2006 and thus conclude that, budget policies of the countries in CEMAC zone were not sustainable and thus ineffective. In the second time, the methodology of endogenous thresholds of Hansen (1996, 1999) made it possible to determine a rate of debt of 79% of the GDP, below which the expansionist policies have favorable effects on the activity, and with the top of which contractionnists policies become effective.
Disk storage management for LHCb based on Data Popularity estimator

This paper presents an algorithm providing recommendations for optimizing the LHCb data storage. The LHCb data storage system is a hybrid system. All datasets are kept as archives on magnetic tapes. The most popular datasets are kept on disks. The algorithm takes the dataset usage history and metadata (size, type, configuration etc.) to generate a recommendation report. This article presents how we use machine learning algorithms to predict future data popularity. Using these predictions it is possible to estimate which datasets should be removed from disk. We use regression algorithms and time series analysis to find the optimal number of replicas for datasets that are kept on disk. Based on the data popularity and the number of replicas optimization, the algorithm minimizes a loss function to find the optimal data distribution. The loss function represents all requirements for data distribution in the data storage system. We demonstrate how our algorithm helps to save disk space and to reduce waiting times for jobs using this data.
Financial Innovation and Arbitrage in the Spanish Bond Market

This paper empirically tests the level of sequential arbitrage in the Spanish bond market. The test is implemented by drawing on default free and option free pure discount and coupon bonds issued by the Spanish government. This fact seems to be a clear distinction between this paper and the related empirical literature since there are no risky bonds or derivative securities involved in our analysis. As a consequence, the sequential arbitrage absence is just equivalent to the existence of a term structure of interest rates matching the whole set of bond prices as provided by The Bank of Spain. Thus, the main conclusions seem to be robust because they only depend on very general and simple hypotheses and, particularly, no dynamic assumptions are required. The results of the empirical analysis may be useful to traders and researchers since it seems to reveal the existence of sequential arbitrage. Furthermore, the number of arbitrage opportunities significantly increased in 1998, when important innovations were implemented and, amongst other new possibilities, agents began trading each whole bond and its coupons (strips) separately. The inexperience associated with financial innovations may lead to ine¢ciencies in the market.
Characterization of Multidrug Resistant Extended-Spectrum Beta-Lactamase-Producing Escherichia coli among Uropathogens of Pediatrics in North of Iran

Escherichia coli remains as one of the most important bacteria causing infections in pediatrics and producing extended-spectrum beta-lactamases (ESBLs) making them resistant to beta-lactam antibiotics. In this study we aimed to genotype ESBL-producing E. coli isolates from pediatric patients for ESBL genes and determine their association with antimicrobial resistance. One hundred of the E. coli isolates were initially considered ESBL producing based on their MIC results. These isolates were then tested by polymerase chain reaction (PCR) for the presence or absence of CTX, TEM, SHV, GES, and VEB beta-lactamase genes. About 30.5% of isolated E. coli was ESBL-producing strain. The TEM gene was the most prevalent (49%) followed by SHV (44%), CTX (28%), VEB (8%), and GES (0%) genes. The ESBL-producing E. coli isolates were susceptible to carbapenems (66%) and amikacin (58%) and showed high resistance to cefixime (99%), colistin (82%), and ciprofloxacin (76%). In conclusion, carbapenems were the most effective antibiotics against ESBl-producing E. coli in urinary tract infection in North of Iran. The most prevalent gene is the TEM-type, but the other resistant genes and their antimicrobial resistance are on the rise.
An Iterative approach to extract dictionaries from Wikipedia for under-resourced languages

The problem of extracting bilingual dictionaries from Wikipedia is well known and well researched. Given the structural and rich multilingual content of Wikipedia, a language independent approach is necessary for extracting dictionaries for various languages more so for under-resourced languages. In our attempt to mine dictionaries for under-resourced languages, we developed an iterative approach to construct parallel corpus for building a dictionary, for which we consider several kinds of Wikipedia article information like title, infobox information, category, article text and dictionaries already built at each phase. The average precision over various datasets is encouraging with maximum precision of 76.7%, performing better than existing systems. As no language-specific resources are used, our method is applicable to any pair of language with special focus on under-resourced languages and hence breaking the language barrier.
A file I/O system for many-core based clusters

A many-core based co-processor, such as the Intel Many Integrated Core (MIC) Architecture, connected to a server-level multi-core host processor via a PCI Express bus, has recently been the subject of a great deal of attention. In such a machine, because the many-core is separated from the host processor with disk I/O and it also has limited cache and memory bandwidth, performance degradation can results from cache pollution and data transfer latency caused by processing file operations. Three types of file I/O mechanisms for the many-core in such a system are designed, implemented, and evaluated in this paper. One mechanism involves the file I/O system calls being performed by the kernel running on the same core that the application program is running on. Another is a mechanism whereby those system calls are offloaded to the kernel running on a dedicated core of the many-core that handles file I/O operations. In either case, the kernel requests file data transfer to the file system on the host processor and file data is cached on the many-core. The third mechanism involves the system calls being offloaded to the kernel running on the host processor so that the host kernel transfers data directly to the user buffer in the many-core. The experimental results show that the first two mechanisms, performing in the many-core, are superior to offloading them to the host when the data size is relatively small because they are designed to conduct file I/O operations through a file cache and fewer of communications occur between the many-core and the host. With larger data sizes, however, file I/O system calls offloaded to the host, which transfer data directly to/from the user buffer, are better than those performed inside the many-core. In view of cache awareness, it is shown that the user code and part of the file I/O system calls can be performed efficiently when the user buffer data is small enough to be on the cache.
Low-density series expansions for directed percolation: II. The square lattice with a wall

A new algorithm for the derivation of low-density expansions has been used to greatly extend the series for moments of the pair-connectedness on the directed square lattice near an impenetrable wall. Analysis of the series yields very accurate estimates for the critical point and exponents. In particular, the estimate for the exponent characterizing the average cluster length near the wall, 1 = 1.00014(2), appears to exclude the conjecture 1 = 1. The critical point and the exponents || and have the same values as for the bulk problem.
The non-parametric problem of Plateau in arbitrary codimension

We study the problem of finding a minimal graph with prescribed boundary data in arbitrary dimension and codimension. Existence, uniqueness, stability and regularity are treated. We first present the well-known results for codimension one: Jenkins-Serrin's existence theorem, convexity properties of the area which give uniqueness and stability and De Giorgi's theorem for regularity. In higher codimension we first discuss the counterexamples of Lawson and Osserman. Then we present the recent results of Mu-Tao Wang: we use the mean curvature flow to show existence for small boundary data and we prove a new Bernstein theorem (also due to Mu-Tao Wang) which holds for every area-decreasing minimal graph. We finally deduce from the Bernstein theorem that an area-decreasing minimal graph is always smooth.
Association between maternal perceptions and actual nutritional status for children in a study group in Mexico.

BACKGROUND Mexico has the highest rates of childhood obesity of any country in the world. As an environmental risk factor, family perception and behavior concerning child obesity is thought to have an important influence on the prevention and treatment of obesity. OBJECTIVE the aim of this study was to evaluate maternal perceptions of children's weight and to determine whether these perceptions are associated with the actual weight of the children. METHODS A descriptive study was conducted that assessed 273 children and included interviews with their mothers. Maternal perceptions of the children's weight status were compared with the measured weights, statures and body mass indices (BMI). RESULTS The mean BMIs of the children and mothers were 18.1 ± 3.6 and 27.7 ± 4.7 kg/m², respectively. The perceptions of mothers of children's weight are not associated with actual BMI category of children (p < 0.0001). We found 74 children who were overweight and obese (27%); 62.2% of the mothers of these children considered them to be overweight and obese and 37.8% of these mothers felt that their sons were of normal weight when in fact they were overweight (p = 0.038). CONCLUSIONS We found no association between actual BMI category of children and their mother's perceptions of their weight. In addition, found that third of mothers with obese or overweight children reported that their children had a normal nutritional status.
Design and analysis of 13.56MHz RFID antenna based on modified wheeler equation: A practical approach

An RFID tag antenna in coil form with typical credit card dimension (78mm × 41mm) which is suitable for ISO 14443 standards was built virtually in Electromagnetic simulator and its result was later validated with identical physical antennas. A simplified equation of inductance in microstrip form by H. A. Wheeler is then compared to result found using data from the simulation and modified to fit it. Consecutively, specific matching impedance for each coil is calculated which the matching impedance represents an RFID tag plus its compensation impedance. The coil and the matching impedance will then be modelled in an RF system simulator and its impedance plot will be explained.
Semantic Web Standard in Cloud Computing

CLOUD computing is an emerging paradigm in the IT and data processing communities. Enterprises utilize cloud computing service to outsource data maintenance, which can result in significant financial benefits. Businesses store and access data at remote locations in the "cloud. " As the popularity of cloud computing grows, the service providers' face ever increasing challenges. They have to maintain huge quantities of heterogenous data while providing efficient information retrieval. Thus, the key emphasis for cloud computing solutions is scalability and query efficiency. At the same time semantic web is also an emerging area to augment human reasoning. Resource Description Framework (RDF) which is semantic web technology that can be utilized to build efficient and scalable systems for Cloud Computing. A framework that can be built using Hadoop to store and retrieve large numbers of RDF triples by exploiting the cloud computing paradigm. To determine the Hadoop jobs, and present an algorithm to generate query plan, whose worst case cost is bounded, based on a greedy approach to answer a SPARQL Protocol and RDF Query Language (SPARQL) query. In the project, Hadoop's Map/Reduce framework is used to answer the queries. the results show that it can store large RDF graphs in Hadoop clusters built with cheap commodity class hardware.
Localization of speech recognition in spoken dialog systems: how machine translation can make our lives easier

The localization of speech recognition for large-scale spoken dialog systems can be a tremendous exercise. Usually, all in- volved grammars have to be translated by a language expert, and new data has to be collected, transcribed, and annotated for statistical utterance classifiers resulting in a time-consuming and expensive undertaking. Often though, a vast number of transcribed and annotated utterances exists for the source lan- guage. In this paper, we propose to use such data and translate it into the target language using machine translation. The trans- lated utterances and their associated (original) annotations are then used to train statistical grammars for all contexts of the target system. As an example, we localize an English spoken dialog system for Internet troubleshooting to Spanish by trans- lating more than 4 million source utterances without any human intervention. In an application of the localized system to more than 10,000 utterances collected on a similar Spanish Internet troubleshooting system, we show that the overall accuracy was only 5.7% worse than that of the English source system. Index Terms: spoken dialog systems, machine translation, lo- calization
Speed of Adjustment and Inflation – Unemployment Tradeoff in Developing Countries – Case of India

This paper estimates the short-run aggregate supply curve for the Indian economy over the period 1950-51 to 2008-09. Methodological improvements in this paper include the technique of estimating adaptive expectations, constrained estimation consistent with long run equilibrium, and introduction of the extended Phillips curve. The study also attempts to investigate the question of speed of recovery and the choice of adjustment paths available to policymakers in face of adverse supply shocks. In order to estimate the inflation-unemployment tradeoff we estimate the regular Phillips curve which lies at the root of the aggregate supply curve. The estimation is based on using adaptive inflationary expectations and supply shocks. We further introduce the extended part of the Phillips curve to analyze the question of speed of recovery and the choice of adjustment path. Contrary to previous studies, the present study finds a regular tradeoff between inflation and output or unemployment with inflationary expectations based on the experience of past three to four years. We also find that the subtle tradeoff between the rate of output recovery and inflation is negative in India thereby implying that a strategy of fast recovery is not likely to result in high inflationary pressures. These findings have important implications for policy choices on growth and strategy for recovery. The current fiscal and monetary policy stance has been strictly anti-inflationary and recognizes that some short-run deceleration in growth is unavoidable for controlling inflation. These policies without any empirical support presuppose the existence of a tradeoff between inflation and output and the choice of strategy for recovery. Our findings show that a strategy of slow recovery and following demand contraction policies to control inflation during the recovery phase could be counterproductive.
Design and test of a scalable security processor

This paper presents a security processor to accelerate cryptographic processing in modern security applications. Our security processor is capable of popular cryptographic functions such as RSA, AES, hashing and random number generation, etc. With proposed crypto-DMA controller, data gathering and scattering become flexible for security processing, using a simple descriptor-based programming model. The architecture of the security processor with its core-based platform is scalable and configurable for security variations in performance, cost and power consumption. Different number of data channels and crypto-engines can be used to meet the specifications. In addition, a DFT platform is also implemented for the design-test integration. The security processor has been fabricated with 0.18/spl mu/m CMOS technology. The core area is 3.899mm /spl times/ 2.296mm (525K gates approximately) and the operating clock rate is 83MHz.
CRNN : Integrating Classification Rule and Neural Network

Association classification has been an important type of the rule based classification. A variety of approaches have been proposed to build a classifier based on classification rules. In the prediction stage of the extant approaches, most of the existing association classifiers use the ensemble quality measurement of each rule in a subset rules to predict the class label of the new data. This method still suffers the following two problems. The classification rules are used individually that the coupling relations between rules are ignored in the prediction. However, in real-word rules set, the rules are often inter related together and many rules are usually satisfied partially when a new data object comes. Furthermore, the classification rule based prediction model lacks a general expression of the decision methodology. This paper proposes a classification method that integrating classification rule and neural network (CRNN for short), which gives a general form of the rule based decision methodology by rules network. In comparison with the extant rule based classifiers, such as C4.5, CBA, CMAR and CPAR, our approach has two advantages. First, CRNN takes into account of the coupling relations between rules from the training data in the prediction step. Second, CRNN obtains higher performance on the structure and parameter learning automatically than traditional neural network. CRNN uses the linear computing algorithm in neural network instead of the costly iterative learning algorithm. Two ways of the classification rule set generation are conducted in this paper for the CRNN evaluation and CRNN achieves the satisfied performance.
Solid Free-Form Fabrication For Self-Sustained Robot Ecologies : Challenges And Opportunities

We report on ongoing investigations of the potential for Solid Free-form Fabrication (SFF) processes to autonomously manufacture entire fully functional complex electromechanical systems, and of the application of such technology to a selfsustained robot ecology. Two complementary approaches are considered: (a) A universal fabrication system capable of autonomously manufacturing arbitrary machines, and (b) a parsimonious fabrication process that can achieve arbitrary functionality by redesign.
The efficacy of mirtazapine in agitated patients with Alzheimer’s disease: A 12-week open-label pilot study

Agitation is one of the most devastating behavioral symptoms in demented patients but there is little evidence about effective and safe pharmacotherapy. We aimed to determine the effectiveness and safety of mirtazapine in treatment of agitated patients with Alzheimer’s disease (AD). The consecutive patients with AD who have significant agitation were assigned to a 12-week open-label, prospective study. Patients received mirtazapine 15–30 mg/day. The changes in Cohen-Mansfield Agitation Inventory-Short form (CMAI-SF) scores were primary outcome measurement. The change in Clinical Global Impression-Severity scale (CGI-S) scores and tolerability-safety profile were the secondary efficacy variables. Thirteen of 16 (81.25%) patients completed the study. There was a significant reduction in CMAI-SF and CGI-S between the pre- and post-treatment with mirtzapaine (p < 0.001). The mean baseline score was 26.54 ( ± 5.4) and mean reduction was 10.6 ( ± 7.5) in CMAI-SF. There was no significant side effect and cognitive deterioration. The results of this open-label pilot study suggest that mirtazapine may be an effective choice for treatment of agitated patients with AD.
QoE-oriented 3D video transcoding for mobile streaming

With advance in mobile 3D display, mobile 3D video is already enabled by the wireless multimedia networking, and it will be gradually popular since it can make people enjoy the natural 3D experience anywhere and anytime. In current stage, mobile 3D video is generally delivered over the heterogeneous network combined by wired and wireless channels. How to guarantee the optimal 3D visual quality of experience (QoE) for the mobile 3D video streaming is one of the important topics concerned by the service provider. In this article, we propose a QoE-oriented transcoding approach to enhance the quality of mobile 3D video service. By learning the pre-controlled QoE patterns of 3D contents, the proposed 3D visual QoE inferring model can be utilized to regulate the transcoding configurations in real-time according to the feedbacks of network and user-end device information. In the learning stage, we propose a piecewise linear mean opinion score (MOS) interpolation method to further reduce the cumbersome manual work of preparing QoE patterns. Experimental results show that the proposed transcoding approach can provide the adapted 3D stream to the heterogeneous network, and further provide superior QoE performance to the fixed quantization parameter (QP) transcoding and mean squared error (MSE) optimized transcoding for mobile 3D video streaming.
An Improved Genetic Algorithm Based on hK1 Triangulation

An improved genetic algorithm based on the hK1 triangulation is proposed for optimization of dual multimodal function. With this algorithm, the optimal problems converse to solution of fixed point problems. The minimum points can be distinguished by using the Hessian Matrix. The test results of many typical functions indicated that the algorithm is valid and highly effective.
Heart Disease Prediction System Using Supervised Learning Classifier

Cardiovascular disease remains the biggest cause of deaths worldwide and the Heart Disease Prediction at the early stage is importance. In this paper Supervised Learning Algorithm is adopted for heart disease prediction at the early stage using the patient's medical record is proposed and the results are compared with the known supervised classifier Support Vector Machine (SVM). The information in the patient record is classified using a Cascaded Neural Network (CNN) classifier. In the classification stage 13 attributes are given as input to the CNN classifier to determine the risk of heart disease. The proposed system will provide an aid for the physicians to diagnosis the disease in a more efficient way. The efficiency of the classifier is tested using the records collected from 270 patients. The results show the CNN classifier can predict the likelihood of patients with heart disease in a more efficient way.