Miracle-dz/UniFAR-FTUs huggingface dataset:
Photometric entropy of stellar populations and related diagnostic tools

We discuss, from a statistical point of view, some leading issues that deal with the study of stellar populations in fully or partially unresolved aggregates, like globular clusters and distant galaxies. A confident assessment of the effective number and luminosity of stellar contributors can provide, in this regard, a very useful interpretative tool to properly assess the observational bias coming from crowding conditions or surface brightness fluctuations. These arguments have led us to introduce a new concept of "photometric entropy" of a stellar population, whose impact on different astrophysical aspects of cluster diagnostic has been reviewed here.
Supplementary Documents for “ Semi-Crowdsource Clustering : Generalizing Crowd Labeling by Robust Distance Metric Learning ”

where T (A′) denotes the space spanned by the elements of the form uky and xv> k , for 1 ≤ k ≤ r, Ω(A′) denotes the space of matrices that have the same support toA′, ‖ ·‖ denotes the spectral norm and ‖ · ‖∞ denotes the largest entry in magnitude. Lemma 1. Let A∗ ∈ RN×N be a similarity matrix of rank r obeying the incoherence properties (A1) and (A2), with μ = max(μ0, μ1). Suppose we observe m1 entries of A∗ recorded in Ã
Object recognition by fast hypothesis generation and reasoning about object interactions

We present a two-step approach for recognizing multiple 3-D objects in single 2-D images. In the first step, hypotheses of object instances are generated using a memory-based technique. This technique relies on an array, which is computed off-line, that associates a large number of object poses with corresponding image features. During actual recognition, the array serves as a discrete approximation of the inverse projection function, and each image feature returns a set of poses that are accumulated by a generalized Hough transform. In the second step, the configuration of hypotheses that best interprets the image is calculated using a Bayesian network. The network represents both visual effects, such as the creation and occlusion of image features, and physical constraints, such as object interference.
Investigating the Concept of Information Security Culture Investigating the Concept of Information Security Culture

The concept of an ‘information security culture’ is relatively new. A review of published research on the topic suggests that it is not the information security panacea that has been suggested. Instead it tends to refer to a range of existing techniques for addressing the human aspect of information security, oversimplifying the link between culture and behaviour, exaggerating the ease with which a culture can be adjusted, and treating culture as a monolith, set from the top. Evidence for some of the claims is also lacking. The paper finds that the term ‘information security culture’ is ambiguous and vague enough to suggest the possibility of achieving an almost mystical state whereby behaviour consistent with information security is second nature to all employees, but when probed does not deliver. Instead, future research should be clear about what it considers information security culture to be, should provide evidence for claims, and should take complexity and context seriously.
The natural selection of fidelity in social learning

Social learning mechanisms are usually assumed to explain both the spread and the persistence of cultural behavior. In a recent article, we showed that the fidelity of social learning commonly found in transmission chain experiments is not high enough to explain cultural stability. Here we want to both enrich and qualify this conclusion by looking at the case of song transmission in song birds, which can be faithful to the point of being true replication. We argue that this high fidelity results from natural selection pressure on cognitive mechanisms. This observation strengthens our main argument. Social learning mechanisms are unlikely to be faithful enough to explain cultural stability because they are generally selected not for high fidelity but for generalization and adjustment to the individual’s needs, capacities and situation.
Assisted oocyte activation overcomes fertilization failure in globozoospermic patients regardless of the DPY19L2 status.

STUDY QUESTION Does DPY19L2 status influence intracytoplasmic sperm injection (ICSI) outcomes with or without assisted oocyte activation (AOA)? SUMMARY ANSWER DPY19L2 mutations have no major impact on ICSI outcomes in globozoospermic patients. WHAT IS KNOWN ALREADY Globozoospermia is a rare and severe teratozoospermia characterized by round-headed spermatozoa lacking an acrosome. Recently, it has been shown that DPY19L2 mutations can be found in a vast majority of, but not all, globozoospermic patients (66.7%). These patients suffer from primary infertility due to a sperm-related oocyte activation deficiency secondary to the absence of an acrosome that can be overcome by the application of AOA. STUDY DESIGN, SIZE, DURATION Cohort study, retrospective, 34 patients, 83 cycles. MATERIALS, SETTING, METHODS Clinical and biologic data were collected from 29 patients mutated for DPY19L2 and 5 non-mutated patients. In total, 35 ICSI cycles using AOA and 48 conventional ICSI cycles were included in the analysis. Patients were divided into groups according to whether or not they were mutated for DPY19L2 and whether or not they received AOA. MAIN RESULTS AND THE ROLE OF CHANCE Regardless of the presence of a DPY19L2 mutation, the fertilization rates with AOA are restored to normal when compared with conventional ICSI in our cohort of globozoospermic patients. Also, when performing ICSI plus AOA, both mutated and non-mutated cases have similar positive hCG rates, ongoing pregnancy rates and live birth rates per transfer. On the contrary, the fertilization rate in globozoospermic patients using conventional ICSI is correlated with the presence of a DPY19L2 mutation, with slightly better, although still very low, fertilization rates in patients carrying a DPY19L2 mutation. Nevertheless, when performing conventional ICSI, both mutated and non-mutated cases have similar very low positive hCG rates, ongoing pregnancy rates and live birth rates per transfer. LIMITATIONS A limitation of this study is the low number of included non-mutated cases. WIDER IMPLICATIONS OF THE FINDINGS We propose a pathway for the clinical management of globozoospermic patients depending on the phenotype that includes several diagnostic and therapeutic steps. STUDY FUNDING/COMPETING INTEREST(S) None.
Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology

Propensity score methods were proposed by Rosenbaum and Rubin (Biometrika 70 (1983) 41-55) as central tools to help assess the causal effects of interventions. Since their introduction more than two decades ago, they have found wide application in a variety of areas, including medical research, economics, epidemiology and education, es- pecially in those situations where randomized experiments are either difficult to perform, or raise ethical questions, or would require exten- sive delays before answers could be obtained. In the past few years, the number of published applications using propensity score methods to evaluate medical and epidemiological interventions has increased dra- matically. Nevertheless, thus far, we believe that there have been few applications of propensity score methods to evaluate marketing inter- ventions (e.g., advertising, promotions), where the tradition is to use generally inappropriate techniques, which focus on the prediction of an outcome from background characteristics and an indicator for the in- tervention using statistical tools such as least-squares regression, data mining, and so on. With these techniques, an estimated parameter in the model is used to estimate some global "causal" effect. This practice can generate grossly incorrect answers that can be self-perpetuating: polishing the Ferraris rather than the Jeeps "causes" them to continue to win more races than the Jeeps , visiting the high-prescribing doc- tors rather than the low-prescribing doctors "causes" them to continue to write more prescriptions. This presentation will take "causality" seri- ously, not just as a casual concept implying some predictive association in a data set, and will illustrate why propensity score methods are gen- erally superior in practice to the standard predictive approaches for estimating causal effects.
Inhibition of antibody-dependent lymphocyte cytotoxicity by homologous restriction factor incorporated into target cell membranes

The 65 kD homologous restriction factor (HRF) was isolated from normal human erythrocytes (E) by immunoadsorption using rabbit anti-human HRF. The protein was radiolabeled and incorporated into the membrane of sheep erythrocytes (Es). Es bearing HRF exhibited a markedly reduced susceptibility to reactive lysis by C5b-9. Es-HRF with 1,000-3,000 HRF molecules per cell and sensitized with rabbit IgG anti-Es also were less susceptible to lysis by human large granular lymphocytes (LGL) than untreated Es sensitized with IgG antibody. Similarly, human E of a patient with paroxysmal nocturnal hemoglobinuria (PNH), lacking HRF and sensitized with IgG antibody underwent lysis by human LGL. Lysis was abrogated by incorporation of isolated human HRF. Incorporation of human decay-accelerating factor (DAF) into sensitized Es had no effect on antibody-dependent, cell-mediated cytotoxicity. Furthermore, lysis of Es by the isolated cytolytic C9-related protein (C9RP) of human cytotoxic lymphocytes could be inhibited by cell bound human HRF. These results suggest that HRF inhibits channel formation not only by C5b-9, but also by cytotoxic lymphocytes.
Curbing Chronic Absence in the Early Grades.

ComstoCk/Getty ImaGes What many educators might not realize, however, is that thousands of our youngest students are at risk because they are chronically absent, missing 10 percent or more of school over the course of an academic year. Nationwide, nearly one out of 10 kindergartners and first graders is chronically absent. While chronic absence is not a problem everywhere, in some communities, it affects as much as a quarter of all the children in kindergarten through third grade across an entire district, or as many as half the students in a single elementary school. If large numbers of students miss school, then academic performance for all students can suffer, as teachers divert their attention from the rest of the class to meet the needs of children who are frequently absent. most educators, especially elementary principals, know from experience and common sense that attending school regularly during the early elementary years is essential. Young children need enough time in the classroom to gain the basic social and academic skills that will equip them to succeed long term in school. This understanding is confirmed by the report Present, Engaged and Accounted For, which documents that missing nearly a month or more of school during kindergarten is associated with lower academic achievement in first grade. The impact was most profound for children in poverty, for whom poor kindergarten attendance predicted low fifth-grade performance. Hedy N. Chang
Welfare Attitudes and Expressions of (Trans)national Solidarity

The article explores the extent to which Europeans’ welfare attitudes explain (trans)national solidarity behavior. We set our analyses against the backdrop of the broader debate of welfare state consequences: Does a strong welfare state that is considered to take care of those in need diminish or strengthen citizens’ motivations to become engaged in helping others? We distinguish individuals’ solidarity behavior toward others within the welfare state, that is, citizens within one’s country, and outside the welfare state community of the respondents’ particular country. We further distinguish different others outside the welfare state, that is, between refugees, taking the refugee crisis in the European Union (EU) as a prime example, and citizens living in other countries—in EU countries and non-EU countries. As far as the main explanatory variables are concerned, we derive from the concept of “multidimensional welfare attitudes” and focus on five crucial dimensions of these attitudes, that is, welfare goals, range, degree, redistribution, and outcome. We draw on data collected within the EU project TransSOL and calculate a set of multilevel logistic regression models controlling for a wide range of individual (sociodemographic, economic, and political) variables. Overall, we observe that a “crowding in” effect, that is, higher support of the welfare state, goes in line with solidarity activity toward others including both “outsiders” and “insiders” of the national community.
Decreased Anxiety, Altered Place Learning, and Increased CA1 Basal Excitatory Synaptic Transmission in Mice with Conditional Ablation of the Neural Cell Adhesion Molecule L1

L1, a neural cell adhesion molecule of the immunoglobulin superfamily, is involved in neuronal migration and differentiation and axon outgrowth and guidance. Mutations in the human and mouse L1 gene result in similarly severe neurological abnormalities. To dissociate the functional roles of L1 in the adult brain from developmental abnormalities, we have generated a mutant in which the L1 gene is inactivated by cre-recombinase under the control of the calcium/calmodulin-dependent kinase II promoter. This mutant (L1fy+) did not show the overt morphological and behavioral abnormalities observed previously in constitutive L1-deficient (L1-/-) mice; however, there was an increase in basal excitatory synaptic transmission that was not apparent in L1-/- mice. Similar to L1-/- mice, no defects in short- and long-term potentiation in the CA1 region of the hippocampus were observed. Interestingly, L1fy+ mice showed decreased anxiety in the open field and elevated plus-maze, contrary to L1-/- mice, and altered place learning in the water maze, similar to L1-/- mice. Thus, mice conditionally deficient in L1 expression in the adult brain share some abnormalities, but also display different ones, as compared with L1-/- mice, highlighting the role of L1 in the regulation of synaptic transmission and behavior in adulthood.
Power-Efficient Work Distribution Method for CPU-GPU Heterogeneous System

As the system scales up continuously, the problem of power consumption for high performance computing (HPC) system becomes more severe. Heterogeneous system integrating two or more kinds of processors, could be better adapted to heterogeneity in applications and provide much higher energy efficiency in theory. Many studies have shown heterogeneous system is preferable on energy consumption to homogeneous system in a multi-programmed computing environment. However, how to exploit energy efficiency (Flops/Watt) of heterogeneous system for a single application or even for a single phase in an application has not been well studied. This paper proposes a power-efficient work distribution method for single application on a CPU-GPU heterogeneous system. The proposed method could coordinate inter-processor work distribution and per-processor’s frequency scaling to minimize energy consumption under a given scheduling length constraint. We conduct our experiment on a real system, which equips with a multi-core CPU and a multi-threaded GPU. Experimental results show that, with reasonably distributing work over CPU and GPU, the method achieves 14% reduction in energy consumption than static mappings for several typical benchmarks. We also demonstrate that our method could adapt to changes in scheduling length constraint and hardware configurations.
A Simple Data-Muling Protocol

More and more industrial infrastructures consider the use of industrial wireless sensor network (IWSN) technology to perform control activities in industry. The benefits stemmed from IWSNs include flexible access (no wires are required) to elements that may interact with the factory floor. However, this support introduces new challenges: muling the almost permanent connectivity given by wired infrastructures is no longer available in IWSNs. To address this challenge, this paper explores data muling as the ability of transferring data between different elements within an industrial infrastructure. The proposed data-muling protocol uses a dual discovery and update (D&U) strategy. The prototype evaluation carried out on 6LowPan over IEEE 802.15. 4 motes provides some insights on the performance that this type of infrastructure may offer within an industrial context. This paper also offers an extensive benchmark that may be used to test bounds and limitations of the proposed D&U protocol on the industrial use-cases.
Weaving Theory into Practice: Preparing Transformative Leaders for Social Justice.

The purpose of this paper is to explore the effects of an alternative, transformative pedagogy that may assist us in responding to the urgent call for changes in the way educational leaders are prepared and developed. Within the contextual loom of preparation programs, the two theoretical perspectives of Transformative Learning Theory and Critical Social Theory are interwoven with the three pedagogical strategies of critical reflection, rational discourse, and policy praxis to increase students’ awareness, acknowledgement, and action regarding issues of social justice and equity.
Association Rule Mining Analyzation Using Column Oriented Database

The logical view of data is a two dimensional table and the physical storage is a single dimensional. Two approaches exist to map two dimensional data on to a single dimensional storage: Row oriented and Column oriented. Common database applications are developed using traditional roworiented database systems. Data Mining (DM) is a promising research area, deals with huge data with large numbers of attributes and records. DM algorithms are more analytical in nature with the goal of reading through the data to gain new insight and use it for planning make Column oriented database systems more preferable. The Column oriented database systems show better performance than traditional database on analytical workloads such as those found in data warehouses, decision support, and business intelligence applications. The Column oriented databases like MonetDB is utilized for performance analysis of SQL queries. This paper is focused on the utilization of Column oriented databases like MonetDB with Oracle 11g the famous Row oriented database for execution time analysis for famous DM algorithm: APRIORI. Experiment results show the faster execution time of MonetDB compare to Oracle for different supports and justifies the suitability of the Column oriented database for such data mining algorithm.
Handling uncertain sensor data in vision-based camera tracking

A hybrid approach for real-time markerless tracking is presented. Robust and accurate tracking is obtained from the coupling of camera and inertial sensor data. Unlike previous approaches, we use sensor information only when the image-based system fails to track the camera. In addition, sensor errors are measured and taken into account at each step of our algorithm. Finally, we address the camera/sensor synchronization problem and propose a method to resynchronize these two devices online. We demonstrate our method in two example sequences that illustrate the behavior and benefits of the new tracking method.
Applying Text Classification Algorithms in Web Services Robustness Testing

Testing web services for robustness is an effective way of disclosing software bugs. However, when executing robustness tests, a very large amount of service responses has to be manually classified to distinguish regular responses from responses that indicate robustness problems. Besides requiring a large amount of time and effort, this complex classification process can easily lead to errors resulting from the human intervention in such a laborious task. Text classification algorithms have been applied successfully in many contexts (e.g., spam identification, text categorization, etc) and are considered a powerful tool for the successful automation of several classification-based tasks. In this paper we present a study on the applicability of five widely used text classification algorithms in the context of web services robustness testing. In practice, we assess the effectiveness of Support Vector Machines, Naïve Bayes, Large Linear Classification, K-nearest neighbor (Ibk), and Hyperpipes in classifying web services responses. Results indicate that these algorithms can be effectively used to automate the identification of robustness issues while reducing human intervention. However, in all mechanisms there are cases of misclassified responses, which means that there is space for improvement.
Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise

Denoising algorithms based on wavelet thresholding replace small wavelet coefficients by zero and keep or shrink the coefficients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use generalized cross validation. This procedure does not require an estimation for the noise energy. Originally, this method assumes uncorrelated noise. In this paper, we describe how we can extend it to images with correlated noise.
2012 International Conference on Information Management, Innovation Management and Industrial Engineering an Improved Particle Swarm Optimization for Multi-objective Discrete Optimization

In this paper an improved multi-objective particle swarm optimization algorithm (IMOPSO) is designed to efficiently solve multi-objective discrete optimization problems. In the IMOPSO, a novel similarity-based selecting scheme is used to selection of the global best solution and individual best solution for each particle, and an external set truncation strategy is used to maintain the diversity in the Pareto optimal solutions. Additionally, a local search subroutine is applied on every particle to improve the search efficiency of optimization. The IMOPSO is compared with two multi-objective particle swarm optimization algorithms proposed in the literature on several test problems, and experimental results show that the IMOPSO has good performance in multi-objective discrete optimization
Application of S-transform in EEG analysis

Since EEG signal has the time-variant nature, time-frequency techniques can provide desired analysis. In this paper, sorts of time-frequency methods were applied to provide time-frequency distributions in a time series, and the results of these analyses were compared, presenting their strengths and weakness, especially finding the advantages of S-transform analyzing the signal with multiple frequencies. Based on these advantages, S-transform was used to detect driving fatigue in EEG analysis. The results show S-transform can distinguish different states of drivers easily. It proves that S-transform can be a feasible method in EEG analysis.