Miracle-dz/UniFAR-FTUs huggingface dataset:
Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods

i This book is also available in Postscript from over the Internet. To retrieve the postscript le you can use one of the following methods: 1. anonymous ftp to www.netlib. org cd templates get templates.ps quit 2. from any machine on the Internet type: rcp anon@www.netlib. org:templatesstemplates.ps templates.ps 3. send email to netlib@ornl.gov and in the message type: send templates.ps from templates The url for this book is We h a ve divided this book into ve main chapters. Chapter 1 gives the motivation for this book and the use of templates. Chapter 2 describes stationary and nonstationary iterative methods. In this chapter we present both historical development and state-of-the-art methods for solving some of the most challenging computational problems facing researchers. Chapter 3 focuses on preconditioners. Many iterative methods depend in part on preconditioners to improve performance and ensure fast convergence. Chapter 4 provides a glimpse of issues related to the use of iterative methods. This chapter, like the preceding, is especially recommended for the experienced user who wishes to have further guidelines for tailoring a speciic code to a particular machine. It includes information on complex systems, stopping criteria, data storage formats, and parallelism. Chapter 5 includes overviews of related topics such as the close connection between the Lanczos algorithm and the Conjugate Gradient algorithm, block iterative methods, reddblack orderings, domain decomposition methods, multigrid-like methods, and row-projection schemes. The Appendices contain information on how the templates and BLAS software can be obtained. A glossary of important terms used in the book is also provided. The eld of iterative methods for solving systems of linear equations is in constant ux, with new methods and approaches continually being created, modiied, tuned, and some eventually discarded. We expect the material in this book to undergo changes from time to time as some of these new approaches mature and become the state-of-the-art. Therefore, we plan to update the material included in this book periodically for future editions. We w elcome your comments and criticisms of this work to help us in that updating process. Please send your comments and questions by email to A T matrix transpose A H conjugate transpose Hermitian of A A ,1 matrix inverse A ,T the inverse of A T a i;j matrix element a :;j jth matrix column A i;j matrix subblock a i vector element u x ; u xx rst, second derivative with respect to x …
The real- and redshift-space density distribution functions for large-scale structure in the spherical collapse approximation

We use the spherical collapse (SC) approximation to derive expressions for the smoothed redshift-space probability distribution function (PDF), as well as the p-order hierarchical amplitudes Sp, in both real and redshift space. We compare our results with numerical simulations, focusing on the standard CDM model, where redshift distortions are strongest. We find good agreement between the SC predictions and the numerical PDF in real space even for , where σL is the linearly evolved rms fluctuation on the smoothing scale. In redshift space, reasonable agreement is possible only for . Numerical simulations also yield a simple empirical relation between the real-space PDF and the redshift-space PDF: we find that for , the redshift-space PDF, [Pδ(z)], is, to a good approximation, a simple rescaling of the real-space PDF, P[δ], i.e., whereσ and σ(z) are the real-space and redshift-space rms fluctuations, respectively. This result applies well beyond the validity of linear perturbation theory, and it is a good fit for both the standard CDM model and the ΛCDM model. It breaks down for SCDM at , but provides a good fit to the ΛCDM models for σL as large as 0.8.
Methods to choose the best Hidden Markov Model topology for improving maintenance policy

Prediction of physical particular phenomenon is based on partial knowledge of this phenomenon. Theses knowledges help us to conceptualize this phenomenon according to di erent models. Hidden Markov Models (HMM) can be used for modeling complex processes. We use this kind of models as tool for fault diagnosis systems. Nowadays, industrial robots living in stochastic environment need faults detection to prevent any breakdown. In this paper, we wish to nd the best Hidden Markov Model topologies to be used in predictive maintenance system. To this end, we use a synthetic Hidden Markov Model in order to simulate a real industrial CMMS. In a stochastic way, we evaluate relevance of Hidden Markov Models parameters, without a priori knowledges. After a brief presentation of a Hidden Markov Model, we present the most used selection criteria of models in current literature. We support our study by an example of simulated industrial process by using our synthetic model. Therefore, we evaluate output parameters of the various tested models on this process: topologies, learning algorithms, observations distributions, epistemic uncertainties. Finally, we come up with the best model which will be used to improve maintenance policy and worker safety.
Data mining for shopping centres - customer knowledge-management framework

Relying on complex interdependencies between shoppers, retailers and owners, shopping centres are ideal for knowledge at management study. Retailers have been the forefront of data mining, but shopping centres have received little attention. Aspects of customer knowledge management for shopping centres are considered using analogies drawn from an exploratory questionnaire survey.
Prospective Predictors of Patellofemoral Pain Syndrome

Context: Patellofemoral pain syndrome (PFPS) is one of the most common overuse injuries. Objective: To assess the collective evidence of predisposing factors to PFPS. Data Sources: MEDLINE (1960–June 2010), EMBASE (1980–June 2010), and CINAHL (1982–June 2010). Study Selection: Studies were included if patients were asymptomatic at baseline testing (free of PFPS) and were prospectively followed for the development of the disorder. Only studies that assessed at least 1 variable that can be measured at a typical clinic were included. After duplicates were removed, 973 studies were assessed from their titles or abstracts, 20 from the full text, and from these, 7 met the inclusion criteria. Data Extraction: Data were extracted for age, weight, height, sample size, patient type (military vs civilian), follow-up periods, diagnostic methods, and diagnostic criteria. Means and standard deviations were extracted for all outcome variables. Results: Meta-analyses were performed for height, weight, leanness, Q angle, number of sit-ups, knee extension strength, and peak knee valgus angle during landing. Lower knee extension strength was the only variable that was predictive of PFPS (P < 0.01). Other variables that were identified as predictive of PFPS by single studies were vertical jump, push-ups, knee flexion and hip abduction strength, thumb-to-forearm flexibility, quadriceps and gastrocnemius flexibility, genu varum, navicular drop, knee valgus moment at initial contact during landing, social support, and palliative reaction. Conclusions: It appears that anthropometric variables are not associated with PFPS, while knee extension strength deficits appear to be predictors of PFPS.
Transmembrane protein MIG-13 links the Wnt signaling and Hox genes to the cell polarity in neuronal migration

Directional cell migration is a fundamental process in neural development. In Caenorhabditis elegans, Q neuroblasts on the left (QL) and right (QR) sides of the animal generate cells that migrate in opposite directions along the anteroposterior body axis. The homeobox (Hox) gene lin-39 promotes the anterior migration of QR descendants (QR.x), whereas the canonical Wnt signaling pathway activates another Hox gene, mab-5, to ensure the QL descendants’ (QL.x) posterior migration. However, the regulatory targets of LIN-39 and MAB-5 remain elusive. Here, we showed that MIG-13, an evolutionarily conserved transmembrane protein, cell-autonomously regulates the asymmetric distribution of the actin cytoskeleton in the leading migratory edge. We identified mig-13 as a cellular target of LIN-39 and MAB-5. LIN-39 establishes QR.x anterior polarity by binding to the mig-13 promoter and promoting mig-13 expression, whereas MAB-5 inhibits QL.x anterior polarity by associating with the lin-39 promoter and downregulating lin-39 and mig-13 expression. Thus, MIG-13 links the Wnt signaling and Hox genes that guide migrations, to the actin cytoskeleton, which executes the motility response in neuronal migration.
Envy-Free Cake-Cutting in Two Dimensions

We consider the problem of fair division of a two dimensional heterogeneous good among several agents. Applications include division of land as well as ad space in print and electronic media. Classical cake cutting protocols either consider a one-dimensional resource, or allocate each agent several disconnected pieces. In practice, however, the two dimensional shape of the allotted piece is of crucial importance in many applications, e.g., squares or bounded aspect-ratio rectangles are most useful for building houses as well as advertisements. We thus introduce and study the problem of envy-free two-dimensional division wherein the utility of the agents depends on the geometric shape of the allocated pieces (as well as the location and size). In addition to envy-freeness, we require that the fraction allocated to each agent be at least a certain constant that depends only on the shape of the cake and the number of agents. We focus on the case where the allotted pieces must be square and the cakes are either squares or the unbounded plane. We provide algorithms for the problem for settings with two and three agents.
Abstract predicate entailment over points-to heaplets is syntax recognition

Abstract predicates are considered in this paper as abstraction technique for heap-separated configurations, and as genuine Prolog predicates which are translated straight into a corresponding formal language grammar used as validation scheme for intermediate heap states. The approach presented is rule-based because the abstract predicates are rule-based, the parsing technique can be interpreted as an automated fold/unfold of the corresponding heap graph.
Energy Aware Dynamic Resource Consolidation Algorithm for Virtualized Service Centers Based on Reinforcement Learning

In this paper we propose an energy aware dynamic consolidation algorithm for virtualized service centers based on reinforcement learning. The energy awareness is enacted by using the Energy Aware Context Model (EACM) to programmatically represent the current service center context situation by means of ontologies. We have defined the EACM model entropy metric for evaluating the service center greenness level. If the entropy value is above a predefined threshold, the service center is not in a green state. As a consequence, consolidation or dynamic power management actions are selected by means of reinforcement learning and executed to bring back the service center in an energy efficient state. The results are promising showing that the proposed energy aware consolidation algorithm decreases the energy consumption with about 26% from the total energy consumption of a service center.
An Iterative Nonlinear Regression Method for Microarray Data Normalization

Normalization is a prerequisite for almost all follow-up steps in microarray data analysis. Accurate normaliza- tion across different experiments and phenotypes assures a common base for comparative yet quantitative studies using gene expression data. In this paper, we report a novel normalization approach, namely iterative nonlinear regression (INR) method, which exploits concurrent identification of invariantly expressed genes (IEGs) and implementation of nonlinear regression normalization. The INR scheme features an iterative process that performs the following two steps alterna- tively: (1) selection of IEGs and (2) estimation of nonlinear regression function for normalization. We demonstrate the principle and performance of the INR approach on two real microarray data sets. As compared to major peer methods (e.g., linear regression method, Loess method and iterative ranking method), INR method shows an improved perform- ance in achieving low expression variance across replicates and excellent fold-change preservation for differently ex- pressed genes.
Solving multiple-instance and multiple-part learning problems with decision trees and decision rules . Application to the mutagenesis problem

In recent work, Dietterich et al. (1997) have presented the problem of supervised multiple-instance learning and how to solve it by building axis-parallel rectangles. This problem is encountered in contexts where an object may have different possible alternative configurations, each of which is described by a vector. This paper introduces the multiplepart problem, which is more general than the multipleinstance problem, and shows how it can be solved using the multiple-instance algorithms. These two so-called "multiple" problems could play a key role both in the development of efficient algorithms for learning the relations between the activity of a structured object and its structural properties and in inductive logic programming. This paper analyzes and tries to clarify multiple-problem solving. It goes on to propose multiple-instance extensions of classical learning algorithms to solve multiple-problems by learning multiple-decision trees (ID3-M, C4.5-M) and multiple-decision rules (AQ-M, CN2-M,Ripper-M). In particular, it suggests a new multiple-instance entropy function and a multiple-instance coverage function. Finally, it successfully applies the multiple-part framework on the well-known mutagenesis prediction problem.
Real-time lineage analysis reveals oriented cell divisions associated with morphogenesis at the shoot apex of Arabidopsis thaliana

Precise knowledge of spatial and temporal patterns of cell division, including number and orientation of divisions, and knowledge of cell expansion, is central to understanding morphogenesis. Our current knowledge of cell division patterns during plant and animal morphogenesis is largely deduced from analysis of clonal shapes and sizes. But such an analysis can reveal only the number, not the orientation or exact rate, of cell divisions. In this study, we have analyzed growth in real time by monitoring individual cell divisions in the shoot apical meristems (SAMs) of Arabidopsis thaliana. The live imaging technique has led to the development of a spatial and temporal map of cell division patterns. We have integrated cell behavior over time to visualize growth. Our analysis reveals temporal variation in mitotic activity and the cell division is coordinated across clonally distinct layers of cells. Temporal variation in mitotic activity is not correlated to the estimated plastochron length and diurnal rhythms. Cell division rates vary across the SAM surface. Cells in the peripheral zone (PZ) divide at a faster rate than in the central zone (CZ). Cell division rates in the CZ are relatively heterogeneous when compared with PZ cells. We have analyzed the cell behavior associated with flower primordium development starting from a stage at which the future flower comprises four cells in the L1 epidermal layer. Primordium development is a sequential process linked to distinct cellular behavior. Oriented cell divisions, in primordial progenitors and in cells located proximal to them, are associated with initial primordial outgrowth. The oriented cell divisions are followed by a rapid burst of cell expansion and cell division, which transforms a flower primordium into a three-dimensional flower bud. Distinct lack of cell expansion is seen in a narrow band of cells, which forms the boundary region between developing flower bud and the SAM. We discuss these results in the context of SAM morphogenesis.
The Korean Perspective of Helicobacter pylori Infection: Lessons from Japan Policy to Prevent Gastric Cancer

The guideline of the Korean College of Helicobacter and Upper Gastrointestinal Research group for Helicobacter pylori infection was first produced in 1998. Definite indication for H. pylori eradication is early gastric cancer in addition to the previous indications of peptic ulcer (PUD) including scar lesion and marginal zone B cell lymphoma (MALT type). Though treatment regimen was similar, Japan government declared the inclusion of H. pylori eradication in patients with H. pylori-associated chronic gastritis, suggesting the treatment guideline is quite different between Korea and Japan from February 21, 2013. The prime rationale of Japanese extended treatment guideline for H. pylori infection was based on the drastic intention to prevent gastric cancer according to their beliefs that H. pylori eradication can decrease gastric cancer incidence as well as mortality. In this review, the discrepancy in treatment guideline between Korea and Japan will be explained.
Millipedes: Distributed and Set-Based Sub-Task Scheduler of Computing Engines Running on Yarn Cluster

Hadoop YARN is evolving to become the de-facto standard that allows multiple data processing engines such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform. And, there are lots of researches about efficiently managing cluster resources and scheduling parallel jobs over YARN clusters. However, the scheduling of sub-tasks derived from one application doesn't receive enough attention, which still managed by independent data processing engines on YARN. In this paper, we analyze the limitation of sub-task scheduling algorithms of popular data processing engines running on YARN, including MapReduce 2.0, Spark Then, we propose Millipedes: a distributed & set-based sub-task scheduling framework. In Millipedes, YARN's application master dispatches a set of sub tasks to data nodes, and then the local task scheduler of each data node schedules the execution of these sub tasks according to real-time dynamic resource usage.
An integrated approach for detection of masses and macro calcification in mammogram images using dexterous variant median fuzzy c-means algorithm

The proposed contribution uses median fuzzy c-means approach for detection of masses and macrocalcificaiton in mammogram images. Median clustering is a powerful methodology for prototype based clustering of similarity/dissimilarity data. In the MFCM instead of calculating the mean for each cluster to determine its centroid, it calculates the median. This has the consequence of reducing error on the whole clusters with respect to the 1-norm distance metric, as divergent to the square of the 2-norm distance metric. The dataset was collected from Mammographic Image Analysis Society. The result shows the efficient detection of masses and macro calcification better than k-means and Fuzzy C-means.
Sectoral Price Changes and Output Growth: Supply and Demand in General Equilibrium

Price changes and output growth, both at the aggregate and the sectoral level, appear to be negatively correlated. At a basic level, this suggests that sectoral “supply” shocks are more prevalent than sectoral “demand” shocks. However, it is not clear what these sectoral price-output correlations mean once one thinks in terms of general equilibrium. To help us understand the implication of these price-output correlations, this paper examines a multi-sector dynamic general equilibrium model that includes sectoral technology shocks and sectoral demand shocks, as well as aggregate money growth shocks. We show that while a model driven solely by sectoral technology shocks can generate “plausible” price-output correlations, “demand” shocks, particularly sectoral demand shocks, are needed for the model to generate the sectoral price-output correlations observed in the data. We also show that technology shocks do not always look like “supply” shocks. Positive technology shocks to sectors producing goods that are used for investment frequently result in increases in output and prices in other sectors while positive technology shocks to sectors producing goods that are used primarily as intermediate inputs look like supply shocks in other sectors.
Recommending Proper API Code Examples for Documentation Purpose

Code examples are important resources for expressing correct application programming interface (API) usages. However, many framework and library APIs fail in offering sufficient code examples in corresponding API documentations. This is because constructing proper code examples for documentation purpose takes significant developers' efforts. To reduce such effort, this work proposes a methodology, PropER-Doc, that recommends proper code examples for documentation purpose. PropER-Doc accepts queries from API developers and utilizes code search engines (CSEs) to collect corresponding code example candidates. The structural and conceptual links between API elements are captured from the API implementation and available API documents to guide candidate recommendation. During recommendation, PropER-Doc groups collected candidates based on involved API types for distinguishing different API usages. To assist API developers in selecting proper candidates, a diagrammatic presentation and three code example appropriateness metrics are also developed in {PropER-Doc}. Two case studies on Eclipse JDT framework are conducted to confirm the effectiveness of PropER-Doc.
Relational on demand data management for IT-services

Database systems are widely used in technical applications. However, it is difficult to decide which database management system fits best for a certain application. For many applications, different workload types often blend to mixed workloads that cause mixed requirements. The selection of an appropriate database management system is more critical for mixed workloads because classical domains with complementary requirements are combined, e.g., OLTP and OLAP. A definite decision for a database management system is not possible. Hybrid database system are developed to accept this challenge, i.e., these systems combine different storage approaches. However, a mutual optimization in hybrid systems is not available for mixed workloads. We develop a decision-support framework to provide application-performance estimation on a certain database management system on the one hand and to provide query optimization for hybrid database systems on the other hand. In this paper, we combine heuristics to a rule-based query optimization framework for hybrid relational database systems. That is, we aim on support for IT-services with volatile requirements. We evaluate the Aqua2 framework on standard database benchmarks. and show an acceleration of query execution on hybrid database systems.
Robust control of applications by hand-gestures

We use the RGB-D technology of Kinect to control an application with hand-gestures. We use PowerPoint for test. The system can start/end PPT, navigate between slides, capture or release the control of the cursor, and control it through natural gestures. Such a system is useful and hygienic in the kitchen, lavatories, hospital ICUs for touch-less surgery, and the like. The challenge is to extract meaningful gestures from continuous hand motions. We propose a system that recognizes isolated gestures from continuous hand motions for multiple gestures in real-time. Experimental results show that the system has 96.48% precision (at 96.00% recall) and performs better than the Microsoft Gesture Recognition library for swipe gestures.