Miracle-dz/UniFAR-FTUs huggingface dataset:
China's Environmental Challenges and Implications for the World

After three decades of exceptional economic growth, China has become a global economic powerhouse. As the economy has grown, though, so have China's environmental challenges, causing enormous socioeconomic consequences for China and the rest of the world. The global financial crisis has prompted China to create more domestic demand for consumption and implement massive infrastructure construction. Although China has the second-largest total gross domestic product (GDP) in the world, its per capita GDP is still much lower than per capita GDP of developed countries: there is much room for further increase in GDP and consequent environmental challenges. Despite China's many efforts to protect the environment and improve resource use efficiency, increasing environmental pollution and resource scarcity have become a severe bottleneck for sustainable development. Because of China's size, these and other challenges and opportunities have huge implications for the world. However, literature related to China's various environmental challenges and rapid changes is scattered. In this article, we outline China's socioeconomic transformation, synthesize China's environmental challenges and their interrelationships, illustrate impacts of environmental challenges on human well-being in China and beyond, and offer a systems approach to addressing environmental sustainability.
The Skill Plot: A Graphical Technique for Evaluating Continuous Diagnostic Tests

Summary We introduce the Skill Plot, a method that it is directly relevant to a decision maker who must use a diagnostic test. In contrast to ROC curves, the skill curve allows easy graphical inspection of the optimal cutoff or decision rule for a diagnostic test. The skill curve and test also determine whether diagnoses based on this cutoff improve upon a naive forecast (of always present or of always absent). The skill measure makes it easy to directly compare the predictive utility of two different classifiers in an analogy to the area under the curve statistic related to ROC analysis. Finally, this article shows that the skill‐based cutoff inferred from the plot is equivalent to the cutoff indicated by optimizing the posterior odds in accordance with Bayesian decision theory. A method for constructing a confidence interval for this optimal point is presented and briefly discussed.
STAT signaling is active during early mammalian development

Cytokine activation of gene expression can be mediated through signal transducer and activator of transcription (STAT) signaling pathways resulting in expression of target genes. Because many cytokines have important regulatory roles during early development, we wanted to ascertain whether STAT signaling was also active at this time and could therefore have important roles in mediating developmental processes. We have found that Stat1 and Stat3 mRNAs are present in both maternal and extraembryonic tissues during early postimplantation stages of murine development. Furthermore, analyses of STAT activity in E4.5–E9.5 decidual swellings by electrophoretic mobility shift assay demonstrated that Stat3 protein was active during this early developmental period. The identification of activated Stat3 demonstrates that STAT signaling functions during early postimplantation development in the mouse are likely to be important during early embryogenesis. Dev. Dyn. 208:190–198, 1997. © 1997 Wiley‐Liss, Inc.
Active fleet balancing in vehicle sharing systems via Feedback Dynamic Pricing

In the last few years, many Vehicle Sharing Systems (VSSs) have been widely promoted around the world as a sustainable urban transport paradigm. Such systems can be divided in two main categories: one-way VSSs and round-trip VSSs. This paper focuses on the issue of keeping the number of vehicles balanced over the stations in one-way VSSs. Specifically, the paper presents a novel approach called Feedback Dynamic Pricing (FDP), based on the modelling of a VSS as a dynamical system and aimed to control the fleet balancing by varying the price of the service in real-time. To show the effectiveness of the proposed approach, a full-fledged simulator has been developed taking into account the stochastic nature of human decisions. The results obtained using controlled and uncontrolled VSSs are finally compared and discussed.
Pedestrian movement monitoring based on face recognition and RFID recognition

In order to achieve object trajectory tracking without blind angle, we propose a pedestrian movement monitoring system with face recognition outdoor and RFID radio frequency equipment indoor, which connect people, object and events. While granting integrated human movement information, we also provide two-dimensional trajectory panoramic map browsing, preview video clips on designated area, video search specified users for object motion trajectory and behavioral abnormalities. The experimental results show that the proposed system method can obtain satisfactory identification result on video test set of china mobile southern base.
Towards a Better Understanding of Learning Objects Content

Usually semantic metadata are introduced to support effective search of relevant learning objects from LOR (Learning Object Repositories). We propose to use them to assist authors of SCORM (Sharable Content Object Reference Model) like learning objects. In fact, when we design new learning objects by reuse of existing ones, there are risks to have weaknesses mainly due to a bad understanding of the true nature of the reused objects. To overcome those risks we have defined an automated authoring assistance approach. In this paper we focus especially on how we use semantic metadata to produce indicators about the content of learning objects, more precisely about their complexity, heterogeneity and imbalance from a semantic perspective. To attend this specific objective we introduce the notion of "semantic space" and "semantic component". Then we use them to compute metrics which are aggregated together to produce meaningful indicators.
A CSCW System Model for Classifying Tailorability Implementations

The paper defines a model for CSCW systems which identifies thr e main system parts. Corresponding to these parts, three kinds of tailorability , as seen from the system’s perspective, are defined. It is shown how the model applies to the main cl sses of CSCW systems. Then, some examples are given and some issues are addressed f or the three tailorability kinds.
Abstract—in Association with Path Dependence, Researchers Often

talk of institutional " lock-in " , thereby indicating that far-reaching path deviation or path departure are to be regarded as exceptional cases. This article submits the alleged general inclination for stability of path-dependent processes to a critical review. The different reasons for path dependence found in the literature indicate that different continuity-ensuring mechanisms are at work when people talk about path dependence (" increasing returns " , complementarity, sequences etc.). As these mechanisms are susceptible to fundamental change in different ways and to different degrees, the path dependence concept alone is of only limited explanatory value. It is therefore indispensable to identify the underlying continuity-ensuring mechanism as well if a statement's empirical value is to go beyond the trivial, always true " history matters " .
View-invariant action recognition based on local linear dynamical system

To address recognition of human actions under view changes, this paper proposes a view-invariant human action recognition approach based on local linear dynamical system and sparse coding. We utilize the bag-of-words (BoW) approach, local patches are modeled as linear dynamical systems and the model parameters are used as the descriptors of local patches. The model parameters capture the dynamics in human actions which is insensitive to view changes. The sparse coding algorithm is then applied to learn discriminative codebook and to avoid the initialization problem in the k-means algorithm. The proposed approach is tested on the IXMAS dataset. The experimental results demonstrate that this approach can recognize the viewinvariant actions, obtain high recognition rates, and achieve comparable results in cross-views action recognition.
MODELING AND FORMAL VERIFICATION OF COUNTING HEADS FOR RAILWAYS

: The demand for safety for electronic systems, especially safety critical systems, is high. Nowadays such systems are tested and simulated with a manually created set of test cases. But testing cannot reach a complete coverage for complex designs. Hence, we present a veri(cid:2)cation (cid:3)ow for Counting Heads for railways which are used by many electronic railway interlocking systems from SIEMENS.Our approach is based on SystemC, a powerful system level description language. Thereby, ef(cid:2)cient modeling and simulation-based validation of railway systems becomes possible. The presented (cid:3)ow allows also for formal veri(cid:2)cation.
Permanent Structural Change in the US Short-Term and Long-Term Interest Rates

This paper uses a time-varying error correction model to examine the structural changes in the rate of adjustment to the long-run equilibrium and the cointegrating vector of the US short- and long-term interest rates. We show that agents’ expectations of interest rate movements vary according to policy changes as reflected by changes in the direction of movements of the underlying parameters.
Spherical Mexican hat wavelet: an application to detect non-Gaussianity in the COBE-DMR maps

ABSTRA C T The spherical Mexican hat wavelet is introduced in this paper, with the aim of testing the Gaussianity of the cosmic microwave background temperature fluctuations. Using the information given by the wavelet coefficients at several scales, we have performed several statistical tests on the COBE-DMR maps to search for evidence of non-Gaussianity. Skewness, kurtosis, scale ‐ scale correlations (for two and three scales) and Kolmogorov‐ Smirnov tests indicate that the COBE-DMR data are consistent with a Gaussian distribution. We have extended the analysis to compare temperature values provided by COBE-DMR data with distributions (obtained from Gaussian simulations) at each pixel and at each scale. The number of pixels with temperature values outside the 95 and 99 per cent limits is consistent with that obtained from Gaussian simulations, at all scales. Moreover, the extrema values for COBE-DMR data (maximum and minimum temperatures in the map) are also consistent with those obtained from Gaussian simulations.
AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS MEDICAL GUIDELINES FOR THE CLINICAL USE OF DIETARY SUPPLEMENTS AND NUTRACEUTICALS AACE Nutrition Guidelines Task Force Chairman

Reviewers Donald A. Bergman, MD, FACP, FACE Jeffrey R. Garber, MD, FACE Carlos R. Hamilton, Jr., MD, FACE Yehuda Handelsman, MD, FACP, FACE Kalman E. Holdy, MD John S. Kukora, MD, FACS, FACE Philip Levy, MD, FACE Pasquale J. Palumbo, MD, MACE Steven M. Petak, MD, JD, FACE Leonid Poretsky, MD Philip Rabito, MD, FACE Herbert I. Rettinger, MD, FACE, MBA Helena W. Rodbard, MD, FACE F. John Service, MD, PhD, FACE, FACP, FRCPC Talla P. Shankar, MD, FACE
Circulating Insulin-Like Growth Factor I Mediates Effects of Exercise on the Brain

Physical exercise increases brain activity through mechanisms not yet known. We now report that in rats, running induces uptake of blood insulin-like growth factor I (IGF-I) by specific groups of neurons throughout the brain. Neurons accumulating IGF-I show increased spontaneous firing and a protracted increase in sensitivity to afferent stimulation. Furthermore, systemic injection of IGF-I mimicked the effects of exercise in the brain. Thus, brain uptake of IGF-I after either intracarotid injection or after exercise elicited the same pattern of neuronal accumulation of IGF-I, an identical widespread increase in neuronal c-Fos, and a similar stimulation of hippocampal brain-derived neurotrophic factor. When uptake of IGF-I by brain cells was blocked, the exercise-induced increase on c-Fos expression was also blocked. We conclude that serum IGF-I mediates activational effects of exercise in the brain. Thus, stimulation of the uptake of blood-borne IGF-I by nerve cells may lead to novel neuroprotective strategies.
Compound Image Segmentation for Multiscale Recurrent Pattern Coding

In this paper we propose a new compound image segmentation algorithm, specially developed for a digital document encoding scheme based on the Multidimensional Multiscale Parser (MMP) encoding algorithm. In order to increase the MMP algorithm performance, a block based segmentation defines a mask that divides the input image into two types of blocks: text-like blocks and non-text, or smooth image-like blocks. Different versions of the MMP algorithm are then applied on each image layer. The segmentation algorithm presented in this paper is based in two steps. The first step relies on morphological filtering and image gradient analysis. In the second step, false text blocks are eliminated using a morphological based algorithm. Experimental results show that this algorithm is not only efficient for our encoder, but also improves the segmentation accuracy in relation to other methods proposed in the literature.
Battle of the Sexes: Non-Cooperative Games in the Theory of the Family

We look at private-provision-of-public goods games. These games share an assumption that family members non-cooperatively use their resources either to acquire a private good or a family-specific good. What exactly constitutes the "private good" and the "public good" will be seen to vary from model to model. The next section suggests that the type of non-cooperative model presented earlier can be used to reformulate the Nash bargaining theory of the household.
ASSESSMENT OF LANGUAGE AFTER STROKE IN ADULTS IN THE STATE OF SERGIPE Avaliação da linguagem após acidente vascular cerebral em adultos no estado de sergipe

Purpose: to study the occurrence of language disorders in subjects who had suffered stroke from February to August 2012, under treatment at the center for education and health of Sergipe State, in the section of physiotherapy referral. The language evaluation was based on the Test of Rio de Janeiro (2005). Methods: data analysis was performed using descriptive statistics and probability through frequency distribution, calculation of mean, standard deviation and percentiles, Mann-Whitney U Test and Chi-Square. Results: the sample was composed of 31 subjects, of both genders, aged in between 30 and 94 years. The data also showed that gender, education and age, influence test performance, verifying that the male subjects below 65 years and those with schooling above incomplete primary school level have higher scores. Conclusion: the data show that a higher percentage of the sample has comprehension disorders, when compared to language expression disorders.
Middle-aged men with increased waist circumference and elevated C-reactive protein level are at higher risk for postoperative atrial fibrillation following coronary artery bypass grafting surgery.

INTRODUCTION We recently demonstrated that metabolic syndrome (MetS) is an independent risk factor for postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG). In the present work, we sought to determine which feature of the MetS is associated with POAF. METHODS AND RESULTS We retrospectively analysed the association between metabolic features and the incidence of new-onset POAF in a total of 2214 male patients <65 years who underwent first isolated CABG. Anthropometric data including waist circumference (WC) and complete preoperative lipid profile were available. We also conducted a nested case-control substudy including 147 patients who developed POAF, and were matched for age with a control population. In these patients, C-reactive protein, interleukin-6 (IL-6), and thiobarbituric acid-reactive substances (TBARS; evaluating the oxidative stress) blood levels were determined. In the whole cohort, 19.6% of patients developed POAF. On univariate analysis, body mass index (BMI; P = 0.002) and WC (P = 0.001) were the only anthropometric variables significantly associated with increased incidence of POAF. In the multivariable logistic model, the only independent predictors of POAF were a WC > 102 cm [odds ratio (OR) = 1.40, P = 0.04)] and older age (OR = 1.08, P < 0.001). In the nested case-control substudy C-reactive protein, IL-6, and TBARS levels were not significantly different in patients with or without POAF. Of particular significance, patients with elevated WC > 102 cm and C-reactive protein > 1.5 mg/L or IL-6 >2.2 pg/mL were at a high risk of developing POAF (respectively, OR = 2.32, P = 0.02 and OR = 2.27, P = 0.03). CONCLUSION Patients with increased WC combined with elevated C-reactive protein levels are at higher risk for POAF. Thus, interventions targeting inflammation related to visceral obesity might help reducing the incidence of POAF.
Reinforce Your Overlay with Shadows : Efficient Dynamic Maintenance of Robust Low Fan-out Overlays for Topic-based Publish / Subscribe under Churn

Overlay network design for topic-based publish/subscribe systems is of primary importance because the overlay directly impacts the system’s performance. Determining a low fan-out topic-connected overlay (TCO) is a fundamental problem. Existing algorithms for constructing TCOs with provably low fan-out build the overlays from scratch. In this paper, we propose the first fully dynamic algorithms for efficiently maintaining TCO in presence of node churn such that both the average and maximum node degrees stay provably low. The main challenge of dynamic maintenance is to efficiently overcome departure of nodes central to topic-connectivity. This is attained by maintaining sets of shadow nodes so that all links adjacent to a failed node can quickly be replaced by adding links among shadow nodes. Compared to constructing TCOs from scratch, the proposed algorithms exhibit two important advantages: When a node joins or leaves, topic connectivity is restored much faster, and changes to the overlay are incremental so that existing links do not need to be torn down. We corroborate these advantages by extensive evaluations on typical workloads with up to 2 000 nodes and 200 topics under 1 000 rounds of churn.
Lower Intensified Target LDL-c Level of Statin Therapy Results in a Higher Risk of Incident Diabetes: A Meta-Analysis

Background A recent meta-analysis has reported that intensive-dose statin drug increases the risk of incident diabetes. However, doubling of the statin dose generates only a further 6% decrease in low-density lipoprotein cholesterol (LDL-c) on average. This study aimed to determine whether statin therapy with lower intensive-target LDL-c level contributes to higher risk of new-onset diabetes. Methods Medline, Embase, and the Cochrane Central Register of Controlled Trials were searched for randomized controlled endpoint trials of statins conducted from 1966 to 2012. We included trials with more than 1000 participants who were followed up for at least 2 years. The included trials were stratified by the target LDL-c level. I 2 statistic was used to measure heterogeneity between trials. We further calculated risk estimates with random-effect meta-analysis. Meta-regression was used to identify the potential risk factors of statin-induced diabetes. Results Fourteen trials with a total of 95 102 non-diabetic participants were included. The risks elevated by 33% [odds ratio (OR) = 1.33; 95% confidence interval (CI) 1.14–1.56; I 2 = 7.7%] and 16% (OR = 1.16; 95% CI 1.06–1.28; I 2 = 0.0%) when the intensified target LDL-c levels were ≤1.8 mmol/L and 1.8–2.59 mmol/L, respectively. The risk of incident diabetes did not increase when the target LDL-c level was ≥2.59 mmol/L. Apart from age, female, and baseline level of total cholesterol, meta-regression analysis showed that the target and baseline levels of LDL-c and relative LDL-c reduction were predictors of statin-induced diabetes. Conclusion A lower intensified target LDL-c level of statin therapy resulted in a higher risk of incident diabetes.
Non-Computer Science Majored Women Students Perspective on a Pictorial Programming Environment

variety of ways and different tools to increase the student retention, women and minorities are still underrepresented in computer science and the awarded degree numbers are still way behind from the 2000-2006 numbers. In order to address this problem, a dragon drop programming environment is created to teach introductory level object oriented programming course for the students who have no prior programming experience. The framework eliminates one of the main challenges, if not the most, namely syntax issues with creating the programming statements automatically. With that way the students focus more on the logical issues of the task, instead of spending time on correcting typos and syntax errors. The applied survey to the student who is majored in another program but taking the Introduction to Programming course revealed that 88 percent of them found the application beneficial. Survey completed by non-Computer Science (CS) majored women students indicates that this new application will increase the interest level in computer science, while 82 percent of the non-CS majored women students prefer to use this application instead of current programming environment.
FEATURE SELECTION METHODS FOR SOFTCOMPUTING CLASSIFICATION

Feature selection and feature creating are two of the most important and difficult tasks in the field of pattern recognition. It involves determining a good feature subset given a set of candidate features. The acoustic analysis of vibration signals in the time and frequency domain frequently generates a large number of features and makes a reduction of dimensionality necessary. The present method is an approach to improve pattern classifier performance using a feature selection process. For this task the two parts feature selection and the inherent classification step are combined. The results obtained from an application for the automatic detection of different surface structures indicates the usefulness of the approach. The algorithm is realized as the Plug-In FeatureSelector for the software tool Data Engine .
Towards Understanding the Impact of Personality Traits on Mobile App Adoption - A Scalable Approach

Smartphones are the most personal devices. The kind of apps we install are therefore closely linked to our habits and personality. In this research-in-progress paper, we aim to provide two key contributions. First, we aim to advance the body of knowledge in technology adoption research by explaining adoption of specific mobile apps by using the Big Five personality traits. Second, we provide a scalable method of deriving personality traits based on easily accessible data. We show that it is possible to determine a user's personality in reasonable accuracy by evaluating her history of app installations and update events.
Risk Sharing and Growth in the Gifts Economy

We consider a two-period overlapping generations model where agents face the uncertainty of intergenerational transfers from their children. To avoid this kind of risk, agents have an incentive to share the risk within the same generation. However, there exists an information asymmetry about the realization of the old period fs income between the insurance company and old agents. By analyzing economies with and without risk sharing, we find that risk sharing decreases the rate of economic growth and accelerates social welfare when the rate of social time preference is sufficiently large.