Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation De La Satisfaction Des Patientes Hospitalisées En Suites De Couches À La Maternité De L’hôpital De Bè (Togo)

Introduction: L’evaluation de la satisfaction montre l’adequation entre les attentes des patients et la qualite des soins recus. Objectif: evaluer la satisfaction des accouchees hospitalisees en suites de couches a la maternite de l’hopital de Be. Patientes et methode: Il s’est agi d’une etude transversale a visee descriptive et analytique menee du 1er juillet au 31 aout 2018 soit une duree de 2 mois. Elle a eu pour cadre, le secteur des suites de couches de la maternite de l’hopital de Be. La collecte des donnees par interview directe a ete faite a l’aide d’une fiche d’enquete anonyme sur le modele Saphora-MCO questionnaires Project COMPAQH et adapte a nos realites. Le traitement et l’analyse des donnees ont ete realisees grâce aux logiciels Epi data et Epi info. Resultats: Au total 300 accouchees ont accepte participer a l’etude. L’âge moyen etait de 29 ± 6,2 ans. La satisfaction globale etait de 94%. Les principaux elements de satisfaction etaient : l’accueil (94,7%), le respect de l’intimite (96,7%), la prise en charge des soins (94%), le delai d’attente (86,7%). L’insatisfaction etait due a l’insuffisance de communication des soignants (65,7%) et de proprete des chambres et des sanitaires (57%). L’âge, le niveau d’instruction, la parite, le mode d’accouchement avaient influence significativement l’insatisfaction des patientes. Conclusion: Cette etude a permis de relever les elements d’insatisfaction des clientes. Il est opportun d’associer les clients dans le processus de la qualite des soins a l’hopital de Be. Introduction: Satisfaction assessment shows the match between patient expectations and the quality of care received. Objective: to assess the satisfaction of women hospitalized in the maternity ward of the obstetrics and gynecology department of the Be hospital in order to contribute to the improvement of the quality of care in the maternity ward. Patients and method: This was a cross-sectional study with descriptive and analytical aims conducted from July 1 to August 31, 2018, i.e., a duration of 2 months. It took place in the puerperium section at the maternity of the Be hospital. Data collection by direct interview was done using an anonymous survey form on the Saphora-MCO questionnaires Project COMPAQH model and adapted to our realities. Data processing and analysis were carried out using Epi data and Epi info software. Results: A total of 300 women agreed to participate in the study. The average age was 29 ± 6.2 years. Overall satisfaction was 94%. The main elements of satisfaction were: reception (94.7%), respect for privacy (96.7%), management of care (94%), waiting time (86.7%). Dissatisfaction was due to insufficient communication from caregivers (65.7%) and cleanliness of rooms and toilets (57%). Age, level of education, parity and mode of delivery had a significant influence on the respondent’s dissatisfaction. Conclusion: This study identified factors that led to patient’s dissatisfaction. It is appropriate to involve patients in the quality of care process at the Be hospital.
Group Scissor: Scaling Neuromorphic Computing Design to Big Neural Networks

Synapse crossbar is an elementary structure in Neuromorphic Computing Systems (NCS). However, the limited size of crossbars and heavy routing congestion impedes the NCS implementations of big neural networks. In this paper, we propose a two-step framework (namely, \textit{group scissor}) to scale NCS designs to big neural networks. The first step is \textit{rank clipping}, which integrates low-rank approximation into the training to reduce total crossbar area. The second step is \textit{group connection deletion}, which structurally prunes connections to reduce routing congestion between crossbars. Tested on convolutional neural networks of \textit{LeNet} on MNIST database and \textit{ConvNet} on CIFAR-10 database, our experiments show significant reduction of crossbar area and routing area in NCS designs. Without accuracy loss, rank clipping reduces total crossbar area to 13.62\% and 51.81\% in the NCS designs of \textit{LeNet} and \textit{ConvNet}, respectively. Following rank clipping, group connection deletion further reduces the routing area of \textit{LeNet} and \textit{ConvNet} to 8.1\% and 52.06\%, respectively.
Neuropsychiatric Symptoms in Mild Cognitive Impairment

Objective: Neuropsychiatric symptoms (NPS) may be the first manifestation of an underlying neurocognitive disorder. We undertook a review to provide an update on the epidemiology and etiological mechanisms of NPS that occur in mild cognitive impairment (MCI) and just before the onset of MCI. We discuss common clinical presentations and the implications for diagnosis and care. Method: The authors conducted a selective review of the literature regarding the emergence of NPS in late life, before and after the onset of MCI. We discuss recent publications that explore the epidemiology and etiological mechanisms of NPS in the earliest clinical stages of these disorders. Results: NPS have been reported in 35% to 85% of adults with MCI and also occur in advance of cognitive decline. The occurrence of NPS for the first time in later life should increase suspicion for an underlying neurocognitive disorder. The presenting symptom may provide a clue regarding the etiology of the underlying disorder, and the co-occurrence of NPS may herald a more accelerated cognitive decline. Conclusions: NPS are prevalent in the early clinical stages of neurocognitive disorders and can serve as both useful diagnostic and prognostic indicators. Recognition of NPS as early manifestations of neurocognitive disorders will become increasingly important as we move towards preventative strategies and disease-modifying treatments that may be most effective when deployed in the earliest stages of disease.
Original article The relationship between left ventricular late-systolic rotation and twist, and classic parameters of ventricular function and geometry

Background: Using speckle tracking echocardiography we investigated left ventricular (LV) twist and rotation (ROT) at the papillary muscle (PM) level and their correlation with standard echocardiographic and demographic parameters. Aim: To assess whether the fulcrum of LV short axis ROT is shifted in myocardial disease. Methods: : The study group consisted of 33 patients (54±13 years old, 18 women). Left ventricular systolic function was normal in 6, and various degrees of wall motion abnormalities were present in the others [LV ejection fraction (LVEF) 49±15, wall motion score index (WMSI) 1.43±0.38]. Short axis images at basal, PM and apical level were analysed offline. The direction of ROT was determined from the apical aspect and expressed in degrees: clockwise (CW) in negative values, counter CW in positive. Twist is the arithmetic difference between apical and basal ROT. Results: : Left ventricular twist was in the range of 0.4-27.5 (14±7) degrees and correlated with LV systolic diameter (LVS), r=–0.46, 95% CI from –0.69 to –0.13, p 11.3 deg and PWS >13 mm. We found counterCW PM ROT in all patients with both conditions, 59% of patients with one, 22% with none. Conclusions: : Left ventricular twist is related to mid segments function and IVSS, while PM ROT value and its direction (associated with ‘zero ROT level’) is related to PWS and apical ROT, rather than to LVEF or WMSI. Thus twist and rotation may reflect novel aspects of LV function.
Bradykinin and angiotensin-converting enzyme inhibition in cardioprotection.

OBJECTIVES To show that angiotensin-converting enzyme (ACE) inhibition potentiates subthreshold ischemic preconditioning (IPC) via the elevation of bradykinin activity, leading to a fully delayed cardioprotective response. METHODS On day 1 of the experiment, pigs were subjected to sham (group 1, controls) or IPC protocols. In groups 2 and 3, 4x5 min and 2x2 min of IPC, respectively, were elicited by occluding the left anterior descending coronary artery with percutaneous transluminal coronary angioplasty inflatable balloon catheter. Group 4 was subjected to the ACE inhibitor perindoprilate only. In group 5, the pigs were pretreated with perindoprilate (0.06 mg/kg) and then subjected to 2x2 min IPC. In group 6, intracoronary HOE 140 (a selective bradykinin B(2) receptor antagonist) was added before the perindoprilateaugmented subthreshold (2x2 min) PC stimulus. On the second day, all animals underwent 40 min left anterior descending coronary artery ligation and 3 h reperfusion, followed by infarct size analysis using triphenyl tetrazolium chloride staining. RESULTS THE RATES OF INFARCT SIZE AND RISK ZONE WERE THE FOLLOWING IN THE EXPERIMENTAL GROUPS: group 1, 42.8%; group 2,19.5% (P<0.05); group 3, ischemia/reperfusion (I/R) 33.4%; group 4, I/R 18.4% (P<0.05); group 5, I/R 31.2%; and group 6, I/R 36.3%. A significant increase of nuclear factor kappa B activation in groups 2 and 4 was seen. CONCLUSIONS Results confirm that ACE inhibitors do not give total pharmacological IPC, but they enhance the induction effect of small ischemic insults, which raises the ischemic tolerance of myocardium. It was determined that enhanced bradykinin activity leads to downstream nuclear factor kappa B activation in this model.
Tree-structured method for LUT inverse halftoning and for image halftoning

Recently, the authors proposed a Look Up Table (LUT) based method for inverse halftoning of images. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. Many of the entries in the LUT are unused because the corresponding binary patterns hardly occur in commonly encountered halftones. These are called nonexistent patterns. In this paper, we propose a tree structure which will reduce the storage requirements of an LUT by avoiding nonexistent patterns. We will demonstrate the performance on error diffused images and ordered dither images. Then, we introduce LUT based halftoning and tree-structured LUT (TLUT) halftoning. Even though TLUT method is more complex than LUT halftoning, it produces better halftones and requires much less storage than LUT halftoning.We will demonstrate how error diffusion characteristics can be achieved with this method. Afterwards, our algorithm will bet rained on halftones obtained by Direct Binary Search (DBS). The complexity of TLUT halftoning is higher than error diffusion algorithm but much lower than DBS algorithm. Also, the halftone quality of TLUT halftoning increases if the size of TLUT gets bigger. Thus, halftone image quality between error diffusion and DBS will be achieved depending on the size of tree-structure in TLUT algorithm.
V " ( s $ ) I a $ ' ( ~ π , ( s $ ) Start token : a 5 End token : a 8 RNN Cell

Generating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users about what it is seeing. Such image captioning methods are typically trained by maximising the likelihood of ground-truth annotated caption given the image. While simple and easy to implement, this approach does not directly maximise the language quality metrics we care about such as CIDEr. In this paper we investigate training image captioning methods based on actor-critic reinforcement learning in order to directly optimise non-differentiable quality metrics of interest. By formulating a per-token advantage and value computation strategy in this novel reinforcement learning based captioning model, we show that it is possible to achieve the state of the art performance on the widely used MSCOCO benchmark.
Frequency-dependent mechanical damping in alloys

We perform oscillatory shear simulations to determine the loss modulus for three solids with identical interaction yet distinct structures: ordered, random and glassy alloys. Random and glassy alloys show more pronounced high-frequency loss in the THz regime than the ordered alloy. Ordered and random alloys exhibit a power-law decay in damping strength as frequency decreases over nearly five decades. Glassy alloy, with a limited frequency range of power-law decay, retains significant damping at low frequencies extending down to ~100 MHz due to slow irreversible deformation of local clusters.
Geometry-Aware Learning of Maps for Camera Localization Supplementary Material

In this supplementary document, we provide more implementation details of our method, names of the sequences used in the experiments on the Oxford RobotCar dataset, more analysis and visualization of the experimental results presented in the main paper, and the detailed derivation of pose-graph optimization (PGO) in MapNet+PGO. Please also refer to the supplementary video for more visualizations of the camera localization results.
Delay integral sliding mode control design of T-S fuzzy stochastic descriptor systems

This paper investigates the delay effects on the integral sliding mode control problems of T-S fuzzy stochastic descriptor systems. A new sliding surface involving an artificial delay is defined. The resulting closed-loop system is a T-S fuzzy stochastic descriptor time-delay system whose dynamics can be regulated by the introduced delay. The restrictive assumption previously employed in the sliding mode control for stochastic systems is also removed. It shows that the integral sliding mode control problems can be handled for a much wider class of T-S fuzzy stochastic descriptor systems when a proper delay term is included in the sliding surface. Finally, a numerical example is presented to demonstrate the effectiveness of the results proposed.
A comparative study of matrix factorization and random walk with restart in recommender systems

Between matrix factorization or Random Walk with Restart (RWR), which method works better for recommender systems? Which method handles explicit or implicit feedback data better? Does additional information help recommendation? Recommender systems play an important role in many ecommerce services such as Amazon and Netflix to recommend new items to a user. Among various recommendation strategies, collaborative filtering has shown good performance by using rating patterns of users. Matrix factorization and random walk with restart are the most representative collaborative filtering methods. However, it is still unclear which method provides better recommendation performance despite their extensive utility. In this paper, we provide a comparative study of matrix factorization and RWR in recommender systems. We exactly formulate each correspondence of the two methods according to various tasks in recommendation. Especially, we newly devise an RWR method using global bias term which corresponds to a matrix factorization method using biases. We describe details of the two methods in various aspects of recommendation quality such as how those methods handle cold-start problem which typically happens in collaborative filtering. We extensively perform experiments over real-world datasets to evaluate the performance of each method in terms of various measures. We observe that matrix factorization performs better with explicit feedback ratings while RWR is better with implicit ones. We also observe that exploiting global popularities of items is advantageous in the performance and that side information produces positive synergy with explicit feedback but gives negative effects with implicit one.
Can Big Media Data Revolutionarize Gun Violence Prevention?

The scientific method drives improvements in public health, but a strategy of obstructionism has impeded scientists from gathering even a minimal amount of information to address America's gun violence epidemic. We argue that in spite of a lack of federal investment, large amounts of publicly available data offer scientists an opportunity to measure a range of firearm-related behaviors. Given the diversity of available data - including news coverage, social media, web forums, online advertisements, and Internet searches (to name a few) - there are ample opportunities for scientists to study everything from trends in particular types of gun violence to gun-related behaviors (such as purchases and safety practices) to public understanding of and sentiment towards various gun violence reduction measures. Science has been sidelined in the gun violence debate for too long. Scientists must tap the big media data stream and help resolve this crisis.
ISSN 2348 – 0319 International Journal of Innovative and Applied Research (2013), Volume 1, Issue (1): 48- 55

1. Benue State University, Makurdi, Benue state, Nigeria. 2. Department of Psychology, Nigeria Police Academy Wudil, Kano state, Nigeria. Abstract: The impact of stress on the general employee wellbeing receives little attention from employers and policy makers in Nigeria. This study examined the extent to which job demands on practicing journalists in Benue State, Nigeria, affect their stress levels and job satisfaction. Anchored on the Job Demands–Control–Support Model, the study surveyed a total of 153 journalists comprising 129 males and 24 females from 31 print and electronic media organisations in Benue state and relied on the Depression, Anxiety Stress Scale and the Job Satisfaction Survey. Using Correlation analysis and T-test (SPSS 16) the study found significant negative relationship between stress and job satisfaction among journalists. A T-test result indicates differences between male and female journalists in their levels of job satisfaction but finds no differences in their level of stress. The research recommends among others that management should organise stress management seminars for journalists in their organizations.
Numerical simulation of transitional flow on a wind turbine airfoil with RANS-based transition model

ABSTRACT This paper presents a numerical investigation of transitional flow on the wind turbine airfoil DU91-W2-250 with chord-based Reynolds number Rec = 1.0 × 106. The Reynolds-averaged Navier–Stokes based transition model using laminar kinetic energy concept, namely the k − kL − ω model, is employed to resolve the boundary layer transition. Some ambiguities for this model are discussed and it is further implemented into OpenFOAM-2.1. 1. The k − kL − ω model is first validated through the chosen wind turbine airfoil at the angle of attack (AoA) of 6.24° against wind tunnel measurement, where lift and drag coefficients, surface pressure distribution and transition location are compared. In order to reveal the transitional flow on the airfoil, the mean boundary layer profiles in three zones, namely the laminar, transitional and fully turbulent regimes, are investigated. Observation of flow at the transition location identifies the laminar separation bubble. The AoA effect on boundary layer transition over wind turbine airfoil is also studied. Increasing the AoA from −3° to 10°, the laminar separation bubble moves upstream and reduces in size, which is in close agreement with wind tunnel measurement.
Computational Statistics and Data Analysis Robust Nonnegative Garrote Variable Selection in Linear Regression

Robust selection of variables in a linear regression model is investigated. Many variable selection methods are available, but very few methods are designed to avoid sensitivity to vertical outliers as well as to leverage points. The nonnegative garrote method is a powerful variable selection method, developed originally for linear regression but recently successfully extended to more complex regression models. The method has good performances and its theoretical properties have been established. The aim is to robustify the nonnega-tive garrote method for linear regression as to make it robust to vertical outliers and leverage points. Several approaches are discussed, and recommendations towards a final good performing robust nonnegative garrote method are given. The proposed method is evaluated via a simulation study that also includes a comparison with existing methods. The method performs very well, and often outperforms existing methods. A real data application illustrates the use of the method in practice.
Convolutional Sequence to Sequence Learning

A. Weight Initialization We derive a weight initialization scheme tailored to the GLU activation function similar to Glorot & Bengio (2010); He et al. (2015b) by focusing on the variance of activations within the network for both forward and backward passes. We also detail how we modify the weight initialization for dropout. A.1. Forward Pass Assuming that the inputs x l of a convolutional layer l and its weights W l are independent and identically distributed (i.i.d.), the variance of its output, computed as y l =W l x l +b l , is
On the Use of MPCCs in Combined Topological and Parametric Design of Genetic Regulatory Circuits

Synthetic biology involves the assembly of biological components in a systematic and rational way to engineer functional devices, systems, and organisms [1]. A number of synthetic devices have been created over the last thirteen years, including a toggle switch [2] and a repressilator [3]. Established strategies from other engineering domains, such as feedback control, have been extended to the design of genetic circuits that govern biological functions within cells [4, 5, 6]. Simulation of the nonlinear dynamic behavior of genetic circuits has been used to explore design options. Existing design methods, however, are limited, particularly in their ability to manage large-scale genetic circuits. The dimension of successfully implemented genetic circuits, measured by the number of regulatory regions, has plateaued at six for several years [7]. While these simple systems can perform simple biological functions, they are not yet sophisticated enough to provide practical benefits. Realizing the vision of using synthetic biology to tackle grand challenges—such as biomedical therapies—will require new design principles and methodologies. Here we introduce a new genetic circuit design technique based on direct transcription (DT), a method for dynamic system optimization. DT transforms infinite-dimensional dynamic optimization problems into finite-dimensional nonlinear programs (NLPs), and is used here to simultaneously simulate and optimize genetic circuits with respect to both time-independent parameters and system topology. This design problem is similar to more conventional engineering design problems that involve both topological and continuous design variables where nonlinear dynamic response is important. When genetic circuits are modeled using nonlinear ordinary differential equations known as the Michaelis-Menten equations, topological design decisions are modeled naturally as
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Supplementary Material

To describe the log-likelihood computation in our model, let us consider a two scale pyramid for the moment. Given a (vectorized) j × j image I , denote by l = d(I) the coarsened image, and h = I − u(d(I)) to be the high pass. In this section, to simplify the computations, we use a slightly different u operator than the one used to generate the images displayed in Figure 3 of the paper. Namely, here we take d(I) to be the mean over each disjoint block of 2× 2 pixels, and take u to be the operator that removes the mean from each 2× 2 block. Since u has rank 3d/4, in this section, we write h in an orthonormal basis of the range of u, then the (linear) mapping from I to (l, h) is unitary. We now build a probability density p on Rd2 by p(I) = q0(l, h)q1(l) = q0(d(I), h(I))q1(d(I)); in a moment we will carefully define the functions qi. For now, suppose that qi ≥ 0, ∫ q1(l) dl = 1, and for each fixed l, ∫ q0(l, h) dh = 1. Then we can check that p has unit integral: ∫