Miracle-dz/UniFAR-FTUs huggingface dataset:
Annexin V Binds to Viable B Cells and Colocalizes with a Marker of Lipid Rafts upon B Cell Receptor Activation1

Recombinant annexin V (rAnV) has been used to identify apoptotic cells based on its ability to bind phosphatidylserine (PS), a lipid normally restricted to the cytoplasmic face of the plasma membrane, but externalized early during apoptosis. However, this association of rAnV binding and apoptosis is not an obligatory one. We demonstrate that rAnV binds to a large fraction of murine B cells bearing selectable Ag receptors despite the fact that these cells are not apoptotic. Phosphatidylserine, which is uniformly distributed on resting B cells, is mobilized to co-cap with IgM on anti-IgM-treated B cells and to colocalize with GM1, a marker of lipid rafts. Cross-linking PS before anti-IgM treatment sequesters this lipid and alters signaling through IgM. Thus, PS exposed on the majority of B cells in vivo does not reflect early apoptosis, but, instead, plays a role in receptor-mediated signaling events.
A service plane over the PCE architecture for automatic multidomain connection-oriented services

In this article we concentrate on the automated provisioning of inter-AS services based on GMPLS-TE technology. We consider a provider alliance where TE connections are established between the members of the alliance. We propose an efficient and economically feasible architecture for the automatic provisioning of inter-AS GMPLS-TE, based on the introduction of a multidomain service plane coupled with the PCE-based architecture.
A Text Alignment Corpus for Persian Plagiarism Detection

This paper describes how a Persian text alignment corpus was constructed to evaluate plagiarism detection systems. This corpus is in PAN format and contains 11,089 documents and more than 11,603 plagiarism cases. Efforts were made to simulate various types of plagiarism manually, semi-automatically, or automatically in this large-scale corpus.
Head teachers’ attitudes towards religious diversity and interreligious dialogue and their implications for secondary schools in Catalonia

ABSTRACT This paper explores the attitudes of secondary-school head teachers towards religious diversity, intercultural and interreligious dialogue and the role of education in fostering intercultural and interreligious dialogue. A sample comprising 275 head teachers in Catalan secondary schools answered an online questionnaire. The results revealed attitudes which were moderately favourable towards cultural and religious diversity, more strongly favourable towards interreligious dialogue, and less favourable towards education playing a major role in managing religious and cultural diversity and in fostering interreligious dialogue. We found significant differences in head teachers’ attitudes in line with the specific features of the schools where they worked. Amongst these differences, it was noticeable that heads of religious and private–public schools had more positive attitudes towards managing religious and cultural diversity and towards education playing a leading role in promoting dialogue. Also, we identified three groups of head teachers who showed differing degrees of positivity according to the perceived religious diversity of their schools. The more diverse the school, the less favourable the attitude, and vice-versa; the most moderate favourability was also associated with the most moderate diversity.
A Methodology for R & D Portfolio Selection : Employing an Integrated DEA and BSC Model

We present a portfolio selection methodology that responds to three major goals that most R&D organizations are concerned with – effectiveness, efficiency, and balance. The methodology uses a strategy-oriented, multi-criterion, evaluation model that integrates concepts from data envelopment analysis (DEA) and balanced scorecard (BSC). It is based on two subsequent phases. At the first phase individual R&D projects are evaluated and project performance indexes are generated (i.e. project-efficiency, project-balance, and project-risk). By setting bounds to these indexes projects are screened and a subset of candidate R&D projects is generated. At the second phase potential portfolios are constructed using a branch-and-bound procedure that takes into account multiple resource constraints. Other possible restricting considerations, such as, the distribution of the length of the projects in the portfolio, may also be considered. The potential portfolios are then evaluated and an integrated portfolio performance index is computed. Based on this index the portfolios are ranked in order of decreasing rating, and a desirable portfolio is selected. Uncertainty is dealt with by adjusting output data to reflect expected values and by performing scenario analysis. The two-phase procedure reduces considerably the complexity involved in generating and evaluating all possible portfolios, and it considers both project and portfolio related factors.
Space Competition and Time Delays in Human Range Expansions. Application to the Neolithic Transition

Space competition effects are well-known in many microbiological and ecological systems. Here we analyze such an effect in human populations. The Neolithic transition (change from foraging to farming) was mainly the outcome of a demographic process that spread gradually throughout Europe from the Near East. In Northern Europe, archaeological data show a slowdown on the Neolithic rate of spread that can be related to a high indigenous (Mesolithic) population density hindering the advance as a result of the space competition between the two populations. We measure this slowdown from a database of 902 Early Neolithic sites and develop a time-delayed reaction-diffusion model with space competition between Neolithic and Mesolithic populations, to predict the observed speeds. The comparison of the predicted speed with the observations and with a previous non-delayed model show that both effects, the time delay effect due to the generation lag and the space competition between populations, are crucial in order to understand the observations.
Physics of Econophysics

Econophysics is a new area developed recently by the cooperation between economists, mathematicians and physicists. It’s not a tool to predict future prices of stocks and exchange rates. It applies idea, method and models in Statistical Physics and Complexity to analyze data from economical phenomena. In this paper, three examples from three active main topics in Econophysics are presented first. Then using these examples, we analyze the role of Physics in Econophysics. Some comments and emphasis on Physics of Econophysics are included. New idea of network analysis for economy systems is proposed, while the actual analysis is still in progress.
Interacting with large displays from a distance with vision-tracked multi-finger gestural input

We explore the idea of using vision-based hand tracking over a constrained tabletop surface area to perform multi-finger and whole-hand gestural interactions with large displays from a distance. We develop bimanual techniques to support a variety of asymmetric and symmetric interactions, including fast targeting and navigation to all parts of a large display from the comfort of a desk and chair, as well as techniques that exploit the ability of the vision-based hand tracking system to provide multi-finger identification and full 2D hand segmentation. We also posit a design that allows for handling multiple concurrent users.
Performance Analysis of Single Relay Cooperative ARQ protocol under Time Correlated Rayleigh Fading Channel

In this paper, we analyze the performances of a cooperative ARQ protocol under Poisson arrivals and the time correlated Rayleigh fading channel environment. A two dimensional discrete time Markov chain is formulated for the analysis. Using the steady state analysis of the Markov chain, the average frame latency and the moment generating function of the service time of a frame are derived. We validate our analytical results by comparing them with simulation results. From our analysis, we show that the cooperative ARQ protocol outperforms the non-cooperative protocol. In addition, we see that the effect of time correlation of the fading channel is not negligible.
Automated Detection of Interictal Spikes in EEG : A literature review

This work offers a preliminary study on methods for automatic detection of interictal epileptiform discharges in electroencephalogram (EEG) signals. The present research investigated recent studies on automated spike detection techniques. From 80 selected papers, we reviewed over 40 according to five indicators (year of publication, feature extraction and classification methods, performance evaluation and dataset). Since feature extraction plays a fundamental role in automatic spike detection, a comprehensive literature review indicated the wavelet-based methods as suitable approaches to address such task. In addition, we covered the main topics related to the theme, such as classifiers, artifact rejection, data reduction techniques and performance metrics. This study provides directions to both conduct future studies and implement automated systems to assist in epilepsy diagnosis.
M-FISH Karyotyping - A New Approach Based on Watershed Transform

Karyotyping is a process in which chromosomes in a dividing cell are properly stained, identified and displayed in a standard format, which helps geneticist to study and diagnose genetic factors behind various genetic diseases and for studying cancer. M-FISH (Multiplex Fluorescent In-Situ Hybridization) provides color karyotyping. In this paper, an automated method for M-FISH chromosome segmentation based on watershed transform followed by naive Bayes classification of each region using the features, mean and standard deviation, is presented. Also, a post processing step is added to re-classify the small chromosome segments to the neighboring larger segment for reducing the chances of misclassification. The approach provided improved accuracy when compared to the pixel-by-pixel approach. The approach was tested on 40 images from the dataset and achieved an accuracy of 84.21 %.
Comparison of MTA and CEM Cement Microleakage in Repairing Furcal Perforation, an In Vitro Study

Statement of Problem: Sealing the perforation defect is an important factor to reduce inflammation in the area and to perform healing. Selecting the appropriate material to repair the defect is an important concern. Among the various available materials, MTA and CEM are used recently for achieving this purpose. In the current study we compare the sealing ability of these materials by evaluating their microleakage by fluid filtration method. Purpose: The purpose of this study was to compare the microleakage of MTA and CEM cement in furcal perforation in different periods of time. Materials and Method: Forty one mandibular molars were selected for this experimental study. The perforation defects were created perpendicular to the long axis of the teeth, on the furcation of the teeth and the samples were divided into 2 experimental and two control groups. The defects were sealed by CEM and MTA in each experimental group. The samples were undergone the fluid filtration test with 20 cm H2O pressure. The amount of fluid filtration was measured for each sample at 24, 72 and 168 hrs and the data were analyzed by using ANOVA and T test. Results: The experimental groups which were sealed with CEM exhibited significantly less microleakage in all determined periods of time (24, 72 and 168 hrs) than MTA groups (p< 0.001). Conclusion: Based on the results of this study, CEM cement has a better sealing ability compared with MTA using fluid filtration method.
A general framework for efficient clustering of large datasets based on activity detection

Data clustering is one of the most popular data mining techniques with broad applications. K‐Means is one of the most popular clustering algorithms, due to its high efficiency/effectiveness and wide implementation in many commercial/noncommercial softwares. Performing efficient clustering on large dataset is especially useful; however, conducting K‐Means clustering on large data suffers heavy computation burden which originates from the numerous distance calculations between the patterns and the centers. This paper proposes framework General Activity Detection (GAD) for fast clustering on large‐scale data based on center activity detection. Within this framework, we design a set of algorithms for different scenarios: (i) exact GAD algorithm, E‐GAD, which is much faster than K‐Means and gets the same clustering result; (ii) approximate GAD algorithms with different assumptions, which are faster than E‐GAD, while achieving different degrees of approximation; and (iii) GAD based algorithms to handle the large clusters problem which appears in many large‐scale clustering applications. The framework provides a general solution to exploit activity detection for fast clustering in both exact and approximate scenarios, and our proposed algorithms within the framework can achieve very high speed. We have conducted extensive experiments on several datasets from various real world applications, including data compression, image clustering, and bioinformatics. By measuring the clustering quality and CPU time, the experiment results show the effectiveness and high efficiency of our proposed algorithms. Copyright © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 11–29 2011
Adaptive indexing in modern database kernels

Physical design represents one of the hardest problems for database management systems. Without proper tuning, systems cannot achieve good performance. Offline indexing creates indexes a priori assuming good workload knowledge and idle time. More recently, online indexing monitors the workload trends and creates or drops indexes online. Adaptive indexing takes another step towards completely automating the tuning process of a database system, by enabling incremental and partial online indexing. The main idea is that physical design changes continuously, adaptively, partially, incrementally and on demand while processing queries as part of the execution operators. As such it brings a plethora of opportunities for rethinking and improving every single corner of database system design. We will analyze the indexing space between offline, online and adaptive indexing through several state of the art indexing techniques, e. g. , what-if analysis and soft indexes. We will discuss in detail adaptive indexing techniques such as database cracking, adaptive merging, sideways cracking and various hybrids that try to balance the online tuning overhead with the convergence speed to optimal performance. In addition, we will discuss how various aspects of modern techniques for database architectures, such as vectorization, bulk processing, column-store execution and storage affect adaptive indexing. Finally, we will discuss several open research topics towards fully automomous database kernels.
TECHNICAL APPENDIX for DEMOCRACY AND ECONOMIC GROWTH: A META-ANALYSIS

Which studies should be included? Three approaches are adopted in the literature. First, analysts take the effort to identify as many studies as possible. This involves a comprehensive search of relevant databases, analysis of citations and careful study of references. The aim is to construct a dataset that represents the population of all publicly available estimates on a given topic. This is our preferred approach. Second, analysts make take a random sample of the available studies. This is easier to do if there is a database available from which samples can be drawn randomly. Such databases are currently unavailable in economics and political science and, hence, this approach is problematic (i.e., what makes the selection of studies random is not clear). Third, analysts may take the population of studies from a given year. For example, analysts may decide to include only studies published since 2000. While this has the benefit of making the data construction process easier, it does raise issues about the representativeness of the sample. Under certain circumstances, however, this approach has its merits. In all cases, care should be taken to ensure that the studies are comparable.
Measurements of the hopcount in internet

End-to-end quality of service is very likely to depend on the hopcount, the number of traversed routers. The hopcount also enhances our understanding of properties of the Internet topology. In this paper we present the results of the measurements of the hopcount from a source at Delft towards several destinations spread over three continents. Fitting the data with our theoretical model illustrates that the number of routers based on our measurements is around 98400. The dynamics in the change of the hopcount is investigated, it was found that though there is virtually no short-term change in the average hopcount the individual hopcounts do change significantly. When investigating the changes in the paths to the sites within these continents, rapid changes in the paths were observed to barely affect the hopcount.
Detection in analog sensor networks with a large scale antenna fusion center

We consider the distributed detection of a zero-mean Gaussian signal in an analog wireless sensor network with a fusion center (FC) configured with a large number of antennas. The transmission gains of the sensor nodes are optimized by minimizing the ratio of the log probability of detection (PD) and log probability of false alarm (PFA). We show that the problem is convex with respect to the squared norm of the transmission gains, and that a closed-form solution can be found using the Karush-Kuhn-Tucker conditions. Our results indicate that a constant PD can be maintained with decreasing sensor transmit gain provided that the number of antennas increases at the same rate. This is contrasted with the case of a singleantenna FC, where PD is monotonically decreasing with transmit gain. On the other hand, we show that when the transmit power is high, the single- and multi-antenna FC both asymptotically achieve the same PD upper bound.
Motif discovery in promoters of genes co-localized and co-expressed during myeloid cells differentiation

Genes co-expressed may be under similar promoter-based and/or position-based regulation. Although data on expression, position and function of human genes are available, their true integration still represents a challenge for computational biology, hampering the identification of regulatory mechanisms. We carried out an integrative analysis of genomic position, functional annotation and promoters of genes expressed in myeloid cells. Promoter analysis was conducted by a novel multi-step method for discovering putative regulatory elements, i.e. over-represented motifs, in a selected set of promoters, as compared with a background model. The combination of transcriptional, structural and functional data allowed the identification of sets of promoters pertaining to groups of genes co-expressed and co-localized in regions of the human genome. The application of motif discovery to 26 groups of genes co-expressed in myeloid cells differentiation and co-localized in the genome showed that there are more over-represented motifs in promoters of co-expressed and co-localized genes than in promoters of simply co-expressed genes (CEG). Motifs, which are similar to the binding sequences of known transcription factors, non-uniformly distributed along promoter sequences and/or occurring in highly co-expressed subset of genes were identified. Co-expressed and co-localized gene sets were grouped in two co-expressed genomic meta-regions, putatively representing functional domains of a high-level expression regulation.
Identifying the financial ratios influencing the explanation of firm values using genetic algorithm and neural networks

Based on the theoretical foundations of financial reporting, the primary objective is to help investors make economic decisions. One of the most important decisions is to sell and buy stock, which is in turn a function of the value of investee. Company valuation and identifying the factors contributing to it in capital markets have been always a challenging issue for investors and financial analysts. They are also seeking the identification of the contributory factors in firm value so as to control the factors and carefully determine firm value. In this study, 68 financial ratios for 122 firms from 2008 to 2012 have been examined. Using genetic algorithm, contributing ratios have been identified, then using the techniques of neural network, 23 selected ratios were used to estimate the values of companies. The results of the study indicate that the financial ratios are capable of explaining the 96.9% of firm value.
How Strongly are Business Cycles and Financial Cycles Linked in the G7 Countries

In this study we examine the dynamic interactions between credit growth and output growth using the spillover index approach of Diebold and Yilmaz (2012). Based on quarterly data on credit growth and GDP growth over the period 1957Q1-2012Q4 for the G7 countries we find that: i) spillovers between credit growth and GDP growth evolve rather heterogeneously over time and across countries, and increase during extreme economic events. ii) Spillovers between credit growth and GDP growth are of bidirectional nature, indicating bidirectional causation between the financial and real sectors. iii) In the period shorty before and on the onset of the global financial crisis, the link between credit growth and GDP growth becomes more pronounced. In particular, the financial sector plays a dominant role during the early stages of the crisis, while the real sector quickly takes over as the dominant source of spillovers. iv) Interestingly, credit growth in the US is the dominant transmitter of shocks internationally, and especially to other countries' real sectors in the run up period to (and during) the global financial crisis. Overall, our results suggest feedback effects between the financial and the real sectors that create rippling effects within and between the G7 countries during the global financial crisis.
A Dynamic Data Structure for Checking Hyperacyclicity

We present a dynamic data structure that keeps track of an acyclic hypergraph (equivalently, a triangulated graph) and enables verifying that adding a candidate hyperedge (clique) will not break the acyclicity of the augmented hypergraph. This is a generalization of the use of Tarjan’s Union-Find data structure for maintaining acyclicity when augmenting forests, and the amortized time per operation has a similar almost-constant dependence on the size of the hypergraph. Such a data structure is useful when augmenting acyclic hypergraphs, e.g.ĩn order to greedily construct a high-weight acyclic hypergraph. In designing this data structure, we introduce a hierarchical decomposition of acyclic hypergraphs that aid in understanding hyper-connectivity, and introduce a novel concept of a hypercycle which is excluded from acyclic hypergraphs.