Miracle-dz/UniFAR-FTUs huggingface dataset:
Maternal factors predisposing to early-onset Neonatal sepsis at Kenyatta National Hospital Maternity unit

Background: Neonatal sepsis, defined as sepsis within the first 28 days of life, is a major risk factor for neonatal mortality and accounts for about 1 million deaths annually. Early onset neonatal sepsis is acquired from the mother during passage through a colonised genital tract during birth. The incidence o f early onset neonatal sepsis (sepsis in neonates less than 7 days old) differs in the general population and is estimated to be between 1-10 cases per 1000 live births. A study done in Kilifi, Kenya documented a prevalence of early neonatal sepsis of 13.5%. Objective: To determine maternal factors predisposing to occurrence of early onset neonatal sepsis among mothers of 3 day old neonates admitted at the KNH New Born Unit (NBU). Study design: This was a hospital based casecontrol study. Methodology-: Cases were defined as mothers of 3 day old neonates admitted in the Kenyatta National Hospital New Bom Unit with a diagnosis of early onset neonatal sepsis and controls were mothers o f 3 day old neonates admitted in the KNH maternity unit without a diagnosis of neonatal sepsis. Data for this study was collected using structured questionnaires, inpatient progress notes from mothers and neonates’ files and laboratory microbiology results of vaginal swabs and urine cultures from the mothers. An access database was designed for data entry and SPSS version 15.0 was used for data analysis. Setting: The study was conducted at the Kenyatta National Hospital’s maternity unit.
A Deep Learning Based Approach to Lung Cancer Identification

Cancer is a very common disease type in worldwide. There are many types of cancer. Lung cancer is the most common type of cancer. Lung cancer which is common in both men and women can be fatal. The initiation of treatment by diagnosing cancer is important in reducing the risk of death. In this paper, classification of lung nodules is performs using CT images of SPIE-AAPM-LungX data. Deep learning has been a popular choice for the classification process in recent years. Especially it is used in the implementation of tensorFlow and 3D convolutional neural network architecture from deep learning libraries.
Learning Whole-Body Motor Skills for Humanoids

This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i.e., ankle, hip, foot tilting, and stepping strategies. The policy is trained in a physics simulator with realistic setting of robot model and low-level impedance control that are easy to transfer the learned skills to real robots. The advantage over traditional methods is the integration of high-level planner and feedback control all in one single coherent policy network, which is generic for learning versatile balancing and recovery motions against unknown perturbations at arbitrary locations (e.g., legs, torso). Furthermore, the proposed framework allows the policy to be learned quickly by many state-of-the-art learning algorithms. By comparing our learned results to studies of preprogrammed, special-purpose controllers in the literature, self-learned skills are comparable in terms of disturbance rejection but with additional advantages of producing a wide range of adaptive, versatile and robust behaviors.
Equivalent Magnetic Circuit Analysis for Linear Transverse Flux Permanent Magnet Machine

The paper proposes a novel linear transverse flux permanent magnet machine with the flux-concentrating of three side wall in the mover which has the characteristics of high force density and high reliability. Firstly, the novel structure and work principle of the linear TFPM machine are introduced. Secondly, the equivalent magnetic circuit model is established by analyzing the magnetic flux distribution between the relative positions with stator and mover. Thirdly, the results of magnetic network model are calculated by the first law of Kirchhoff magnetic circuit, and are compared with the three-dimensional finite element model. Finally, a prototype is constructed and experiments show good agreement with the finite element analysis.
Towards Manipulability of Bilateral Teleoperators With Time-Varying Delay

In this paper, we systematically formulate the concept of infinite (dynamical) manipulability for general dynamical systems, and by exploiting this concept to develop a new class of dynamic feedback, we solve the problem of bilateral control for nonlinear teleoperators with arbitrary bounded unknown time-varying delay. Specifically, based on a new dynamic feedback design of the reference velocities and accelerations, we propose an adaptive controller for nonlinear teleoperators that is robust with respect to arbitrary bounded unknown time-varying delay. It is shown that the proposed controller achieves position synchronization of the master and slave robots in free motion. In addition, the proposed controller ensures static torque reflection and the infinite manipulability of the teleoperator system with degree one provided that the gravitational torques are compensated a priori. The performance of the proposed adaptive controller is shown by numerical simulation results.
Parameter Estimation of Delay-Doppler Underwater Acoustic Multi-Path Channel Based on Iterative Fractional Fourier Transform

The characterization of wideband underwater acoustic (UWA) multi-path signals is investigated in this paper. The UWA multi-path channel can be considered as a sparse model and it causes changes of the transmitted signal on Doppler factor, time delay, and amplitude, thus the signal component from each path can be differentiated from the set of parameters. This paper proposes an FrFT-based method to estimate the channel parameters, which can decompose and retrieve the multi-path signals from the UWA channels. The proposed algorithm proceeds in an iterative manner and returns a set of estimated parameters at each turn. The estimated time delay is obtained directly without any extra correlation computation. Then, the estimated component is extracted by eliminating its FrFT result from the fractional domain. Compared to the existing methods, the most improvement is that the method can be applied to the wideband LFM signals with a wider frequency range in low signal-to-noise environment. Because the indirect subtractions avoid the bad chain effect caused by the low resolution or estimation error. The effectiveness of the method is confirmed by several simulations with low and high frequency. The performance is compared with a matching pursuit-based algorithm and an FrFT-based algorithm. It is shown that the proposed method outperforms the existing algorithms in low-frequency multi-component separation as well as estimation accuracy.
Kinematic Analysis of Trajectory Dimension-Dependent Sensorimotor Control in Arm Tracking

This paper aims to examine how the trajectory dimension influences sensorimotor control during arm tracking. We designed three trajectories with different dimensions in a three-dimensional (3D) immersive virtual reality environment and instructed the subjects to control a virtual hand to follow a cubic target that moved along the designed trajectories. The position of the virtual hand was determined by the position of the actual hand captured with a high-resolution 3D motion capture system in real time. Five kinematic measures were calculated: the root mean square error (RMSE), the standard deviation of the speed (speedsd), the magnitude of the jerk (Jerkm), the integral of the speed power spectrum (IVPS), and the 3D fuzzy approximate entropy (fApEn3D). All the kinematic measures increased significantly with increasing trajectory dimensions, except for the IVPS between the 1D and 2D conditions and the fApEn3D between the 2D and 3D conditions. The increase in time-domain parameters (i.e., RMSE, speedsd, and Jerkm) showed degradation in accuracy, energy efficiency, and multijoint coordination, respectively, in the higher dimensions. An increase in the frequency-domain measure (i.e., IVPS) in higher dimensional condition reflected an increase of visual feedback-related intermittency in manual control when increasing the trajectory dimension. The larger nonlinear fApEn3D values in the 2D and 3D conditions might have been due to the higher level neuromotor noise and increased sensory inputs. The selected parameters could provide a comprehensive method for evaluating motor performance from different perspectives. The findings in this paper shed light on the underlying sensorimotor control that is caused by the trajectory dimension in arm tracking tasks.
Image Denoising via Nonlocal Low Rank Approximation With Local Structure Preserving

The nuclear norm minimization method emerged from a patch-based low-rank model leads to an excellent image denoising performance, where the non-local self-similarity over image patches is exploited. However, natural images are normally with complex and irregular image patches, which cannot be well represented using only a low-rank model, and thus most of them suffer from the over-penalty problem especially for images with lots of local irregular structures (e.g., fine details or sharp edges), and then results in over-smoothing problem after denoising. On the other hand, in order to represent the irregular components, edges defined over pixel level are often exploited. While the total variation (TV) is a well-known prior to remove noises and preserve edges, it might yield undesired staircase artifacts. The total generalized variation (TGV), a generalization of TV, can largely alleviate such staircase artifacts. Consequently, in order to deal with the over-smoothing problem aroused by a low-rank model, we propose a re-weighted TGV regularized nuclear norm minimization model for local structure preserving image denoising. Thanks to the split Bregman method, our proposed model can be effectively solved. A re-weighted strategy is developed to adaptively update the weight parameters of TGV regularization. The encouraging experimental results on noisy images demonstrate the effectiveness of our proposed method.
Realtime Robustification of Interdependent Networks under Cascading Attacks

This paper studies the problem of robustifying an interdependent network by rewiring a small number of links in realtime during a cascading attack. Interdependent networks have been widely used to model interconnected complex systems such as a critical infrastructure network including both the power grid and the Internet. Realtime robustification of interdependent networks, therefore, has significant practical importance. This paper formulates the problem using the Markov decision process (MDP) framework. We first show the problem is NP-hard and then develop an effective and efficient greedy algorithm, named REALWIRE, to robustify the network in realtime. REALWIRE scores each link (and each node) based on the expected number of links failures resulted from the failure of the link (or the node), and rewires the links greedily according to the scores. Extensive experimental results show that REALWIRE outperforms other algorithms on multiple trobustness metrics.
Feature Fusion for Weakly Supervised Object Localization

Improving the precision of weakly supervised multiscale objects localization is of significant challenge in computer vision, especially when tackling the small objects. in this paper, we propose to integrate the feature pyramid network (FPN) with convolutional neural network (CNN) for weakly supervised object localization, where the FPN is built upon the outputs of different layers of the CNN. Then, we upsample the high-level maps by nearest-neighbor interpolation and fuse with the low-level maps in the FPN to produce multi-scale fused maps which features of both high resolution and strong semantics. Finally, we produce class activation maps by each layer of the FPN and gain multiple prediction scores by wildcat spatial pooling. To acquire more precise localization, we select the class activation map that corresponds to the highest score across all multiscale maps for object localization. In particular, we choose the maximum response regions of the class activation map for pointwise localization and choose the largest connected component above the threshold in the class activation map for bounding box localization. By applying the proposed strategy over PASCAL VOC dataset and MS COCO dataset, it is demonstrated that our strategy is highly effective in improving the precision of weakly supervised object localization as compared with some of the state-of-the-art weakly supervised methods.
Recurrent Models of Visual Co-Attention for Person Re-Identification

Person re-identification (re-id) refers to matching people across disjoint camera views. Most of person re-id methods extract discriminative features from the whole images or fixed regions and develop their metrics. However, these methods ignore that the attention regions with temporal cues in the pedestrian image pair hold discriminative information. In this paper, we propose the recurrent models of visual co-attention that aim to simulate human eye movement, focusing on the sequential concurrent attention (co-attention) regions of the same locations when comparing image pairs. Since reinforcement learning provides a flexible learning strategy for sequential decision-making, it is naturally applied to perform the temporal re-id co-attention learning task. The reward functions are designed to recursively optimize the prediction by rewarding or punishing the learning process. The recurrent models are used to extract information from a sequence of attention regions. Finally, person re-id is performed based on the whole image feature and the features from the recurrent models. Our contributions are: 1) the visual mechanism, which can dynamically locate the optimal co-attention regions to simulate the human re-id process; 2) the design of reward functions in reinforcement learning, which aims to recursively optimize the prediction process; and 3) experimental results, which demonstrate the advantages of our method compared with the state-of-the-art methods.
An Interactive Algorithm for Synchronizing from Burst Deletions

We study the efficient synchronization between two distant nodes, A and B, that are connected through a lossless bidirectional communication channel. In our setup, Node A contains file X, and node B contains file Y that is generated through burst deletions from X. The burst deletion pattern is modeled by a stationary two-state Markov chain. Inspired by the previously proposed synchronization protocol of Yazdi and Dolecek that is specifically designed to handle i.i. d. deletions, we create a new synchronization algorithm that is efficient for the case of burst deletions. Utilizing the previous overall framework from Yazdi and Dolecek, we first prove that the matching module still works well in our scenario. We then invent two new schemes for the implementation of the burst deletion recovery module. The remaining errors created in the first two modules are eliminated by the final LDPC decoder. Lastly, we provide experimental results of the proposed synchronization algorithm for both i.i. d. and realistic sources.
Sparse Scanning Electron Microscopy for Imaging and Segmentation in Connectomics

Sparse Scanning Electron Microscopy can be used in combination with Inpainting algorithms to reduce acquisition time and electron dose. Especially for three-dimensional (3D) or very large field of view imaging, acquisition time reductions are of large importance to the community. Dose reduction is of importance in imaging material that is sensitive to electron radiation. In this study we demonstrate a workflow that acquires data by performing a sparse scan at random positions on a specimen. The sparse data is reconstructed to a full grid image by a GPU accelerated dictionary based inpainting algorithm. The reconstructed data is suitable to be used for automatic semantic segmentation of neuron structures. We demonstrate the procedure on two key segmentation applications in connectomics (cell membranes and mitochondria) and show that the overall segmentation quality improves notably compared to data from a conventional raster scan acquired with the same total dose per image. Alternatively, the total dwell time per pixel can be reduced by 33% while maintaining the same level of quality of the segmentation. These results demonstrate that sparse scanning and reconstruction can increase the effective data acquisition rates without sacrificing on quality for the end user segmentation application.
Creencias de eficacia académica y engafement en estudiantes universitarios

El objetivo de este trabajo es poner a prueba un modelo de engagement en una muestra de 193 estudiantes universitarios. Se analiza el ajuste de un modelo que contempla por un lado, las variables ‘creencias de eficacia’ y percepcion de facilitadores como antecedentes y por otro lado el ‘engagement’ (vigor y dedicacion) como consecuente. Los facilitadores fueron medidos con una escala auto-construida de 32 items, las creencias de eficacia con la escala de eficacia percibida (6 items) del Maslach Burnout Inventory-Student Survey, (MBI-SS; Schaufeli, Salanova, Gonzalez-Roma y Bakker, 2002). Finalmente la variable engagement se midio con la traduccion de los 11 items del Student Academia Engagement Scale (SAIS; Schaufeli et al., 2002) de las dimensiones de vigor y dedicacion. Los analisis estructurales realizados haciendo uso del programa AMOS (Arbuckle, 1997) ofrecen evidencia del rol mediador de las ‘creencias de eficacia’ entre la percepcion de facilitadores y el engagement de los estudiantes. Por lo tanto se concluye que aumentar las creencias de eficacia tiene un efecto positivo significativo sobre la el engagement academico y que a su vez, la percepcion de facilitadores es una manera de incrementar las creencias de eficacia.
Differential Dynamic Programming for Multi-Phase Rigid Contact Dynamics

A common strategy to generate efficient locomotion movements is to split the problem into two consecutive steps: the first one generates the contact sequence together with the centroidal trajectory, while the second step computes the whole-body trajectory that follows the centroidal pattern. While the second step is generally handled by a simple program such as an inverse kinematics solver, we propose in this paper to compute the whole-body trajectory by using a local optimal control solver, namely Differential Dynamic Programming (DDP). Our method produces more efficient motions, with lower forces and smaller impacts, by exploiting the Angular Momentum (AM). With this aim, we propose an original DDP formulation exploiting the Karush- Kuhn-Tucker constraint of the rigid contact model. We experimentally show the importance of this approach by executing large steps walking on the real HRP-2 robot, and by solving the problem of attitude control under the absence of external contact forces.
A Novel Concept of Pseudo Harmonic Transmission Ratio in PM Vernier Motor for In-Wheel Traction Application

This paper proposes the novel concept titled as pseudo harmonic transmission ratio (PHTR) in permanent magnet vernier motor (PMVM) for in-wheel traction. The PHTR is equivalent to the mechanical gear in conventional transmission. Hence, the torque expressions of PMVM and PMSM plus mechanical gear are both derived and analyzed by theoretical analysis and FEA respectively. Moreover, the output capability of PMVM with same PHTR is compared with the PMSM plus mechanical reduction gear under the same in-wheel space. Meanwhile, in order to evaluate the harmonic discount degree comparing with the mechanical reduction gear, the harmonic transmission factor khtf is introduced and defined in PMVM, which will contribute to further understanding the harmonic working principle. Finally, the results between theoretical analysis and FEA method match well, which exactly validates the accuracy of results.
Classification of TrashNet Dataset Based on Deep Learning Models

Waste recycling is very important in terms of economy and climate balance of the world. For this reason, classifying recyclable garbage is an important goal for humanity and Deep Learning models can be used for this purpose. In this study, we tested well-known Deep Learning models to provide the most efficient approach. In this study, Densenet121, DenseNet169, InceptionResnetV2, MobileNet, Xception architectures were used for Trashnet dataset and Adam and Adadelta were used as the optimizer in neural network models. Based on the findings obtained in this study, Adam provided better test accuracies compared to Adadelta. Besides, the data augmentation process was applied to increase classification accuracy because of limited samples of the Trashnet dataset. As a result of the conducted experiments, the best results were found in the DenseNet121 using fine-tuning with a test accuracy rate of 95%. A similar success rate was also found in the InceptionResNetV2 model using fine-tuning with a test accuracy of 94%.
Alzheimer’s Disease and Hearing Loss among Older Adults: A Literature Review

Older adults with hearing loss are more likely to develop Alzheimer's disease (AD) or dementia compared to those with normal hearing. Hearing loss can be consecutive to presbycusis and/or central auditory dysfunction. The current study reviewed the literature concerning the relationship between hearing loss and AD among older adults. Articles included in this review were identified through a search of the databases PubMed, Medline, Scopus, Google Scholar, and Scientific Information Database (SID) using the search terms Alzheimer’s disease, dementia, presbycusis, hearing loss, and hearing impairment. The literature search was restricted to the years 1989 to 2018 and to articles published in the English language. Of 54 primary articles, 38 potentially eligible articles were reviewed. Although cognitive decline has been shown to be slowed by the use of hearing aids in older adults, a few studies have investigated the effects of other factors such as presbycusis-related tinnitus and length of use of hearing aids by older adults. High-quality clinical trials are needed to determine whether AD can be delayed or prevented in older adults. Moreover, it seems to be very important to evaluate hearing status (peripheral and central) in older adults to diagnose hearing impairment early and initiate the use of hearing aids.
A Multi-Criteria Experimental Ranking of Distributed SPARQL Evaluators

SPARQL is the standard language for querying RDF data. There exists a variety of SPARQL query evaluation systems implementing different architectures for the distribution of data and computations. Differences in architectures coupled with specific optimizations, for e.g. preprocessing and indexing, make these systems incomparable from a purely theoretical perspective. This results in many implementations solving the SPARQL query evaluation problem while exhibiting very different behaviors, not all of them being adapted in any context. We provide a new perspective on distributed SPARQL evaluators, based on multi-criteria experimental rankings. Our suggested set of 5 features (namely velocity, immediacy, dynamicity, parsimony, and resiliency) provides a more comprehensive description of the behaviors of distributed evaluators when compared to traditional runtime performance metrics. We show how these features help in more accurately evaluating to which extent a given system is appropriate for a given use case. For this purpose, we systematically benchmarked a panel of 10 state-of-the-art implementations. We ranked them using a reading grid that helps in pinpointing the advantages and limitations of current technologies for the distributed evaluation of SPARQL queries.