Miracle-dz/UniFAR-FTUs huggingface dataset:
Global demographic history of human populations inferred from whole mitochondrial genomes

The Neolithic transition has led to marked increases in census population sizes across the world, as recorded by a rich archaeological record. However, previous attempts to detect such changes using genetic markers, especially mitochondrial DNA (mtDNA), have mostly been unsuccessful. We use complete mtDNA genomes from over 1700 individuals, from the 1000 Genomes Project Phase 3, to explore changes in populations sizes in five populations for each of four major geographical regions, using a sophisticated coalescent-based Bayesian method (extended Bayesian skyline plots) and mutation rates calibrated with ancient DNA. Despite the power and sophistication of our analysis, we fail to find size changes that correspond to the Neolithic transitions of the study populations. However, we do detect a number of size changes, which tend to be replicated in most populations within each region. These changes are mostly much older than the Neolithic transition and could reflect either population expansion or changes in population structure. Given the amount of migration and population mixing that occurred after these ancient signals were generated, we caution that modern populations will often carry ghost signals of demographic events that occurred far away from their current location.
Deep Residual Learning for Model-Based Iterative CT Reconstruction Using Plug-and-Play Framework

Model-Based Iterative Reconstruction (MBIR) has shown promising results in clinical studies as they allow significant dose reduction during CT scans while maintaining the diagnostic image quality. MBIR improves the image quality over analytical reconstruction by modeling both the sensor (e.g., forward model) and the image being reconstructed (e.g., prior model). While the forward model is typically based on the physics of the sensor, accurate prior modeling remains a challenging problem. Markov Random Field (MRF) has been widely used as prior models in MBIR due to simple structure, but they cannot completely capture the subtle characteristics of complex images. To tackle this challenge, we generate a prior model by learning the desirable image property from a large dataset. Toward this, we use Plug-and-Play (PnP) framework which decouples the forward model and the prior model in the optimization procedure, replacing the prior model optimization by a image denoising operator. Then, we adopt the state-of-the-art deep residual learning for the image denoising operator which represents the prior model in MBIR. Experimental results on real CT scans demonstrate that our PnP MBIR with deep residual learning prior significantly reduces the noise and artifacts compared to analytical reconstruction and standard MBIR with MRF prior.
Robust Parameter Identification for Railway Suspension Systems

Detecting faults at the right time is crucial both for adequate maintenance and optimal operation of railway vehicles. The increasing complexity of the modern railway system pose a challenge for appropriate fault diagnosis and isolation (FDI). In this paper, a sliding-mode-based parameter identification algorithm is proposed as a solution for the identification of parameters in a simplified railway system. It is assessed on different faulty cases where the accuracy of the estimation and sensitivity to faults are shown, as well as robustness to model uncertainties. The data used for simulations and algorithm evaluation is generated using a validated Multibody Simulation (MBS) model implemented in Simpack, while the numerical simulations are carried out through MATLAB/Simulink.
Selection and Compression of Local Binary Features for Remote Visual SLAM

In the field of autonomous robotics, Simultaneous Localization and Mapping (SLAM) is still a challenging problem. With cheap visual sensors attracting more and more attention, various solutions to the SLAM problem using visual cues have been proposed. However, current visual SLAM systems are still computationally demanding, especially on embedded devices. In addition, collaborative SLAM approaches emerge using visual information acquired from multiple robots simultaneously to build a joint map. In order to address both challenges, we present an approach for remote visual SLAM where local binary features are extracted at the robot, compressed and sent over a network to a centralized powerful processing node running the visual SLAM algorithm. To this end, we propose a new feature coding scheme including a feature selection stage which ensures that only relevant information is transmitted. We demonstrate the effectiveness of our approach on well-known datasets. With the proposed approach, it is possible to build an accurate map while limiting the data rate to 75 kbits/frame.
A Second-Order Variational Framework for Joint Depth Map Estimation and Image Dehazing

Outdoor images captured in poor weather conditions (e.g., fog or haze) commonly suffer from reduced contrast and visibility. Increasing attention has recently been paid to single image dehazing, i.e., improving image contrast and visibility. It is generally thought that the dehazing performance highly depends on the accurate depth information. In this work, we first obtain the initial depth map by using the popular dark channel prior. A unified second-order variational framework is then proposed to refine the depth map and restore the haze-free image. The introduced second-order framework has the capacity of preserving important structures in both depth map and haze-free image. Furthermore, the proposed framework performs well for several different types of haze situations. The resulting optimization problems related to depth map estimation and latent image restoration can be effectively handled using the primal-dual algorithm under a two-step numerical framework. The effectiveness of our proposed method has been demonstrated by comparing the imaging performance with several state-of-the-art dehazing methods.
Ultra-wideband Passive radars Fundamental Theory and Applications

Passive radars are such radars that record and process signals of the objects own radiation within the radio wave band. They are used to solve a wide range of remote sensing problems. The observations registered by such radars are a mixture of stochastic signals of the investigated and interfering objects, as well as the internal noise of the receiver. Signals and noise are completely random, and their statistical characteristics are the same. Another feature of the transmitting radio thermal signals is that their emission spectrum is ultra-wideband (UWB) (except for polyatomic gases, which are characterized by discrete absorption and emission lines). Therefore, it is expedient to provide wide- or UWB for the passive radars operating frequencies band. Transition to UWB signal processing requires the improvement of the passive UWB radars theory. This theory should be able to synthesize new algorithms for signal processing and reveal the physical essence of parameter estimates at the output of the radar. This will allow solving the inverse problems of estimating the parameters and characteristics of the objects. Results of many years of research are summarized in this paper, a logical connection is formed between the key elements of the microwave radiation theory, the mathematical apparatus for processing UWB signals and the fundamentals of the optimal signal processing algorithms synthesis. Examples of the signal processing algorithm synthesis in a single- and a multichannel passive radar are also given.
Piezoelectric Ultrasonic Microsensors on Buckled Diaphragms Using Sol-Gel Derived PZT Films

Piezoelectric ultrasonic microsensors with a PZT (lead-zirconate-titanate) capacitor on a buckled diaphragm were fabricated and deposition condition of the sol-gel derived PZT films was investigated from the viewpoint of high mechano-electrical conversion efficiency. The buckling of the diaphragm affects the conversion efficiency in the geometrical aspect and is influenced by the residual stress of the diaphragm. The films were deposited through various thermal treatment in the sol-gel process; four pyrolysis temperatures and two first-crystallized gel film thickness, and were evaluated in their stress and ferroelectric property. The thickness of the gel film affected the crystallographic orientation, (100) or (111), of the film, and dominated the film stress and remanent polarization, e.g., the stress and polarization of the (100)-oriented films were smaller than those of the (lll)-oriented films, meaning the (100) orientation is better in the stress but worse in the remanent polarization than (111) orientation. The pyrolysis temperature was then precisely controlled and finally orientation-distributed samples were fabricated and evaluated. The samples with mixed orientation of (100) and (111) in around 50% showed intermediate stress and remanent polarization, and their sensitivity was enhanced by 1.4-4 times to those with (100) or (111) single oriented films.
Influence of Cognitive Resource Limitation on Consumer Purchasing Decision: An Event-Related Potentials Perspective

This research aims to study the neural influence of conflict between product appearance and performance on consumer decision when consumers' cognitive resources are limited. In order to manipulate the cognitive resources, 20 subjects were asked to memorize a 20-digit number in 20s. Then, stimuli of products with appearance and performance information were displayed randomly. Subjects need to decide whether to buy, while the Electroencephalogram (EEG) was recorded. The stimuli were divided into four conditions based on the consistency of appearance and performance (condition 1: more attractive and superior performance; condition 2: more attractive and inferior performance; condition 3: less attractive and superior performance; condition 4: less attractive and inferior performance). Results showed that conditions 1 and 2 had higher purchase rate than other two conditions, but the difference between conditions 1 and 2 was not significant. Although both conditions 2 and 3 have conflicting information, condition 3 evoked the highest amplitude of conflict-related N270 than other conditions in the prefrontal regions. Aesthetics-related P2 were also elicited among posterior-parietal regions in all configurations, but the P2 amplitude triggered by condition 2 was significantly larger than other conditions. The findings indicated that consumers need to call more cognitive resources to integrate several pieces of product information when product appearance conflicts with its performance. When their cognitive resources were limited, they tended to use automatic, affective processes over thoughtful, cognitive ones which leaded to a high purchase rate, a smaller N270 and a larger P2 for the more attractive but inferior performance products. Hence, P2 and N270 may be useful neural endogenous indicators to reflect the process of handling conflicts in consumer decision.
Educational Data Mining to Improve Learner Performance in Gauteng Primary Schools

Educational institutions' main goal is to contribute to the improvement of quality in education. The useful information obtained from the Educational Database Systems can be used in predicting learners' performance. Educational data mining (EDM) is a new emerging data mining (DM) technique used to improve the quality of education. The dataset of learner academic records was applied on Naïve Bayes, BayesNet, JRip and J48 classification algorithms using the Weka tool. The learners were classified based on their city, school, grades, and Mathematics results. About 678 learner's data was covered. This paper showcases the comparison of four classifiers and finds the best performing classification algorithm among all. Based on the results obtained, we found that J48 algorithm outperformed the other algorithms with 99.13% prediction accuracy.
Windmill Climbing Robot

Having the windmill main components at the top of its high tower made its maintenance a very risky job for workers, and with an increase of use of wind turbines, the risk of accidents occurring is proportionally increasing too. For this, engineers started working on climbing robots and mechanisms to replace risking human lives. This research works on adding to this research topic, by proposing a full study of a new mechanism enhanced with calculations and modeling for it to be applicable and reliable. The proposed robot mechanism can circumference the tower of the wind mill and climb upwards through means of rubber chains.
Robust and customized methods for real-time hand gesture recognition under object-occlusion

Dynamic hand tracking and gesture recognition is a hard task since there are many joints on the fingers and each joint owns many degrees of freedom. Besides, object occlusion is also a thorny issue in finger tracking and posture recognition. Therefore, we propose a robust and customized system for realtime hand tracking and gesture recognition under occlusion environment. First, we model the angles between hand keypoints and encode their relative coordinate vectors, then we introduce GAN to generate raw discrete sequence dataset. Secondly we propose a time series forecasting method in the prediction of defined hand keypoint location. Finally, we define a sliding window matching method to complete gesture recognition. We analyze 11 kinds of typical gestures and show how to perform gesture recognition with the proposed method. Our work can reach state of the art results and contribute to build a framework to implement customized gesture recognition task.
Multi-View Audio-Articulatory Features for Phonetic Recognition on RTMRI-TIMIT Database

In this paper, we investigate the use of articulatory information, and more specifically real time Magnetic Resonance Imaging (rtMRI) data of the vocal tract, to improve speech recognition performance. For the purpose of our experiments, we use data from the rtMRI-TIMIT database. Firstly, Scale Invariant Feature Transform (SIFT) features are extracted for each video frame. Afterwards, the SIFT descriptors of each frame are transformed to a single histogram per picture, by using the Bag of Visual Words methodology. Since this kind of articulatory information is difficult to acquire in typical speech recognition setups we only consider it to be available in the training phase. Thus, we use a multi-view setup approach by applying Canonical Correlation Analysis (CCA) to visual and audio data. By using the transformation matrix, acquired during the training stage, we transform both train and test audio data to produce MFCC-articulatory features, which form the input for the recognition system. Experimental results demonstrate improvements in phone recognition in comparison with the audio-based baseline.
A Comparison of Interactive and Passive Quality Assessment for Gaming Research

Subjective tests to assess the Quality of Experience (QoE) of gaming services are necessary to enable service providers to ensure the satisfaction of their customers. Since gaming is an interactive activity, interactive tests are typically conducted to measure the full spectrum of the player experience. However, carrying out such tests is expensive and time-consuming. Furthermore, the results can be influenced by the behavior and abilities of participants. For this reason, it is of high interest whether such interactive tests can be partially replaced with passive viewing-and-listening tests. In this paper, we present a comparison of an interactive gaming test with passive tests using two different durations. To investigate the differences between the test paradigms, we assessed the overall quality, the video quality and the reactiveness of the game as well as other player experience aspect for different frame rates and bit rates. Results show that once certain requirements are fulfilled, passive tests offer indeed a valuable quality assessment method. However, if the duration of the presented video material is too short, the passive test overestimated the gaming and video quality. Furthermore, we show that the player performance has no impact on the video quality ratings.
Active Learning from Noisy Tagged Images

Learning successful image classiﬁcation models requires large quantities of labelled examples that are generally hard to obtain. On the other hand, the web provides an abundance of loosely labelled images, i.e. tagged in websites such as Flickr. Although these images are cheap and massively available, their tags are typically noisy and unreliable. In an attempt to use such images for training a classiﬁer, we propose a simple probabilistic model to learn a latent semantic space from which deep vector representations of the images and tags are generated. This latent space is subsequently used in an active learning framework based on adaptive submodular optimisation that selects informative images to be labelled. Afterwards, we update the classiﬁer according to the importance of each labelled image to best capture the information they provide. Through this simple approach, we are able to train a classiﬁer that performs well using a fraction of the effort that is typically required for image labelling and classiﬁer training.
Deployment of a Second Victim Peer Support Program: A Replication Study

Background: The second victim phenomenon occurs when health-care providers experience significant professional distress (compassion dissatisfaction, burnout, secondary traumatic stress) and psychological distress (shame, anxiety, and depression) as a result of medical errors or adverse patient outcomes. Few hospitals have institution-wide systems in place to assist employees through the recovery process. Methods: At Nationwide Children’s Hospital (NCH), a peer-based support program called “YOU Matter” was executed and spread hospital-wide. The program emulated the framework and execution strategy designed by University of Missouri Health Care’s (MUHC) “forYOU” Team. Strategic elements of the program’s structure were reviewed and adapted for NCH with system-wide deployment and enhancement to include electronic peer support reporting. This article summarizes program implementation, management, and sustainment over the past 2 years. Results: By following University of Missouri Health Care’s model, we successfully deployed an institution-wide second victim program. Since the November 2013 initiation, we have documented 232 peer and 21 group encounters. High-risk clinical areas for second victimization at NCH included the emergency department (ED), pediatric intensive care unit (PICU), cardiothoracic intensive care unit (CTICU), and pharmacy department. Registered nurses (RNs) and licensed practical nurses (LPNs) have had the highest number of encounters necessitating second victim support (32%). Supported staff reported improved emotional state and improved return-to-work metrics. Conclusions: An organization’s culture of patient safety can be enhanced by ensuring staff psychological safety. Programs like “YOU Matter” and the “forYOU” Team are essential building blocks to improve the overall safety culture and quality of care. Implementation of “YOU Matter” at NCH validates the MUHC program and demonstrates its generalizability to other health-care institutions.
Ex Situ Fabrication of Polypyrrole-Coated Core-Shell Nanoparticles for High-Performance Coin Cell Supercapacitor

Silica-conducting polymer (SiO2-CP) has the advantages of high electrical conductivity, structural stability, and the facile formation of thin-film. This work deals with the preparation and optimization of polypyrrole (PPy)-encapsulated silica nanoparticles (SiO2 NPs) using an ex situ method. The SiO2-PPy core-shell NPs prepared by the ex situ method are well dispersed in water and facilitate the mass production of thin-film electrodes with improved electrical and electrochemical performances using a simple solution process. As-prepared SiO2-PPy core-shell NPs with different particle sizes were applied to electrode materials for two-electrode supercapacitors based on coin cell batteries. It was confirmed that the areal capacitance (73.1 mF/cm2), volumetric capacitance (243.5 F/cm3), and cycling stability (88.9% after 5000 cycles) of the coin cell employing the ex situ core-shell was superior to that of the conventional core-shell (4.2 mF/cm2, 14.2 mF/cm3, and 82.2%). Considering these facts, the ex situ method provides a facile way to produce highly-conductive thin-film electrodes with enhanced electrical and electrochemical properties for the coin cell supercapacitor application.
Face Verification from Depth using Privileged Information

In this paper, a deep Siamese architecture for depth-based face veriﬁcation is presented. The proposed approach efﬁciently veriﬁes if two face images belong to the same person while handling a great variety of head poses and occlusions. The architecture, namely JanusNet , consists in a combination of a depth, a RGB and a hybrid Siamese network. During the training phase, the hybrid network learns to extract complementary mid-level convolutional features which mimic the features of the RGB network, simultaneously leveraging on the light invariance of depth images. At testing time, the model, relying only on depth data, achieves state-of-art results and real time performance, despite the lack of deep-oriented depth-based datasets.
Retention on ART and predictors of disengagement from care in several alternative community‐centred ART refill models in rural Swaziland

A broad range of community‐centred care models for patients stable on anti‐retroviral therapy (ART) have been proposed by the World Health Organization to better respond to patient needs and alleviate pressure on health systems caused by rapidly growing patient numbers. Where available, often a single alternative care model is offered in addition to routine clinical care. We operationalized several community‐centred ART delivery care models in one public sector setting. Here, we compare retention in care and on ART and identify predictors of disengagement with care.
Hybrid Lstm-Fsmn Networks for Acoustic Modeling

This paper describes a series of experiments with neural networks containing long short-term memory (LSTM) [1] and feedforward sequential memory network (FSMN) [2]–[4] layers trained with the connectionist temporal classification (CTC) [5] criteria for acoustic modeling. We propose using a hybrid LSTM/FSMN (FLMN) architecture as an enhancement to conventional LSTM-only acoustic models. The addition of FSMN layers allows the network to model a fixed size representation of future context suitable for online speech recognition. Our experiments show that FLMN acoustic models significantly outperform conventional LSTM. We also compare the FLMN architecture with other methods of modeling future context. Finally, we present a modification of the FSMN architecture that improves performance by reducing the width of the FSMN output.
Data Driven Convolutional Sparse Coding for Visual Recognition

Convolutional sparse coding (CSC) has become an important method in image processing and computer vision. In this paper we focus on visual recognition problems and apply CSC as a feature learning method. We propose a task-specific approach to treat the dictionary of CSC as parameters for a larger learning framework. These parameters are differentiable under mild conditions, and could be updated end-to-end using back-propagation when the errors from the task objectives are provided. We perform several experiments to show that such method provides a more discriminate representation compared with previous CSC methods, and this data driven approach is effective for visual recognition problems.