Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlates of Condom Use among Male and Female aged 18-24 Years in South Africa

Condom use is one of the effective means of HIV prevention among sexually active people. Even though HIV incidence has slightly decreased among the youth in South Africa, many young people still expose themselves to sexual risk behaviours. The aim of the study was to investigate factors that are associated with condom use and exposure to loveLife programmes among young people aged 18 to 24 years in South Africa. A cross-sectional population-based household survey in selected provinces in South Africa was conducted using a multi-stage disproportionate stratified cluster sampling approach. The sample (N=2138) used in the analysis for this paper only includes sexually active youth (18-24 years). Among the sexually active youth surveyed it was found that 78.9% reported using a condom at their last sexual intercourse; this was significantly higher among men (85.4%) than women (72.0%). In multivariate analysis, condom use among males was significantly associated with having not agreed with the statement ―It is acceptable to have sex with my sex partner even though my partner does not want to‖, having partner risk reduction self-efficacy, having talked with their partner about condoms in past 12 months, having had two or more sexual partners in the past year and loveLife programme exposure. Among females, condom use was significantly associated with partner risk reduction self-efficacy, and having talked with their partner about condoms in the past 12 months. The findings indicate higher condom use at last sex among young men when compared to women in South Africa. Condom use with non-regular partners is generally high and even higher among men compared to women. However, condom use with transactional partners is very low. Of concern are young mens‘ reported risk behaviours in having multiple partners. loveLife programme exposure has had a positive effect on condom use among young men. Therefore strategies are needed to improve regular condom use among youth with regular and transactional partners and reinforcing regular condom use with non-regular partners. Keywords: Condom use, loveLife, multiple partners, youth, gender, South Africa Gender & Behaviour , 10(2), December 2012
Research through Design for accounting values in design

Although Value Sensitive Design offers a theoretical and methodological framework to account for values in design, many questions and controversies are left. The current work aims to contribute to this value debate, by taking stock of large Research through Design (RtD) programs including their developed artifacts, to explore to what extent the explicit and tacit knowledge generated enabled actors to make public and cultural values explicit. Differently put, seven ongoing RtD projects have been studied in an elaborate RtD process articulated in three phases, differentiating in their focus: 1) understanding the values involved in the RtD projects; 2) share insights to steer peer debate on Research on Values, and 3) co-analyse the data and generate further insights. The current research brings forward two main contributions to the RTD community. On the one hand, using ongoing RtD projects in an RtD approach provides a kaleidoscopic perspective on how research and design constantly inform each other through the application of design. On the other hand, the adoption of this kaleidoscopic RtD approach in the context of multidisciplinary research on values acts as a catalyst that generated knowledge and insights to stimulate the debate on accounting values in design research.
ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks

We present Ordinary Differential Equation Variational Auto-Encoder (ODE$^2$VAE), a latent second order ODE model for high-dimensional sequential data. Leveraging the advances in deep generative models, ODE$^2$VAE can simultaneously learn the embedding of high dimensional trajectories and infer arbitrarily complex continuous-time latent dynamics. Our model explicitly decomposes the latent space into momentum and position components and solves a second order ODE system, which is in contrast to recurrent neural network (RNN) based time series models and recently proposed black-box ODE techniques. In order to account for uncertainty, we propose probabilistic latent ODE dynamics parameterized by deep Bayesian neural networks. We demonstrate our approach on motion capture, image rotation and bouncing balls datasets. We achieve state-of-the-art performance in long term motion prediction and imputation tasks.
A study into the factors influencing the choice-making process of Indian students when selecting an international university for graduate studies using grounded theory

Universities operate in an increasingly competitive market place facing new and complex socio-technical and economical challenges. For many universities international student recruitment is desirable and necessary for survival. Universities knowledge in this area is often an imperfect tool as the changing environment and diversity of cultures with which it must interact challenge previous assumptions and common wisdom. The overall goal of this study is to identify those factors responsible for influencing Indian students’ choice of international university for graduate studies. The results are based on a longitudinal study that was carried out using the Grounded Theory research method. This qualitative methodology provides a good framework for rigorous and relevant research of emerging phenomena in student mobility. Primary data consisted of unstructured interviews, focus groups and questionnaire surveys among participants of the sample population. The literature was used as a source of secondary data. A narrative style and thick description were used to report the research findings. Four major influencers emerged from the analysis, which are referred to as programme content, international reputation, funding and job prospects and quality. Drawing together these findings the study examines the implications for recruiting graduate students from India and reveals that there are a number of ways in which the university can influence the choice-making process. The results clearly provide a sound basis for future study.
How well do NLI models capture verb veridicality?

In natural language inference (NLI), contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world. We investigate whether a state-of-the-art natural language inference model (BERT) learns to make correct inferences about veridicality in verb-complement constructions. We introduce an NLI dataset for veridicality evaluation consisting of 1,500 sentence pairs, covering 137 unique verbs. We find that both human and model inferences generally follow theoretical patterns, but exhibit a systematic bias towards assuming that verbs are veridical–a bias which is amplified in BERT. We further show that, encouragingly, BERT’s inferences are sensitive not only to the presence of individual verb types, but also to the syntactic role of the verb, the form of the complement clause (to- vs. that-complements), and negation.
HMEAE: Hierarchical Modular Event Argument Extraction

Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles. In this paper, we propose a Hierarchical Modular Event Argument Extraction (HMEAE) model, to provide effective inductive bias from the concept hierarchy of event argument roles. Specifically, we design a neural module network for each basic unit of the concept hierarchy, and then hierarchically compose relevant unit modules with logical operations into a role-oriented modular network to classify a specific argument role. As many argument roles share the same high-level unit module, their correlation can be utilized to extract specific event arguments better. Experiments on real-world datasets show that HMEAE can effectively leverage useful knowledge from the concept hierarchy and significantly outperform the state-of-the-art baselines. The source code can be obtained from https://github.com/thunlp/HMEAE.
Efficient Pure Exploration in Adaptive Round model

In the adaptive setting, many multi-armed bandit applications allow the learner to adaptively draw samples and adjust sampling strategy in rounds. In many real applications, not only the query complexity but also the round complexity need to be optimized. In this paper, we study both PAC and exact top-$k$ arm identification problems and design efficient algorithms considering both round complexity and query complexity. For PAC problem, we achieve optimal query complexity and use only $O(\log_{\frac{k}{\delta}}^*(n))$ rounds, which matches the lower bound of round complexity, while most of existing works need $\Theta(\log \frac{n}{k})$ rounds. For exact top-$k$ arm identification, we improve the round complexity factor from $\log n$ to $\log_{\frac{1}{\delta}}^*(n)$, and achieve near optimal query complexity. In experiments, our algorithms conduct far fewer rounds, and outperform state of the art by orders of magnitude with respect to query cost.
Contrastive Attention Mechanism for Abstractive Sentence Summarization

We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence. The proposed contrastive attention mechanism accommodates two categories of attention: one is the conventional attention that attends to relevant parts of the source sentence, the other is the opponent attention that attends to irrelevant or less relevant parts of the source sentence. Both attentions are trained in an opposite way so that the contribution from the conventional attention is encouraged and the contribution from the opponent attention is discouraged through a novel softmax and softmin functionality. Experiments on benchmark datasets show that, the proposed contrastive attention mechanism is more focused on the relevant parts for the summary than the conventional attention mechanism, and greatly advances the state-of-the-art performance on the abstractive sentence summarization task. We release the code at https://github.com/travel-go/ Abstractive-Text-Summarization.
Can You Unpack That? Learning to Rewrite Questions-in-Context

Question answering is an AI-complete problem, but existing datasets lack key elements of language understanding such as coreference and ellipsis resolution. We consider sequential question answering: multiple questions are asked one-by-one in a conversation between a questioner and an answerer. Answering these questions is only possible through understanding the conversation history. We introduce the task of question-in-context rewriting: given the context of a conversation’s history, rewrite a context-dependent into a self-contained question with the same answer. We construct, CANARD, a dataset of 40,527 questions based on QuAC (Choi et al., 2018) and train Seq2Seq models for incorporating context into standalone questions.
Joint Transmission Scheduling and Power Allocation in Non-Orthogonal Multiple Access

Multi-carrier based non-orthogonal multiple access (NOMA) is an effective method to meet the ever-increasing demands of both user throughput and energy efficiency by multiplexing multiple users on the same carrier. Since interference from users with a poorer channel gain can be canceled at a user with a strong channel gain by successive interference cancellation, NOMA can enhance the system performance. To improve the downlink system performance, it is crucial to appropriately determine users scheduled on each carrier and power allocation at the base station. However, the existing works are generally either heuristic or local optimal due to the mixed optimization problem. In this paper, we focus on the global optimal solutions to maximize user throughput and energy efficiency in NOMA, respectively. In particular, we first formulate the mixed integer optimization problem which are intractable to be solved. Fortunately, by the provided analytical results, the optimization models can be largely simplified. Then, we propose the architectures of joint user scheduling and power allocation in NOMA, as well as the corresponding optimal algorithms. Simulation results demonstrate that our proposed algorithms indeed outperform existing works in terms of the user throughput and energy efficiency, respectively.
VIEW: A CONCEPT FOR AN IMMERSIVE VIRTUAL REALITY IMAGE INTERPRETATION WORKBENCH

Virtual Reality (VR) and serious gaming can increase the learning motivation and outcome in image interpretation. This paper presents the concept of an VR game-like assistance system called Virtual Reality Image Exploitation Workspace (VIEW). It can be used to evaluate the learning-supporting immersion or presence level of image interpreters while solving typical interpretation tasks. The problem statement is centered on the realization aspects of a VR assistance system for image interpretation which provides a high level of immersion. This work combines the setting of a real serious game for formal image interpretation training with virtual reality to increase the immersion level of the users. Enhanced image quality and organizational requirements from professional image interpretation impose additional complexity on VR for image interpretation. The presented VIEW concept deals with multiple aspects in respect to e-learning, professional training and virtual reality for image interpretation. Preliminary evaluation results from a realized prototype show the feasibility and user acceptance of the VIEW concept.
Learning Auctions with Robust Incentive Guarantees

We study the problem of learning Bayesian-optimal revenue-maximizing auctions. The classical approach to maximizing revenue requires a known prior distribution on the demand of the bidders, although recent work has shown how to replace the knowledge of a prior distribution with a polynomial sample. However, in an online setting, when buyers can participate in multiple rounds, standard learning techniques are susceptible to \emph{strategic overfitting}: bidders can improve their long-term wellbeing by manipulating the trajectory of the learning algorithm in earlier rounds. For example, they may be able to strategically adjust their behavior in earlier rounds to achieve lower, more favorable future prices. Such non-truthful behavior can hinder learning and harm revenue. In this paper, we combine tools from differential privacy, mechanism design, and sample complexity to give a repeated auction that (1) learns bidder demand from past data, (2) is approximately revenue-optimal, and (3) strategically robust, as it incentivizes bidders to behave truthfully.
Coordinated hippocampal-entorhinal replay as structural inference

Constructing and maintaining useful representations of sensory experience is essential for reasoning about ones environment. High-level associative (topological) maps can be useful for efficient planning and are easily constructed from experience. Conversely, embedding new experiences within a metric structure allows them to be integrated with existing ones and novel associations to be implicitly inferred. Neurobiologically, the synaptic associations between hippocampal place cells and entorhinal grid cells are thought to represent associative and metric structures, respectively. Learning the place-grid cell associations can therefore be interpreted as learning a mapping between these two spaces. Here, we show how this map could be constructed by probabilistic message-passing through the hippocampal-entorhinal system, where messages are scheduled to reduce the propagation of redundant information. We propose that this offline inference corresponds to coordinated hippocampal-entorhinal replay during sharp wave ripples. Our results also suggest that the metric map will contain local distortions that reflect the inferred structure of the environment according to associative experience, explaining observed grid deformations.
Pharmacogenetic Epidemiology of Statins in an Ageing Population

__Abstract__ Cardiovascular disease (CVD) is an important cause of morbidity and mortality worldwide. The increasing incidence and prevalence of CVD constitutes a considerable disease burden and major health challenge for prevention and treatment. CVD frequently co-exists with other diseases such as type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver diseases (NAFLD), which are both strongly related to the metabolic syndrome. The 3- hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase inhibitors or statins are cholesterol-lowering drugs that are beneficial in the primary and secondary prevention of CVD. With an approximately 20-25% reduction of the risk of major cardiovascular endpoints, these drugs have definitely entered daily clinical practice, and their use is expected to increase even more with a widening of therapeutic indications. However, current pharmacotherapy may not be optimal for all patients, and some individuals may not respond adequately to statins. This inadequate response may consist of a lack of therapeutic effect or the occurrence of adverse drug reactions (ADRs), and can have several underlying reasons. Insight into why people do not respond adequately to statins might improve clinical practice, can partly avoid these events, and may finally lead to tailored drug therapy. Moreover, in 2013, the American guidelines on primary prevention of CVD lowered the threshold for the indication for statin treatment, and thereby widened the target population for these already frequently prescribed drugs. This might have implications for current clinical practice, since prescribers should not ignore the potential risk of overtreatment or unintended effects that may go with this increased use. In this thesis we focused on the pharmacogenetic epidemiology of statins in an ageing population. We describe genetic factors that modify the response to statins, whereby we discovered new genetic variation and validated previous findings from genome-wide research and other relevant literature on the pharmacogenetics of statins. Furthermore, we describe unintended effects of the use of statins in clinical practice, and methodological approaches to estimate statin effectiveness in observational studies. Overall, in this thesis we have provided more insight into the use of statins in an ageing population. Stalins are expected to be used even more frequently in clinical practice, i.e. through widening of the indication for statins, the aging population and the increasing prevalence of welfare diseases associated with dyslipidemia and an increased CVD risk. Future research should investigate the consequences of this increased use for daily clinical practice. Hopefully, there will be a future role for genotype-based clinical decision making, with statin therapy tailored to an individual's needs on the basis of patient's specific characteristics.
Human, Robotics Close Motion Classification Based on the Micro Doppler Signature Using HSVD

: In many applications, in industry and laboratory, there is a robot with a human, in close motion, to satisfy the work requirement. Therefore, for safety and monitoring the quality, the classification between them is important. The robot is considered as a semi-rigid object because it has many parts in the half upper, while the lower half is rigid, and a human is a non-rigid object. This paper presents a practical result which investigates the classification between these two objects based on the micro-Doppler signatures. The object's data were acquired using an S-band 2.4 GHz radar. The improved Stockwell transform was used to analyze the radar received signal in the time-frequency domain and feature extraction. The singular value decomposition is used for data filtering based on the Hankel matrix (HSVD) while using the Temporal Radial Basis Function to satisfy the classification. The classification accuracies gained are; 96.4%-100% for semi-rigid, 92%-95% for non-rigid closed objects.
Two-step path loss prediction by artificial neural network for wireless service area planning

In recent years, wireless network systems are utilized in various industry fields and the wireless service area planning became one of the important tasks to realize efficient and high-quality wireless communication service. The machine learning technology attracts the interests of researchers to improve the efficiency of the area planning task because the radio propagation loss in unknown locations can be predicted by the training data without explicit algorithms. Our previous work showed that the path loss (PL) characteristics become complicated in the high PL region, and it can degrade the entire prediction accuracy. In this paper, we propose the two-step PL prediction method by the artificial neural network (ANN) to solve the issue. Firstly, the area is classified into several zones according to the PL range. And then the PL is predicted by ANNs that were trained for respective zones. Our proposal was evaluated by the ray-tracing simulation data, and the result showed that it improved the root mean square error (RMSE) of PL prediction from 7.9 dB to 4.1 dB. The method is expected to be utilized for the wireless service area planning in various environments.
MRFN: Multi-Receptive-Field Network for Fast and Accurate Single Image Super-Resolution

Recently, convolutional neural network (CNN) based models have shown great potential in the task of single image super-resolution (SISR). However, many state-of-the-art SISR solutions are reproducing some tricks proven effective in other vision tasks, such as pursuing a deeper model. In this paper, we propose a new solution (named as Multi-Receptive-Field Network - MRFN), which outperforms existing SISR solutions in three different aspects. First, from receptive field: a novel multi-receptive-field (MRF) module is proposed to extract and fuse features in different receptive fields from local to global. Integrating these hierarchical features can generate better mappings on recovering high-fidelity details at different scales. Second, from network architectures: both dense skip connections and deep supervision are utilized to combine features from the current MRF module and preceding ones for training more representative features. Moreover, a deconvolution layer is embedded at the end of the network to avoid artificial priors induced by numerical data pre-processing (e.g., bicubic stretching), and speed up the restoration process. Finally, from error modeling: different from $L1$ and $L2$ loss functions, we proposed a novel two-parameter training loss called Weighted Huber loss function which can adaptively adjust the value of back-propagated derivative according to the residual value, thus fit the reconstruction error more effectively. Extensive qualitative and quantitative evaluation results on benchmark datasets demonstrate that our proposed MRFN can achieve more accurate recovering results than most state-of-the-art methods with significantly less complexity.