Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprung's disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package.
3-D Video based Disparity Estimation and Object Segmentation

Stereo video object segmentation is a critical technology of the new generation of video coding, video retrieval and other emerging interactive multimedia fields. Determinations of distinctive depth of a frame features have become more popular in everyday life for automation industries like machine vision and computer vision technologies. This paper examines the evaluation of depth cues through dense of two frame stereo correspondence method. Experimental results show that the method can segment the stationary and moving objects with better accuracy and robustness. The contributions have higher accuracy in matching and reducing time of convergence.
An Empirical Investigation of Relationship between Software Metrics

In this paper, we empirically explore the relationship between existing size and object oriented metrics with four quality measures i.e. abstractness, bug density, method to refactor and dead code over the different versions of an open source software JStock. In order to fulfil our goal, we performed an experiment on by calculating size and OO metrics for different versions of an open source software, analyze them and concluded the behavior over time. Next, we perform Pearson correlation analysis between the chosen metrics and four quality measures because exploring the strength of relationship between these metrics can suggest additional significant understandings than studying them in segregation.
Almost-Unsupervised Cross-Language Opinion Analysis at NTCIR-7

Abstract We describe the Sussex NLCL System entered in the NTCIR-7 Multilingual Opinion Analysis Task (MOAT). Our main focus is on the problem of portability of natural language processing systems across languages. Our system was the only one entered for all four of the MOAT languages, Japanese, English, and Simplified and Traditional Chinese. The system uses an almost-unsupervised approach applied to two of the sub-tasks: opinionated sentence detection and topic relevance detection. Keywords: NTCIR, opinion analysis, relevance detection, cross-language portability. 1 Introduction In our entry to the NTCIR-7 Multilingual Opinion Analysis Task (MOAT) [1] we focused on developing a system that can be easily ported from one language to another.The most obvious way to design such a system is to make it as unsupervised as possible. Unsupervised systems derive all their information from raw, unannotated language data. Unsupervised techniques are promising for systems that need to be ported across domains, text types and languages. However, one of the main problems of unsupervised systems is that they are usually less accurate than supervised systems, which have access to annotated training data. Another problem is that unsupervised methods often need a large amount of raw data to be able derive any useful information. As well as not using annotated data, unsupervised systems typically also contain few built-in assumptions about how a language is structured, encoded for example as rules or via a lexicon. Rule-based systems might consist of hundreds of rules and writing these manually may be as costly in time and linguistic expertise as annotating a corpus to be used by a supervised system. We therefore use no language-specific rules or other kinds of processing which would render our system less portable.In this paper we present a system for opinion analysis which uses an unsupervised approach combined with a very limited amount of manual intervention. We participated in two of the MOAT sub-tasks: opinionated sentence detection (required for all participants) and topic relevance detection (an optional sub-task). We applied our system to all four of the languages, Japanese, English, and Simplified and Traditional Chinese.
Experiences in Close Relationships Revised Child version (ECR-RC): Psychometric evidence in support of a Security factor

Abstract Attachment refers to the innate tendency to form strong and close interpersonal bonds, from infancy through adulthood. Many different ways to assess attachment have been developed, one of them being the use of self-report questionnaires. The Experiences in Close Relationships-Revised is one of the most commonly used instruments to assess adult and late adolescent attachment. Recently, a new and brief child version of this instrument has been published, the 12-item ECR-RC short form. The purpose of the current study was threefold: (1) to test the factorial structure of this form in a sample of Italian adolescents, (2) to test the loadings overlap between the mother and the father forms, and (3) to investigate factors reliability. Using a sample of 961 adolescents (Mage = 14.25, SDage = 1.57), a series of Confirmatory Factor Analyses were performed. The Avoidance – Anxiety structure was not supported, whereas a factor structure including Anxiety, Avoidance, and Security, had a very good fit. Item loadings on these factors were largely equal across mother and father, and internal reliability was high. The results of this study show that the ECR-RC short form is a quick and reliable way to assess attachment in early adolescents. This study also initiates the proposal of an ECR-RC Security factor, to be further validated in future studies.
Planar difference functions

In 1980 Alltop produced a family of cubic phase sequences that nearly meet the Welch bound for maximum non-peak correlation magnitude. This family of sequences were shown by Wooters and Fields to be useful for quantum state tomography. Alltop's construction used a function that is not planar, but whose difference function is planar. In this paper we show that Alltop type functions cannot exist in fields of characteristic 3 and that for a known class of planar functions, x3 is the only Alltop type function.
Molecular Characterization of Transgene Integration by Next-Generation Sequencing in Transgenic Cattle

As the number of transgenic livestock increases, reliable detection and molecular characterization of transgene integration sites and copy number are crucial not only for interpreting the relationship between the integration site and the specific phenotype but also for commercial and economic demands. However, the ability of conventional PCR techniques to detect incomplete and multiple integration events is limited, making it technically challenging to characterize transgenes. Next-generation sequencing has enabled cost-effective, routine and widespread high-throughput genomic analysis. Here, we demonstrate the use of next-generation sequencing to extensively characterize cattle harboring a 150-kb human lactoferrin transgene that was initially analyzed by chromosome walking without success. Using this approach, the sites upstream and downstream of the target gene integration site in the host genome were identified at the single nucleotide level. The sequencing result was verified by event-specific PCR for the integration sites and FISH for the chromosomal location. Sequencing depth analysis revealed that multiple copies of the incomplete target gene and the vector backbone were present in the host genome. Upon integration, complex recombination was also observed between the target gene and the vector backbone. These findings indicate that next-generation sequencing is a reliable and accurate approach for the molecular characterization of the transgene sequence, integration sites and copy number in transgenic species.
Poster abstract: MaWi: A hybrid Magnetic and Wi-Fi system for scalable indoor localization

We present MaWi - a smart phone based scalable indoor localization system. Central to MaWi is a novel framework combining two self-contained but complementary localization techniques: Wi-Fi and Ambient Magnetic Field. Combining the two techniques, MaWi not only achieves a high localization accuracy, but also effectively reduces human labor in building fingerprint databases: to avoid war-driving, MaWi is designed to work with low quality fingerprint databases that can be efficiently built by only one person. Our experiments demonstrate that MaWi, with a fingerprint database as scarce as one data sample at each spot, outperforms the state-of-the-art proposals working on a richer fingerprint database.
Matching Methods with Problems: A Comparative Analysis of Constructive Induction Approaches

This paper provides a taxonomy of constructive induction problems and reports on an empirical comparison of several constructive induction methods. In this paper a representation space is said to be poorly suited for learning because of three types of problems: 1) inappropriate attributes or attribute values, 2) incomplete attribute values, attribute sets or examples and 3) incorrect attributes or examples. Most current constructive induction methods are designed to correct one of these types (or sub-types) of problems which limits the types of problems for which this method is effective. In order to build a more general multistrategy method of constructive induction an understanding of when some methods for constructive induction are useful and when they fail is important. Five methods of constructive induction are evaluated: DCI attribute construction (AQDCI), HCI attribute construction (AQ-HCI(ADD)), HCI attribute removal (AQ-HCI(REMOVE)), HCI construction and removal (AQ-HCI), and attribute-value removal (AQ-SCALE). The results point to the need for a multistrategy constructive induction approach for solving a wide variety of induction problems.
The Risk of Neutropenia and Leukopenia in Advanced Non-Small Cell Lung Cancer Patients Treated With Erlotinib

AbstractEpidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are a critical member of systemic therapy for advanced non-small-cell lung cancer (NSCLC). Erlotinib is the first-generation EGFR-TKIs, the National Comprehensive Cancer Network (NCCN) guidelines recommend it as a first-line agent in patients with sensitizing EGFR mutations. However, the safety of erlotinib plus chemotherapy (CT) or erlotinib alone for advanced NSCLC remains controversial. We carried out a systematic meta-analysis to determine the overall risk of neutropenia and leukopenia associated with erlotinib.PubMed, EMBASE, CBM, CNKI, WanFang database, The Cochrane library, Web of Science, as well as abstracts presented at ASCO conferences and ClinicalTrials.gov were searched to identify relevant studies. RR with 95% CIs for neutropenia and leukopenia were all extracted. The random-effects model was used to calculate pooled RRs and 95% CIs. Power calculation was performed using macro embedded in SAS software after all syntheses were conducted.We identified 12 eligible studies involving 3932 patients. Erlotinib plus CT or alone relative to CT is associated with significantly decreased risks of neutropenia and leukopenia in patients with advanced NSCLC (RR, 0.38; 95% CI, 0.21–0.71; P = 0.00; incidence: 9.9 vs. 35.2%) and (RR, 0.32; 95% CI, 0.11–0.93; P = 0.04; incidence: 3.5 vs. 11.6%), respectively. The subgroup analysis by erlotinb with or without CT showed that erlotinib combine with CT have no significance decrease the relative risks of neutropenia or leukopenia (RR, 0.98; 95% CI, 0.78–1.23; P = 0.87; incidence: 26.2 vs. 30.5%) and (RR, 0.81; 95% CI, 0.34–1.95; P = 0.64; incidence: 6.5 vs. 9.3%), respectively. However, erlotinib alone could decrease incidence of neutropenia (RR, 0.14; 95% CI, 0.07–0.27; P = 0.00; incidence: 3.7 vs. 40.8%) or leukopenia (RR, 0.07; 95% CI, 0.01–0.45; P = 0.01; incidence: 0.8 vs. 15.7%). The power analysis suggests that a power of 61.31% was determined to detect an RR of 0.38 for neutropenia, and 78.03% for an RR of 0.32 for leukopenia.The present meta-analysis suggested that erlotinib could decrease the incidence of neutropenia and leukopenia in patients with advanced NSCLC undergoing erlotinib regardless of whether combined with CT or not. The subgroup analysis revealed that erlotinib combine with CT did not affect the incidence; however, erlotinib alone could significantly decrease the incidence of neutropenia and leukopenia compared with CT alone.
Histogram based watermarking algorithm resist to geometric attacks

The image's histogram use the statistical features of an image and is regardless of the specific distribution of image pixels. With this feature, the histogram-based image watermark algorithm can avoid the desynchronization that geometric attacks bring to image and is robust to geometric attacks. Typical histogram algorithms can resist translation and crop attacks, but the ability to resist rotation, scaling and JPEG compression attacks need improvement. In this paper we divide a group into more bins, give triple relationships of bins. We use election mechanism to improve detection efficiency. Experiment results show our algorithm have higher detection correct rate in rotation, scaling, JPEG compression attacks than previous algorithm.
Manufacture of Ag, Cr and Cu-doped ZnO pellets for gas sensor applications

Starting from a mixture of zinc acetate and potassium hydroxide, zinc oxide (ZnO) powders were prepared by the homogenous precipitation method. The powders were calcined at 800°C for 2h, and subsequently milled in a planetary ball milling during 8 h at a constant speed of 400 rpm. The crystalline phase was determined from X-Ray diffraction (XRD). The morphology of the particles was analyzed by using Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM). From XRD results the wurtzite structure of ZnO was corroborated. SEM images showed a homogenous distribution of the particle size, whereas from TEM analysis the particle size was determined with a higher precision. From the milled and calcined powders, a mixture of ZnO powders with alumina (Al2O3) was developed at different weight ratios for manufacturing 1 mm thick pellets. After measuring the sensing properties of undoped pellets, the [ZnO]/[Al2O3] ratio presenting the maximum sensitivity was taken as reference, and then, doped by impregnation pellets were manufactured to analyze the dopant effect. Three different dopants were used, namely, Cu, Ag, and Cr. The sensing properties of the pellets were tested in an atmosphere of propane, 500 ppm, and different operation temperatures, 200 and 300°C.
Estimate-related disclosures, investor mindset, and the illusion of precision in financial statement estimates

This study examines the joint effect of disclosed measurement information and investor mindset on investors' judgments of estimate precision and firm value. Consistent with psychology theory, results reveal that short horizon investors adopt a relatively concrete mindset and long horizon investors adopt a relatively abstract mindset. As a consequence, disclosing that the fair value of an impaired asset was derived using a model with unobservable inputs strongly influences short horizon investors’ judgments of estimate precision. By contrast, disclosing this information has significantly less impact on the precision judgments of long horizon investors. Further evidence indicates that mindset and disclosed measurement information jointly influence investors’ valuation judgments in a way that is inconsistent with standard models of firm value. Specifically, long horizon investors’ valuation judgments reflect the negative effect of an asset impairment with no significant effect of differences in measurement, consistent with concerns about an illusion of precision in financial statement estimates. Contrary to such concerns, however, short horizon investors’ concrete mindsets lead them to interpret higher (lower) estimate precision as a positive (negative) signal about firm value in its own right. Thus, rather than placing greater weight on a more precisely measured estimate, short horizon investors’ fixation on measurement causes them to discount the negative effect of the asset impairment on firm value when it is measured relatively precisely, but amplify the negative effect in response to imprecise measurement. These results may be useful in judging the effectiveness of existing and proposed disclosures in communicating information about estimate precision. Further, this study makes a unique contribution to the accounting literature by providing theory and empirical evidence that differences in mindset influence investors’ interpretation and use of accounting information in ways that are likely unanticipated by regulators and standard setters relying on standard economic theory.
Feature extraction of face based on the sparse manifold configuration

In the field of recognition, it is a way to improve the rate of recognition by extracting the key feature of the target effectively. In this paper, we proposed an improved method of sparse manifold configuration to solve the problem of feature extraction in face recognition, which is based on manifold learning and the sparsity, and then we used this method to build the configuration and finish the tasks of subspace learning. After a large number of image experiments, we completed the categorization of these images.
Robust distributed indexing for locality-skewed workloads

Multidimensional indexing is crucial for enabling a fast search over large-scale data. Owing to the unprecedented scale of data, extending such indexing technology has recently gained attention in distributed environments. The goal of existing efforts in distributed indexing has been the localization of queries to data residing at a small number of nodes (i.e., locality-preserving indexing) to minimize communication cost. However, considering that workloads often correlate with data locality, such indexing often generates hotspots. Location-based queries are typically skewed to disaster areas during certain periods of time, e.g., during Hurricane Irene, search traffic increased by more than 2000%. To alleviate such hotspots, we propose workload-balancing as an optimization goal. A cost model analytically supporting the need for load balancing is first developed, then a distributed index that evenly distributes the workload is presented. Our empirical study suggests that hotspots degrading search performance can be effectively alleviated. Specifically, when deployed to Amazon EC2, our proposed scheme showed maximum speed-up of 127.7%. Even in hostile settings where workload is not at all correlated with the search criteria, the proposed scheme's performance is comparable to existing approaches optimized for such settings.
Mercurium: Design Decisions for a S2S Compiler

Current research on heterogeneous multiand manycore architectures requires powerful compilers. They must provide flexibility to try new language constructs, and be extensible, to increase the capacity of experimentation with different program transformations. And they must be easy to use, to reduce as much as possible the introduction of defects in the transformations, and allow rapid prototyping. Since 1996, our group has been doing research on program transformations useful to extract parallelism from serial codes, and also improve serial execution. In this period, we have mainly used three compiler platforms to base our work: Parafrase-2 [12], from the Univ. of Illinois at Urbana-Champaign; we did an attempt to use the Open64 infrastructure; and currently we are using Mercurium, our own approach for research on program transformations [4], [6]. Mercurium has gone through several revisions, starting with a C compiler (mcc, 2004), a Fortran95 infrastructure (mf95, 2005), and to the current approach for C/C++ (mcxx, since 2007), and our current work on Fortran 95 (mf95, 2011). We have used this infrastructure in the research for the proper OpenMP extensions to deal with Tasking, Error Management, and User-Defined Reductions, in collaboration with the OpenMP ARB. Ours was the first implementation of OpenMP Tasking. Regarding Error Management and User-Defined Reductions, we expect our contributions to be eventually included in a later specification of OpenMP. Mercurium has supported in-house developments, such as the support of the StarSs collection of extensions – CellSs [11] for the heterogeneous Cell/B.E. architecture, SMPSs [13], [3] for SMP environments, GPUSs for environments with GPUs, and a prototype generating code for FPGAs. Currently, these approaches are being integrated into OmpSs [2], [5], supporting accelerators and cluster environments. We also used it to implement a first approach for a transactional memory OpenMP [9]. The compiler has also been used to generate various kinds of code instrumentation, for later analysis with Paraver [1]. We have also used this infrastructure in a variety of projects: Acotes; SARC; Encore; TEXT; TERAFLUX; and in Mareincognito, the collaboration between BSC and IBM.
A Level Set Method for Anisotropic Geometric Diffusion in 3D Image Processing

A new morphological multiscale method in three-dimensional (3D) image processing is presented which combines the image processing methodology based on nonlinear diffusion equations and the theory of geometric evolution problems. Its aim is to smooth the level sets of a 3D image while simultaneously preserving geometric features such as edges and corners on the level sets. This is obtained by an anisotropic curvature evolution, where time serves as the multiscale parameter. Thereby the diffusion tensor depends on a regularized shape operator of the evolving level sets. As one suitable regularization, local L2 projection onto quadratic polynomials is considered. The method is compared to a related parametric surface approach, and a geometric interpretation of the evolution and its invariance properties is given. A spatial finite element discretization on hexahedral meshes and a semi-implicit, regularized backward Euler discretization in time are the building blocks of the easy-to-code algorithm. Different a.
Resource-Bounded Measure and Randomness

We survey recent results on resource-bounded measure and random-ness in structural complexity theory. In particular, we discuss applications of these concepts to the exponential time complexity classes E and E2. Moreover, we treat time-bounded genericity and stochasticity concepts which are weaker than time-bounded randomness but which suuce for many of the applications in complexity theory.