Miracle-dz/UniFAR-FTUs huggingface dataset:
The algorithm for calculating integrals of hypergeometric type functions and its realization in REDUCE system

The most voluminous bibliography of the analytical methods for calculating of integrals is represented in the article [19]. It is shown there that the most effective and the simplest algorithm of analytical integration was made by O.I. Marichev [8, 9, 12]. Later it was realized in the reference-books [16-18, 20]. This algorithm allows us to calculate definite and indefinite integrals of the products of elementary and special functions of hypergeometric type. It embraces about 70 per cent of integrals which are included in the world reference-literature. It allows to calculate many other integrals too. The present article contains short description of this algorithm and its realization in the REDUCE system during the process of creation of INTEGRATOR system. Only one general method of integration is known to be realized on the computers, i.e. criterion algorithm for calculating of indefinite integrals of elementary functions through elementary functions by themselves (the authors of it are M. Bronstein and other). The idea of our algorithm is in the following. The initial integrals is transformed to contour integral from the ratio of products of gamma-functions by means of Mellin transform and parseval equality. The residue theorem is used for the calculating of the received integral which due to the strict rules results in sums of hypergeometric series. The value of integral itself and the integrand functions are the special cases of the well-known Meijer's G-function [4, 7, 8, 12, 14, 18]. Programming packet is realized in programming languages PASCAL and REDUCE. It also offers the opportunity of finding the values for some classical integral transforms (Laplace, Hankel, Fourier, Mellin and etc.). The REDUCE's part of packet contains the main properties of the well-known special functions, such as the Bessel and gamma-functions and kindred functions, Anger function, Weber function, Whittaker functions, generalized hypergeometric functions. Special place in the packet is occupied by Meijers's G-function for which the main properties such as finding the particular cases and representation by means of hypergeometric series are realized.
A Visual Query Language for Relational Knowledge Discovery TITLE2

qGraph is a visual query language for knowledge discovery in relational data. Using qGraph, a user can query and update relational data in ways that support data exploration, data transformation, and sampling. When combined with modeling algorithms, such as those developed in inductive logic programming and relational learning, the language assists analysis of relational data, such as data drawn from the Web, chemical structure-activity relationships, and social networks. Several features distinguish qGraph from other query languages such as SQL and Datalog. It is a visual language, so its queries are annotated graphs that reflect potential structures within a database. qGraph treats objects, links, and attributes as first-class entities, so its queries can dynamically alter a data schema by adding and deleting those entities. Finally, the language provides grouping and counting constructs that facilitate calculation of attributes that can capture features of local graph structure. We describe the language in detail, discuss key aspects of the underlying data model and implementation, and discuss several uses of qGraph for knowledge discovery.
A survey on Neyman‐Pearson classification and suggestions for future research

In statistics and machine learning, classification studies how to automatically learn to make good qualitative predictions (i.e., assign class labels) based on past observations. Examples of classification problems include email spam filtering, fraud detection, market segmentation. Binary classification, in which the potential class label is binary, has arguably the most widely used machine learning applications. Most existing binary classification methods target on the minimization of the overall classification risk and may fail to serve some real‐world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman‐Pearson (NP) paradigm was introduced in this context as a novel statistical framework for handling asymmetric type I/II error priorities. It seeks classifiers with a minimal type II error subject to a type I error constraint under some user‐specified level. Though NP classification has the potential to be an important subfield in the classification literature, it has not received much attention in the statistics and machine learning communities. This article is a survey on the current status of the NP classification literature. To stimulate readers' research interests, the authors also envision a few possible directions for future research in NP paradigm and its applications. WIREs Comput Stat 2016, 8:64–81. doi: 10.1002/wics.1376
Circulating miR-148b and miR-133a as biomarkers for breast cancer detection

Circulating microRNAs have drawn a great deal of attention as promising novel biomarkers for breast cancer. However, to date, the results are mixed. Here, we performed a three-stage microRNA analysis using plasma samples from breast cancer patients and healthy controls, with efforts taken to address several pitfalls in detection techniques and study design observed in previous studies. In the discovery phase with 122 Caucasian study subjects, we identified 43 microRNAs differentially expressed between breast cancer cases and healthy controls. When those microRNAs were compared with published data from other studies, we identified three microRNAs, including miR-148b, miR-133a and miR-409-3p, whose plasma levels were significantly higher in breast cancer cases than healthy controls and were also significant in previous independent studies. In the validation phase with 50 breast cancer cases and 50 healthy controls, we validated the associations with breast cancer detection for miR-148b and miR-133a (P = 1.5×10−6 and 1.3×10−10, respectively). In the in-vitro study phase, we found that both miR-148b and miR-133a were secreted from breast cancer cell lines, showing their secretory potential and possible tumor origin. Thus, our data suggest that both miR-148b and miR-133a have potential use as biomarkers for breast cancer detection.
Continuous-time based Multiple Model System Control with Reliability Constraint

In this paper, a new active control frame- work is developed for non-LTI continuous-time sys- tem, which is closer to the real world. Like other control system developments, it consists of two major parts: observer design and controller design. For the observer, the dynamic system is modelled as a jump Markov linear system whose parameters evolve along with the running mode of plant, then the continuous- time based interacting multiple model(CT-IMM) al- gorithm is proposed to estimate the full state more precisely. For the controller, a dynamic reliability constraint is added and the optimal control problem is reformulated to guarantee the reliability of sys- tem. Illustrative examples showed the accuracy of CT-IMM and the validity of controller with reliabil- ity constraint.
Estuarine flood modelling using artificial neural networks

Prediction of water levels at estuaries poses a significant challenge for modelling of floods due to the influence of tidal effects. In this study, a two-stage forecasting system is proposed. In the first stage, the tidal portion of the available records is used to develop a tidal prediction system. The predictions of the first stage are used for flood modelling in the second. Experimental results suggest that the proposed flood modelling approach is advantageous for forecasting flood levels with more than 1 hour lead times.
Flood modelling with hydraTE: 2+1-dimensional smoothed-particle hydrodynamics

We present HydraTE, our own implementation of the smoothed-particle hydrodynamics technique for shallow water that uses the adaptive size of the smoothing kernel as a proxy for the local water depth. We derive the equa- tions of motion for this approach from the Lagrangian before demonstrating that we can model the depth of water in a trough, implement vertical walls, recover the correct acceleration and terminal velocity for water flowing down a slope and obtain a stable hydraulic jump with the correct jump condition. We demonstrate that HydraTE performs well on two of the UK Environ- ment Agency flood modelling benchmark tests. Benchmark EA3 involves flow down an incline into a double dip depression and studies the amount of water that reaches the second dip. Our results are in agreement with those of the other codes that have attempted this test. Benchmark EA6 is a dam break into a horizontal channel containing a building. HydraTE again pro- duces results that are in good agreement with the other methods and the experimetal validation data except where the vertical velocity structure of the flow is expected to be multi-valued, such as the hydralic jump where the precise location is not recovered even though the pre- and post- jump water heights are. We conclude that HydraTE is suitable for a wide range of flood modelling problems as it preforms at least as well as the best available commercial alternatives for the problems we have tested.
Engaging Civil Society in the Nagorno Karabakh Conflict: What Role for the EU and its Neighbourhood Policy?

The conflict over Nagorno Karabakh, opposing Armenia and Azerbaijan, is the longest conflict in the OSCE area and a fundamental security threat to the South Caucasus and surrounding regions, preventing full and inclusive economic development and constraining regional relations. This chapter takes the ENP as a conflict transformation tool and looks at how the EU has used this initiative to reach civil society organisations (CSOs) and improve their performance as peace-builders in this protracted conflict. Building on the theoretical framework presented by Tocci (2008), the chapter assesses EU involvement in the civil society domain, mapping the types of organisations privileged by the EU and the potential impact of their activities on the conflict. It puts forward relevant arguments regarding the suitability of the EU’s goals and instruments to the dynamics on the ground and concludes with a categorisation of the EU’s approach according to three hypotheses: The Liberal Peace, the Leftist Critique and the Realist hypothesis. It is argues that work with civil society is a crucial part of the EU’s approach, despite the difficulties of making such engagement a central part of its peace-building and conflict transformation activities.
A detailed study of the enigmatic cluster M82F

We present a detailed study of the stellar cluster M82F, using multiband high-resolution Hubble Space Telescope (HST) imaging and deep ground-based optical slit and integral field spectroscopy. Using the imaging, we create colour maps of the cluster and surrounding region in order to search for substructure. We find a large amount of substructure, which we interpret as the result of differential extinction across the projected face of the cluster. With this interpretation, we are able to construct a spatially resolved extinction map across the cluster which is used to derive the intrinsic flux distribution. Fitting cluster profiles (King and Elson, Fall & Freeman) to the intrinsic images, we find that the cluster is 15-30 per cent larger than previous estimates, and that no strong evidence of mass segregation in this cluster exists. Using the optical spectra, we find that the age of M82F is 60-80 Myr and from its velocity conclude that the cluster is not physically associated with a large H II region that it is projected upon, both in agreement with previous studies. The reconstructed integral field maps show that that majority of the line emission comes from a nearby H II region. The spatial dependence of the line widths (implying the presence of multiple components) measured corresponds to the extinction map derived from photometry, indicating that the gas/dust clouds responsible for the extinction are also partially ionized. Even with the wealth of observations presented here, we do not find a conclusive solution to the problem of the high light-to-mass ratio previously found for this cluster and its possible top-heavy stellar initial mass function.
Calculating the Required Number of Bits in the Function of Confidence Level and Error Probability Estimation

This paper proposes the calculation of the required number of bits transmitted in the system, in order to achieve the desired level of confidence. Proposed solution is based on the selected value for the bit error probability, and statistical confidence levels. In addition, required error probability for telecom- munications protocols and data transfer protocols is discussed, overview of the BER (Bit Error Ratio) testing equipment performance is provided, and compro- mise of testing time in relation to the statistical level of confidence depending on BER is examined.
Distributed anomaly detection using 1‐class SVM for vertically partitioned data

There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth science data are collected from modern satellites, in situ sensors and different climate models. Similarly, huge amount of flight operational data is downloaded for different commercial airlines. These different types of data sets need to be analyzed for finding outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only because of the massive volume of data but also because these data sets are physically stored at different geographical locations with only a subset of features available at any location. Moving these petabytes of data to a single location may waste a lot of bandwidth. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the entire data without moving all the data to a single location. The method we propose only centralizes a very small sample from the different data subsets at different locations. We analytically prove and experimentally verify that the algorithm offers high accuracy compared to complete centralization with only a fraction of the communication cost. We show that our algorithm is highly relevant to both earth sciences and aeronautics by describing applications in these domains. The performance of the algorithm is demonstrated on two large publicly available data sets: (i) the NASA MODIS satellite images and (ii) a simulated aviation data set generated by the ‘Commercial Modular Aero‐Propulsion System Simulation’ (CMAPSS). © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 393–406, 2011
Adaptable bandwidth system for reliable wireless transmission

The concept of a simple adaptable bandwidth system is investigated which can be implemented to maintain reliability of the communication link in a non-directed diffused indoor optical wireless environment under varying channel conditions. The proposed system is found to be more sensitive to any variations in signal-to-noise ratio (SNR) at higher noise magnitudes compared to SNR variations at lower noise magnitudes.
Analysis and control of large shunt capacitor bank switching transients

Large capacitor banks at medium voltage levels are finding applications and greater acceptability in industrial distribution systems. However, these can give rise to current and voltage transients, stress the switching devices and insulation systems, and can be detrimental to the sensitive loads, i.e., drive systems. The paper discusses the methodology of the analysis and control of the switching transients. The results of EMTP simulation are presented, where applicable
Citrus Yield Mapping System on a Canopy Shake and Catch Harvester

Machine vision based citrus yield mapping and fruit quality inspection system was developed. The system consisted of 3CCD camera, four halogen lamps, a DGPS receiver, an encoder and a laptop computer. A total of 3,653 images were taken during an experiment on the test bench at the Citrus Research and Education Center, Lake Alfred, Florida and 703 images were used for analysis. The system was also tested on a canopy shake and catch harvester at the Lykes grove located in Fort Basinger, FL. A total of 773 images were acquired as well. Fruit weight was measured in 14 test trials of image acquisitions during the test bench experiment as well as in two test trials of image acquisitions during a field trial with a commercial canopy shake and catch harvester. An image processing algorithm that could identify and inspect fruit qualities was developed. The number of fruit and total fruit areas were measured from the sets of color images using the developed algorithm. Finally number of citrus fruit that was found by the image processing algorithm during the test-bench experiment was compared against actual fruit weight. The coefficient of determination, R2, was 0.962 between them. For a validation purpose of the canopy shake and catch harvester experiment, number of fruit was counted manually from a total of 60 images. Human counting was compared with algorithm counting. The coefficient of determination, R2, was 0.891 between actual and estimated number of fruit.
Nasopharyngeal Colonization and Invasive Disease Are Enhanced by the Cell Wall Hydrolases LytB and LytC of Streptococcus pneumoniae

Background Streptococcus pneumoniae is a common colonizer of the human nasopharynx and one of the major pathogens causing invasive disease worldwide. Dissection of the molecular pathways responsible for colonization, invasion, and evasion of the immune system will provide new targets for antimicrobial or vaccine therapies for this common pathogen. Methodology/Principal Findings We have constructed mutants lacking the pneumococcal cell wall hydrolases (CWHs) LytB and LytC to investigate the role of these proteins in different phases of the pneumococcal pathogenesis. Our results show that LytB and LytC are involved in the attachment of S. pneumoniae to human nasopharyngeal cells both in vitro and in vivo. The interaction of both proteins with phagocytic cells demonstrated that LytB and LytC act in concert avoiding pneumococcal phagocytosis mediated by neutrophils and alveolar macrophages. Furthermore, C3b deposition was increased on the lytC mutant confirming that LytC is involved in complement evasion. As a result, the lytC mutant showed a reduced ability to successfully cause pneumococcal pneumonia and sepsis. Bacterial mutants lacking both LytB and LytC showed a dramatically impaired attachment to nasopharyngeal cells as well as a marked degree of attenuation in a mouse model of colonization. In addition, C3b deposition and phagocytosis was more efficient for the double lytB lytC mutant and its virulence was greatly impaired in both systemic and pulmonary models of infection. Conclusions/Significance This study confirms that the CWHs LytB and LytC of S. pneumoniae are essential virulence factors involved in the colonization of the nasopharynx and in the progress of invasive disease by avoiding host immunity.
Real Time, Non Linear, Dynamic Simulation In Teaching Structures: Elementary To Advanced

The paper describes a project to develop software and teaching methods which employ real-time non-linear dynamic structural simulation in topics ranging from introductory statics to advanced steel design and earthquake engineering. The software is called Arcade and its computation method is based on a physics engine, a method which has been widely applied in computer games. The physics engine approach can easily model unstable structures with very large displacements, making it well suited to study post-buckling behavior, cable structures, and plastic collapse mechanisms. The program performs computations in real time, so that models respond instantly to input from the keyboard and mouse with a game-like interface. While the program has proven effective in lecture demonstrations and course assignments for studying nonlinear behavior in an advanced steel design course, its ability to model unstable structures also makes it useful in teaching elementary statics, since it can model the response of free-floating bodies to unbalanced forces. In addition, its visual presentation makes it suitable for nonengineering students, and the program has been used to teach statics in architecture courses. The program is also ideally suited to teaching concepts of structural redundancy and anti-terror design, since it is possible to see the response of a structure when members are removed interactively: e.g. removing a member from a loaded truss by clicking on the member. The paper describes the program and the teaching methods that have employed it, including lecture demonstrations, a homework problem, and a laboratory exercise.
Efficient circuits for quantum walks

We present an efficient general method for realizing a quantum walk operator corre-sponding to an arbitrary sparse classical random walk. Our approach is based on Groverand Rudolph's method for preparing coherent versions of efficiently integrable probabil-ity distributions [1]. This method is intended for use in quantum walk algorithms withpolynomial speedups, whose complexity is usually measured in terms of how many timeswe have to apply a step of a quantum walk [2], compared to the number of necessary clas-sical Markov chain steps. We consider a finer notion of complexity including the numberof elementary gates it takes to implement each step of the quantum walk with somedesired accuracy. The difference in complexity for various implementation approaches isthat our method scales linearly in the sparsity parameter and poly-logarithmically withthe inverse of the desired precision. The best previously known general methods eitherscale quadratically in the sparsity parameter, or polynomially in the inverse precision.Our approach is especially relevant for implementing quantum walks corresponding toclassical random walks like those used in the classical algorithms for approximating per-manents [3, 4] and sampling from binary contingency tables [5]. In those algorithms,the sparsity parameter grows with the problem size, while maintaining high precision isrequired.
Exploring Protein Binding of Uremic Toxins in Patients with Different Stages of Chronic Kidney Disease and during Hemodialysis

As protein binding of uremic toxins is not well understood, neither in chronic kidney disease (CKD) progression, nor during a hemodialysis (HD) session, we studied protein binding in two cross-sectional studies. Ninety-five CKD 2 to 5 patients and ten stable hemodialysis patients were included. Blood samples were taken either during the routine ambulatory visit (CKD patients) or from blood inlet and outlet line during dialysis (HD patients). Total (CT) and free concentrations were determined of p-cresylglucuronide (pCG), hippuric acid (HA), indole-3-acetic acid (IAA), indoxyl sulfate (IS) and p-cresylsulfate (pCS), and their percentage protein binding (%PB) was calculated. In CKD patients, %PB/CT resulted in a positive correlation (all p < 0.001) with renal function for all five uremic toxins. In HD patients, %PB was increased after 120 min of dialysis for HA and at the dialysis end for the stronger (IAA) and the highly-bound (IS and pCS) solutes. During one passage through the dialyzer at 120 min, %PB was increased for HA (borderline), IAA, IS and pCS. These findings explain why protein-bound solutes are difficult to remove by dialysis: a combination of the fact that (i) only the free fraction can pass the filter and (ii) the equilibrium, as it was pre-dialysis, cannot be restored during the dialysis session, as it is continuously disturbed.