Miracle-dz/UniFAR-FTUs huggingface dataset:
Top- en breedtesport: een Siamese tweeling?

Breedtesport en topsport ontwikkelen zich in een verschillende richting, maar blijven elkaar beinvloeden. Deze beinvloeding kan worden onderscheiden naar effecten die samenhangen met de veronderstelde aanvoerfunctie van de breedtesport en effecten die voortkomen uit de veronderstelde aanjaagfunctie van de topsport. Beide functies maken onderdeel uit van de zogenoemde 'double pyramid theory'. Volgens deze theorie brengen duizenden sportbeoefenaars een paar Olympische kampioenen voort (aanvoerfunctie), terwijl deze kampioenen door hun rolmodel weer duizenden mensen stimuleren om een of andere vorm van sport te gaan beoefenen (aanjaagfunctie). Onderzoek wijst echter uit dat er geen sprake is van een gelijke mate van beinvloeding tussen topsport en breedtesport. De topsport kan niet zonder breedtesport, maar andersom is dit in veel mindere mate het geval. Breedtesporters ontlenen vooral als publiek veel plezier aan de topsport, maar een groot deel van de sportbeoefenaars, vooral buiten verenigingsverband, zal in hun sportbeoefening nauwelijks een relatie met de topsport ervaren.
An Aboriginal Women’s Song from Arrwek, Central Australia

Songs are a fundamental part of Indigenous Australian cultures. They are vehicles for transmitting experiences about a region, including the stories and ancestral entities from the creation. Yet relatively little is known about the musical characteristics of songs from many parts of Australia. In this article we present the first musical analysis of traditional songs of the Anmatyerr people of central Australia. We analyse a selection of songs from recordings of a women’s song set owned and performed by the landholders of Arrwek, an estate that straddles the Anmatyerr-speaking and Warlpiri-speaking regions. We analyse three basic acoustic elements of a central Australian Aboriginal song: rhythmic text, pitch, and the relationship between them. Our analysis finds that these have much in common with Warlpiri songs, despite their Anmatyerr geographic affiliation.
A study of the electrocardiogram in infants of normal conditions and during treatment with cod liver oil and vigantol. 1

The communication between the venous and the arterial part of the circulatory system during fetal life causes the blood pressure to be the same within t h i right and left ventricle of the heart, the walls of the right ventricle being then of about the same strength as those of the left. Just after birth this communication is closed, and the difference in peripheral resistance in the vessels of the systemic circulation as compared with that in the pulmonary vessels causes the strength of the ventricular muscle to adapt itself accordingly so that the wall of the left Ventricle grows considerably stronger and thicker than that of the right. The way in which the balance between the muscular strength of the left and the right ventricular walls may influence the features of the electrocardiogram (Ecg.) is known from the investigations of Lewis (1) and others, and we also know, that the Ecg. of the new born child has features indicating a right ventricular preponderance, but, as far as we are aware no detailed study has been made
BİLİŞİM TEKNOLOJİLERİ VE YAZILIM DERSİ ÖĞRETİM PROGRAMINA YÖNELİK ÖĞRENCİ GÖRÜŞLERİNİN DİJİTAL VATANDAŞLIK BAĞLAMINDA İNCELENMESİ

Bu calisma Bilisim Teknolojileri ve Yazilim (BTY) dersi ogretim programina yonelik ogrenci goruslerinin dijital vatandaslik baglaminda incelenmesi amaciyla gerceklestirilmistir. Arastirmaya, Adana ili merkez ilcelerindeki ortaokullara devam eden 264 ogrenci katilmistir. Ogrencilerin 151’i (%57.2) besinci sinif, 113’u (%42.8) altinci sinif ogrencisidir ve 139’u (% 52.7) kiz, 125’i (%47.3) erkektir. Veriler, Elci ve Sari (2016) tarafindan gelistirilen “BTY Dersinde Dijital Vatandaslik Olcegi (BTY-DVO) kullanilarak toplanmistir. Verilerin analizinde t-testi ve varyans analizi kullanilmistir. Elde edilen bulgulara gore dijital etik-kanun ve buna bagli hak ve sorumluluklar boyutuyla ilgili bilgi, beceri ve degerlerin kazandirilmasinda BTY dersi ortanin uzerinde; dijital okuryazarlik boyutuyla ilgili bilgi, beceri ve degerlerin kazandirilmasinda ortanin cok az altinda, dijital guvenlik ve dijital iletisim boyutlariyla ilgili bilgi, beceri ve degerlerin kazandirilmasinda ise ortanin biraz uzerinde katkida bulunmaktadir. Arastirmanin sonuclarina gore BTY-DVO puanlari arasinda sinif duzeyi ve cinsiyete gore anlamli farklar bulunmamakta iken; evinde bilgisayar ve internet baglantisi bulunan ogrenciler lehine anlamli farklar belirlenmistir.
A Case of Tetanus Treated with Propofol and Shakuyakukanzoto

芍薬甘草湯は筋弛緩目的に使用される漢方薬である。 今回，破傷風の全身痙攣の緩和目的に芍薬甘草湯を併用した1例を経験した。 患者は32歳の男性で，飼い犬に咬まれた咬傷から破傷風を発症した。 全身痙攣に対してプロポフォールを投与し，芍薬甘草湯も併用したところ，プロポフォールを漸減することができた。 持続勃起症，腹痛，振戦，不眠などが一過性にみられたが，芍薬甘草湯の服用を継続することで消失した。 患者は後遺症なく第14病日に退院した。 これまでに破傷風に芍薬甘草湯を使用した報告はないが，筋痙攣の制御や自律神経系過緊張の緩和を目標に使用できると思われた。
Breech presentation at a district level hospital in South Africa

Abstract Introduction: The Term Breech Trial has led to obstetricians opting for Caesarean section as the mode of delivery for this presentation, even in poor countries. Concerns related to this approach are the resultant increase in Caesarean section rates and their associated complications, particularly in under-resourced countries, which are faced with financial and human-resource expertise constraints. Method: This was a retrospective chart review of women who presented at term with a singleton breech presentation at the antenatal clinic and in labour, from January 2005 to December 2007, at a district level hospital in South Africa. Results: There was a total of 19 197 deliveries, of which 466 were singleton term breech deliveries, giving a rate of 2.4%. Of the 297 women who had antenatal care and had been allocated to planned Caesarean section, 271 had the planned operation. There were no neonatal deaths in the planned Caesarean section group. The emergency Caesarean section group and the group in which no decision was made on the mode of delivery were associated with higher maternal complication rates than in the group that had planned Caesarean sections. The highest neonatal complication rate was in the group that had unplanned vaginal deliveries. Conclusion: In a district hospital in South Africa, the mode of delivery for breech presentations is usually a planned Caesarean section. Unplanned vaginal deliveries are associated with significant perinatal mortality.
STUDIES ON LYMPHOGRANULOMA VENEREUM INFECTION OF THE RECTUM

The purpose of this paper is to call attention to the frequency of lymphogranuloma venereum infection of the rectum, to emphasize the rather characteristic clinical and diagnostic features and to comment on the therapeutic value of sulfanilamide and its derivatives. Lymphogranuloma venereum typically is a subacute, or rather a chronic, venereal disease1characterized primarily by the bubo. Spontaneous recovery may occur, or the disease may lapse into a chronic indolent state. The course may be acute, almost typhoidal in character, and, particularly in extragenital infections, its true nature may not be suspected. Generalized manifestations, such as fever and multiple arthritis, may be present. There is evidence of the occurrence of a subclinical form. For instance, the husband of our patient E. S. (case 1) has no clinical evidence of disease, gives no history suggestive of such infection and yet has been found, on the two occasions on which he
Bevolkingsprognose 2012-2060: veronderstellingen over de geboorte

In de laatste bevolkingsprognose van het CBS, die in december 2012 uitkwam, wordt verondersteld dat jonge generaties vrouwen gemiddeld 1,75 kind per vrouw zullen krijgen. Al ruim een decennium zijn er geen aanwijzingen gevonden om het toekomstig kindertal bij te stellen. Dat heeft vooral te maken met de waarneming dat het uitstelgedrag van het eerste kind nagenoeg voltooid is en ‘uitgestelde kinderen’ zijn ‘ingehaald’. De leeftijd- en rangnummerspecifieke vruchtbaarheidscijfers worden dus constant verondersteld.
Procedural animation : towards studio solutions for believability

This thesis sets out to investigate the understanding of the relationship between key-frame movement performances and procedural animation. It is geared towards building a theory of practice that would help develop a succinct method for generating believable character animation using procedural animation. This research places an emphasis on a practical aproach to the theory of animation and movement, and investigates the historical development of character animation and the notion of believability. It uses Laban Movement Analysis as a method in the application of procedural animation. The study seeks to address the following objectives: (1) To examine what areas of procedural animation may enhance the believability of a key-framed movement performance; (2) To identify the areas of procedural animation that are or could be used within professional studio practice; (3) To examine the potential of procedural animation to help develop convincing and life-like character movements; (4) To identify where and how a keyframed character movement can be enhanced procedurally; (5) To carry out empirical studies in order to analyse the effects and possible benefits of procedural enhancements on a key-framed movement.
An Appraisal of Immunisation in Nigeria: Towards Improving Coverage

Nigeria has achieved modest progress in immunization coverage particularly in the 1980-1990 periods. However, coverage has declined since the achievement of this peak. One factor important in sustainability of coverage is routine immunization and increased awareness to increase the knowledge of mothers on immunization. Within the country also there’s a wide interstate variation in coverage figures with a generally greater percentage of full immunization Coverage obtained in the southern zones than in the Northern zones. Even though the Nigeria’s universal child immunization coverage is said to have improved in the last two years much is still needed to bring the coverage target to at least 75% throughout the nation for effective control of all Vaccine Preventable Diseases. Targeted “mop-up” campaigns should be intensified for the wild polio virus transmission as found limited to a specific focal area, as found presently in some northern parts of Nigeria. As for the control of measles, experience globally also shows that routine immunization together with supplemental measles immunization can reduce measles deaths. Key words: Immunization coverage; appraisal; routine immunization; mop-up campaigns
From Statistics to Neural Networks: Theory and Pattern Recognition Applications

This volume provides a unified approach to the study of predictive learning, i.e., generalization from examples. It contains an up-to-date review and in-depth treatment of major issues and methods related to predictive learning in statistics, Artificial Neural Networks (ANN), and pattern recognition. Topics range from theoretical modeling and adaptive computational methods to empirical comparisons between statistical and ANN methods, and applications. Most contributions fall into one of the three themes: unified framework for the study of predictive learning in statistics and ANNs; similarities and differences between statistical and ANN methods for nonparametric estimation (learning); and fundamental connections between artificial and biological learning systems.
Multiple-antenna capacity in correlated Rayleigh fading with channel covariance information

For a block fading multiple input multiple output (MIMO) wireless link comprising M transmit and N receive antennas operating in a spatially correlated Rayleigh flat fading environment with coherence time T and only knowledge of channel correlations at the transmitter and receiver, we show that the channel capacity is independent of the smallest M-T eigenvalues of the transmit fade covariance matrix. We characterize the structure of the input signal that achieves capacity. In contrast to the previously reported results for the spatially white fading model where adding more transmit antennas beyond the coherence interval length (M>T) does not increase capacity, we find that additional transmit antennas always increase capacity as long as their channel fading coefficients are spatially correlated, making the case for smaller spacing between transmit antennas. For fast fading channels the capacity gained from multiple transmit antennas is bounded by the array gain 10 log M.
Adjunctive Devices for Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

Coronary stents and adjunctive pharmacologic agents—including glycoprotein IIb/IIIa receptor inhibitors and thienopyridines—have improved the efficacy of percutaneous coronary intervention (PCI). However, dislodgement of atherothrombotic material from coronary lesions during PCI can result in distal embolization that leads to what is commonly referred to as the “noreflow phenomenon. ” This phenomenon, characterized by inadequate flow at the cardiac tissue level despite patent coronary vessels, is often defined as (1) a thrombolysis in myocardial infarction (TIMI) flow grade ≤2 despite vessel patency and the absence of dissection, spasm, or distal macroembolus, or (2) a myocardial blush grade (MBG) of 0 or 1. No reflow has been associated with larger infarcts, significant left ventricular systolic dysfunction, and an increased risk of a major adverse cardiovascular event (MACE) or death. Depending on the exact clinical definition used, the incidence of no reflow has been found to range from 12 to 39 percent of patients undergoing PCI.
Review on Graph Feature Learning and Feature Extraction Techniques for Link Prediction

The problem of link prediction has recently attracted considerable attention by research community. Given a graph, which is an abstraction of the relationships among entities, the task of link prediction is to anticipate future connections among entities in the graph, concerning its current state. Extensive studies have examined this problem from different aspects and proposed various methods, some of which might work very well for a specific application but not as a global solution. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used to study link prediction. To the best of our knowledge, this survey is the first comprehensive study that considers all of the mentioned challenges and solutions for link prediction in graphs with the improvements in the recent years, including the unsupervised and supervised techniques and their evolution over the recent years.
Preventing Posterior Collapse with delta-VAEs

Due to the phenomenon of"posterior collapse,"current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\times 32$.
Mean Estimation From One-Bit Measurements

We consider the problem of estimating the mean of a symmetric log-concave distribution under the constraint that only a single bit per sample from this distribution is available to the estimator. We study the mean squared error as a function of the sample size (and hence the number of bits). We consider three settings: first, a centralized setting, where an encoder may release <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> bits given a sample of size <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, and for which there is no asymptotic penalty for quantization; second, an adaptive setting in which each bit is a function of the current observation and previously recorded bits, where we show that the optimal relative efficiency compared to the sample mean is precisely the efficiency of the median; lastly, we show that in a distributed setting where each bit is only a function of a local sample, no estimator can achieve optimal efficiency uniformly over the parameter space. We additionally complement our results in the adaptive setting by showing that <italic>one</italic> round of adaptivity is sufficient to achieve optimal mean-square error.
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries

Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that enable misleading classification. Although this has questioned their suitability for this task, it is not yet clear why such algorithms are easily fooled also in this particular application domain. In this work, we take a first step to tackle this issue by leveraging explainable machine-learning algorithms developed to interpret the black-box decisions of deep neural networks. In particular, we use an explainable technique known as feature attribution to identify the most influential input features contributing to each decision, and adapt it to provide meaningful explanations to the classification of malware binaries. In this case, we find that a recently-proposed convolutional neural network does not learn any meaningful characteristic for malware detection from the data and text sections of executable files, but rather tends to learn to discriminate between benign and malware samples based on the characteristics found in the file header. Based on this finding, we propose a novel attack algorithm that generates adversarial malware binaries by only changing few tens of bytes in the file header. With respect to the other state-of-the-art attack algorithms, our attack does not require injecting any padding bytes at the end of the file, and it is much more efficient, as it requires manipulating much fewer bytes.
Context Aware Machine Learning

We propose a principle for exploring context in machine learning models. Starting with a simple assumption that each observation may or may not depend on its context, a conditional probability distribution is decomposed into two parts: context-free and context-sensitive. Then by employing the log-linear word production model for relating random variables to their embedding space representation and making use of the convexity of natural exponential function, we show that the embedding of an observation can also be decomposed into a weighted sum of two vectors, representing its context-free and context-sensitive parts, respectively. This simple treatment of context provides a unified view of many existing deep learning models, leading to revisions of these models able to achieve significant performance boost. Specifically, our upgraded version of a recent sentence embedding model not only outperforms the original one by a large margin, but also leads to a new, principled approach for compositing the embeddings of bag-of-words features, as well as a new architecture for modeling attention in deep neural networks. More surprisingly, our new principle provides a novel understanding of the gates and equations defined by the long short term memory model, which also leads to a new model that is able to converge significantly faster and achieve much lower prediction errors. Furthermore, our principle also inspires a new type of generic neural network layer that better resembles real biological neurons than the traditional linear mapping plus nonlinear activation based architecture. Its multi-layer extension provides a new principle for deep neural networks which subsumes residual network (ResNet) as its special case, and its extension to convolutional neutral network model accounts for irrelevant input (e.g., background in an image) in addition to filtering.
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors

Accurate state estimation is a fundamental problem for autonomous robots. To achieve locally accurate and globally drift-free state estimation, multiple sensors with complementary properties are usually fused together. Local sensors (camera, IMU, LiDAR, etc) provide precise pose within a small region, while global sensors (GPS, magnetometer, barometer, etc) supply noisy but globally drift-free localization in a large-scale environment. In this paper, we propose a sensor fusion framework to fuse local states with global sensors, which achieves locally accurate and globally drift-free pose estimation. Local estimations, produced by existing VO/VIO approaches, are fused with global sensors in a pose graph optimization. Within the graph optimization, local estimations are aligned into a global coordinate. Meanwhile, the accumulated drifts are eliminated. We evaluate the performance of our system on public datasets and with real-world experiments. Results are compared against other state-of-the-art algorithms. We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization. Our implementations are open source\footnote{https://github.com/HKUST-Aerial-Robotics/VINS-Fusion}.
Machine Learning Automation Toolbox (MLaut)

In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the python data science ecosystem. MLaut automates large-scale evaluation and benchmarking of machine learning algorithms on a large number of datasets. MLaut provides a high-level workflow interface to machine algorithm algorithms, implements a local back-end to a database of dataset collections, trained algorithms, and experimental results, and provides easy-to-use interfaces to the scikit-learn and keras modelling libraries. Experiments are easy to set up with default settings in a few lines of code, while remaining fully customizable to the level of hyper-parameter tuning, pipeline composition, or deep learning architecture. As a principal test case for MLaut, we conducted a large-scale supervised classification study in order to benchmark the performance of a number of machine learning algorithms - to our knowledge also the first larger-scale study on standard supervised learning data sets to include deep learning algorithms. While corroborating a number of previous findings in literature, we found (within the limitations of our study) that deep neural networks do not perform well on basic supervised learning, i.e., outside the more specialized, image-, audio-, or text-based tasks.