Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Pressure Modulates Electrophysiological Correlates of Early Visual Processing

Background Reactions to sensory events sometimes require quick responses whereas at other times they require a high degree of accuracy–usually resulting in slower responses. It is important to understand whether visual processing under different response speed requirements employs different neural mechanisms. Methodology/Principal Findings We asked participants to classify visual patterns with different levels of detail as real-world or non-sense objects. In one condition, participants were to respond immediately, whereas in the other they responded after a delay of 1 second. As expected, participants performed more accurately in delayed response trials. This effect was pronounced for stimuli with a high level of detail. These behavioral effects were accompanied by modulations of stimulus related EEG gamma oscillations which are an electrophysiological correlate of early visual processing. In trials requiring speeded responses, early stimulus-locked oscillations discriminated real-world and non-sense objects irrespective of the level of detail. For stimuli with a higher level of detail, oscillatory power in a later time window discriminated real-world and non-sense objects irrespective of response speed requirements. Conclusions/Significance Thus, it seems plausible to assume that different response speed requirements trigger different dynamics of processing.
Using Enhanced Concept Map for Student Modeling in Programming Tutors

We have been using the concept map of the domain, enhanced with pedagogical concepts called learning objectives, as the overlay student model in our intelligent tutors for programming. The resulting student model is finegrained, and has several advantages: (1) it facilitates better adaptation during problem generation; (2) it makes it possible for the tutor to automatically vary the level of locality during problem generation to meet the needs of the learner; (3) it clarifies to the learner the relationship among domain concepts when opened to scrutiny; (4) the tutor can estimate the level of understanding of a student in any higher-level concept, not just the concepts for which it presents problems; and (5) two tutors in a domain can affect each other’s adaptation of problems. Prior evaluations have shown that tutors that use enhanced concept maps help improve learning.
Reactive current control of a multi-DC-voltage Cascade STATCOM

This paper presents the reactive power control by a multi-DC-voltage Cascade Static Synchronous Compensator (STATCOM). In order to suppress the harmonics of the output arm voltage, the DC voltages of the series three module in one arm are designed as the voltage ratio of 1.2 : 2 : 6. As a result, 19 voltage levels in the output arm voltage waveform are obtained. The authors propose the voltage control of all the module capacitors based on the stored energy. The effectiveness of the proposed control of the Cascade STATCOM has been verified by experiments.
Usefulness of hyperlink structure for query-biased topic distillation

In this paper, we introduce an information theoretic method for estimating the usefulness of the hyperlink structure induced from the set of retrieved documents. We evaluate the effectiveness of this method in the context of an optimal Bayesian decision mechanism, which selects the most appropriate retrieval approaches on a per-query basis for two TREC tasks. The estimation of the hyperlink structure's usefulness is stable when we use different weighting schemes, or when we employ sampling of documents to reduce the computational overhead. Next, we evaluate the effectiveness of the hyperlink structure's usefulness in a realistic setting, by setting the thresholds of a decision mechanism automatically. Our results show that improvements over the baselines are obtained.
Perception and Importance of Entrepreneurial Leadership in Small and Medium Sized Companies

The competitive advantage is difficult to obtain in ever-changing business environment. A solution to this problem is entrepreneurial leadership which is vital to attain and uphold competitive advantage in the competitive markets. Hence, this research study was aimed at finding the perception and the importance of entrepreneurial leadership for the UK retail sector particularly the small and medium sized retailers. The small and medium sized retailers registered in the Chamber and Commerce Industry in UK were selected for survey intention. The link to a web-designed questionnaire was sent to business managers and CEOs of these retailers. The responses of survey participants showed a positive perception and the importance of the role of entrepreneurial leadership. However, the UK retail sector needs to work on improving the perception of chaos in order to avoid the high failure rate of small and medium retailers.
Statistical Approach of Functional Profiling for a Microbial Community

Background Metagenomics is a relatively new but fast growing field within environmental biology and medical sciences. It enables researchers to understand the diversity of microbes, their functions, cooperation, and evolution in a particular ecosystem. Traditional methods in genomics and microbiology are not efficient in capturing the structure of the microbial community in an environment. Nowadays, high-throughput next-generation sequencing technologies are powerfully driving the metagenomic studies. However, there is an urgent need to develop efficient statistical methods and computational algorithms to rapidly analyze the massive metagenomic short sequencing data and to accurately detect the features/functions present in the microbial community. Although several issues about functions of metagenomes at pathways or subsystems level have been investigated, there is a lack of studies focusing on functional analysis at a low level of a hierarchical functional tree, such as SEED subsystem tree. Results A two-step statistical procedure (metaFunction) is proposed to detect all possible functional roles at the low level from a metagenomic sample/community. In the first step a statistical mixture model is proposed at the base of gene codons to estimate the abundances for the candidate functional roles, with sequencing error being considered. As a gene could be involved in multiple biological processes the functional assignment is therefore adjusted by utilizing an error distribution in the second step. The performance of the proposed procedure is evaluated through comprehensive simulation studies. Compared with other existing methods in metagenomic functional analysis the new approach is more accurate in assigning reads to functional roles, and therefore at more general levels. The method is also employed to analyze two real data sets. Conclusions metaFunction is a powerful tool in accurate profiling functions in a metagenomic sample.
Scalable Block Scheduling for Efficient Multi-database Record Linkage

Record linkage (RL) is a task in data integration that aims to identify matching records that refer to the same entity from different databases. When records from more than two databases are to be linked RL is significantly challenged by the intrinsic exponential growth in the number of potential record comparisons to be conducted. We propose a scalable meta blocking protocol to be used for Multi-Database RL (MDRL) to significantly reduce the complexity of the matching (comparison and classification) phase. Our approach uses a graph structure to schedule the comparison of pairs of blocks with the aim of minimizing the number of repeated and superfluous comparisons between records. We provide an analysis of our approach and conduct an empirical study on large real-world databases.
Exact inference for adaptive group sequential designs

Methods for controlling the type‐1 error of an adaptive group sequential trial were developed in seminal papers by Cui, Hung, and Wang (Biometrics, 1999), Lehmacher and Wassmer (Biometrics, 1999), and Müller and Schäfer (Biometrics, 2001). However, corresponding solutions for the equally important and related problem of parameter estimation at the end of the adaptive trial have not been completely satisfactory. In this paper, a method is provided for computing a two‐sided confidence interval having exact coverage, along with a point estimate that is median unbiased for the primary efficacy parameter in a two‐arm adaptive group sequential design. The possible adaptations are not only confined to sample size alterations but also include data‐dependent changes in the number and spacing of interim looks and changes in the error spending function. The procedure is based on mapping the final test statistic obtained in the modified trial into a corresponding backward image in the original trial. This is an advance on previously available methods, which either produced conservative coverage and no point estimates or provided exact coverage for one‐sided intervals only. Copyright © 2013 John Wiley & Sons, Ltd.
Investigating variability in classroom performance amongst children exhibiting difficulties with early arithmetic

Researchers in both psychology and mathematics education acknowledge that children’s mathematical performance can vary inexplicably from day to day, though there has been little detailed investigation of the form of variability discussed in this paper. The paper builds on research suggesting this might be a particular issue for children considered to have learning difficulties in mathematics. The children concerned were seven- to nine-year-olds taught together for mathematics in a small group with high levels of adult help in assessment, planning and teaching. Observational research was conducted, with the researcher making weekly visits over the course of a year. Findings synthesise a range of evidence for each child gathered both during planned assessment tasks and as part of routine classroom activity. The data are used to chart the performance of individuals over this period. Findings suggest that arithmetical capacities were not fixed and easily assessed, but varied from day to day. This variability is considered in some detail with the aim of offering explanations for perceived differences. Elements such as task presentation and subtle mathematical differences between tasks provide partial explanations. Many differences remain unexplained, and it is argued that variability is in fact a feature of learning. Finally, implications for practice in assessing children and planning for their mathematical development, are considered.
Regression results

RESULTS This chapter describes the results of our analyses to account for the patient and facility characteristics that influence the VA's patient care costs. These results were generated by applying the regression analysis techniques described in the preceding chapter. Additionally, the chapter presents the results of simulations, based on the regression models, of how VISN allocations would change if VERA incorporated various factors that affect patient care costs but that it currently omits. In this chapter, we have focused our discussion on the set of results that we believe to be most relevant for policy purposes. Complete findings from both the regression and simulation analyses are contained in Appendix D. In this section, we summarize the results from the patient-and facility-level regression models. We focus our discussion on the results from the policy model; however, we also include a brief discussion of the key differences between the results of the policy model and those of the fully specified model. As noted in the previous chapter, we restricted the sample in our primary analysis to those veterans who are funded via the current VERA allocation methodology. Specifically , the primary analysis sample excludes Priority 7 veterans who are in the Basic Care patient categories that are currently excluded from VERA workload estimates and patients who are not veterans, such as non-veteran employees. The final sample for FY 2000 includes 3,000,563 veterans. Descriptive statistics are presented in Table 3.1. Approximately 44 percent of the sample is 65 years of age or older, 95 percent is male, 52 percent is married, and 59 percent reported an annual income of $20,000 or less. However, the income data must be interpreted with caution, because the data are based on voluntary self-reports and are missing for 13 percent of the sample. Missing data are also an issue for a number of the demographic variables. For example, we do not have information on race for 32 percent of the sample or on marital status for 5 percent. Again, these data are based on self-reports, and the information is not required for treatment within the system. A plurality of veterans (48 percent) is in Priority Group 5 (see Table 1.1 for definitions of patient priority groups). Priority Groups 1 and 3 are the second
The Particle Swarm : Explosion , Stability , and Convergence in a Multi-Dimensional Complex Space

The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained how it works. Further, traditional versions of the algorithm have had some dynamical properties that were not considered to be desirable, notably the particles’ velocities needed to be limited in order to control their trajectories. The present paper analyzes the particle’s trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A 5-dimensional depiction is developed, which completely describes the system. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system’s convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the optimization power of the particle swarm.
Comparative Evaluation of Semi Fragile Watermarking Algorithms for Image Authentication

Technology has no limits today; we have lots of software available in the market by which we can alter any image. People usually copies image from the internet and after some changes they claim that these are their own properties. Insuring digital image integrity has therefore become a major issue. Over the past few years, watermarking has emerged as the leading candidate to solve problems of ownership and content authentications for digital multimedia documents. To protect authenticity of images semi fragile watermarking is very concerned by researchers because of its important function in multimedia content authentication. The aim of this paper is to present a survey and a comparison of emerging techniques for image authentication using semifragile watermarking. In present paper comprehensive overview of insertion and extraction methods used in different semi fragile water marking algorithm are studied using image parameters, potential application, different algorithms are described and focus is on their comparison according to the properties cited above and future directions for developing a better image authentication algorithm are suggested.
Fetal Heart Rate Analysis for Automatic Detection of Perinatal Hypoxia Using Normalized Compression Distance and Machine Learning

Accurate identification of Perinatal Hypoxia from visual inspection of Fetal Heart Rate (FHR) has been shown to have limitations. An automated signal processing method for this purpose needs to deal with time series of different lengths, recording interruptions, and poor quality signal conditions. We propose a new method, robust to those issues, for automated detection of perinatal hypoxia by analyzing the FHR during labor. Our system consists of several stages: (a) time series segmentation; (b) feature extraction from FHR signals, including raw time series, moments, and usual heart rate variability indices; (c) similarity calculation with Normalized Compression Distance, which is the key element for dealing with FHR time series; and (d) a simple classification algorithm for providing the hypoxia detection. We analyzed the proposed system using a database with 32 fetal records (15 controls). Time and frequency domain and moment features had similar performance identifying fetuses with hypoxia. The final system, using the third central moment of the FHR, yielded 92% sensitivity and 85% specificity at 3 h before delivery. Best predictions were obtained in time intervals more distant from delivery, i.e., 4–3 h and 3–2 h.
Policy-makers’ perceptions on their sex offender laws: the good, the bad, and the ugly

The views and attitudes that lawmakers have about sexual offenders and sexual victimization can be influential in criminal lawmaking. And given the popularity of sex offender laws, policy-makers are central players in how state justice systems respond to sex crimes. Therefore, state-level policy-makers from across the country, who sponsored and passed at least one sex offender law in their state, (n = 61) were interviewed about sex offenders and sex crimes. Policy-makers believe sex offender laws are too broad. The laws extend to nonviolent offenses, low-risk offenders, and thus dilute the law enforcement potency of sex offender registries. Policy-makers view existing sex offender laws as necessary to enhance public safety and as proof that lawmakers are responding to the needs and concerns of the public. Sex offender laws were also discussed as a source of political capital; a way to help ensure reelection. The sexual victimization of children and the media’s coverage of sex crimes were instrumental in these state-level policy-makers’ decisions to sponsor sex offender laws. Policy and research recommendations are offered.
Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface

Linear discriminant analysis (LDA) has been widely adopted to classify event-related potential (ERP) in brain-computer interface (BCI). Good classification performance of the ERP-based BCI usually requires sufficient data recordings for effective training of the LDA classifier, and hence a long system calibration time which however may depress the system practicability and cause the users resistance to the BCI system. In this study, we introduce a spatial-temporal discriminant analysis (STDA) to ERP classification. As a multiway extension of the LDA, the STDA method tries to maximize the discriminant information between target and nontarget classes through finding two projection matrices from spatial and temporal dimensions collaboratively, which reduces effectively the feature dimensionality in the discriminant analysis, and hence decreases significantly the number of required training samples. The proposed STDA method was validated with dataset II of the BCI Competition III and dataset recorded from our own experiments, and compared to the state-of-the-art algorithms for ERP classification. Online experiments were additionally implemented for the validation. The superior classification performance in using few training samples shows that the STDA is effective to reduce the system calibration time and improve the classification accuracy, thereby enhancing the practicability of ERP-based BCI.
A Simple SVD Algorithm for Finding Hidden Partitions

Finding a hidden partition in a random environment is a general and important problem which contains as subproblems many important questions, such as finding a hidden clique, finding a hidden colouring, finding a hidden bipartition, etc. In this paper we provide a simple SVD algorithm for this purpose, addressing a question of McSherry. This algorithm is easy to implement and works for sparse graphs under optimal density assumptions. We also consider an approximating algorithm, which on one hand works under very mild assumptions, but on other hand can sometimes be upgraded to give the exact solution.
Transcriptional Downregulation of Rice rpL32 Gene under Abiotic Stress Is Associated with Removal of Transcription Factors within the Promoter Region

Background The regulation of ribosomal proteins in plants under stress conditions has not been well studied. Although a few reports have shown stress-specific post-transcriptional and translational mechanisms involved in downregulation of ribosomal proteins yet stress-responsive transcriptional regulation of ribosomal proteins is largely unknown in plants. Methodology/Principal Findings In the present work, transcriptional regulation of genes encoding rice 60S ribosomal protein L32 (rpL32) in response to salt stress has been studied. Northern and RT-PCR analyses showed a significant downregulation of rpL32 transcripts under abiotic stress conditions in rice. Of the four rpL32 genes in rice genome, the gene on chromosome 8 (rpL32_8.1) showed a higher degree of stress-responsive downregulation in salt sensitive rice variety than in tolerant one and its expression reverted to its original level upon withdrawal of stress. The nuclear run-on and promoter:reporter assays revealed that the downregulation of this gene is transcriptional and originates within the promoter region. Using in vivo footprinting and electrophoretic mobility shift assay (EMSA), cis-elements in the promoter of rpL32_8. 1 showing reduced binding to proteins in shoots of salt stressed rice seedlings were identified. Conclusions The present work is one of the few reports on study of stress downregulated genes. The data revealed that rpL32 gene is transcriptionally downregulated under abiotic stress in rice and that this transcriptional downregulation is associated with the removal of transcription factors from specific promoter elements.
VLog: A Column-Oriented Datalog System for Large Knowledge Graphs

Rule-based ontology languages are well-suited for data-intensive applications, and rules therefore are an important topic in Semantic Web applications and research up to the present day [7,8,9,10]. The common foundation of many rule languages is Datalog, which was recently the focus of much renewed interest [4]. Modern Datalog systems such as LogicBlox, SocialLite, or EmptyHeaded, now often compete successfully with state-of-the-art techniques in their target area. In spite of these advances, scalability remains a big challenge. We therefore have recently presented a new Datalog system, VLog (Vertical Datalog) [10], which exploits data management technologies used in column stores to compute efficiently rule-based computation on large Knowledge Graphs (KGs). Compared to traditional row stores, column-based approaches have shown performance advantages on analytical workloads [6], but were so far deployed mostly in relational DBMSs. With VLog, we show that Datalog too can benefit from column-store technology. This is not obvious, since the advantages of column stores are set off by a comparatively high cost of updates [2], whereas the iterative computation of Datalog query results can produce large numbers of derived facts that need to be inserted. Indeed, as far as we know, ours is the first work to successfully use column-based technology for Datalog processing. VLog can process arbitrary Datalog rules on top of a range of RDF stores and relational databases. The heart of VLog is an efficient materialization procedure that computes derivations bottom-up. To improve performance, VLog packs a significant number of carefully engineered optimizations on several levels. The goal of this demonstration is to provide hands-on insights into the workings of the system. We will show Datalog processing performed live, on a laptop, through a graphical monitoring interface that displays details of the computation. We will use different datasets, and show the impact of several optimizations. A demonstration provides an ideal format for discussing implementation aspects and practical experiences that would be difficult to present in a paper. Moreover, we will show important features that have never been published before, especially VLog’s new RDBMS bindings, which were introduced only recently.