Miracle-dz/UniFAR-FTUs huggingface dataset:
“She must have been sleeping around”…: Contextual interpretations of cervical cancer and views regarding HPV vaccination for adolescents in selected communities in Ibadan, Nigeria

Background Human Papilloma virus (HPV) vaccines for adolescents are pivotal in the control of cervical cancer, the commonest women specific malignancy in sub-Saharan Africa. Knowledge about cervical cancer have been reported to be low in Africa but expressed acceptability for HPV vaccines have been high. The reason for this mismatch is not clear. An understanding of the interpretation of cervical cancer and views about HPV vaccine are important as they can affect actual decision making regarding adolescents’ uptake of HPV vaccine. This study explored contextual interpretations of stakeholders regarding cervical cancer and HPV vaccines for adolescents in five selected communities in Ibadan, Nigeria. Methods Qualitative data were collected through twenty focus group discussions among parents of adolescents, religious and traditional leaders, school teachers and adolescents; and four key informant interviews with the identified traditional healers in the selected communities. Constant comparison analysis was done after transcription. Results Almost every group had at least one person who had seen a cervical cancer patient. Cervical cancer was widely viewed as being caused by promiscuity of women while older participants believed that the alteration of lifestyle by civilization was a major contributory factor. There was also a general notion that it was due to a curse. The role of HPV was generally not known. Most participants were favourable towards HPV immunization for adolescents but traditional healers and some religious leaders were not. The high cost of the vaccines and possibility of side effects where the main concerns about the introduction of HPV vaccine. Decision to take the vaccine rest with the fathers whose views were heavily influenced by traditional and religious leaders. Conclusions Awareness about cervical cancer may not be as low as earlier reported and there were many misconceptions about cervical cancer in the study communities. It is important to address these misconceptions to ensure successful introduction of HPV vaccine for adolescents in future.
Improving Fast Segmentation With Teacher-Student Learning

Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks. However, these models are very heavy and generally suffer from low inference speed, which limits their application scenarios in practice. Meanwhile, existing fast segmentation models usually fail to obtain satisfactory segmentation accuracies on public benchmarks. In this paper, we propose a teacher-student learning framework that transfers the knowledge gained by a heavy and better performed segmentation network (i.e. teacher) to guide the learning of fast segmentation networks (i.e. student). Specifically, both zero-order and first-order knowledge depicted in the fine annotated images and unlabeled auxiliary data are transferred to regularize our student learning. The proposed method can improve existing fast segmentation models without incurring extra computational overhead, so it can still process images with the same fast speed. Extensive experiments on the Pascal Context, Cityscape and VOC 2012 datasets demonstrate that the proposed teacher-student learning framework is able to significantly boost the performance of student network.
Cumulative effects of triadic closure and homophily in social networks

Dynamic interplay of choice homophily and triadic closure amplifies homophily and creates core-peripheries in social networks. Social network structure has often been attributed to two network evolution mechanisms—triadic closure and choice homophily—which are commonly considered independently or with static models. However, empirical studies suggest that their dynamic interplay generates the observed homophily of real-world social networks. By combining these mechanisms in a dynamic model, we confirm the longheld hypothesis that choice homophily and triadic closure cause induced homophily. We estimate how much observed homophily in friendship and communication networks is amplified due to triadic closure. We find that cumulative effects of homophily amplification can also lead to the widely documented core-periphery structure of networks, and to memory of homophilic constraints (equivalent to hysteresis in physics). The model shows that even small individual bias may prompt network-level changes such as segregation or core group dominance. Our results highlight that individual-level mechanisms should not be analyzed separately without considering the dynamics of society as a whole.
Availability of Additional Mediolateral Implant Option During Total Knee Arthroplasty Improves Femoral Component Fit Across Ethnicities

Background: Anatomical variation may represent a challenge in achieving a close fit between a prosthesis and a patient’s osseous geometry in total knee arthroplasty (TKA). The purposes of this study were to determine whether the shape of the distal part of the femur differs among ethnicities, whether these differences affect the fit of the femoral component of a standard prosthesis, and whether the additional availability of a femoral component with a reduced mediolateral dimension for the same anteroposterior dimension improves femoral component fit across ethnicities. Methods: Femoral dimensions were measured intraoperatively during 967 TKAs performed using the same type of prosthesis in patients of 5 different ethnicities. Aspect ratios were calculated to determine whether the shapes of the femora differed among ethnicities. The component fit (“perfect,” overhang, or underhang) when only standard prostheses were available was compared with the fit when both standard and narrow prostheses were available in all ethnic groups. This enabled us to determine whether the femoral component fit was improved by the additional availability of the narrow version. Results: Wide variations in shape were found among ethnicities as were variations among individuals of the same ethnicity. Differences in shape among ethnicities influenced the rate of overhang. However, overhang was more frequent at the trochlear than at the condylar level across all ethnicities. The availability of both the standard and the narrow femoral components improved the rate of a perfect fit in women in 3 of the 5 ethnic groups and reduced the overhang rate in women in all 5 of the ethnic groups. In contrast, only modest improvements in femoral component fit, which were not statistically significant, were seen in men. Conclusions: The shape of the distal part of the femur varies not only among ethnicities but also within ethnic groups, leading to a high prevalence of overhang when only standard prostheses are available. The additional availability of a femoral component with a reduced mediolateral dimension for the same anteroposterior size can reduce overhang and improve component fit across ethnicities.
Design of a Novel 3-DoF Leg with Series and Parallel Compliant Actuation for Energy Efficient Articulated Robots

This work presents the development of a 3-DoF leg with series and parallel compliant actuation. Series-elastic main actuators are combined with parallel high efficiency energy storage branches, to substantially improve energy efficiency. The leg design is semi-anthropomorphic, with similar mass and mass distribution to the human limb, and includes a biarticulated actuation configuration. The parallel branches are driven by secondary motors and their design parameters are optimised. The mechanical design of the prototype leg is presented, introducing details of the actuation configuration principles employed. Preliminary experimental data are presented, in which a baseline series-elastic-only configuration is compared with configurations with mono- and biarticulated parallel branches, respectively. The results effectively demonstrate the concept's potential, showing improvements of 53% and 60% in electrical power consumption while the leg is executing loaded cyclic motion profiles.
A Graph-CNN for 3D Point Cloud Classification

Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph. Major challenges when working with data on graphs are that the support set (the vertices of the graph) do not typically have a natural ordering, and in general, the topology of the graph is not regular (i.e., vertices do not all have the same number of neighbors). Thus, Graph-CNNs have huge potential to deal with 3D point cloud data which has been obtained from sampling a manifold. In this paper we develop a Graph-CNN for classifying 3D point cloud data, called PointGCN1. The architecture combines localized graph convolutions with two types of graph downsampling operations (also known as pooling). By the effective exploration of the point cloud local structure using the Graph-CNN, the proposed architecture achieves competitive performance on the 3D object classification benchmark ModelNet, and our architecture is more stable than competing schemes.
Autonomous Agents in Snake Game via Deep Reinforcement Learning

Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has become a commonly adopted method to enable the agents to learn complex control policies in various video games. However, similar approaches may still need to be improved when applied to more challenging scenarios, where reward signals are sparse and delayed. In this paper, we develop a refined DRL model to enable our autonomous agent to play the classical Snake Game, whose constraint gets stricter as the game progresses. Specifically, we employ a convolutional neural network (CNN) trained with a variant of Q-learning. Moreover, we propose a carefully designed reward mechanism to properly train the network, adopt a training gap strategy to temporarily bypass training after the location of the target changes, and introduce a dual experience replay method to categorize different experiences for better training efficacy. The experimental results show that our agent outperforms the baseline model and surpasses human-level performance in terms of playing the Snake Game.
A Joint Multi-Task Learning Framework for Spoken Language Understanding

Spoken language understanding (SLU), which mainly involves intent prediction and slot filling, is a core component of a spoken dialogue system. Usually, intent determination and slot filling are carried out independently. Recently, joint learning of intent determination and slot filling has been proved effective in SLU. In this paper, we propose a novel joint multi-task learning framework for SLU, which predicts user intent and slot label via shared LSTM architecture, as well as next word's part of speech (POS) via neural language model. The proposed model exploits the correlation among different tasks and makes full advantage of all supervised signals. We conduct experiments on popular benchmark ATIS dataset, which consists of rich dialogues collected from real world. The experiment results show that our model achieves state-of-the-art in terms of several popular metrics.
Identification of potential biomarkers in cholestasis and the therapeutic effect of melatonin by metabolomics, multivariate data and pathway analyses

The present study investigated the anti-cholestatic effect of melatonin (MT) against α-naphthyl isothiocyanate (ANIT)-induced liver injury in rats and screened for potential biomarkers of cholestasis. Rats were administered ANIT by intraperitoneal injection and then sacrificed 36 h later. Serum biochemical parameters were measured and liver tissue samples were subjected to histological analysis. Active components in the serum were identified by gas chromatography-mass spectrometry, while biomarkers and biochemical pathways were identified by multivariate data analysis. The results revealed that the serum levels of alanine aminotransferase, aspartate aminotransferase, total bilirubin, direct bilirubin, γ-glutamyl transpeptidase, and alkaline phosphatase were reduced in rats with ANIT-induced cholestasis that were treated with MT. The histological observations indicated that MT had a protective effect against ANIT-induced hepatic tissue damage. Metabolomics analysis revealed that this effect was likely to be associated with the regulation of compounds related to MT synthesis and catabolism, and amino acid metabolism, including 5-aminopentanoate, 5-methoxytryptamine, L-tryptophan, threonine, glutathione, L-methionine, and indolelactate. In addition, principal component analysis demonstrated that the levels of these metabolites differed significantly between the MT and control groups, providing further evidence that they may be responsible for the effects induced by MT. These results provide an insight into the mechanisms underlying cholestasis development and highlight potential biomarkers for disease diagnosis.
Hough Transform Guided Deep Feature Extraction for Dense Building Detection in Remote Sensing Images

Detecting dense buildings without elevation information is an important and challenging task in remote sensing applications. In this paper, we present a novel cascaded deep neural network architecture, incorporating multi -stage region proposal detection and Hough transform to obtain better mid-level semantic information for man-made objects. This proposed network can be trained end-to-end by multi-loss jointly. We train and test it on a large building dataset collected from Google Earth, including buildings from urban, suburban and rural areas. Experiments demonstrate great robustness and superiority of our method to various buildings over other convolutional neural network (CNN) based detection methods.
Directional Priors for Multi-Frame Optical Flow

Pipeline approaches that interpolate and reﬁne an initial set of point correspondences have recently shown a good performance in the ﬁeld of optical ﬂow estimation. However, so far, these methods are typically restricted to two frames which makes exploiting temporal information difﬁcult. In this paper, we show how such pipeline approaches can be extended to the temporal domain and how directional constraints can be incorporated to further improve the estimation. To this end, we not only suggest to exploit temporal information in the preﬁltering step, we also propose a trajectorial reﬁnement method that lifts successful concepts of recent variational two-frame methods to the multi-frame domain. Experiments demonstrate the usefulness of our pipeline approach. They do not only show good results in general, they also demonstrate the clear beneﬁts of using multiple frames and of imposing directional constraints on the preﬁltering step and the reﬁnement.
Zap Meets Momentum: Stochastic Approximation Algorithms with Optimal Convergence Rate

There are two well known Stochastic Approximation techniques that are known to have optimal rate of convergence (measured in terms of asymptotic variance): the Ruppert-Polyak averaging technique, and stochastic Newton-Raphson (SNR) (a matrix gain algorithm that resembles the deterministic Newton-Raphson method). The Zap algorithms introduced by the authors are a version of SNR designed to behave more closely like their deterministic cousin. It is found that estimates from the Zap Q-learning algorithm converge remarkably quickly, but the per-iteration complexity can be high. This paper introduces an entirely new class of stochastic approximation algorithms based on matrix momentum. For a special choice of the matrix momentum and gain sequences, it is found in simulations that the parameter estimates obtained from the algorithm couple with those obtained from the more complex stochastic Newton-Raphson algorithm. Conditions under which coupling is guaranteed are established for a class of linear recursions. Optimal finite-$n$ error bounds are also obtained. The main objective of this work is to create more efficient algorithms for applications to reinforcement learning. Numerical results illustrate the value of these techniques in this setting.
First-Order Continuous Adaptive Sliding Mode Control for Robot Manipulators with Finite-Time Convergence of Trajectories to Real Sliding Mode

In this paper the design, analysis and implementation of improved first-order continuous adaptive sliding mode control (CASMC) based on online estimation of the lumped time-varying uncertainties for tracking control of the robot manipulators is presented. The proposed method allows to address the main drawbacks of conventional sliding mode control: the chattering phenomenon, and the requirement for a priori knowledge of the bounds of the uncertainties, and also the chattering problem associated with adaptive discontinuous sliding mode controllers, while the robustness property of the conventional sliding mode control is preserved. Furthermore, in the previously published version of the controller [1], the estimate of robot inertia matrix is needed to realize the adaptive component of the control law. In this work, the skew-symmetry property (passivity property) of robot dynamic is used to eliminate that requirement. The global stability and robustness of the proposed controller are established in the presence of time-varying uncertainties using Lyapunov's approach and fundamentals of sliding mode theory. The robustness is achieved without knowing the bound of uncertainties. The dynamic model of a two-degrees of freedom (2-DOF) rigid robot is used for simulation study and a 2-DOF flexible-link robot is used as an experimental test-bed to evaluate the performance, and robustness of the controller. Based on the simulations and experimental results, the proposed controller performs remarkably well in terms of the tracking error convergence, estimation of lumped uncertain parameter. And it is robust against un-modeled dynamics and external disturbances.
Anticipating Many Futures: Online Human Motion Prediction and Generation for Human-Robot Interaction

Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions. The bottleneck of most methods is the lack of an accurate model of natural human motion. In this work, we present a conditional variational autoencoder that is trained to predict a window of future human motion given a window of past frames. Using skeletal data obtained from RGB depth images, we show how this unsupervised approach can be used for online motion prediction for up to 1660 ms. Additionally, we demonstrate online target prediction within the first 300–500 ms after motion onset without the use of target specific training data. The advantage of our probabilistic approach is the possibility to draw samples of possible future motion patterns. Finally, we investigate how movements and kinematic cues are represented on the learned low dimensional manifold.
Do interventions containing risk messages increase risk appraisal and the subsequent vaccination intentions and uptake? – A systematic review and meta‐analysis

Purpose There is good evidence that for many behaviours, increasing risk appraisal can lead to a change in behaviour, heightened when efficacy appraisals are also increased. The present systematic review addressed whether interventions presenting a risk message increase risk appraisal and an increase in vaccination intentions and uptake. Method A systematic search identified randomized controlled trials of interventions presenting a risk message and measuring risk appraisal and intentions and uptake post‐intervention. Random‐effects meta‐analyses investigated the size of the effect that interventions had on vaccination risk appraisal and on vaccination behaviour or intention to vaccinate, and the size of the relationship between vaccination risk appraisal and vaccination intentions and uptake. Results Eighteen studies were included and 16 meta‐analysed. Interventions overall had small significant effects on risk appraisal (d = 0.161, p = .047) and perceptions of susceptibility (d = 0.195, p = .025), but no effect on perceptions of severity (d = −0.036, p = .828). Interventions showed no effect on intention to vaccinate (d = 0.138, p = .195) and no effect on vaccination behaviour (d = 0.043, p = .826). Interventions typically did not include many behaviour change techniques (BCTs), with the most common BCT unique to intervention conditions being ‘Information about Health Consequences’. Few of the included studies attempted to, or successfully increased, efficacy appraisals. Conclusions Overall, there is a lack of good‐quality primary studies, and existing interventions are suboptimal. The inclusion of additional BCTs, including those to target efficacy appraisals, could increase intervention effectiveness. The protocol (CRD42015029365) is available from http://www.crd. york.ac. uk/PROSPERO/. Statement of contribution What is already known on this subject? Previous research indicates that an increase in risk appraisal is associated with increased uptake in health‐related behaviours. Research suggests that interventions increasing risk appraisal have a greater effect on intention when elements of efficacy appraisals are simultaneously increased. What does this study add? This is the first systematic review to examine the effect of interventions on risk appraisal and vaccination uptake using only experimental studies. Limitations of the interventions themselves, and those caused by study methods and reporting, mean that the potential value of this type of review is lost. Instead, its value is in shining a light on the paucity of experimental studies in this area, and the quality of methods and reporting used. Future experimental studies should examine interventions that focus exclusively on increasing risk and efficacy appraisal compared to controls, use conditional measures of risk, and improve reporting to enable both more accurate coding of intervention content and more accurate assessments of study bias.
Mesenchymal Stem Cells as New Therapeutic Approach for Diabetes and Pancreatic Disorders

Diabetes is a worldwide disease which actually includes different disorders related to glucose metabolism. According to different epidemiological studies, patients affected by diabetes present a higher risk to develop both acute and chronic pancreatitis, clinical situations which, in turn, increase the risk to develop pancreatic cancer. Current therapies are able to adjust insulin levels according to blood glucose peak, but they only partly reach the goal to abrogate the consequent inflammatory milieu responsible for diabetes-related diseases. In recent years, many studies have investigated the possible use of adult mesenchymal stem cells (MSCs) as alternative therapeutic treatment for diabetes, with promising results due to the manifold properties of these cells. In this review we will critically analyze the many different uses of MSCs for both diabetes treatment and for the reduction of diabetes-related disease development, focusing on their putative molecular mechanisms.