Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk factors and health consequences of physical and emotional violence against children in Zimbabwe: a nationally representative survey

Introduction This study provides, for the first time, comparable national population-based estimates that describe the nature and magnitude of physical and emotional violence during childhood in Zimbabwe. Methods From August to September 2011, we conducted a national population-based survey of 2410 respondents aged 13–24 years, using a two-stage cluster sampling. Regression models were adjusted for relevant demographics to estimate the ORs for associations between violence, risk factors and various health-related outcomes. Results Respondents aged 18–24 years report a lifetime prevalence (before the age of 18) of 63.9% (among girls) to 76% (among boys) for physical violence by a parent or adult relative, 12.6% (girls) to 26.4% (boys) for humiliation in front of others, and 17.3% (girls) to 17.5% (boys) for feeling unwanted. Almost 50% of either sex aged 13–17 years experienced physical violence in the 12 months preceding the survey. Significant risk factors for experiencing physical violence for girls are ever experiencing emotional abuse prior to age 13, adult illness in the home, socioeconomic status and age. Boys’ risk factors include peer relationships and socioeconomic status, while caring teachers and trusted community members are protective factors. Risk factors for emotional abuse vary, including family relationships, teacher and school-level variables, socioeconomic status, and community trust and security. Emotional abuse is associated with increased suicide attempts for both boys and girls, among other health outcomes. Conclusion Physical and emotional violence often work in tandem causing poor mental and physical health outcomes. Understanding risk factors for violence within the peer or family context is essential for improved violence prevention.
Complexity Dynamic Character Analysis of Retailers Based on the Share of Stochastic Demand and Service

Apart from the price fluctuation, the retailers’ service level becomes another key factor that affects the market demand. This paper depicts a modified price and demand game model based on the stochastic demand and the retailer’s service level which influences the market demand decided by customers’ preference, while the market demand is stochastic in this model. We explore how the price adjustment speed affects the stability of the supply chain system with respect to service level and stochastic demand. The dynamic behavior of the system is researched by simulation and the stability domain and the bifurcation phenomenon are shown clearly. The largest Lyapunov exponent and the chaotic attractor are also given to confirm the chaotic characteristic of the system. The simulation results indicate that relatively small price adjustment speed may maintain the system at stable state. With the price adjustment speed gradually increasing, the price system gets unstable and finally becomes chaotic. This chaotic phenomenon will perturb the product market and this phenomenon should be controlled to keep the system stay in the stable region. So the chaos control is done and the chaos can be controlled completely. The conclusion makes significant contribution to the system referring to the price fluctuation based on the service level and stochastic demand.
Are Strategic Customers Bad for a Supply Chain?

We consider a manufacturer serving a retailer that sells its product to customers over two periods. Each firm determines its unit price. The retailer orders the product from the manufacturer prior to the beginning of the selling periods. We consider two types of customers: (1) strategic customers who take their future options into account and time their purchases accordingly and (2) myopic customers who consider only their current options. We compare the resulting equilibria for these two scenarios and evaluate the impact of customers’ strategic behavior. We find that strategic customer behavior always benefits the manufacturer. Interestingly, it may also improve the profitability of the retailer and the entire supply chain in some cases. Strategic customer behavior and decentralization are often regarded as detriments to supply chain performance. We find that, however, the combination of these two factors may not result in the worst supply chain performance. When customers are sufficiently patient, a dec.
Impact of Time Delay in Perceptual Decision-Making: Neuronal Population Modeling Approach

Impairments in decision-making are frequently observed in neurodegenerative diseases, but the mechanisms underlying such pathologies remain elusive. In this work, we study, on the basis of novel time-delayed neuronal population model, if the delay in self-inhibition terms can explain those impairments. Analysis of proposed system reveals that there can be up to three positive steady states, with the one having the lowest neuronal activity being always locally stable in nondelayed case. We show, however, that this steady state becomes unstable above a critical delay value for which, in certain parameter ranges, a subcritical Hopf bifurcation occurs. We then apply psychometric function to translate model-predicted ring rates into probabilities that a decision is being made. Using numerical simulations, we demonstrate that for small synaptic delays the decision-making process depends directly on the strength of supplied stimulus and the system correctly identifies to which population the stimulus was applied. However, for delays above the Hopf bifurcation threshold we observe complex impairments in the decision-making process; that is, increasing the strength of the stimulus may lead to the change in the neuronal decision into a wrong one. Furthermore, above critical delay threshold, the system exhibits ambiguity in the decision-making.
Web Assurance Seals: How and Why They Influence Consumers' Decisions

Internet commerce is exploding and predicted to continue growing at a rapid rate for several more years. Online businesses that have a desire to tap into this Internet commerce explosion are seeking ways to convince online browsers to become online purchasers. To achieve this goal, businesses need to find ways to alleviate consumers' fears and concerns about making online purchases. This paper reports on a series of three studies focused on (1) determining the fears and concerns that online consumers have, (2) examining whether the leading brands of web assurance seals (Verisign®, TRUSTe, Good House Keeping, and CPA WebTrust) can help alleviate those fears and concerns, and (3) gaining insights into the process by which web assurance seals can influence consumers' online purchase decisions. This study identified seven distinct concerns that consumers had with purchasing goods/services online. Factor analysis revealed that these concerns were along two dimensions: concerns about the firm and concerns about.
Evolution of the Chinese Industry-University-Research Collaborative Innovation System

The goal of this study was to reveal the mechanism of the Chinese industry-university-research collaborative innovation (IURCI) and interactions between the elements in the system and find issues that exist in the collaborative innovation process. Based on the theoretical perspective of innovation and complexity science, we summarized the elements of the IURCI as innovation capability, research and development (R&D) configuration, and knowledge transfer and established a theoretical model to describe the evolution of the IURCI system. We used simulation technology to determine the interactions among variables and the evolution trend of the system. The results showed that the R&D configuration can promote the evolution of innovation capability and knowledge transfer and that innovation capacity is the current dominant factor in the evolution of the Chinese IURCI system and is highly positively correlated with R&D configuration. The evolutionary trend of knowledge transfer was gentler, and its contribution to the evolution of the Chinese IURCI system was less than that of R&D configuration. When innovation, R&D configuration, and knowledge transfer are relatively balanced, the collaborative innovation system can achieve high speed and stable evolution.
Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks

Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 -measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk.
Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment

With the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named , is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.
Sparse Causality Network Retrieval from Short Time Series

We investigate how efficiently a known underlying sparse causality structure of a simulated multivariate linear process can be retrieved from the analysis of time series of short lengths. Causality is quantified from conditional transfer entropy and the network is constructed by retaining only the statistically validated contributions. We compare results from three methodologies: two commonly used regularization methods, Glasso and ridge, and a newly introduced technique, LoGo, based on the combination of information filtering network and graphical modelling. For these three methodologies we explore the regions of time series lengths and model-parameters where a significant fraction of true causality links is retrieved. We conclude that when time series are short, with their lengths shorter than the number of variables, sparse models are better suited to uncover true causality links with LoGo retrieving the true causality network more accurately than Glasso and ridge.
Empirical Analysis and Agent-Based Modeling of the Lithuanian Parliamentary Elections

We analyze a parties’ vote share distribution across the polling stations during the Lithuanian parliamentary elections of 1992, 2008, and 2012. We find that the distribution is rather well fitted by the Beta distribution. To reproduce this empirical observation, we propose a simple multistate agent-based model of the voting behavior. In the proposed model, agents change the party they vote for either idiosyncratically or due to a linear recruitment mechanism. We use the model to reproduce the vote share distribution observed during the election of 1992. We discuss model extensions needed to reproduce the vote share distribution observed during the other elections.
Involvement of anti-tumor miR-124-3p and its targets in the pathogenesis of pancreatic ductal adenocarcinoma: direct regulation of ITGA3 and ITGB1 by miR-124-3p

MicroRNAs (miRNAs) are unique in that a single miRNA molecule regulates a vast number of RNA transcripts. Thus, aberrantly expressed miRNAs disrupt tightly controlled RNA networks in cancer cells. Our functional screening showed that expression of miR-124-3p was downregulated in pancreatic ductal adenocarcinoma (PDAC) tissues. Here, we aimed to investigate the anti-tumor roles of miR-124-3p in PDAC cells and to identify miR-124-3p-mediated oncogenic signaling in this disease. Ectopic expression of miR-124-3p inhibited cancer cell migration and invasion in PDAC cells. Moreover, restoration of miR-124-3p suppressed oncogenic signaling, as demonstrated by reduced phosphorylation of focal adhesion kinase, AKT, and extracellular signal-regulated kinase, in PDAC cells. Our in silico database analyses and luciferase reporter assays showed that two cell-surface matrix receptors, integrin α3 (ITGA3) and integrin β1 (ITGB1), were directly regulated by miR-124-3p in PDAC cells. Overexpression of ITGA3 and ITGB1 was confirmed in PDAC clinical specimens. Interestingly, a large number of cohort analyses from TCGA database showed that high expressions of ITGA3 and ITGB1 were significantly associated with poor prognosis of patients with PDAC. Knockdown of ITGA3 and ITGB1 by siRNAs markedly suppressed the migration and invasion abilities of PDAC cells. Moreover, downstream oncogenic signaling was inhibited by ectopic expression of miR-124-3p or knockdown of the two integrins. The discovery of anti-tumor miRNAs and miRNA-mediated oncogenic signaling may provide novel therapeutic targets for the treatment of PDAC.
Automatic Emergence Detection in Complex Systems

Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent) system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis results. In this paper, we propose a quantitative definition of emergence for complex systems. We also propose a framework to detect emergent properties given observations of its subsystems. This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs), learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. Fusion is the central element of our approach to account for situations when a common variable may have different probabilistic distributions in different subsystems. We evaluate our detection performance against a baseline approach (Bayesian Network ensemble) on synthetic testbeds from UCI datasets. To do so, we also introduce a method to simulate and a metric to measure discrepancies that occur with shared/common variables. Experiments demonstrate that our framework outperforms the baseline. In addition, we demonstrate that this framework has uniform polynomial time complexity across all three learning, fusion, and reasoning procedures.
The efficiency of mutual funds

This paper analyzes the short-term market efficiency of the mutual fund industry around the world. Using a unique database of worldwide domestic equity funds, it employs a parametric (regression model) and non-parametric (data envelopment analysis (DEA) model) approaches to establish a relation between cost (expense ratio, turnover, loads, and risk) and benefit (return) of mutual funds. The empirical results of the parametric approach show a statistically significant negative relationship between expenses and risk-adjusted performance across countries. When we reexamine this relationship using a non-parametric approach, we show, in contrast to our previous result, a positive relationship between expenses and risk-adjusted performance. Thus, using the DEA methodology, we find strong evidence that equity mutual funds around the world are approximately mean–variance efficient.
Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes

Differential expression plays an important role in cancer diagnosis and classification. In recent years, many methods have been used to identify differentially expressed genes. However, the recognition rate and reliability of gene selection still need to be improved. In this paper, a novel constrained method named robust nonnegative matrix factorization via joint graph Laplacian and discriminative information (GLD-RNMF) is proposed for identifying differentially expressed genes, in which manifold learning and the discriminative label information are incorporated into the traditional nonnegative matrix factorization model to train the objective matrix. Specifically, -norm minimization is enforced on both the error function and the regularization term which is robust to outliers and noise in gene data. Furthermore, the multiplicative update rules and the details of convergence proof are shown for the new model. The experimental results on two publicly available cancer datasets demonstrate that GLD-RNMF is an effective method for identifying differentially expressed genes.
Müziksel ve Matematiksel Öğrenme Başarısı Arasındaki İlişki

Bu calismada, matematiksel ve muziksel ogrenme arasindaki iliski incelenmistir. Arastirmaci Uludag Universitesi Devlet Konservatuvari Ilkogretim Okulu 6. , 7. ve 8. sinif ogrencilerine verdigi egitim sirasinda ayni muzik yetenegi sinaviyla okula alinan ogrencilerden bir kisminin yeteneklerini gelistirmede zorluk cektigini ve bunlarin cogunun Matematik dersinde de zorluklarla karsilastiklarini gozlemistir. Olusan kanaati arastirmak amaciyla 2004-2005 ogretim yilinda 6. ve 7. siniflarda ogrenim goren iki ogrenci grubunun Matematik dersi ile muziksel isitme yeteneginin gelistirilerek muzik okur yazarliginin saglanmaya calisildigi “Solfej-Dikte-Teori” dersi yil sonu basari puanlari karsilasti- rilmistir. Bulgularin bu iki ders arasinda pozitif yonde anlamli bir iliski oldugunu gostermesi uzerine 2005-2006 ogretim yilinda (bu kez 7. ve 8. sinifta olan) ayni ogrenci gruplarina ait basari puanlari yeniden karsilastirilmis ve benzer sonuclara ulasilmistir.
When Prospect Theory Meets Consumer Choice Models: Assortment and Pricing Management with Reference Prices

Problem definition: Reference prices arise as price expectations against which consumers evaluate products in their purchase scenarios. We investigate what will happen when prospect theory (e.g., reference prices) meets consumer choice models from the perspectives of both the consumers and the firm. Academic/practical relevance: Consumers see multiple relevant products on a particular purchase occasion and often compare their prices to form the willingness to pay when considering whether to purchase a particular product. Reference prices, which are not included in many choice models, may impact consumer choice behavior, so we incorporate reference prices into consumer choice models and investigate the operations management problems. Methodology: We take the widely used multinomial logit model as a showcase to examine the effects of reference prices through analytical and empirical study. We consider the optimization problems on assortment planning and pricing under consumer choice models with a variety of.
Unbundling of Ancillary Service: How Does Price Discrimination of Main Service Matter?

We consider a setting where the firm sells a main service (e.g., air travel) and an ancillary service (e.g., baggage delivery) to two types of consumers (high-type and low-type, e.g., business travelers and leisure travelers). We study the firm's decision of whether to unbundle the ancillary service from the main service and charge separate prices. We analyze two types of firms: firms that use uniform pricing of main service and firms that use discriminatory pricing of main service. Our analysis highlights the differences in the structure of optimal ancillary service strategies between the two types of firms. First, a uniform-pricing firm unbundles the ancillary service if the consumers that value the main service higher are also very likely to purchase the ancillary service. On the other hand, a discriminatory-pricing firm unbundles the ancillary service if the consumers that value the main service higher are not very likely to purchase the ancillary service. Second, under uniform pricing, as the firm's proportion of high-type consumers increases, unbundling is more likely to be optimal if the correlation between consumers' main service valuations and ancillary service valuations is high, and bundling is more likely to be optimal if the correlation is low. On the contrary, under discriminatory pricing, as the firm's proportion of high-type consumers increases, bundling is more likely to be optimal if the correlation is high and unbundling is more likely to be optimal if the correlation is low. The differences hinge on the rationale that under uniform pricing, unbundling allows the firm to extract more ancillary surplus from high-type consumers; whereas under discriminatory pricing, bundling allows the firm to extract more ancillary surplus from high-type consumers. Finally, we use these findings to discuss industry practice.
Structured Inference for Recurrent Hidden Semi-markov Model

Segmentation and labeling for high dimensional time series is an important yet challenging task in a number of applications, such as behavior understanding and medical diagnosis. Recent advances to model the nonlinear dynamics in such time series data, has suggested to involve recurrent neural networks into Hidden Markov Models. However, this involvement has caused the inference procedure much more complicated, often leading to intractable inference, especially for the discrete variables of segmentation and labeling. To achieve both flexibility and tractability in modeling nonlinear dynamics of discrete variables, we present a structured and stochastic sequential neural network (SSNN), which composes with a generative network and an inference network. In detail, the generative network aims to not only capture the long-term dependencies but also model the uncertainty of the segmentation labels via semi-Markov models. More importantly, for efficient and accurate inference, the proposed bi-directional inference network reparameterizes the categorical segmentation with the Gumbel-Softmax approximation and resorts to the Stochastic Gradient Variational Bayes. We evaluate the proposed model in a number of tasks, including speech modeling, automatic segmentation and labeling in behavior understanding, and sequential multi-objects recognition. Experimental results have demonstrated that our proposed model can achieve significant improvement over the state-of-the-art methods.