Miracle-dz/UniFAR-FTUs huggingface dataset:
Range queries in OLAP data cubes

A range query applies an aggregation operation over all selected cells of an OLAP data cube where the selection is specified by providing ranges of values for numeric dimensions. We present fast algorithms for range queries for two types of aggregation operations: SUM and MAX. These two operations cover techniques required for most popular aggregation operations, such as those supported by SQL. For range-sum queries, the essential idea is to precompute some auxiliary information (prefix sums) that is used to answer ad hoc queries at run-time. By maintaining auxiliary information which is of the same size as the data cube, all range queries for a given cube can be answered in constant time, irrespective of the size of the sub-cube circumscribed by a query. Alternatively, one can keep auxiliary information which is 1/bd of the size of the d-dimensional data cube. Response to a range query may now require access to some cells of the data cube in addition to the access to the auxiliary information, but the overall time complexity is typically reduced significantly. We also discuss how the precomputed information is incrementally updated by batching updates to the data cube. Finally, we present algorithms for choosing the subset of the data cube dimensions for which the auxiliary information is computed and the blocking factor to use for each such subset. Our approach to answering range-max queries is based on precomputed max over balanced hierarchical tree structures. We use a branch-and-bound-like procedure to speed up the finding of max in a region. We also show that with a branch-and-bound procedure, the average-case complexity is much smaller than the worst-case complexity.
Role Model Behavior and Youth Violence: A Study of Positive and Negative Effects

The study investigated how role models’ behavior may positively or negatively influence African American early adolescents’ attitudes toward violence and violent behavior. Participants in this study included 331 African American seventh and eighth graders from low-income neighborhoods in an urban, Midwestern city. The study used a model developed and tested to assess the relationships between role model prosocial behavior, role model antisocial behavior, adolescents’ attitudes toward violence, and adolescents’ violent behavior. The model developed was assessed using structural equation modeling. Results revealed the following: (a) Role model prosocial behavior is indirectly related to less violent behavior through adolescents’ attitudes toward violence, (b) role model antisocial behavior is directly linked to increased violent behavior and indirectly linked to increased violent behavior through adolescents’ attitudes toward violence, and (c) role model antisocial behavior appear to have a stronger effect on African American early adolescents’ violent behavior than role model prosocial behavior. Possible explanations for study findings and implications are discussed.
Stochastic, Dynamic Modelling and Signal Processing: Time Variable and State Dependent Parameter Estimation

Previous publications (e.g. Young, 1978, 1983, 1993a,b, 1998a,b, 1999a,b; Young and Runkle, 1989; Young and Minchin, 1991; Young et al., 1991; Young and Lees, 1993; Young and Beven, 1994; Young and Pedregal, 1997, 1998, 1999) have discussed an approach to nonstationary and nonlinear signal processing based on the identification and estimation of stochastic models with time variable (TVP) or state dependent (SDP) parameters. Here the term ‘nonstationarity’ is assumed to mean that the statistical properties of the signal, as defined by the parameters in an associated stochastic model, are changing over time at a rate which is ‘slow’ in relation to the rates of change of the stochastic state variables in the system under study. Although such nonstationary systems exhibit nonlinear behaviour, this can often be approximated well by TVP (or piece-wise linear) models, the parameters of which can be estimated using recursive methods of estimation in which the parameters are assumed to evolve in a simple stochastic manner (e.g. Young, 1984, 1999a). On the other hand, if the changes in the parameters are functions of the state or input variables (i.e. they actually constitute stochastic state variables), then the system is truly nonlinear and likely to exhibit severe nonlinear behaviour. Normally, this cannot be approximated in a simple TVP manner; in which case, recourse must be made to the alternative, and more powerful SDP modelling methods that are the main topic of this chapter. The extension of the TVP estimation methods to allow for state dependency, as described here, involves two statistical stages.
Female sex as an independent risk factor for stroke in atrial fibrillation: Possible mechanisms

Summary Atrial fibrillation (AF) is an independent risk factor for thromboembolism and stroke. Women with AF are at a higher overall risk for thromboembolic stroke when compared to men with AF. Recent evidence suggests that female sex, after adjusting for stroke risk profile and sex differences in utilisation of anticoagulation, is an independent stroke risk factor in AF. The inclusion of female sex has improved the accuracy of the CHADS2 stroke risk stratification schema (Congestive heart failure, Hypertension, Age 75 years or greater, Diabetes mellitus, and prior Stroke or TIA). The newly revised and validated schema, CHA2DS2-VASc, dichotomises age and incorporates female sex and vascular disease history. The pathophysiological mechanisms to explain this increased risk in women are not well understood. According to Virchow’s triad, thrombosis that leads to stroke in AF should arise from three co-existing phenomena: structural abnormalities, blood stasis, and a hypercoagulable state. Herein, we explore the sex differences in the biological processes that lead to thrombus formation as applied to Virchow’s Triad. The objective of this review is to describe the potential mechanisms behind the increased risk of stroke in AF associated with female sex.
Electoral Support for Islamic Parties in the Middle East and North Africa

The Middle East and North Africa (MENA) region is often regarded as an area where it is difficult to stabilize democracy. This notion is reinforced by the fact that, in the most recent set of elections held in the region, Islamic-orientated political parties perceived to be anti-democratic have achieved important electoral victories. This article analyses these parties in the MENA region in an attempt to discern whether support is related to a preference for anti-democratic forms of government. The research takes the form of a quantitative analysis using survey data and results indicate that electoral support for Islamic political parties is in fact driven by a rejection of the present state configuration, which in turn leads to a demand for religion to exert greater influence on the state.
Repeated learning makes cultural evolution unique

Although genetic information is acquired only once, cultural information can be both abandoned and reacquired during an individual's lifetime. Therefore, cultural evolution will be determined not only by cultural traits' ability to spread but also by how good they are at sticking with an individual; however, the evolutionary consequences of this aspect of culture have not previously been explored. Here we show that repeated learning and multiple characteristics of cultural traits make cultural evolution unique, allowing dynamical phenomena we can recognize as specifically cultural, such as traits that both spread quickly and disappear quickly. Importantly, the analysis of our model also yields a theoretical objection to the popular suggestion that biological and cultural evolution can be understood in similar terms. We find that the possibility to predict long-term cultural evolution by some success index, analogous to biological fitness, depends on whether individuals have few or many opportunities to learn. If learning opportunities are few, we find that the existence of a success index may be logically impossible, rendering notions of “cultural fitness” meaningless. On the other hand, if individuals can learn many times, we find a success index that works, regardless of whether the transmission pattern is vertical, oblique, or horizontal.
Application of fungal complexes to improve flax dew-retting.

The effect of fungal complexes on the flax dew-retting accelerating under the field conditions was examined. Active strains of fungi – Chrysosporium merdarium, Sarcopodium tortuosum, Geomyces pannorum, Oidiodendron griseum, Alternaria alternata, Cladosporium tenuissimum, Cladosporium cladosporioides, Cladosporium herbarum – were selected and four complexes for a field treatment were formed. The fungal spore suspensions were used for the additional contamination of laid flax at the moment of harvesting or swath returning. The amount of fungi on flax stems, species composition and density of Cladosporium population as well as meteorological conditions were estimated periodically during the flax dew-retting. The analysis showed that the largest amount of fungi persisted on the flax treated with fungal complex N 3 containing 6 fungal strains. Totally 160 fungal species were isolated from the retted flax, but only 29–35 of them were found more frequently, and Alternaria alternata, Cladosporium cladosporioides, C. herbarum prevailed among them. The best results of fibre separation were observed in the variants where the population density of species Cladosporium was comparatively high (25–29%) at the end of retting. The additional contamination of laid flax had a positive effect on the fiber separation index and the quality of flax fiber. Fungi, flax, dew-retting, fiber separation index.
On the Description of Spectrogram Probabilities With a Chi-Squared Law

Given a correlated Gaussian signal, may a chi-squared law of probability always be used to describe a spectrogram coefficient distribution? If not, would a "chi-squared description" lead to an acceptable amount of error when detection problems are to be faced in the time-frequency domain? These two questions prompted the study reported in this paper. After deriving the probability distribution of spectrogram coefficients in the context of a non centered Gaussian correlated signal, the Kullback-Leibler divergence is first used to evaluate to what extent the nonwhiteness of the signal and the Fourier analysis window impact the probability distribution of the spectrogram. To complete the analysis, a detection task formulated as a binary hypothesis test is considered. We evaluate the error committed on the probability of false alarm when the likelihood ratio test is expressed with chi-squared laws. From these results, a chi-squared description of the spectrogram distribution appears accurate when the analysis window used to construct the spectrogram decreases to zero at its boundaries, regardless of the level of correlation contained in the signal. When other analysis windows are used, the length of the window and the correlation contained in the analyzed signal impact the validity of the chi-squared description.
An Improved Fault Tolerant System Using Clustering for Multiple Object Tracking in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), Efficient clustering is key for optimal use of available nodes. Fault tolerance to any failure on the network or node level is an essential requirement in this context. Hence, a novel approach towards clustering and multiple object tracking in WSNs is being explored. The Proposed method employs improved K-means clustering algorithm which employs judicious mix of burdening all available nodes including CH (Cluster Head) to earn energy efficiency and fault tolerance. Initially, node with the maximum residual energy and least distance with fewer faults prone in a cluster become cluster head and node with the second maximum residual energy with minimum distance as next in hierarchy ranked accordingly. Later on, selection of cluster head will be based on available residual energy with no permanent fault due to energy drain and hence the object is being tracked with least fault occurrence. We use Matlab software as simulation platform to check energy consumption at cluster by evaluation of proposed algorithm. Eventually we evaluated and compare this proposed method against previous method and we demonstrate our model is better optimization than other method such as Traditional clustering in energy consumption rate.
Social Networks of the Marginal Young: A Study of Young People's Social Exclusion in Finland

It has been argued by many social network analysts that community ties and social support provided by them still continue to flourish in the late-modern societies, despite the general fear of the loss of community as a result of such large-scale social changes as urbanization, individualization and globalization. This article examines the role of community ties in the lives of young people who hold marginal positions in the Finnish labour market and are considered as being at risk of social exclusion. Based on survey data (n= 144) collected from young people aged 18-27 years, the article explores whether young people's exclusion from paid employment also leads to their exclusion from all significant social networks, as has often been assumed. It will be argued that the link is not automatic. Instead, the community ties continue to flourish and provide social support to young people also at the margins of society.
Measuring test coverage of SoA services

One of the challenges of testing in a SoA environment is that testers do not have access to the source code of the services they are testing. Therefore they are not able to measure test coverage at the code level, as is done in conventional white-box testing. They are compelled to measure test coverage in other ways which satisfy the constraints of black-box testing. We propose some alternate means of measuring test coverage by focusing on the structure and content of the service interface without regarding the code. The result is a new way of measuring test coverage which can apply to testing in a SoA environment.
Discovery of Changes from the Global Carbon Cycle and Climate System Using Data Mining

The goal of our NASA sponsored project, “Discovery of Changes from the Global Carbon Cycle and Climate System Using Data Mining,” is to better understand global scale patterns in biosphere processes, especially relationships between the global carbon cycle and the climate system. To that end, we have developed data mining techniques to efficiently find spatio-temporal patterns in large Earth Science data sets. One such technique finds ecosystem disturbances in long-term, nonstationary fractional photosynthetically active radiation (FPAR) time series data. A key contribution of this work, which was the subject of a recent NASA press release, is the development of an automated technique for detecting abrupt changes in FPAR that take into account the timing, location, and magnitude of such changes. Using this technique, scientists were able to estimate that nearly 9 Pg (peta-grams) of carbon have been lost from the terrestrial biosphere to the atmosphere as a result of large-scale ecosystem disturbance events over an 18-year time period. Also, a novel clustering technique was developed to identify regions of uniform behavior in spatio-temporal data. The clusters produced by these methods are useful in discovering climate indices because they identify significant regions of the ocean or atmosphere where the behavior is relatively uniform over the entire area. Some of the discovered clusters correspond to known climate indices while other clusters are variants of known indices that appear to provide better predictive power for some land areas and still other clusters may represent potentially new Earth science phenomena. Other contributions include visualization techniques for finding interesting associations using land cover information and a filter-and-refine approach for efficiently processing correlation-based queries. Such innovative data analysis tools and techniques can aid NASA scientists in analyzing the growing datasets generated by NASA’s global observing satellites and offer an unprecedented opportunity for predicting and preventing future ecological problems by managing the ecology and health of our planet.
Experiments with clustering as a software remodularization method

As valuable software systems get old, reverse engineering becomes more and more important to the companies that have to maintain the code. Clustering is a key activity in reverse engineering to discover a better design of the systems or to extract significant concepts from the code. Clustering is an old activity, highly sophisticated, offering many methods to answer different needs. Although these methods have been well documented in the past, these discussions may not apply entirely to the reverse engineering domain. We study some clustering algorithms and other parameters to establish whether and why they could be used for software remodularization. We study three aspects of the clustering activity: abstract descriptions chosen for the entities to cluster; metrics computing coupling between the entities; and clustering algorithms. The experiments were conducted on three public domain systems (gcc, Linux and Mosaic) and a real world legacy system (2 million LOC). Among other things, we confirm the importance of a proper description scheme of the entities being clustered, we list a few good coupling metrics to use and characterize the quality of different clustering algorithms. We also propose novel description schemes not directly based on the source code and we advocate better formal evaluation methods for the clustering results.
Identification of miRNAs and Their Target Genes in Peach (Prunus persica L.) Using High-Throughput Sequencing and Degradome Analysis

MicroRNAs play critical roles in various biological and metabolic processes. The function of miRNAs has been widely studied in model plants such as Arabidopsis and rice. However, the number of identified miRNAs and related miRNA targets in peach (Prunus persica) is limited. To understand further the relationship between miRNAs and their target genes during tissue development in peach, a small RNA library and three degradome libraries were constructed from three tissues for deep sequencing. We identified 117 conserved miRNAs and 186 novel miRNA candidates in peach by deep sequencing and 19 conserved miRNAs and 13 novel miRNAs were further evaluated for their expression by RT-qPCR. The number of gene targets that were identified for 26 conserved miRNA families and 38 novel miRNA candidates, were 172 and 87, respectively. Some of the identified miRNA targets were abundantly represented as conserved miRNA targets in plant. However, some of them were first identified and showed important roles in peach development. Our study provides information concerning the regulatory network of miRNAs in peach and advances our understanding of miRNA functions during tissue development.
Exercise prescription and the patient with type 2 diabetes: A clinical approach to optimizing patient outcomes

Purpose: To review the current recommendations for physical activity in type 2 diabetes mellitus (T2DM) and propose methods to optimize compliance, reduce the pharmaceutical burden, and improve the general health and well‐being of patients with T2DM. Data sources: PubMed, SportDiscus, Ovid MEDLINE, Psychinfo, Web of Science, LexisNexis, and EBM reviews. Conclusions: T2DM is a condition in which physical activity has been documented to improve patient outcomes, yet research has noted that healthcare professionals inadequately address this issue, resulting in physical activity being an underutilized therapy. Implications for practice: An exercise prescription consists of mode (type), frequency, intensity, duration, and progression. Determining the appropriate mode depends upon patient preference and safety issues regarding the state of T2DM or other conditions. Frequency, intensity, and duration are specific to the type of activity and should be tailored to the patient's abilities to safely perform the activity. Finally, the health professional addresses periodic progression in order to maintain the exercise stimulus needed to promote continued health improvements and prevent “plateauing. ” In this article, on the basis of the current scientific research, we propose recommendations that enable healthcare professionals to advocate for their patients with T2DM by offering safe and effective treatment options.
Toxicity of chlordane at early developmental stage of zebrafish

Chlordane is highly toxic organochlorine pesticides that have been widely used throughout the world for decades and posing adverse effects on the environment. Contents detected in tissue and blood samples have resulted in a raising concern for their potential effects on wildlife and humans. In this study, we investigate the potential effect of chlordane on the development of zebrafish embryos. Zebrafish larvae were treated with different concentrations (0, 25, 50, 100, 200 ng/L) of chlordane from 12 hours postfertilization (hpf). Different early stage parameters were observed at 1, 2, 3 and 4 day post-fertilization (dpf). Chlordane-exposed zebrafish larvae appeared significant lower survival rate, developmental and hatching time delay and decreased embryo productivity. The heartbeat rate and blood flow were decreased in a dose dependent manner. These results suggested that exposure to real life of chlordane led to direct morphological and phenotypic changes and effects systems related to development and reproduction even in short-term manner.
A novel, low-latency algorithm for multiple Group-By query optimization

Data summarization is essential for users to interact with data. Current state of the art algorithms to optimize its most general form, the multiple Group By queries, have limitations in scalability. In this paper, we propose a novel algorithm, Top-Down Splitting, that scales to hundreds or even thousands of attributes and queries, and that quickly and efficiently produces optimized query execution plans. We analyze the complexity of our algorithm, and evaluate, empirically, its scalability and effectiveness through an experimental campaign. Results show that our algorithm is remarkably faster than alternatives in prior works, while generally producing better solutions. Ultimately, our algorithm reduces up to 34% the query execution time, when compared to un-optimized plans.
Statistical metric fusion for image quality assessment

In this paper, we propose two novel Statistical Metric Fusion (SMF) methods for Image Quality Assessment (IQA) metric enhancement. First, local quality map is constructed from existing state-of-the-art IQA algorithm. After that several statistical indices are extracted from local quality map. Finally, the extracted statistical indices are fused by Supervised Statistical Metric Fusion (SMF-S) based on Support Vector Regression (SVR) and Unsupervised Statistical Metric Fusion (SMF-U) based on Reciprocal Rank Fusion (RRF) to obtain the final quality score, respectively. Experimental results on the largest public IQA database TID2013 have demonstrated that the two proposed SMF methods can generally enhance the quality prediction performance of the fused IQA metric in terms of high correlation with human opinion scores.
Sensors and control concept of a biped robot

The biped robot "Johnnie" is designed to achieve a dynamically stable gait pattern, allowing for high walking velocities. Very accurate and fast sensors were developed for the machine. In particular, the design of the three-dimensional-orientation sensor and the six-axes force-torque sensor are presented. The control scheme is based on the information from these sensors to deal with unstructured terrain and disturbances. Two different implementations are investigated: a computed torque approach and a trajectory control with adaptive trajectories. Walking speeds of 2.4 km/h have been achieved in experiments.