Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on complexity about a class of complex system

In accordance with the limitation of traditional research on the stock and real estate market, attempt to substitute new research methods and setup for traditional mathematical modeling framework by applying the latest achievements of complexity science and the interdisciplinary approach in the paper. Trying to apply complex adaptive system theory to the stock and real estate market, the system framework of the stock and real estate market based on multi-agent technology is set up, and the key technologies involved were discussed. At first, The internal consistency of the stock and real estate system and complex adaptive system is analyzed, which proves that stock and real estate system is a complex adaptive system. Secondly, a new method of researching on such systems was proposed. Finally, some phenomena occurring in reality system was simulated through the program of Object-c based on swarm. Therefore, the feasibility and validity of applying complexity theory and method to a class of complex adaptive system is verified.
Fast multi-camera reconstruction and surveillance with human tracking and optimized camera configurations

Human-robot cooperation and coexistence requires robust surveillance. Our system creates a visual hull as volume approximation of humans within the work cell from images of multiple static color cameras. We present a fast, exact, and conservative reconstruction from high-resolution input data and apply temporal filtering after the reconstruction to address detection failure of the cameras. Furthermore, we avoid incorrect placement of cameras by automatically optimizing the camera positions. All processing steps handle occluding obstacles.
N-Best Re-scoring Approaches for Mandarin Speech Recognition

The predominant language model for speech recognition is n-gram language model, which is locally learned and usually lacks global linguistic information such as long-distance syntactic constraints. We first explore two n-best re-scoring approaches for Mandarin speech recognition to overcome this problem. The first approach is linear re-scoring that can combine several language models from various perspectives. The weights of these models are optimized using minimum error rate learning method. Discriminative approach can also be used for re-scoring with rich syntactic features. To overcome the speech text insufficiency problem for discriminative model, we propose a domain adaptation method that trains the model using Chinese pinyin-to-character conversion dataset. Then we present a cascaded approach to combine the two re-scoring models in pipeline that takes the probability output of linear re-scoring model as the initial weight of the discriminative model. Experimental results show that both re-scoring approaches outperform the baseline system, and the cascaded approach achieves the best performance.
Parental Influences on the Physical Activity Behavior of Children of Various Ethnic Backgrounds

SMITH, MILLAGE ZACHARY. Parental Influences on the Physical Activity Behavior of Children of Various Ethnic Backgrounds. (Under the direction of Dr. Laura Burton and Dr. Edwin Lindsay.) Childhood overweight is a serious public health concern. Approximately 15% of adolescents and children ages 6-19 are overweight. Promoting physical activity is one key in fighting the obesity epidemic. The purpose of this study was to explore parental influences on the physical activity behavior of children of various ethnic backgrounds and of low socioeconomic status. Although the majority of the children self-identified as Black or African American, the full sample was considered to be children of various ethnic backgrounds since there was representation from other ethnic groups. This effort addressed the void of ethnically diverse youth physical activity research. Using the Youth Physical Activity Promotion Model as a theoretical framework (Welk, 1999), direct and indirect parental influences were investigated. Four specific parental influencing strategies including role modeling, encouragement, involvement, and facilitation were also examined. This study replicated a previous study by Welk, Wood, and Morss (2003). The survey administered combined the Physical Activity Questionnaire for Children (Kowalski, Crocker, & Faulkner, 1997), the Children’s Attraction to Physical Activity scale (Brustad, 1993, 1996), the perceived athletic competence scale from the Self-Perception Profile for Children (Harter, 1985), and a series of parental influence measures designed by Welk et al. (2003). Parents in this sample influence children’s physical activity behavior both directly and indirectly. Parental support strategies other than role modeling were found to significantly predict physical activity, perceived athletic competence, and attraction to physical activity. There was evidence that suggested the general measure of physical activity and the measure of perceived competence introduced cultural bias and therefore did not fully capture the experiences of the children in this sample. The access and opportunities available to this sample may also affect the choices parents make in how they influence children’s physical activity behavior. With knowledge of the results of this study, professionals committed to promoting physical activity in children can more effectively educate and involve parents, create culturally sensitive environments for participation, and recognize the multiple determinants in children’s physical activity participation in order to deliver comprehensive programs. PARENTAL INFLUENCES ON THE PHYSICAL ACTIVITY BEHAVIOR OF CHILDREN OF VARIOUS ETHNIC BACKGROUNDS by MILLAGE ZACHARY SMITH A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Master of Science PARKS, RECREATION AND TOURISM MANAGEMENT
TOWARDS A MULTIVIEWS COMPONENT BASED MODEL DRIVEN APPROACH

A Multiviews Component is an extension of a UML2.0 component [El Asri (2005a)], that offers a richer behavior by adopting the concept of views. The problem of its representation has been tackled by VUML [Nassar (2004)] [El Asri (2005b)], which is a UML extension (UML profile). In this paper, we propose an approach to implement Multiviews Components under the MDA (Model Driven Architecture) initiative [Soley (2000)]. This process follows two sequential steps: The first step is a PIM-to-PIM transformation. It transforms and converts a Multiviews Component (which is a Platform Independent Model) into a conventional UML component by applying a proprietary design pattern [Hain (2006)] [Hain (2007)]. The second step transforms the resulting PIM into a PSM (Platform Specific Model) that targets Java / EJB platform. Our approach offers two major advantages. The first advantage reduces the complexity related to VUML Multiviews Component handling by making it UML based. The second step implements these components into a specific development platform, which can be taken as an example for other target platforms.
Post-Traumatic Stress Disorder, Sensory Integration, and Aquatic Therapy: A Scoping Review

ABSTRACT The prevalence of post-traumatic stress disorder (PTSD) continues to increase. Symptomology of PTSD include sensory deficits much like those displayed in children with sensory integration (SI) disorder. The aim of this scoping review is to review symptoms of PTSD and SI, and make a case of how aquatic therapy may be an effective modality in treating clients with PTSD. Eighteen articles were selected for review. From this review, it is suggested that aquatic therapy can be an effective modality in reducing symptoms of PTSD; however, future research is essential in determining its effectiveness.
Fiscal Transparency and Fiscal Policy Outcomes in OECD Countries

It is widely believed and often argued that fiscal, or budgetary, transparency has large, positive effects on fiscal performance. However, the evidence linking transparency and fiscal policy outcomes is far from compelling. We present a career-concerns model with political parties to analyze the effects of fiscal transparency on public debt accumulation. To test the predictions of the model, we construct a replicable index of fiscal transparency. Simultaneous estimates of debt and transparency on 19-country OECD data strongly confirm that a higher degree of fiscal transparency is associated with lower public debt and deficits.
Association between Alcohol Consumption and Cancers in the Chinese Population—A Systematic Review and Meta-Analysis

Background Alcohol consumption is increasing worldwide and is associated with numerous cancers. This systematic review examined the role of alcohol in the incidence of cancer in the Chinese population. Methods Medline/PubMed, EMBASE, CNKI and VIP were searched to identify relevant studies. Cohort and case-control studies on the effect of alcohol use on cancers in Chinese were included. Study quality was evaluated using the Newcastle-Ottawa Scale. Data were independently abstracted by two reviewers. Odds ratios (OR) or relative risks (RR) were pooled using RevMan 5.0. Heterogeneity was evaluated using the Q test and I-squared statistic. P<. 01 was considered statistically significant. Results Pooled results from cohort studies indicated that alcohol consumption was not associated with gastric cancer, esophageal cancers (EC) or lung cancer. Meta-analysis of case-control studies showed that alcohol consumption was a significant risk factor for five cancers; the pooled ORs were 1.79 (99% CI, 1.47–2.17) EC, 1.40 (99% CI, 1.19–1.64) gastric cancer, 1.56 (99% CI, 1.16–2.09) hepatocellular carcinoma, 1.21 (99% CI, 1.00–1.46) nasopharyngeal cancer and 1.71 (99% CI, 1.20–2.44) oral cancer. Pooled ORs of the case-control studies showed that alcohol consumption was protective for female breast cancer and gallbladder cancer: OR 0.76 (99% CI, 0.60–0.97) and 0.70 (99% CI, 0.49–1.00) respectively. There was no significant correlation between alcohol consumption and lung cancer, colorectal cancer, pancreatic cancer, cancer of the ampulla of Vater, prostate cancer or extrahepatic cholangiocarcinoma. Combined results of case-control and cohort studies showed that alcohol consumption was associated with 1.78- and 1.40-fold higher risks of EC and gastric cancer but was not significantly associated with lung cancer. Conclusions Health programs focused on limiting alcohol intake may be important for cancer control in China. Further studies are needed to examine the interaction between alcohol consumption and other risk factors for cancers in Chinese and other populations.
A novel sodium channel mutation causing a hyperkalemic paralytic and paramyotonic syndrome with variable clinical expressivity

A point mutation A4078G predicting the amino acid exchange Met1360Val in segment IV/S1 of the human muscle sodium channel α-subunit was identified in a family presenting features of hyperkalemic periodic paralysis and paramyotonia congenita with sex-related modification of expression. In this family, only one male member is clinically affected, presenting episodes of flaccid weakness as well as paradoxical myotonia and cold-induced weakness. Three female family members who have the same mutation show only myotonic bursts on EMG. We studied the functional defect caused by this mutation by investigating recombinant wild type (WT) and mutant sodium channels expressed in a mammalian cell line (HEK293) using the patch-clamp technique. With mutant channels, the decay of the sodium currents was two times slower than with WT, the steady-state inactivation curve was shifted by-13 mV, and recovery from inactivation was 1.5 times faster. High extracellular potassium (9 mM) did not affect channel gating. Single-channel measurements revealed prolonged mean open times and an increased number of reopenings. The results are remarkable with respect to the lack of complete penetrance usually seen with sodium channelopathies and the site of mutation that was formerly not thought to be involved in channel inactivation.
Argentina's Lost Decade Forthcoming in Review of Economic Dynamics

Argentina suffered a depression in the 1980s that was as severe as the Great Depression experienced in the United States and Germany in the interwar period. Our paper examines this depression from the perspective of growth theory, taking total factor productivity as exogenous. The predictions of the growth model conform rather well with the observations during the ”lost decade” years. Journal of Economic Literature Classification Codes: E32.
A study of transformation-invariances of deep belief networks

In order to learn transformation-invariant features, several effective deep architectures like hierarchical feature learning and variant Deep Belief Networks (DBN) have been proposed. Considering the complexity of those variants, people are interested in whether DBN itself has transformation-invariances. First of all, we use original DBN to test original data. Almost same error rates will be achieved, if we change weights in the bottom interlayer according to transformations occurred in testing data. It implies that weights in the bottom interlayer can store the knowledge to handle transformations such as rotation, shifting, and scaling. Along with the continuous learning ability and good storage of DBN, we present our Weight-Transformed Training Algorithm (WTTA) without augmenting other layers, units or filters to original DBN. Based upon original training method, WTTA is aiming at transforming weights and is still unsupervised. For MNIST handwritten digits recognizing experiments, we adopted 784-100-100-100 DBN to compare the differences of recognizing ability in weights-transformed ranges. Most error rates generated by WTTA were below 25% while most rates generated by original training algorithm exceeded 25%. Then we also did an experiment on part of MIT-CBCL face database, with varying illumination, and the best testing accuracy can be achieved is 87.5%. Besides, similar results can be achieved by datasets covering all kinds of transformations, but WTTA only needs original training data and transform weights after each training loop. Consequently, we can mine inherent transformation-invariances of DBN by WTTA, and DBN itself can recognize transformed data at satisfying error rates without inserting other components.
A passion for fashion: The impact of social influence, vanity and exhibitionism on consumer behaviour

Purpose The purpose of this paper is to explore the effect of social influence and individual vanity on passion for fashion of clothes and accessories and the mediating role of exhibitionist tendency. Design/methodology/approach The study was conducted in two phases. The first was exploratory (n=109), using online panel interviews, carried out among a sample of fashion enthusiasts. The quantitative phase (n=425). Shopping mall intercept field survey methodology has been utilised to collect data. Consumers who just completed their shopping and were about to leave the shopping malls were approached by trained interviewers. Findings The content analysis of phase 1 yielded four major aspects and more two aspects less cited that participants seek in posts and online information that motivate them for shopping, such as inspirational outfits, products and brands posted, self-identification with the style, value for money, friends and fashion magazines and runway shows. The findings of phase 2 reveal that the social influence is more important than individual vanity in enhancing the desire to buy and use fashion clothes and accessories. Further, the exhibitionist tendency acts as a mediator between passionate desire for fashion and self-expression word-of-mouth. Originality/value As far as authors know, this is the first attempt to explore the effect of two components of narcissism in fashion context and to analyse the social and individual influence on passionate desire to use fashion.
LabelSOM: on the labeling of self-organizing maps

Self-organizing maps are a prominent unsupervised neural network model providing cluster analysis of high-dimensional input data. However, in spite of enhanced visualization techniques for self-organizing maps, interpreting a trained map proves to be difficult because the features responsible for a specific cluster assignment are not evident from the resulting map representation. In this paper we present our LabelSOM approach for automatically labeling a trained self-organizing map with the features of the input data that are the most relevant ones for the assignment of a set of input data to a particular cluster. The resulting labeled map allows the user to understand the structure and the information available in the map and the reason for a specific map organization, especially when only little prior information on the data set and its characteristics is available. We demonstrate the applicability of the LabelSOM method in the field of data mining providing an example from real world text mining.
Does Benefit Receipt Affect Future Income? An Econometric Explanation

This paper provides an econometric analysis of the effects of receiving welfare benefits on individuals’ future income, using longitudinal administrative data on individual incomes. After controlling for heterogeneous differences in individual incomes, spurious effects of contemporaneous benefit receipt and possible endogeneity with incomes, there is no systematic evidence of a positive or negative effect of benefit receipt on incomes. The results are generally imprecisely estimated and sensitive to the choice of specification. Also, a simple first-order specification with unobserved heterogeneity provides a reasonable characterisation of individual income dynamics, although formal statistical tests tend to reject this specification as being too parsimonious.
Strategic Asymmetry in Dynamic Coordination Games with Learning

This paper studies a simple model of dynamic coordination with learning under incomplete information. We show that strategic asymmetry arises where the predecessor’s strategy is a strategic substitute for the successor’s while the latter is a strategic complement for the former. The role of dynamics and learning is identified by comparing the strategic interactions in the coordination games with different information and timing structures. We also demonstrate that dynamics with learning induces players to take more aggressive strategies.
Effective Online Group Discovery in Trajectory Databases

GPS-enabled devices are pervasive nowadays. Finding movement patterns in trajectory data stream is gaining in importance. We propose a group discovery framework that aims to efficiently support the online discovery of moving objects that travel together. The framework adopts a sampling-independent approach that makes no assumptions about when positions are sampled, gives no special importance to sampling points, and naturally supports the use of approximate trajectories. The framework's algorithms exploit state-of-the-art, density-based clustering (DBScan) to identify groups. The groups are scored based on their cardinality and duration, and the top-k groups are returned. To avoid returning similar subgroups in a result, notions of domination and similarity are introduced that enable the pruning of low-interest groups. Empirical studies on real and synthetic data sets offer insight into the effectiveness and efficiency of the proposed framework.
Gene expression Large scale data mining approach for gene-specific standardization of microarray gene expression data

Motivation: The identification of the change of gene expression in multifactorial diseases, such as breast cancer is a major goal of DNA microarray experiments. Herewepresent a newdatamining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of gene’s behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes
A pan-cancer signature of neutral tumor evolution

Despite extraordinary efforts to profile cancer genomes on a large scale, interpreting the vast amount of genomic data in the light of cancer evolution and in a clinically relevant manner remains challenging. Here we demonstrate that cancer next-generation sequencing data is dominated by the signature of growth governed by a power-law distribution of mutant allele frequencies. The power-law signature is common to multiple tumor types and is a consequence of the effectively-neutral evolutionary dynamics that underpin the evolution of a large proportion of cancers, giving rise to the abundance of mutations responsible for intra-tumor heterogeneity. Importantly, the law allows the measurement, in each individual cancer, of the in vivo mutation rate and the timing of mutations with remarkable precision. This result provides a new way to interpret cancer genomic data by considering the physics of tumor growth in a way that is both patient-specific and clinically relevant.
Assessing the Innovation Efficiency of Chinese Domestic-funded Enterprises

The purpose of this research is to analyze the innovation efficiency of Chinese domestic-funded enterprises. Data envelopment analysis (DEA) and Malmquist index are used to evaluate innovation efficiency changes. CCR model is applied to measure technical innovation efficiency scores, radial movements, and slack movements. BCC model is extended to analyze the factors that contribute to technical innovation inefficiency. Based on data from the large- and medium-size industrial enterprises of different types of registration status, we find that the means of innovation efficiency of Chinese domestic-funded enterprises progressed except limited liability corporations and private enterprises during 2000-2008. Technical innovation inefficiency of cooperative enterprises and limited liability corporations was due to scale innovation inefficient in 2006-2008. Cooperative enterprises were on the increasing returns to scale portion, however, limited liability corporations were on the decreasing returns to scale portion during 2006-2008.
Decentralized Control of a Formation Involving a Miniature Helicopter and a Team of Ground Robots Based on Artificial Vision

This work addresses a coordinate control scheme for a helicopter and a formation of ground robots, based on artificial vision. First, a control scheme previously proposed is implemented to guide a formation of three ground mobile robots while they track a desired trajectory. In the sequel, a nonlinear controller based on the inverse dynamics of the aerial vehicle, focusing on its under actuated character, is used to guide its navigation. The mission to be accomplished by the helicopter consists in tracking the centroid of the ground formation. An important aspect to be emphasized is that the two controllers adopted operate totally independently, thus characterizing a decentralized control scheme. An artificial vision system onboard the rotorcraft is used to capture the positions of the ground vehicles, which are used to define the 3D-path to be followed by the helicopter. Simulation results validating the proposed system are presented and discussed.