Miracle-dz/UniFAR-FTUs huggingface dataset:
Curve evolution with a dual shape similarity and its application to segmentation of left ventricle

Automated image segmentation has been playing a critical role in medical image analysis. Recentl, Level Set methods have shown an efficacy and efficiency in various imaging modalities. In this paper, we present a novel segmentation approach to jointly delineate the boundaries of epi- and endocardium of the left ventricle on the Magnetic Resonance Imaging (MRI) images in a variational framework using level sets, which is in great demand as a clinical application in cardiology. One strategy to tackle segmentation under undesirable conditions such as subtle boundaries and occlusions is to exploit prior knowledge which is specific to the object to segment, in this case the knowledge about heart anatomy. While most left ventricle segmentation approaches incorporate a shape prior obtained by a training process from an ensemble of examples, we exploit a novel shape constraint using an implicit shape prior knowledge, which assumes shape similarity between epi- and endocardium allowing a variation under the Gaussian distribution. Our approach does not demand a training procedure which is usually subject to the training examples and is also laborious and time-consuming in generating the shape prior. Instead, we model a shape constraint by a statistical distance between the shape of epi- and endocardium employing signed distance functions. We applied this technique to cardiac MRI data with quantitative evaluations performed on 10 subjects. The experimental results show the robustness and effectiveness of our shape constraint within a Mumford-Shah segmentation model in the segmentation of left ventricle from cardiac MRI images in comparison with the manual segmentation results.
‘Girls don’t play soccer’: children policing gender on the playground in a township primary school in South Africa

ABSTRACT This paper is based on an ethnographic study conducted between 2012 and 2014 with a group of 64 boys and girls aged 6–10, all attending the same township primary school in South Africa. The paper explores how the young children construct gender ‘boundaries’ and ‘police’ gender ‘transgressions’ on the school playground during break-time. The findings illustrate the ways in which these young boys and girls learn to ‘do’ gender through practices of inclusion, exclusion and ‘policing’ during play. The findings challenge dominant teacher constructions of the playground as a ‘free space’ and demonstrate how the playground operates as a site of learning gender through forms of ‘policing’ that involve boys bullying girls and boys who do not conform to gender norms. The findings raise implications for the development of curriculum material and teaching practices that would assist primary schoolteachers to reflect more deeply on young learners’ personal experiences and perspectives around gender and play.
Evaluation of doctors’ performance as facilitators in basic medical science lecture classes in a new Malaysian medical school

Background Didactic lecture is the oldest and most commonly used method of teaching. In addition, it is considered one of the most efficient ways to disseminate theories, ideas, and facts. Many critics feel that lectures are an obsolete method to use when students need to perform hands-on activities, which is an everyday need in the study of medicine. This study evaluates students’ perceptions regarding lecture quality in a new medical school. Methods This was a cross-sectional study conducted of the medical students of Universiti Sultan Zainal Abidin. The study population was 468 preclinical medical students from years 1 and 2 of academic year 2012–2013. Data were collected using a validated instrument. There were six different sections of questions using a 5-point Likert scale. The data were then compiled and analyzed, using SPSS version 20. Results The response rate was 73%. Among 341 respondents, 30% were male and 70% were female. Eighty-five percent of respondents agree or strongly agree that the lectures had met the criteria with regard to organization of lecture materials. Similarly, 97% of students agree or strongly agree that lecturers maintained adequate voices and gestures. Conclusion Medical students are quite satisfied with the lecture classes and the lectures. However, further research is required to identify student-centered teaching and learning methods to promote active learning.
Identifying Topics about Leadership and Entrepreneurship using Topic Modelling

Terms like leadership and entrepreneurship have reached great importance and popularity. A lot of information has appeared related with these topics and searching for it has become a bit complicated for people interested in leadership and entrepreneurship. With so much information, it is difficult to identify a topic among the huge quantity of concepts involved in these themes. This work presents a process based on text processing, text mining and topic modelling for finding topics and patterns in interviews about these topics in order to identify the main themes that leaders and entrepreneurs talk about.
Improved count suffix trees for natural language data

With more and more natural language text stored in databases, handling respective query predicates becomes very important. Optimizing queries with predicates includes (sub)string estimation, i.e., estimating the selectivity of query terms based on small summary statistics before query execution. Count Suffix Trees (CST) are commonly used to this end. While CST yield good estimates, they are expensive to build and require a large amount of memory to be stored. To fit in the data dictionary of database systems, they have to be severely pruned. Existing pruning techniques are based on suffix frequency or tree depth. In this paper, we propose new filtering and pruning techniques that reduce both the size of CST over natural-language texts and the cost of building them. The core idea is to exploit features of the natural language data, i.e., regarding only the suffixes that are useful in a linguistic sense. The most important innovations are (a) a new aggressive approximate syllabification technique to filter out suffixes, (b) a new affix and prefix stripping procedure that conflates more terms than conventional stemming techniques, (c) the deployment of state-of-the-art trigram techniques and a new syllable-based mechanism to filter out non-words (i.e., misspellings and other language anomalies like foreign words), which would cause an over-proportional growth of the CST otherwise. -- Our evaluation with large English text corpora shows that our new mechanisms in combination decrease the size of a CST by up to 80% and shorten the build phase significantly. From a different perspective, if storage space remains unchanged, the accuracy of selectivity estimates computed from the CST increases by up to 70%.
Unsupervised Co-segmentation of 3D Shapes via Functional Maps

We present an unsupervised method for co-segmentation of a set of 3D shapes from the same class with the aim of segmenting the input shapes into consistent semantic parts and establishing their correspondence across the set. Starting from meaningful pre-segmentation of all given shapes individually, we construct the correspondence between same candidate parts and obtain the labels via functional maps. And then, we use these labels to mark the input shapes and obtain results of co-segmentation. The core of our algorithm is to seek for an optimal correspondence between semantically similar parts through functional maps and mark such shape parts. Experimental results on the benchmark datasets show the efficiency of this method and comparable accuracy to the state-of-the-art algorithms.
Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization, and the automatic estimation of network size and architecture.
The Psychological Trauma,Empathy Deficits and Antisocial Personality Disorders

Objective:The current research aimed to explore the relationship between trauma,empathy deficits and antisocial personality disorders (APD) during childhood. Methods: The Personality Diagnostic Questionnaire (PDQ+4) was used to assess 2690 prisoners. A semi structured interview Personality Disorder interview (PDI+4) was employed to study 182 prisoners for diagnosis. The empathy deficits of APD subjects were measured by the Interpersonal Reactivity Index (IRI). Results: 47.10% subjects experienced sex abuse,39.10% body neglect,23.40% emotion neglect,14.20% body abuse and 11.4% emotion abuse when they were in their childhood. The subjects who experienced more severe trauma in childhood had higher scores on 12 types of personality disorders. The results of multiple linear regression analysis showed that trauma in childhood were effective predictors of empathy deficits. Conclusion: The trauma in childhood will influence the development of the empathy while the empathy deficits will lead to personality disorders.
Multiple Imputation to Account for Missing Data in a Survey: Estimating the Prevalence of Osteoporosis

Background. Nonresponse bias is a concern in any epidemiologic survey in which a subset of selected individuals declines to participate. Methods. We reviewed multiple imputation, a widely applicable and easy to implement Bayesian methodology to adjust for nonresponse bias. To illustrate the method, we used data from the Canadian Multicentre Osteoporosis Study, a large cohort study of 9423 randomly selected Canadians, designed in part to estimate the prevalence of osteoporosis. Although subjects were randomly selected, only 42% of individuals who were contacted agreed to participate fully in the study. The study design included a brief questionnaire for those invitees who declined further participation in order to collect information on the major risk factors for osteoporosis. These risk factors (which included age, sex, previous fractures, family history of osteoporosis, and current smoking status) were then used to estimate the missing osteoporosis status for nonparticipants using multiple imputation. Both ignorable and nonignorable imputation models are considered. Results. Our results suggest that selection bias in the study is of concern, but only slightly, in very elderly (age 80+ years), both women and men. Conclusions. Epidemiologists should consider using multiple imputation more often than is current practice.
Model Predictive Control of a Direct Fire Projectile Equipped with Canards

Launch uncertainties in uncontrolled direct fire projectiles can lead to significant impact point dispersion, even at relatively short range. A model predictive control scheme for direct fire projectiles is investigated to reduce impact point dispersion. The control law depends on projectile linear theory to create an approximate linear model of the projectile and quickly predict states into the future. Control inputs are based on minimization of the error between predicted projectile states and a desired trajectory leading to the target. Through simulation, the control law is shown to work well in reducing projectile impact point dispersion. Parametric trade studies on an example projectile configuration are reported that detail the effect of prediction horizon length, gain settings, model update interval, and model step size.
An information spectrum approach to the capacity region of GIFC

In this paper, we present a general formula for the capacity region of a general interference channel with two pairs of users. The formula shows that the capacity region is the union of a family of rectangles, where each rectangle is determined by a pair of spectral inf-mutual information rates. Although the presented formula is usually difficult to compute, it provides us useful insights into the interference channels. In particular, when the inputs are discrete ergodic Markov processes and the channel is stationary memoryless, the formula can be evaluated by BCJR algorithm. Also the formula suggests us that the simplest inner bounds (obtained by treating the interference as noise) could be improved by taking into account the structure of the interference processes. This is verified numerically by computing the mutual information rates for Gaussian interference channels with embedded convolutional codes. Moreover, we present a coding scheme to approach the theoretical achievable rate pairs. Numerical results show that decoding gain can be achieved by considering the structure of the interference.
Efficient architecture for circle detection using Hough transform

This paper presents an efficient architecture for circle detection using Hough transform. The architecture adopts the scanline-based ball detection algorithm for the edge detection stage and edge-flag algorithm for the voting process. To bolster the performance of the voting process, when drawing a circle, we divide it into 16 sub-parts and compute the parts in parallel. The proposed design employs an internal memory block for the edge list. Our simulation results show that the benchmark images in the VGA size of 640 × 480 are processed within 10ms, which indicates that the proposed architecture can satisfy the speed requirements of most real world applications.
Vortioxetine restores reversal learning impaired by 5-HT depletion or chronic intermittent cold stress in rats

Current treatments for depression, including serotonin-specific reuptake inhibitors (SSRIs), are only partially effective, with a high incidence of residual symptoms, relapse, and treatment resistance. Loss of cognitive flexibility, a component of depression, is associated with dysregulation of the prefrontal cortex. Reversal learning, a form of cognitive flexibility, is impaired by chronic stress, a risk factor for depression, and the stress-induced impairment in reversal learning is sensitive to chronic SSRI treatment, and is mimicked by serotonin (5-HT) depletion. Vortioxetine, a novel, multimodal-acting antidepressant, is a 5-HT3, 5-HT7 and 5-HT1D receptor antagonist, a 5-HT1B receptor partial agonist, a 5-HT1A receptor agonist, and inhibits the 5-HT transporter. Using adult male rats, we first investigated the direct effects of vortioxetine, acting at post-synaptic 5-HT receptors, on reversal learning that was compromised by 5-HT depletion using 4-chloro-DL-phenylalanine methyl ester hydrochloride (PCPA), effectively eliminating any contribution of 5-HT reuptake blockade. PCPA induced a reversal learning impairment that was alleviated by acute or sub-chronic vortioxetine administration, suggesting that post-synaptic 5-HT receptor activation contributes to the effects of vortioxetine. We then investigated the effects of chronic dietary administration of vortioxetine on reversal learning that had been compromised in intact animals exposed to chronic intermittent cold (CIC) stress, to assess vortioxetine's total pharmacological effect. CIC stress impaired reversal learning, and chronic vortioxetine administration prevented the reversal-learning deficit. Together, these results suggest that the direct effect of vortioxetine at 5-HT receptors may contribute to positive effects on cognitive flexibility deficits, and may enhance the effect of 5-HT reuptake blockade.
An Ameliorative Whale Optimization Algorithm for Multi-Objective Optimal Allocation of Water Resources in Handan, China

With the deepening discrepancy between water supply and demand caused by water shortages, alleviating water shortages by optimizing water resource allocation has received extensive attention. How to allocate water resources optimally, rapidly, and effectively has become a challenging problem. Thus, this study employs a meta-heuristic swarm-based algorithm, the whale optimization algorithm (WOA). To overcome drawbacks like relatively low convergence precision and convergence rates, when applying the WOA algorithm to complex optimization problems, logistic mapping is used to initialize swarm location, and inertia weighting is employed to improve the algorithm. The resulting ameliorative whale optimization algorithm (AWOA) shows substantially enhanced convergence rates and precision than the WOA and particle swarm optimization algorithms, demonstrating relatively high reliability and applicability. A water resource allocation optimization model with optimal economic efficiency and least total water shortage volume is established for Handan, China, and solved by the AWOA. The allocation results better reflect actual water usage in Handan. In 2030, the p = 50% total water shortage is forecast as 404.34 × 106 m3 or 14.8%. The shortage is mainly in the primary agricultural sector. The allocation results provide a reference for regional water resources management.
Analysis and Visualization of Time Series Data from Consumer-Generated Media and News Archives

Internet has become an indispensable part of everyday life with millions of people around the globe using it for a wide range of daily activities such as monitoring stock prices, posting blogs, and browsing online newspapers. Though a vast amount of information can be easily searched and obtained in seconds simply by pressing a click with a fingertip, the overflow of information popping up may not be something really relevant to what we need and therefore, it creates a headache to us when it comes to scanning and extracting relevant and useful information. Finding a wise way of extracting only the useful data for further analysis plays a significant role in promoting the efficient and effective use of the internet. In this paper, we present a system which performs the analysis and visualization of the emerging consumer generated media (CGM) posts and online news archives in a more user-friendly way. In order to overcome the heavy time complexity incurred, we would employ an approach to extract only the useful data from the CGM by means of the time series data processing technique, namely, the perceptual important point (PIP). By correlating the sorted out time series data with the online texts, further analysis could be done in a more effective and efficient way. With valuable and easy-to-understand information generated by using the perceptual important point (PIP), many businesses could gain the upper hand in today's competitive world market.
A New Composition Method of Admissible Support Vector Kernel Based on Reproducing Kernel

Kernel function, which allows the formulation of nonlinear variants of any algorithm that can be cast in terms of dot products, makes the Support Vector Machines (SVM) have been successfully applied in many fields, e.g. classification and regression. The importance of kernel has motivated many studies on its composition. It’s well-known that reproducing kernel (R.K) is a useful kernel function which possesses many properties, e.g. positive definiteness, reproducing property and composing complex R.K by simple operation. There are two popular ways to compute the R.K with explicit form. One is to construct and solve a specific differential equation with boundary value whose handicap is incapable of obtaining a unified form of R.K. The other is using a piecewise integral of the Green function associated with a differential operator L. The latter benefits the computation of a R.K with a unified explicit form and theoretical analysis, whereas there are relatively later studies and fewer practical computations. In this paper, a new algorithm for computing a R.K is presented. It can obtain the unified explicit form of R.K in general reproducing kernel Hilbert space. It avoids constructing and solving the complex differential equations manually and benefits an automatic, flexible and rigorous computation for more general RKHS. In order to validate that the R.K computed by the algorithm can be used in SVM well, some illustrative examples and a comparison between R.K and Gaussian kernel (RBF) in support vector regression are presented. The result shows that the performance of R.K is close or slightly superior to that of RBF. Keywords—admissible support vector kernel, reproducing kernel, reproducing kernel Hilbert space, Green function, support vector regression
Density‐dependent and geographical variation in bird immune response

Latitudinal gradients in parasitism are common, causing differences in the intensity of parasite-mediated natural selection. Such differences in selection pressures should affect optimal investment in anti-parasite defense, because defense levels should increase in response to increased intensity of parasite-induced selection. Likewise, latitudinal differences in population density may affect immune responses either by selecting for higher levels or defense, or by suppressing resources needed for mounting efficient immune responses. We tested these predictions in a study of T-cell mediated immune response in altricial bird species in subtropical Spain and temperate Denmark. There were highly consistent levels of T-cell mediated response between nestlings and adults in the two areas, with nestlings having stronger responses than adults. In addition, there were highly consistent levels of immune response in nestlings and adults between the two areas, with responses being consistently stronger in Denmark than in Spain, particularly in adults. Population density was much higher in Denmark than in Spain. We found evidence of density-dependent immune response in nestlings and adults, as shown by differences in T-cell response between study areas being positively related to differences in density. Given that the relationship between density and immune response was positive, we can reject the hypothesis that higher population densities suppressed immune response. Therefore, our results support the hypothesis that birds in areas with higher density allocate more resources to immune response, particularly in adults, suggesting that density-dependent effects of parasitism have selected for allocation strategies that minimize the risk of parasitism.
Piercing Axis-Parallel Boxes

Let $\F$ be a finite family of axis-parallel boxes in $\R^d$ such that $\F$ contains no $k+1$ pairwise disjoint boxes. We prove that if $\F$ contains a subfamily $\M$ of $k$ pairwise disjoint boxes with the property that for every $F\in \F$ and $M\in \M$ with $F \cap M \neq \emptyset$, either $F$ contains a corner of $M$ or $M$ contains $2^{d-1}$ corners of $F$, then $\F$ can be pierced by $O(k)$ points. One consequence of this result is that if $d=2$ and the ratio between any of the side lengths of any box is bounded by a constant, then $\F$ can be pierced by $O(k)$ points. We further show that if for each two intersecting boxes in $\F$ a corner of one is contained in the other, then $\F$ can be pierced by at most $O(k\log\log(k))$ points, and in the special case where $\F$ contains only cubes this bound improves to $O(k)$.
Robust mean-shift tracking with extended fast colour thresholding

We propose a novel adaptive approach for object tracking using fast colour thresholding and region merging. It proves to be an effective measure against large variations between consecutive frames during a mean-shift process. The approach retains mean-shift's property of efficiency and improves mean-shift's drawback of robustness. It can track non-rigid objects with significant occlusion.