Miracle-dz/UniFAR-FTUs huggingface dataset:
Public reconciliation at the South African TRC: the impact of the victim hearings hearings

The Truth and Reconciliation Commission (TRC) has been a turning point in South African history. By listening to the experiences of apartheid victims and by considering applications for amnesty by apartheid perpetrators, the TRC embodied the entire transition to democracy. This article is based on a discursive analysis of the language created at the Human Rights Violations (HRV) hearings – the victim hearings – of the TRC. This was a language in which reconciliation and forgiveness were prominently present, hence the label ‘reconciliation discourse’. One of the key features of this reconciliation discourse was that the concept of reconciliation was interpreted in a highly inclusive way, leading to a set of multidimensional definitions. By referring to a number of testifying victims, I will demonstrate the multilayered interpretation of the term reconciliation at the HRV hearings. The testifiers were allowed to frame reconciliation in different ways, be it religious, political, cultural, or by referring to national unity. Additionally, victims were also allowed to be highly critical about reconciliation or to only conditionally accept the notion of reconciliation. My central argument is that the TRC reconciliation discourse strengthened the willingness to reconcile among South African citizens (see Verdoolaege, 2008). One of the main reasons for this ‘maximalist’ perspective, according to my research, lies in the inclusive nature of the concept of reconciliation. This inclusive nature urged South Africans to accept reconciliation, to relate to the term reconciliation and to identify with this concept in many divergent ways. As a result of the discourse taking shape at the victim hearings, South Africans could now recognize themselves in one central and unifying concept. Consequently, as I will argue in the final part of this article, reconciliation became a central feature in post-TRC South Africa. At that time, the public debate on reconciliation was omnipresent and reconciliation-oriented initiatives were taking place all over the country. On a national level, this might have been favourable for the post-apartheid peace process in South Africa, and on an international level this led to reconciliation becoming an identifying label for South Africa as a whole.
Reply to comments from Victor Brovkin

1. “The manuscript “Bimodality of woody cover and biomass in semi-arid regime” by Yin et al. reveals a very interesting analysis of distribution of tree cover in the precipitation space. The research follows the study by Hirota et al. (2011) but with a focus on a region with a relatively narrow range of precipitation in central Africa. The authors found that savanna and forest could co-exist for the same precipitation regime, and illustrate how vegetation structure (horizontal vs. vertical leaf extent) could a↵ect the bistability. The study combines analysis of observations with modeling experiments, which makes the paper very informative but di cult to read (see comments below). ” A: Thanks for your interest in this work.
English Greek Dictionary A Vocabulary Of The Attic Language

english greek dictionary a vocabulary of the attic language is available in our digital library an online access to it is set as public so you can get it instantly. Our digital library saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the english greek dictionary a vocabulary of the attic language is universally compatible with any devices to read.
Towards a holistic prioritization of climate-change risks for bridges

Although the projected changes in the climate include considerable uncertainties, especially with downscaling, there is irrefutable evidence that the climate is changing at an unprecedented and alarming rate. In recent studies, some of the potential climate-change risks on bridges have been identified. The sheer number of these potential risks provokes two questions. Firstly, for a certain risk of interest (e.g. increased scour rate) which bridges should be prioritized from an inventory of bridges? Secondly, for a specific bridge, which of these risks are more critical? This paper proposes a method that can be used for addressing these two questions while considering the uncertainties intrinsic to the problem. Although this paper focuses on addressing the first question, a discussion on how the proposed method can be used for answering the second is also presented. The suggested method is based on four risk components; namely: hazard, exposure, vulnerability, and consequences. In spite of being specifically tailored for bridges, the developed method can be easily extended to other types of infrastructure. The proposed method is a step towards an improved climate-change risk management. (Less)
Small-cell lung cancer growth inhibition: synergism between NMDA receptor blockade and chemotherapy

Background Small-cell lung cancer (SCLC) has a poor prognosis since there is currently no effective therapy for commonly recurring disease. In our previous study, both primary and recurrent human tumors have been shown to express functional N-methyl-D-aspartate (NMDA) receptors, and blockade of these receptors with GluN1 and GluN2B antagonists decreased tumor cell viability in vitro, and growth of tumor xenografts in nu/nu mice. Materials and methods In this study, we examine the influence of the GluN2B antagonist ifenprodil and the channel-blocker antagonist memantine, on cell viability and growth of tumor xenografts of recurrent SCLC (rSCLC) in mice. Results Both antagonists significantly reduced cell viability and levels of components of the ERK1/2 pathway, increased apoptosis, and at very safe levels significantly reduced the growth of tumors in mice. Each antagonist and topotecan had additive effects to reduce cell viability with significant synergy demonstrated for the case of memantine. More significantly, combination treatments of xenografts in mice with ifenprodil and the chemotherapeutic agent topotecan produced clear additive effects that completely stopped tumor growth. Moreover, the ifenprodil and topotecan combination showed excellent supra-addition or synergy of inhibition for tumors ≤300 mm in size (P=4.7E−4). Combination treatment of memantine with topotecan also showed clear addition but, unlike ifenprodil, no synergy for the doses chosen. Conclusion Since topotecan is a drug of choice for treatment of rSCLC, our findings suggest that combining this agent with NMDA receptor blockade using the GluN2B antagonist, ifenprodil, will significantly improve patient outcomes.
Computational modelling of expressive music performance in jazz guitar: a machine learning approach

Computational modelling of expressive music performance deals with the analysis and characterization of performance deviations from the score that a musician may introduce when playing a piece in order to add expression. Most of the work in expressive performance analysis has focused on expressive duration and energy transformations, and has been mainly conducted in the context of classical piano music. However, relatively little work has been dedicated to study expression in popular music where expressive performance involves other kinds of transformations. For instance in jazz music, ornamentation is an important part of expressive performance but is seldom indicated in the score, i.e. it is up to the interpreter to decide how to ornament a piece based on the melodic, harmonic and rhythmic contexts, as well as on his/her musical background. In this dissertation we present an investigation in the computational modelling of expressive music performance in jazz music, using the guitar as a case study. High-level features are extracted from the scores, and performance data is obtained from the corresponding audio recordings from which a set of performance actions are obtained semi automatically (including timing/energy deviations, and ornamentations). After each note is characterized by its musical context description, several machine learning techniques are explored to, on one hand, induce regression models for timing, onset and dynamics transformations, and classification models for ornamentation to render expressive performances of new pieces, and, on the other hand, learn expressive performance rules to analyse its musical meaning. Finally. we report on the relative importance of the considered features, quantitatively evaluate the accuracy of the induced models, and discuss some of the learnt expressive performance rules. Moreover, we present different approaches for semi-automatic data extraction-analysis, as well as, some applications in other research fields. The findings, methods, data extracted, and libraries developed for this work are a contribution to expressive music performance field, as well to other related fields.
Prevalence and molecular detection of fluoroquinolone-resistant genes (qnrA and qnrS) in Escherichia coli isolated from healthy broiler chickens

Aim: The present study was carried out to determine the prevalence and molecular detection of fluoroquinolone-resistant Escherichia coli carrying qnrA and qnrS genes in healthy broiler chickens in Mymensingh, Bangladesh, and also to identify the genes responsible for such resistance. Materials and Methods: A total of 65 cloacal swabs were collected from apparently healthy chickens of 0-14 days (n=23) and 15-35 days (n=42) old. The samples were cultured onto Eosin Methylene Blue Agar, and the isolation and identification of the E. coli were performed based on morphology, cultural, staining, and biochemical properties followed by polymerase chain reaction (PCR) targeting E. coli 16S rRNA genes. The isolates were subjected to antimicrobial susceptibility test against five commonly used antibiotics under fluoroquinolone (quinolone) group, namely gatifloxacin, levofloxacin, moxifloxacin, ofloxacin, and pefloxacin by disk diffusion method. Detection of qnrA and qnrS genes was performed by PCR. Results: Among the 65 cloacal samples, 54 (83.08%) were found to be positive for E. coli. Antibiotic sensitivity test revealed that, of these 54 isolates, 18 (33.33%) were found to be resistant to at least one fluoroquinolone antibiotic. The highest resistance was observed against pefloxacin (61.11%). By PCR, of 18 E. coli resistant to fluoroquinolone, 13 (72.22%) were found to be positive for the presence of qnrS. None of the isolates were found positive for qnrA. Conclusion: Fluoroquinolone-resistant E. coli harboring qnrS genes is highly prevalent in apparently healthy broiler chickens and possesses a potential threat to human health.
A fruitful tree: developing the dendritic nucleation model of actin-based cell motility

A fundamental question in cell biology concerns how cells move, and this has been the subject of intense research for decades. In the 1990s, a major leap forward was made in our understanding of cell motility, with the proposal of the dendritic nucleation model. This essay describes the events leading to the development of the model, including findings from many laboratories and scientific disciplines. The story is an excellent example of the scientific process in action, with the combination of multiple perspectives leading to robust conclusions.
VOCABULARY ACQUISITION WITH AFFIXATION: LEARNING ENGLISH WORDS BASED ON PREFIXES & SUFFIXES

This study was designed to investigate vocabulary learning. Non-native speakers have trouble in learning English word systems that have roots and standard rules for prefixes and suffixes that extend the way these core items are used. Therefore, I wanted to investigage which ways of studying vocabulary work best for non-native students: formally learning the underlying prefixes and suffixes or just incidentally learning without using the knowledge of affixation. This experiment was carried out over 10 weeks. Only10 minutes out of each 50minute class were used for studying the English words that I selected. A total of 54 students took part in this experiment at a private English School in Korea. In the main experiment, the groups of students were organized into two groups: one group was scheduled to memorize English words using knowledge gained from their teachers’ affixation lectures, while the other group studied without instruction about affixation. The goal of the posttest was to determine how students acquired affixation patterns of English words and expanded their vocabulary knowledge and to investigate differences between the two groups. The assumption was that basic knowledge of commonly used affixes would help students learn English vocabulary much faster and avoid the need to repeatedly look up words.
Facial Expression Analysis via Transfer Learning

Automated analysis of facial expressions has remained an interesting and challenging research topic in the field of computer vision and pattern recognition due to vast applications such as human-machine interface design, social robotics, and developmental psychology. This dissertation focuses on developing and applying transfer learning algorithms – multiple kernel learning (MKL) and multi-task learning (MTL) – to resolve the problems of facial feature fusion and the exploitation of multiple facial action units (AUs) relations in designing robust facial expression recognition systems. MKL algorithms are employed to fuse multiple facial features with different kernel functions and tackle the domain adaption problem at the kernel level within support vector machines (SVM). lp-norm is adopted to enforce both sparse and non-sparse kernel combination in our methods. We further develop and apply MTL algorithms for simultaneous detection of multiple related AUs by exploiting their inter-relationships. Three variants of task structure models are designed and investigated to obtain fine depiction of AU relations. lp-norm MTMKL and TDMTMKL (Task-Dependent MTMKL) are group-sensitive MTL methods that model the co-occurrence relations among AUs. On the other hand, our proposed hierarchical multi-task structural learning (HMTSL) includes a latent layer to learn a hierarchical structure to exploit all possible AU inter-relations for AU detection. Extensive ex-
Scalability of modern scatterplot visualizations for large image datasets

In dieser Arbeit befassen wir uns mit der Darstellung von grosen hochdimensionalen Bilddatenbanken mit modernen Streudiagramm-basierten Visualisierungstechniken. Wir erforschen die Skalierbarkeit von Streudiagrammen sowohl in Bezug auf Machbarkeit (Performance-Skalierbarkeit), als auch in Bezug auf Sinnhaftigkeit (Informationsskalierbarkeit). Wir erstellen zwei interaktive Streudiagramm-basierte Visualisierungen fur grose Bilddatensatze, der Global View und der Interactive Cell Plot. Global View ist eine Desktopanwendung zur Visualisierung von Petascale-Simulationen durch in-situ generierte Bilddatenbanken, die in Zusammenarbeit mit dem Los Alamos National Laboratory entwickelt wurde. Global View liest Bilddatenbanken im Cinema Datenformat ein. Die geladenen Bilder werden durch geskriptete visuelle Zuordnungen, von unserer neuartigen Skriptsprache fur visuelle Zuordnungen, visualisiert. Wir vergleichen verschiedene visuelle Zuordnungen fur den MPAS-Ocean-Datensatz mittels Global View und schliesen daraus, dass die verschiedenen visuellen Zuordnungen einen Kompromiss zwischen intuitiver Darstellung und der gleichzeitigen Darstellung mehrerer Bilder darstellen. Sobald eine visuelle Zuordnung definiert ist, kann die Bilddatenbank durch die Durchquerung einer dreidimensionalen virtuellen Umgebung von Bildern erforscht werden. Unser Texture-Streaming-Algorithmus ladt und entladt Bilder dynamisch, wahrend die Bilddatenbank erforscht wird, indem die zugewiesene Menge an Grafikspeicher uberwacht wird. Der Interactive Cell Plot ist eine JavaScript-Bibliothek zum Rendern groser Streudiagramme mittels WebGL, die in Zusammenarbeit mit dem Allen Institute for Cell Science entwickelt wurde. Unsere effiziente Renderingtechnik ermoglicht es uns, eine Million zweidimensionale Datenpunkte mit 60 Bildern pro Sekunde und funf Millionen Punkte mit 25 Bildern pro Sekunde zu rendern. Wir evaluieren Visualisierungsparameter des Interactive Cell Plot und funf verschiedene Plazierungsstrategien fur Vorschaubilder in einer qualitativen Anwenderstudie. Unsere neuartigen Algorithmen fur rand- und dichtebasierte Beschriftung minimieren sowohl Uberlappungen von Datenpunkten, als auch die Distanz zwischen Beschriftung und Referenz. Wir stellen Dichtekarten als Zwischenstruktur fur schnelles Clustering und schnelle Beschriftung, Stichprobengenerierung und Erkennung Charakteristischer Datenpunkte vor. Durch die Laufzeitschatzung der Dichtekartengenerierung gibt unser Algorithmus dem Benutzer direkte Kontrolle uber den Kompromiss zwischen Laufzeit und Genauigkeit der Dichtekartenerstellung.
Contralog: a Prolog conform forward-chaining environment and its application for dynamic programming and natural language parsing

Abstract The backward-chaining inference strategy of Prolog is inefficient for a number of problems. The article proposes Contralog: a Prolog-conform, forward-chaining language and an inference engine that is implemented as a preprocessor-compiler to Prolog. The target model is Prolog, which ensures mutual switching from Contralog to Prolog and back. The Contralog compiler is implemented using Prolog's de facto standardized macro expansion capability. The article goes into details regarding the target model. We introduce first a simple application example for Contralog. Then the next section shows how a recursive definition of some problems is executed by their Contralog definition automatically in a dynamic programming way. Two examples, the well-known matrix chain multiplication problem and the Warshall algorithm are shown here. After this, the inferential target model of Prolog/Contralog programs is introduced, and the possibility for implementing the ReALIS natural language parsing technology is described relying heavily on Contralog's forward chaining inference engine. Finally the article also discusses some practical questions of Contralog program development.
Generating dynamic street view images

In the contemporary age, usage of the street view systems like Google Street View is becoming increasingly common. These systems, make the visualization of the real street ambience easier on the Geographic Information System and also aids the users to effortlessly plan and determine their intended place of travel. However, such street view systems are generally not updated on a regular basis because the street view service provider requires numerous cars mounted with panoramic cameras to capture the real roadside surroundings. The main aim of this paper is to propose a novel model that can create image sequences of random street view at any place by employing the images acquired from automotive video recorders. The notion of this model is identical to the theory of dynamic street view frames preserved by the common handlers. The proposed model is employed to design a framework that can map any frame recorded by a video event data recorder to the already available databank consisting of several panoramic street view images. A new image warping technique has been proposed to reduce the geometric distortion present in the recreated street view frames.
A wavelet domain LSB insertion algorithm for high capacity audio steganography

In this work, we increased the capacity of the classic LSB insertion method by performing the embedding process in the wavelet domain. The algorithm uses perfect reconstruction filterbanks and embeds additional information inside wavelet domain of audio signal by modifying LSB values of wavelet coefficients. Objective and subjective tests show large advantage of the proposed method over time insertion method. For the same SNR values, proposed algorithm outperforms classical LSB algorithm by 150-200 kbps of hidden data. Subjective experiments showed that wavelet domain information hiding scheme is acoustically more transparent as well.
Mono (Developer's Notebook)

This no-fluff, lab-style guide jumps right into Mono 1.0 as you work through nearly fifty mini-projects that introduce you to the most important and compelling aspects of the 1.0 release. You'll learn how to acquire, install, and run Mono on Linux, Windows, or Mac OS X. You'll work with the various Mono components: Gtk#, the Common Language Runtime, the class libraries (both .NET and Mono-provided class libraries), IKVM and the Mono C# compiler.
INVARIANT DESCRIPTOR LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK

Abstract. In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.
Fast kmer counting table algorithm using perfect hash function: C++ pseudo-code integration into R using Rcpp API

Counting kmers (substrings of length k in DNA sequence data) is an essential component of many methods in bioinformatics, including data preprocessing for de novo assembly, repeat detection, and sequencing coverage estimation. We proposed a simple algorithm to calculate the kmer count using perfect hash table implemented in C++ and using Rcpp API to be able exported into R. The source code is available at : https://figshare.com/s/5c3a4242c4ca6cdd5282
A New Calibration Method for Shore-base Search Radars

It is necessary for the shore-base search radars to be termly calibrated to ensure the high precision. Due to the special landform,it has no effective calibration method at present. In this paper,a real time dynamic calibration method for shore-base search radars is proposed,its basic principles and key technologies are introduced,and the application result is also presented. This new technology has been applied to many radar's calibration.
Least square-based vehicle position estimation algorithm

A vehicle position estimation algorithm base on least square optimization was prposed.Firstly,the algorithm deduced the convex constraint condition which the distance matrix had to conform within a short time interval.Then,the least square method was employed to estimate the vehicles’ relative position.Finally,the local maps were merged into the global map.Simulation results demonstrate that the algorithm can achieve preferable estimation accuracy,and meet the real time constraint in vehicular networks.
Investigative Behavioral Intention to Knowledge Acceptance and Motivation in Cloud Computing Applications

Recently the number of Cloud Computing users in educational institutions has increased. Students have the chance to access various applications and this gives the opportunity to take advantage of those applications. This study examined the behavioral Intention toward Cloud Computing Applications and evaluated the acceptance of these Applications. Participants population consisted of 110 students from different Jordanian universities. The results showed that Performance Expectancy, Effort Expectancy, Attitude toward using Technology, Social Influence, Self-Efficacy, Attention and Relevance have different levels of correlation with Behavioral Intention in Cloud Computing Applications, and there was no correlation between Anxiety and Behavioral Intention in Cloud Computing Applications.