Miracle-dz/UniFAR-FTUs huggingface dataset:
Desisting and persisting gender dysphoria after childhood: A qualitative follow-up study

The aim of this qualitative study was to obtain a better understanding of the developmental trajectories of persistence and desistence of childhood gender dysphoria and the psychosexual outcome of gender dysphoric children. Twenty five adolescents (M age 15.88, range 14–18), diagnosed with a Gender Identity Disorder (DSM-IV or DSM-IV-TR) in childhood, participated in this study. Data were collected by means of biographical interviews. Adolescents with persisting gender dysphoria (persisters) and those in whom the gender dysphoria remitted (desisters) indicated that they considered the period between 10 and 13 years of age to be crucial. They reported that in this period they became increasingly aware of the persistence or desistence of their childhood gender dysphoria. Both persisters and desisters stated that the changes in their social environment, the anticipated and actual feminization or masculinization of their bodies, and the first experiences of falling in love and sexual attraction had influenced their gender related interests and behaviour, feelings of gender discomfort and gender identification. Although, both persisters and desisters reported a desire to be the other gender during childhood years, the underlying motives of their desire seemed to be different.
Predicting sports results using latent features: A case study

Predicting sports results is normally a challenging task, even more in case of a sport that shows a highly stochastic nature. In football, for example, numerous features are tracked and combined with expert knowledge, yielding various predicting algorithms. Our work however, is based on a case where there is no expert knowledge available and the only data comes from previous match results. We built a goal score prediction model that uses latent features obtained from matrix factorization process. We also added a Naive Bayes Classifier to be able to predict outcome of the match. The algorithm has been tested on results of the FIFA World Cup 2014. We also built a match result predictor based on the betting quotas. As these are derived from a complex algorithms that encompass also the expert knowledge, our algorithm can be used to estimate accuracy of an expert knowledge-based system. This case study shows that there is no significant difference between the two algorithms that we tested and that the latent features may provide a valid substitute for real features, when the later ones are not available.
Preservation Watch: What to monitor and how

For successful preservation operations, a preservation system needs to be capable of monitoring compliance of preservation operations to specifications, alignment of these operations with the organisation’s preservation objectives, and associated risks and opportunities. This requires linking a number of diverse information sources and specifying complex conditions. For example, the content to be preserved needs to be related to specifications of significant properties, to the file formats used to represent that content, to the software environments available to analyse, render and convert it and the technical environments available to the designated user communities that will consume this content. This article analyses aspects of interest in a preservation context that call for automated monitoring and investigates the feasibility of drawing sufficient information from diverse sources together and linking it in a meaningful way. We define a number of preservation triggers that lead to preservation planning activities and present the requirements and a high-level design of a preservation watch system that is currently being developed. We demonstrate the value of such a monitoring approach on a number of scenarios and cases.
Magnesium Alloy AZ31 - Short Carbon Fiber Composite Obtained by Pressure Die Casting

The application of short carbon fibers in magnesium alloy AZ31 matrix composite fabrication by cold chamber pressure die casting was presented. A technological procedure of small-sized and complex-shaped composite casts manufacturing was shown. The microstructure of the composite was characterized as well as its mechanical properties, friction coefficient and wear resistance. The application of mechanical stirring of melted AZ31 alloy with short fibers and then AZ31-Cf suspension pressure die casting ensured obtaining casts with the reinforcing phase correctly distributed and well bonded with the matrix. Comparision of the AZ31-Cf composite with the AZ31 alloy properties, cast in the same conditions, revealed a considerable increase in bending strength and hardness, and some improvement of ductility and sliding friction parameters as a result of short carbon fibers application.
Histogram equalization and noise masking for robust speech recognition

Mismatch between training and test conditions deteriorates the performance of speech recognizers. This paper investigates the combination of parametric histogram equalization (pHEQ) and noise masking to compensate for the mismatch caused by additive noise. The proposed front-end maps the distribution of the observed power spectrum vectors to a target distribution. The target distribution matches the distribution of the noise free training data except for an artificially reduced signal-to-noise ratio. Different power spectrum estimation algorithms are used to estimate the noise distribution as used internally by pHEQ more reliably under non-stationary noise conditions. The proposed front-end is evaluated on the Aurora4 database and shows a significant improvement w.r. t. mean-normalized Mel-frequency spectral coefficients. Moreover, the performance could be further improved if better estimates of the instantaneous noise power spectrum were available.
Protein‐protein recognition: exploring the energy funnels near the binding sites

We present a rapidly executable minimal binding energy model for molecular docking and use it to explore the energy landscape in the vicinity of the binding sites of four different enzyme inhibitor complexes. The structures of the complexes are calculated starting with the crystal structures of the free monomers, using DOCK 4.0 to generate a large number of potential configurations, and screening with the binding energy target function. In order to investigate possible correlations between energy and variation from the native structure, we introduce a new measure of similarity, which removes many of the difficulties associated with root mean square deviation. The analysis uncovers energy gradients, or funnels, near the binding site, with decreasing energy as the degree of similarity between the native and docked structures increases. Such energy funnels can increase the number of random collisions that may evolve into productive stable complex, and indicate that short‐range interactions in the precomplexes can contribute to the association rate. The finding could provide an explanation for the relatively rapid association rates that are observed even in the absence of long‐range electrostatic steering. Proteins 1999; 34:255–267. © 1999 Wiley‐Liss, Inc.
Discretization methods with analytical characteristic methods for advection-diffusion-reaction equations and 2d applications

Our studies are motivated by a desire to model long-time simulations of possible scenarios for a waste disposal. Numerical methods are developed for solving the arising systems of convection-diffusion-dispersion-reaction equations, and the received results of several discretization methods are presented. We concentrate on linear reaction systems, which can be solved analytically. In the numerical methods, we use large time-steps to achieve long simulation times of about 10 000 years. We propose higher-order discretization methods, which allow us to use large time-steps without losing accuracy. By decoupling of a multi-physical and multi-dimensional equation, simpler physical and one-dimensional equations are obtained and can be discretized with higher-order methods. The results can then be coupled with an operator-splitting method. We discuss benchmark problems given in the literature and real-life applications. We simulate a radioactive waste disposals with underlying flowing groundwater. The transport and reaction simulations for the decay chains are presented in 2d realistic domains, and we discuss the received results. Finally, we present our conclusions and ideas for further works.
Improvement of light harvesting by inserting an optical spacer (ZnO) in polymer bulk heterojunction solar cells: A theoretical and experimental study

By introducing a thin ZnO layer as an optical spacer, we have demonstrated that inserting this layer between an active layer and a reflective electrode results in a re-distribution of the optical electric field inside bulk heterojunction solar cells. A theoretical analysis by optical modeling showed that the thin ZnO layer could shift the position of the maximum of the electric field into the absorbing layer. Theoretical calculations were compared with experimental results for devices with and without an optical spacer. By using a ZnO optical spacer layer, a significant increase was observed in the short circuit current density of J-V curves. This increase might be due to harvesting more lights and also hole-blocking by the ZnO layer. Both electrical and optical characteristics of the device provided improved results in the power conversion efficiency of the bulk heterojunction solar cell up to 3.49%.
Comprehensive autoregressive modeling for classification of genomic sequences

In this paper, we propose the novel use of an autoregressive (AR) model to produce a multi-dimensional feature for distinguishing between genomic protein coding and non-coding regions, at their nucleotide level. In contrast to previous research, in which AR models were used to estimate a single frequency, here AR model parameters characterizing the entire short-term sequence spectrum are employed as a feature in conjunction with Gaussian mixture model-based classification. The optimized AR-based features are then combined with other signal processing based time-domain and frequency-domain features to advance detection accuracy for the coding/non-coding region classification problem. The system described herein is shown to produce identification accuracies of more than 78.9%, and 81.6% respectively for protein coding and non-coding nucleotides, when evaluated on the GENSCAN test set.
On encouraging multiple views for visualization

Visualization enables 'seeing the unseen', and provides new insight into the underlying data. However, users far too easily believe or rely on a single representation of the data; this view may be a favourite method, the simplest to perform, or a method that has always been used. A single representation may generate a misinterpretation of the information or provide a situation where the user is missing the 'richness' of the data content. By displaying the data in multiple ways a user may understand the information through different perspectives, overcome possible misinterpretations and perform interactive investigative visualization through correlating the information between views. Thus, the use of multiple views of the same information should be encouraged. We believe the visualization system itself should actively encourage the generation of multiple views by providing appropriate tools to aid in this operation. We present and categorise issues for encouraging multiple views and provide a framework for the generation, management and manipulation of such views.
Parallelization of Recursive Preorder Traversal Based on Building and Winding Call Stacks

This paper proposes a method to parallelize the recursive preorder traversal algorithm on an ordered general tree. The method is based on building and winding call stacks, which are achieved by statically prepared initializing and terminating functions. In the method, an ordered general tree is split in a manner in which the nodes of a consecutive preorder range are assigned to a worker thread. At that time, all worker threads are aligned in order of the worker IDs, and an earlier worker thread is assigned an earlier preorder range. Each node is processed exactly once by only one worker thread. This paper also provides a set of proofs to show that each worker thread can start/stop its recursive preorder traversal from/at an intended place in a tree. The method is sufficiently general to be applicable to existing applications that adopt the recursive preorder traversal such as Protocol Buffers, which is a type of serializer/deserializer tool. The evaluation results show that the serialization result writing time of the parallelized Protocol Buffers is 1.78 fold shorter than that for normal Protocol Buffers when serializing 40,320 double values using 5 worker threads.
Sepsis in children

#### The bottom line Sepsis is a clinical syndrome resulting from a dysregulated systemic inflammatory response to infection.1 It is characterised by a generalised pro-inflammatory cascade, which may lead to widespread tissue injury.2 It encompasses a clinical spectrum of severity, including severe sepsis, septic shock, and multi-organ failure.3 Sepsis is a leading cause of morbidity and mortality in children worldwide.4 International consensus guidelines define sepsis as the presence of systemic inflammatory response syndrome plus suspected or proven infection.3 A more pragmatic definition of sepsis has been proposed—that sepsis is a systemic response to infection with the presence of some degree of organ dysfunction.5 This definition is not currently widely accepted, but new criteria are under discussion and due to be published later in the year. Global data on sepsis in children are incomplete, but it is estimated that infection accounts for most deaths (almost 60%) in children aged under 5 years.4 The World Health Organization has stated that the four big causes of death in children worldwide are infectious diseases: pneumonia (1.9 million deaths/year), diarrhoea (1.6 million deaths/year), malaria (1.1 million deaths/year), and measles (550 000 deaths/year). 6 The largest epidemiological reports of the incidence …
Dynamics of Antibiotic Resistant Mycobacterium tuberculosis during Long-Term Infection and Antibiotic Treatment

For an infecting bacterium the human body provides several potential ecological niches with both internally (e.g. host immunity) and externally (e.g. antibiotic use) imposed growth restrictions that are expected to drive adaptive evolution in the bacterium, including the development of antibiotic resistance. To determine the extent and pattern of heterogeneity generated in a bacterial population during long-term antibiotic treatment, we examined in a monoclonal Mycobacterium tuberculosis infection antibiotic resistant mutants isolated from one patient during a 9-years period. There was a progressive accumulation of resistance mutations in the infecting clone. Furthermore, apparent clonal sweeps as well as co-existence of different resistant mutants were observed during this time, demonstrating that during treatment there is a high degree of dynamics in the bacterial population. These findings have important implications for diagnostics and treatment of drug resistant tuberculosis infections.
Significance of Big Data and Analytics in Higher Education

purpose and value of higher education is transforming. Technology is strengthening the capabilities of institutions to face the new challenges. Higher education has access to realms of data which can be used to improve decision making. The use of Big Data and analytics in higher education is relatively new area. The relevance of analytics is profoundly seen in many areas and higher education is no different. This paper examines the role of Big Data and analytics in higher education.
Search-based entity disambiguation with document-centric knowledge bases

Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. One possibility to describe these entities within a knowledge base is via entity-annotated documents (document-centric knowledge base). It has been shown that entity disambiguation with search-based algorithms that use document-centric knowledge bases perform well on the biomedical domain. In this context, the question remains how the quantity of annotated entities within documents and the document count used for entity classification influence disambiguation results. Another open question is whether disambiguation results hold true on more general knowledge data sets (e.g. Wikipedia). In our work we implement a search-based, document-centric disambiguation system and explicitly evaluate the mentioned issues on the biomedical data set CALBC and general knowledge data set Wikipedia, respectively. We show that the number of documents used for classification and the amount of annotations within these documents must be well-matched to attain the best result. Additionally, we reveal that disambiguation accuracy is poor on Wikipedia. We show that disambiguation results significantly improve when using shorter but more documents (e.g. Wikipedia paragraphs). Our results indicate that search-based, document-centric disambiguation systems must be carefully adapted with reference to the underlying domain and availability of user data.
Traffic cone detection and localization in TechX Challenge 2013

This paper presents the detection and localization methods of entrance and staircase markers for the team E-Mobile in TechX Challenge 2013. Autonomous vehicles are required to detect and locate traffic cones beside the indoor entrance and staircase. One big challenge is from the unpredictable lighting conditions and environment. Different practical techniques such as color space selection, segmentation, shape analysis, distance estimation, and detector training are combined to obtain good detection rate and localization accuracy. The proposed methods can achieve satisfactory performance in real-world experiments.
Molecular marker assisted gene stacking for biotic and abiotic stress resistance genes in an elite rice cultivar

Severe yield loss due to various biotic stresses like bacterial blight (BB), gall midge (insect) and Blast (disease) and abiotic stresses like submergence and salinity are a serious constraint to the rice productivity throughout the world. The most effective and reliable method of management of the stresses is the enhancement of host resistance, through an economical and environmentally friendly approach. Through the application of marker assisted selection (MAS) technique, the present study reports a successful pyramidization of genes/QTLs to confer resistance/tolerance to blast (Pi2, Pi9), gall Midge (Gm1, Gm4), submergence (Sub1), and salinity (Saltol) in a released rice variety CRMAS2621-7-1 as Improved Lalat which had already incorporated with three BB resistance genes xa5, xa13, and Xa21 to supplement the Xa4 gene present in Improved Lalat. The molecular analysis revealed clear polymorphism between the donor and recipient parents for all the markers that are tagged to the target traits. The conventional backcross breeding approach was followed till BC3F1 generation and starting from BC1F1 onwards, marker assisted selection was employed at each step to monitor the transfer of the target alleles with molecular markers. The different BC3F1s having the target genes/QTLs were inter crossed to generate hybrids with all 10 stress resistance/tolerance genes/QTLs into a single plant/line. Homozygous plants for resistance/tolerance genes in different combinations were recovered. The BC3F3 lines were characterized for their agronomic and quality traits and promising progeny lines were selected. The SSR based background selection was done. Most of the gene pyramid lines showed a high degree of similarity to the recurrent parent for both morphological, grain quality traits and in SSR based background selection. Out of all the gene pyramids tested, two lines had all the 10 resistance/tolerance genes and showed adequate levels of resistance/tolerance against the five target stresses. The study demonstrates the potential of MAS for stacking of several genes into a single line with a high degree of parental genome recovery.
Computational video editing for dialogue-driven scenes

We present a system for efficiently editing video of dialogue-driven scenes. The input to our system is a standard film script and multiple video takes, each capturing a different camera framing or performance of the complete scene. Our system then automatically selects the most appropriate clip from one of the input takes, for each line of dialogue, based on a user-specified set of film-editing idioms. Our system starts by segmenting the input script into lines of dialogue and then splitting each input take into a sequence of clips time-aligned with each line. Next, it labels the script and the clips with high-level structural information (e.g., emotional sentiment of dialogue, camera framing of clip, etc.). After this pre-process, our interface offers a set of basic idioms that users can combine in a variety of ways to build custom editing styles. Our system encodes each basic idiom as a Hidden Markov Model that relates editing decisions to the labels extracted in the pre-process. For short scenes (< 2 minutes, 8--16 takes, 6--27 lines of dialogue) applying the user-specified combination of idioms to the pre-processed inputs generates an edited sequence in 2--3 seconds. We show that this is significantly faster than the hours of user time skilled editors typically require to produce such edits and that the quick feedback lets users iteratively explore the space of edit designs.
Intelligent Emergency Message Broadcasting in VANET Using PSO

The new type of Mobile Ad hoc Network which is called Vehicular Ad hoc Networks (VANET) created a fertile environment for research. In this research, a protocol Particle Swarm Optimization Contention Based Broadcast (PCBB) is proposed, for fast andeffective dissemination of emergency messages within a geographical area to distribute the emergency message and achieve the safety system, this research will help the VANET system to achieve its safety goals in intelligent and efficient way.
Personalized Social Recommendations - Accurate or Private?

With the recent surge of social networks such as Facebook, new forms of recommendations have become possible -- recommendations that rely on one's social connections in order to make personalized recommendations of ads, content, products, and people. Since recommendations may use sensitive information, it is speculated that these recommendations are associated with privacy risks. The main contribution of this work is in formalizing trade-offs between accuracy and privacy of personalized social recommendations.We study whether "social recommendations", or recommendations that are solely based on a user's social network, can be made without disclosing sensitive links in the social graph. More precisely, we quantify the loss in utility when existing recommendation algorithms are modified to satisfy a strong notion of privacy, called differential privacy. We prove lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We then adapt two privacy preserving algorithms from the differential privacy literature to the problem of social recommendations, and analyze their performance in comparison to our lower bounds, both analytically and experimentally. We show that good private social recommendations are feasible only for a small subset of the users in the social network or for a lenient setting of privacy parameters.