Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Topography of Evoked Brain Activity during Mental Tasks Involving Whole Number Operations.

Students start to memorize arithmetic facts from early elementary school mathematics activities. Their fluency or lack of fluency with these facts could affect their efforts as they carry out mental calculations as adults. This study investigated participants’ levels of brain activation and possible reasons for these levels as they solved arithmetic exercises mentally. It used Optical Topography an imaging technique that measures brain changes in blood flow using near-infrared light. Twelve undergraduate and graduate students participated in scanning sessions and follow-up interviews. Different levels of brain activity were found during tasks: low, moderate and high. Answers during interviews indicated that differences in brain activation were related to students’ effectiveness in solving exercises and prior experiences with memorization of facts. These findings indicate that especial attention should be placed on how facts are memorized, which could have effects on how efficiently adult students’ brains work when solving mental computation exercises.
Mission Impossible ? : Automated Norm Analysis of Legal Texts

1 Introduction Although many legal experts thought this would be impossible we are very close in creating an algorithm for automated norm analysis from legal texts. This algorithm makes use of invariant linguistic structures at the syntactical level that characterises specific normative expressions in natural language. Since the algorithm has not been realised and tested completely jet, we will limit ourselves in this article to explaining the invariances in the natural language representations in which norms in legal texts are expressed. As part of the POWER research program [1], the Dutch Tax and Customs Administration has created a method to formalise normative expressions in legal texts in UML/OCL models. These UML/OCL representations of the legal texts have showed to be quite suitable. To support knowledge analysts in creating these UML/OCL-models, an automated concept ex-tractor was created, which allows a computer to identify the different concepts that exist in a given legal text [2]. This automated concept extractor reduces the amount of work of the knowledge analysts and results in more uniform models as well. The research described here is aimed at further automating the translation of a legal text to a model. Automated generation of models would not only lead to a reduction in the amount of work needed, it would also increase inter-analyst independency. Normally, models created by different analysts could differ in various small details. Removing those difference would lead to more uniform models, which can more easily be understood, and are also easier to process when creating applications based on these models. This article discusses the first results of this research into automated analysis of legal texts.
Information-preserving temporal reallocation of speech in the presence of fluctuating maskers

How can speech be retimed so as to maximise its intelligibility in the face of competing speech? We present a general strategy which modifies local speech rate to minimise overlap with a known fluctuating masker. Continuous time-scale factors are derived in an optimisation procedure which seeks to minimise overall energetic masking of the speech by the masker while additionally unmasking those speech regions potentially most important for speech recognition. Intelligibility increases are evaluated with both objective and subjective measures and show significant gains over an unmodified baseline, with larger benefits at lower signal-to-noise ratios. The retiming approach does not lead to benefits for speech mixed with stationary maskers, suggesting that the gains observed for the fluctuating masker are not simply due to durational expansion.
Parsing minimization when extracting information from code in the presence of conditional compilation

Exploring and understanding a software system requires extracting meaningful information from it. This in turn involves syntactical analysis of source code, an activity that can be complicated by the use of conditional compilation. In this paper we discuss difficulties when parsing code with conditional compilation. We argue that the effective way to ensure the extraction of all meaningful information from a source file is to parse a set of versions of that file defined by conditional compilation. We then describe a heuristic-based approach to minimize the amount of parsing.
Immobilized Lipases on Functionalized Silica Particles as Potential Biocatalysts for the Synthesis of Fructose Oleate in an Organic Solvent/Water System

Lipases from Thermomyces lanuginosus (TLL) and Pseudomonas fluorescens (PFL) were immobilized on functionalized silica particles aiming their use in the synthesis of fructose oleate in a tert-butyl alcohol/water system. Silica particles were chemically modified with octyl (OS), octyl plus glutaraldehyde (OSGlu), octyl plus glyoxyl (OSGlx), and octyl plus epoxy groups (OSEpx). PFL was hyperactivated on all functionalized supports (more than 100% recovered activity) using low protein loading (1 mg/g), however, for TLL, this phenomenon was observed only using octyl-silica (OS). All prepared biocatalysts exhibited high stability by incubating in tert-butyl alcohol (half-lives around 50 h at 65 °C). The biocatalysts prepared using OS and OSGlu as supports showed excellent performance in the synthesis of fructose oleate. High ester synthesis was observed when a small amount of water (1%, v/v) was added to the organic phase, allowing an ester productivity until five times (0.88–0.96 g/L.h) higher than in the absence of water (0.18–0.34 g/L.h) under fixed enzyme concentration (0.51 IU/g of solvent). Maximum ester productivity (16.1–18.1 g/L.h) was achieved for 30 min of reaction catalyzed by immobilized lipases on OS and OSGlu at 8.4 IU/mL of solvent. Operational stability tests showed satisfactory stability after four consecutive cycles of reaction.
Efficient Item Set Mining Supported by IMine Index

This paper presents the IMine index, a general and compact structure which provides tight integration of item set extraction in a relational DBMS. Since no constraint is enforced during the index creation phase, IMine provides a complete representation of the original database. To reduce the I/O cost, data accessed together during the same extraction phase are clustered on the same disk block. The IMine index structure can be efficiently exploited by different item set extraction algorithms. In particular, IMine data access methods currently support the FP-growth and LCM v.2 algorithms, but they can straightforwardly support the enforcement of various constraint categories. The IMine index has been integrated into the PostgreSQL DBMS and exploits its physical level access methods. Experiments, run for both sparse and dense data distributions, show the efficiency of the proposed index and its linear scalability also for large data sets. Item set mining supported by the IMine index shows performance always comparable with, and often (especially for low supports) better than, state-of-the-art algorithms accessing data on flat file. Index Terms — Data mining, item set extraction, indexing.
Molecular Characterization of Spontaneous Mesenchymal Stem Cell Transformation

Background We previously reported the in vitro spontaneous transformation of human mesenchymal stem cells (MSC) generating a population with tumorigenic potential, that we termed transformed mesenchymal cells (TMC). Methodology/Principal Findings Here we have characterized the molecular changes associated with TMC generation. Using microarrays techniques we identified a set of altered pathways and a greater number of downregulated than upregulated genes during MSC transformation, in part due to the expression of many untranslated RNAs in MSC. Microarray results were validated by qRT-PCR and protein detection. Conclusions/Significance In our model, the transformation process takes place through two sequential steps; first MSC bypass senescence by upregulating c-myc and repressing p16 levels. The cells then bypass cell crisis with acquisition of telomerase activity, Ink4a/Arf locus deletion and Rb hyperphosphorylation. Other transformation-associated changes include modulation of mitochondrial metabolism, DNA damage-repair proteins and cell cycle regulators. In this work we have characterized the molecular mechanisms implicated in TMC generation and we propose a two-stage model by which a human MSC becomes a tumor cell.
Low-Complexity Adaptive Set-Membership Reduced-rank LCMV Beamforming

This paper proposes a new adaptive algorithm for the implementation of the linearly constrained minimum variance (LCMV) beamformer. The proposed algorithm utilizes the set-membership filtering (SMF) framework and the reduced-rank joint iterative optimization (JIO) scheme. We develop a stochastic gradient (SG) based algorithm for the beamformer design. An effective time-varying bound is employed in the proposed method to adjust the step sizes, avoid the misadjustment and the risk of overbounding or underbounding. Simulations are performed to show the improved performance of the proposed algorithm in comparison with existing full-rank and reduced-rank methods.
Beyond the Theory of Planned Behaviour — Viewing Entrepreneurial Intent Through the Lens of Economic Powerlessness

Based on a sample of 400 subjects enrolled in business courses at the National University of Lesotho, the aim of this paper is to examine the influence of economic powerlessness on entrepreneurial intent. Correlation, hierarchical regression, stepwise regression and usefulness analyses are used to analyse data. The results reveal that economic powerlessness is not only negatively associated with entrepreneurial intent, but also accounts for the unique variance above that accounted for by demographic factors and the elements of theory of planned behaviour in the explanation of entrepreneurial intent. While subjective norm fails to influence entrepreneurial intent, attitude towards entrepreneurship and perceived behavioural control respectively are the strongest predictors of entrepreneurial intent in this paper. Implications for policy and education are discussed.
Deer Detection in Thermal Images for Traffic Safety Using Contour Based Histogram of Oriented Gradient Met hod

Car accidents due to Deer-Vehicle Crashes (DVC's) are one of the major safety concerns for the driving on rural roads in Europe and North America. Many attempts have been made to reduce the occurrences of these accidents, but none of them has yet proved effective. With the development of infrared imaging technology, using thermal images to identify the presence of deer in night has become possible. A pattern recognition method, Histogram of Oriented Gradient (HOG), can be used to deer detection to reduce such accidents. However, the length of time required to process one image makes the method incapable of real-time deer identification and tracking. Based on the HOG method and a known classifier - Support Vector Machine (SVM), this research developed a contour based HOG + SVM method, called CNT- HOG method to process thermal images and to detect the presence of deer. Experimental results have demonstrated that this algorithm has the advantages of both high accuracy (up to 94.2%) and short computation time (0.1s) when compared to traditional HOG + SVM method. In this research, further analysis has been performed to evaluate the influence of body postures and occlusions on detection accuracy. By achieving such computation speed and accuracy, this CNT-HOG algorithm proves that deer can be tracked in real-time. By using such a system, drivers on road can be warned of the presence of deer in real time and the frequency of DVC's can be effectively reduced.
The Margins of US Trade

Recent research in international trade emphasizes the importance of firms' extensive margins for understanding overall patterns of trade as well as how firms respond to specific events such as trade liberalization. In this paper, we use detailed US trade statistics to provide a broad overview of how the margins of trade contribute to variation in US imports and exports across trading partners, types of trade (i.e., arm's length versus related party) and both short and long time horizons. Among other results, we highlight the differential behavior of related-party and arm's-length trade in response to the 1997 Asian financial crisis.
Asymmetrically designed siRNAs and shRNAs enhance the strand specificity and efficacy in RNAi

RNAi can mediate allele-specific silencing, and offers an attractive approach for treatment of human diseases caused by dominant, gain-of-function gene mutations. However, allele-specific silencing requires that the RNAi target the mutated region of the mRNA, limiting the choices of the target sequences. This often results in the use of a suboptimal siRNAs or shRNAs. Unfavorable strand asymmetry, which leads to the sense strand rather than the antisense strand to be incorporated into RNA-induced silencing complex (RISC), can cause poor RNAi efficacy. We systematically tested an approach that places mismatches at or near the 5′ of the antisense strand to create favorable strand asymmetry. Here we show that this approach can enhance the RNAi efficacy of not only siRNAs but also shRNAs synthesized from genes, which can be placed in various viral vectors. Thus, this design of asymmetric shRNAs could be potentially used in silencing dominant, gain-of-function gene mutations for gene therapy.
Ceria–Vanadia/Silica‐Catalyzed Cascade for C−C and C−O Bond Activation: Green One‐Pot Synthesis of 2‐Amino‐3‐cyano‐4H‐pyrans

Abstract We designed a ceria–vanadia/silica (Ce–V/SiO2) heterogeneous catalyst and used it for the green and efficient synthesis of 2‐amino‐3‐cyano‐4H‐pyran derivatives. The green reaction was a multicomponent one‐pot condensation of 5,5‐dimethylcyclohexane‐1,3‐dione, aromatic aldehyde, and malononitrile in an eco‐compatible solvent (ethanol). The catalyst was synthesized and fully characterized by powder X‐ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) surface area analysis. The reported procedure offers a number of advantages including decreased reaction times, mild conditions, high yields, operational simplicity, and environmentally benign and simple work‐up procedures. Furthermore, the catalyst is economical, fully recyclable, and reusable for over five runs while preserving its high activity. The synthesized 2‐amino‐3‐cyano‐4H‐pyran products can later be used for pharmaceutical purposes.
The role of brief CBT in the treatment of anxiety and depression for young adults at a UK university: a pilot prospective audit study

Abstract Cognitive behaviour therapy (CBT) has rarely been offered to students seeking professional psychological help at universities in the UK. Here, we aimed to investigate whether a brief course of CBT would improve anxious and depressive symptoms. Forty-eight student patients received a brief course of CBT at a university National Health Service, Student Health Centre in England. Patients completed weekly self-report measures of anxiety and depression at the commencement of each CBT session. Student patients receiving CBT showed significant decreases in anxiety and depression. These effects remained after controlling for a range of potential covariates (e.g. primary problem, total time in treatment, therapist qualifications). Findings suggest CBT is effective in reducing anxiety and depressive symptoms in a ‘real-world’ university clinic.
The Rise of Potential Fields in Real Time Strategy Bots

Bots for Real Time Strategy (RTS) games are challenging to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. We show that the use of potential fields for implementing a bot for a real time strategy game gives us a very competitive, configurable, and non-conventional solution.
ID identification by using face detection and recognition systems

In recent years, significant achievements have been achieved in the field of face recognition. Face recognition are special pattern recognition which are used in banking for identity approving and the entrance of controlled areas, the places where the security control impending to airports, to control machines, to follow-up of persons. In this study, face recognition applications on the Yale face databases have been performed by using PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), LBP (Local Binary Patterns). Database consist of right-light, center-light, left-right, no glasses, glasses, normal, wink, sleepy, surprised, happy, sad images. In the face recognition pre-processing steps, HE (Histogram Equalization), HE+ Median Filter, HE+Gaussian filter, He+Laplace Filter was used. It is provided to select the appropriate method from the system depending on state of image. Thus the system recognition rate increases of up to 6% was observed. Application was developed by using Microsoft Visual Studio 2010 C #. EMGU CV library for image processing algorithms and SQL Server 2008 Express for database processing were used.
Dupuytren’s disease

Dupuytren’s Disease (DD) has surely plagued people for hundreds of years at least. Whilst the 20th century saw surgery for any condition – DD included – becoming safer, the ideal treatment was not established. For a disease with an inexorable tendency to progress until treatment was inevitable and to recur after treatment, opinions ranged from the most radical excision with grafting, to the most simple least invasive option of needle fasciotomy. The wound could be left open to heal by secondary intention, or closed with a variety of flaps. In the 21st century, treatment paradigms have been challenged with the option of collagenase which can dissolve a segment of disease – a “surgical drug”.
Neuroinformatics: what are us, where are we going, how to measure our way?

What is neuroinformatics? We can define it as a direction of science and information technology, dealing with development and study of the methods for solution of problems by means of neural networks. A field of science cannot be determined only by fixing what it is "dealing with". The main component, actually constituting a scientific direction, is "THE GREAT PROBLEM", around which the efforts are concentrated. One may state even categorically: if there is no a great problem, there is no a field of science, but only more or less skilful imitation. What is "THE GREAT PROBLEM" for neuroinformatics? The problem of effective parallelism, the study of brain (solution of mysteries of thinking), etc are discussed. The neuroinformatics was considered not only as a science, but as a services sector too. The main ideas of generalized technology of extraction of explicit knowledge from data are presented. The mathematical achievements generated by neuroinformatics, the problem of provability of neurocomputations, and benefits of neural network realization of solution of a problem are discussed.
Virtual Resource Management Based on Software Transactional Memory

One of the main challenges to exploit the potential of Cloud computing is the efficient management of virtual resources supplied by cloud providers. In this paper, we deal with a business model of cloud computing, where third-part entities, the cloud brokers, work as intermediary between cloud providers and clients. Brokers are assumed to provide the intermediation service by addressing multiple cloud providers and this make the cloud environment very complex and dynamic. So, we present a new resource management architecture, able to support concurrent activities over the virtual resources. It abstracts all the resources given by different providers in a single framework, in order to allow brokers to perform seamless management services. To synchronize all the activities on the virtual resources, we propose the adoption of Transactional Memory (TM), a mechanism for controlling accesses to shared memory in concurrent computing. We present details on the implementation of a prototype of our resource management system and some preliminary results obtained through simulation analysis.
Altruistic Distributed Target Allocation for Stable Navigation in Formation of Multi-Robot System

Abstract This paper deals with keeping the formation of a group of mobile robots. A set of virtual targets (points) form a virtual structure of the same shape as the desired formation. Hence, to join and to keep this formation, each robot has only to track one of these targets. The objective of the paper is to propose a cooperative strategy between the robots in order to rapidly join the virtual structure: instead of assigning ahead the targets to the robots, this strategy consists of making each one able to negotiate the closest target. If the latter is desired by a lot of robots, it is left to the robot which meets more difficulties to find an other target. Negotiation is based on a minimalist communication of relative cost coefficients between the robots. Simulation and experimental results validate the proposed contributions.
Static timing analysis of embedded software on advanced processor architectures

This paper examines several techniques for static timing analysis. In detail, the first part of the paper analyzes the connection of prediction accuracy (worst case execution time) and applicability of a methodology for modeling and analysis of instruction as well as data cache behavior. The second part of the paper proposes a timing analysis technique for super-scalar processors. The objects of our studies are two processors of the PowerPC family, in particular the PPC403 and the MPC750.
Internet and democracy in Morocco: A force for change and an instrument for repression

The article explores the extent to which the future of democracy in Morocco is healthier with the Internet as the primary means by which people create a public sphere where they communicate and form public opinion. A study on digital media in Morocco was carried out to examine the emergence of new media platforms and the impact of digitization on democracy and journalists’ activity. The study relies on in-depth interviews with digital activists and online journalists. The study found that the Internet had created a dynamic and networked public space where lively debates can take place on many issues still considered off-limits to mainstream media. It has also been used as a tool for nascent political movements to organize and mobilize supporters across the country, particularly in the context of the ongoing Arab Spring. Morocco was spared from the draconian measures used in other authoritarian countries and the Internet was relatively free. The February 20 Movement in 2011, the Amina Filali campaign in 2012, the Daniel Gate case in 2013 and the Free Ali Anouzla campaign are all significant moments of victory that relied on social media for political mobilization. However, with the surveillance technology available and its use justified (since the most democratic countries such as the United States and France use it), the state is clamping down on Internet freedom. In the past, the government controlled the online information landscape through a series of restrictive laws that can be manipulated to serve political purposes. Today, activists and journalists identify surveillance as the most dangerous instrument in the hands of the regime. The regime has learned to use the empowering potential of the Internet to serve its own repressive agenda.