Miracle-dz/UniFAR-FTUs huggingface dataset:
PARTY - A Software Library for Graph Partitioning

The problem of partitioning a graph into a number of pieces is one of the fundamental tasks in computer science and has a number of applications e.g. in computational mechanics or VLSI design. Finding optimal partitions according to diierent measures is in most cases NP-complete. Nevertheless, a large number of eecient partitioning heuristics have been developed during recent years. The performance of these methods in terms of computation time as well as quality of approximation is heavily innuenced by choices of parameters and certain implementation details. Fortunately, the partitioning problem itself is clearly deened and its description leads to a small interface. Thus, eecient implementations of approximation heuristics can be re-used for diierent applications. The PARTY partitioning library serves a variety of different partitioning methods in a very simple and easy way. Instead of implementing the methods directly, the user may take advantage of the ready implemented methods of the library. All implementations include latest developments to increase the performance of the partitioning heuristics. Two kinds of interfaces allow the use as stand-alone tool as well as the inclusion into application codes. The data-structures used as interface to PARTY are simple and easy to generate. Several research projects currently use the PARTY partitioning library to solve the partitioning problem.
The CCND 1 G 870 A Gene Polymorphism and Leukemia or Non-Hodgkin Lymphoma Risk : a Meta-analysis

Leukemia and non-Hodgkin lymphoma have been selected in our study because of their high incidence rates and mortality rates. Based on observation of WHO, incidence rates of them are estimated at 8.8 and 9.8 per 100, 000 persons in Europe, respectively, and the mortality is 5.1 and 3.5. The incidence and mortality of men are higher than women (http://eco.iarc.fr/EUCAN/Country. aspx? ISOCountryCd=968). According to the degree of cell differentiation and the length of the natural course of the disease, leukemia is classified as acute leukemia and chronic leukemia in general. Acute leukemia is a malignant tumor of the hematopoietic system. With respect to the cell affected, the disease can be subdivided into two major groups as acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), respectively. ALL, with a high degree of malignancy, recurrence rate, poor prognosis features and multiple drug resistance, is a greater difficulty in its treatment. The etiology is not yet fully understood. ALL is the most common diseases of children malignant tumor (Advani et al., 2009; Stieglitz et al., 2013). NonHodgkin lymphoma (NHL), a heterogeneous disease, results from the malignant transformation of lymphocytes and contains multiple subtypes, each with specific
Assessing the Microbial Community and Functional Genes in a Vertical Soil Profile with Long-Term Arsenic Contamination

Arsenic (As) contamination in soil and groundwater has become a serious problem to public health. To examine how microbial communities and functional genes respond to long-term arsenic contamination in vertical soil profile, soil samples were collected from the surface to the depth of 4 m (with an interval of 1 m) after 16-year arsenic downward infiltration. Integrating BioLog and functional gene microarray (GeoChip 3.0) technologies, we showed that microbial metabolic potential and diversity substantially decreased, and community structure was markedly distinct along the depth. Variations in microbial community functional genes, including genes responsible for As resistance, carbon and nitrogen cycling, phosphorus utilization and cytochrome c oxidases were detected. In particular, changes in community structures and activities were correlated with the biogeochemical features along the vertical soil profile when using the rbcL and nifH genes as biomarkers, evident for a gradual transition from aerobic to anaerobic lifestyles. The C/N showed marginally significant correlations with arsenic resistance (p = 0.069) and carbon cycling genes (p = 0.073), and significant correlation with nitrogen fixation genes (p = 0.024). The combination of C/N, NO3 − and P showed the highest correlation (r = 0.779, p = 0.062) with the microbial community structure. Contradict to our hypotheses, a long-term arsenic downward infiltration was not the primary factor, while the spatial isolation and nutrient availability were the key forces in shaping the community structure. This study provides new insights about the heterogeneity of microbial community metabolic potential and future biodiversity preservation for arsenic bioremediation management.
Contourlet detection and feature extraction for automatic target recognition

This research presents a contourlet based detection and feature extraction method for underwater targets. The method operates on Side Scan Sonar (SSS) images and is designed to automatically detect and generate target features for classification. Kernel based classifiers are used to determine the best boundary for separating targets and clutter. A statistically significant target data set is generated by embedding additional synthetic targets into SSS data collected during sea tests. Feature trade off studies show an improvement in classification results with the addition of directional based features.
Analyzing Social Behavior of Software Developers Across Different Communication Channels (S)

Software developers use different project repositories (i.e., mailing list, bug tracking repositories, discussion forums etc.) to interact with each other or to solve software related problems. The growing interest in the usage of social media channels (i.e., Twitter, Facebook, LinkedIn) have also attracted the open source software community and software developers to adopt an identity in order to disseminate project-related information to a wider audience. Much research has been carried out to analyze the social behavior of software developers in different project repositories but so far no one has tried to study the social communication patterns of developers in other social media channels. We in this paper presents a new dimension to the social aspects of software developers and study if the social communication patterns of software developers is different on project repositories and social media channels (i.e., Twitter).
Investigation of BOLD Signal Dependence on CBF and CM&,: The Deoxyhemoglobin Dilution Model

(1) The exponents a and /3 reflect, respectively, the influences of venous blood volume and dHb concentration. The parameter M, which is sample and pulse sequence-specific, reflects the maximum possible increase in fractional BOLD signal that would be produced by complete dilution of venous dHb in the theoretical limit of very high perfusion rates. At constant CMRoz (e.g. during a hypercapnia experiment) using experimentally determined values for a and p of 0.38 [4] and 1.5 [5], respectively, the model predicts an approximately linear relationship between BOLD and perfusion responses for CBF increases of up to 50%. At higher perfusion rates, the predicted relationship becomes increasingly sub-linear and eventually plateaus to an asymptotic level of M. Plots of Eqn. 1 on the BOLD-CBF plane at different levels of relative oxygen consumption can be viewed as iso-CMRO, contours.
Study on frequency control technology of resonant wireless power transmission system

In order to solve the possible detuning problem which may appear in the resonant wireless power transmission (WPT) system, a new type of the frequency control technology is proposed. The frequency of the power supply and the inherent frequency of the transmitting circuit are controlled to scan the possible frequency range of the inherent frequency of the receiving circuit in order to analyze the inherent frequency of the receiving circuit by using the microprocessor, capacitance matrix and other circuit device in the transmit terminal of the system. And the frequency of the power supply and the inherent frequency of the transmitting circuit are made to track synchronously the inherent frequency of the receiving circuit. The circuit model of the frequency control technology of the resonant WPT system is established. The control principle of the system is then analyzed. The frequency control process of the system is given. The experimental system is designed and the correlative experiments are made. The results show that the frequency control technology can effectively solve the possible detuning problem in the system. This provides a powerful technology support for the practical application of the resonant WPT system.
2D Flow around a Rectangular Cylinder: A Computational Study

The unsteady flow around a rectangular cylinder is an area of great research for scientist for several years. A two-dimensional unsteady flow past a rectangular cylinder has been investigated numerically for the low Reynolds numbers (flow is laminar). Gambit has been used throughout this work to generate the geometry and meshes and the computational fluid dynamics analysis is done using fluent software. The influence of vortical structure and pressure distribution around the section of rectangular cylinders are
Test Coverage Metric for Two-Staged Language with Abstract Interpretation

As a program written in multi-staged language can generate and execute code fragments in execution time, it is hard to predict how many code fragments will be generated in execution time. Therefore, current test coverages are not likely to give right answers when they are apply to a program written in multi-staged language because the program size could not be estimated easily. In this paper, we present static analysis which detects code fragments generated in execution time using abstract interpretation and prove the correctness of analyzer. Moreover we propose new test coverage for multi-staged language using the result of analysis.
Low Rank Metric Learning with Manifold Regularization

In this paper, we present a semi-supervised method to learn a low rank Mahalanobis distance function. Based on an approximation to the projection distance from a manifold, we propose a novel parametric manifold regularizer. In contrast to previous approaches that usually exploit side information only, our proposed method can further take advantages of the intrinsic manifold information from data. In addition, we focus on learning a metric of low rank directly, this is different from traditional approaches that often enforce the l_1 norm on the metric. The resulting configuration is convex with respect to the manifold structure and the distance function, respectively. We solve it with an alternating optimization algorithm, which proves effective to find a satisfactory solution. For efficient implementation, we even present a fast algorithm, in which the manifold structure and the distance function are learned independently without alternating minimization. Experimental results over 12 standard UCI data sets demonstrate the advantages of our method.
Epigenetic disruption of cadherin-11 in human cancer metastasis

Little is known about the molecular events occurring in the metastases of human tumours. Epigenetic alterations are dynamic lesions that change over the natural course of the disease, and so they might play a role in the biology of cancer cells that have departed from the primary tumour. Herein, we have adopted an epigenomic approach to identify some of these changes. Using a DNA methylation microarray platform to compare paired primary tumour and lymph node metastatic cell lines from the same patient, we observed cadherin‐11 promoter CpG island hypermethylation as a likely target of the process. We found that CDH11 DNA methylation‐associated transcriptional silencing occurred in the corresponding lymph node metastases of melanoma and head and neck cancer cells but not in the primary tumours. Using in vitro and in vivo cellular and mouse models for depleted or enhanced CDH11 activity, we also demonstrated that CDH11 acts as an inhibitor of tumour growth, motility and dissemination. Most importantly, the study of CDH11 5′‐CpG island hypermethylation in primary tumours and lymph node metastases of cancer patients showed this epigenetic alteration to be significantly confined to the disseminated cells. Overall, these results indicate the existence of metastasis‐specific epigenetic events that might contribute to the progression of the disease. Copyright © 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Knowledge Management for Product Development: A Review

Engineering product development is becoming increasingly collaborative and knowledge-intensive in today's industry. On one hand, the product development process comprises various tasks that involve extensive knowledge exchange and communication among geographically distributed teams. On the other hand, the product development teams usually reuse engineering knowledge from a vast well of previous design experience when solving new design problems. Hence, there is a strong need to capture, store and reuse the knowledge for product development. In this paper, the different understanding and classifications of engineering knowledge are presented. Two aims of knowledge management for product development are analyzed. Furthermore, the researches of engineering knowledge management are summarized based on critical phases of knowledge management process. Finally, future research directions of knowledge management in engineering are discussed.
On routing asymmetry in the Internet

Routing asymmetry in the Internet can significantly affect the manner in which we model and simulate its behavior. In this paper, we study routing asymmetry in the Internet and present quantitative evaluations on the extent of such asymmetry today. Our quantitative evaluations provide a measure of the difference between the forward and reverse paths between two end points. Routing asymmetry has not been studied extensively before; this is primarily due to the lack of a systematic approach for quantifying asymmetry except for simply computing the difference between the forward and reverse path lengths. By applying our framework for representing asymmetry, we quantify routing asymmetry for both US higher education academic networks and general commercial networks at two different levels: the autonomous system (AS) level and the router (or link) level. We take into consideration, not only the difference in the forward and reverse path lengths, but also the AS and link identities and the sequence in which these entities appear on the paths. We measure the AS level routing asymmetry, and provide upper lower bounds on link level routing asymmetry. Our studies show that academic networks appear to be more symmetric than general commercially deployed networks. Furthermore, our studies demonstrate that routing asymmetry exhibits a skewed distribution i.e., a few end-points seem to display a higher extent of participation on asymmetric routes.
Hilbert pair of almost symmetric orthogonal wavelets with arbitrary center of symmetry

This paper proposes a new method for designing a class of Hilbert pairs of almost symmetric orthogonal wavelets with arbitrary center of symmetry. Two scaling low-pass filters are designed simultaneously to satisfy the specified degree of flatness of group delays, vanishing moments and orthogonality condition of wavelets, along with improved analyticity. Therefore, the resulting scaling low-pass filters have flat group delay responses and the specified number of vanishing moments. Moreover, the difference of the frequency responses between two scaling low-pass filters can be effectively minimized to improve the analyticity of complex wavelets. The condition of orthogonality is linearized, and then an iterative procedure is used to obtain the filter coefficients. Finally, several examples are presented to demonstrate the effectiveness of the proposed design procedure.
Learning Predictive Features in Affordance based Robotic Perception Systems

This work is about the relevance of Gibson's concept of affordances for visual perception in interactive and autonomous robotic systems. In extension to existing functional views on visual feature representations, we identify the importance of learning in perceptual cueing for the anticipation of opportunities for interaction of robotic agents. We investigate how the originally defined representational concept for the perception of affordances - in terms of using either optical flow or heuristically determined 3D features of perceptual entities should be generalized to using arbitrary visual feature representations. In this context we demonstrate the learning of causal relationships between visual cues and predictable interactions, and emphasize on a novel framework for cueing and hypothesis verification of affordances that could play an important role in future robot control architectures. We argue that affordance based perception should enable systems to react to environment stimuli both more efficient and autonomous, and provide a potential to plan on the basis of responses to more complex perceptual configurations. We verify the concept with a concrete implementation applying state-of-the-art visual descriptors and regions of interest within a simulated robot scenario and prove that these features were successfully selected for predicting opportunities of robot interaction
CELLULAR POLYPROPYLENE FERROELECTRET FILM: PIEZOELECTRIC MATERIAL FOR NON-CONTACT ULTRASONIC TRANSDUCERS

Electromechanical properties of the cellular polypropylene ferroelectret films (EMFIT) evidence their high potential for the air-coupled ultrasonic applications. The disadvantage with respect to their low coupling factor could be compensated by the extre mely low acoustic impedance. A figure of merit is proposed to compare and estimate piezoelectric materials intended for the air-coupled ultrasonic applications. Electron beam evaporation technology was adapted to the EMFIT films and films with two-sided Au and Al electrodes were prepared without reducing or suppressing of the electromechanical properties. Finally, air-coupling ultra sound transducers based on the EMFIT films were developed and characterized.
Discovering biological processes from microarray data using independent component analysis Extended Abstract

We propose a hypothesis-free methodology for discovering genome-wide expression patterns specific to underlying biological processes from DNA microarray expression data. We apply linear and nonlinear independent component analysis (ICA) as a tool for decomposing microarray data into statistically independent components. Each component represents a gene expression pattern of a putative underlying biological process. Genes that exhibit significant upregulation or downregulation within each component are grouped into clusters and putative biological meaning is assigned to each component by using the annotations of genes within the component’s clusters. We test the statistical significance of enrichment of specific gene annotations within each cluster. ICAbased clustering outperforms other leading methods in constructing functionally coherent clusters on a variety of datasets, supporting our model of expression data as a mixture of statistically independent biological processes. Comparison of the performance of several ICA algorithms, including a novel nonlinear method, shows that a natural-gradient maximum-likelihood method performs well in all datasets, while the nonlinear method performs best in the smaller datasets. We conclude that ICA is a versatile technique that can extract linear and nonlinear features in gene expression, and can group genes in non-mutually exclusive clusters with strong functional coherence. INTRODUCTION Microarray technology has enabled high-throughput genome-wide measurements of gene transcript levels, promising to provide insight into biological processes involved in gene regulation. To aid such discoveries, mathematical and computational tools are needed that are versatile enough to capture the underlying biology, and simple enough to be applied efficiently on large datasets. Analysis tools fall broadly in two categories: supervised and unsupervised approaches. When prior knowledge can group samples into different classes (e.g., normal versus cancer tissue), supervised approaches can be used for finding gene expression patterns (features) specific to each class, and for class prediction of new samples (Ando et al. 2002, Brown et al. 2000, Golub et al. 1999, Mukherjee et al. 1999, Atul 2002). Unsupervised (hypothesis-free) approaches are important for discovering novel biological mechanisms, for revealing genetic regulatory networks and for analyzing large datasets for which little prior knowledge is available. Here we apply linear and nonlinear independent component analysis (ICA) as a versatile unsupervised approach for microarray analysis, and evaluate its performance against other leading unsupervised methods. Unsupervised analysis methods for microarray data can be divided into three categories: clustering approaches, model-based approaches, and projection methods. Clustering approaches group genes and experiments with similar behavior (Eisen et al. 1998, Tavazoie et al. 1999, Tamayo et al. 1999, Ben-Dor et al. 1999, Kim et al. 2001), making the data simpler to analyze (Kaminski
Assessment and comparison of conventional motor starters and modern power electronic drives for induction motor starting characteristics

Induction motor is the workhouse of industry and particular attention is required to monitor and control it. An induction motor draws a high starting current and develops a high torque during start-up. The high starting current often causes problem such as voltage dips which will be penalized by the energy companies. Various motor starters were introduced to overcome this problem. The different motors starters available in Malaysian market are discussed and analyzed in this paper; these include both conventional electromechanical starters and power electronic drives. The advantages, disadvantages and energy saving features of each type of starter mentioned will also be discussed. An analysis of the industrial applications and the demand of these starters in Malaysia are also made and to finally conclude, this paper will also provide a suggestion of which starter is most suitable for an application based on the stated constraints.
Preserving QoI in participatory sensing by tackling location-spoofing through mobile WiFi hotspots

The Quality of Information (QoI) in Participatory Sensing (PS) systems largely depends on the location accuracy of participating users. However, users could easily provide false information through Location Spoofing Attacks (LSA). Existing PS systems are not able to efficiently validate the position of users in large-scale outdoor environments, thus being prone to reduced QoI. In this paper we present an efficient scheme to secure PS systems from LSAs. In particular, the user location is verified with the help of mobile WiFi hot spots (MHSs), which are users activating WiFi interface on their smart phones and waiting connections from nearby users, and thereby validating their position inside the sensing area. A reputation-based algorithm is proposed to rule out sensing reports of location-spoofing users, thereby increasing the reliability of the PS system. The effectiveness of our scheme is analyzed by real-world experiments and simulation study.