Miracle-dz/UniFAR-FTUs huggingface dataset:
The Prevalence of Vitamin A Deficiency and Associated Factors in Pregnant Women Receiving Prenatal Care at a Reference Maternity Hospital in Northeastern Brazil

Vitamin A is essential for mother and child; however, vitamin A deficiency (VAD) remains a public health issue in various countries, affecting around 19 million pregnant women. In Brazil, the scarcity and inconsistency of data have prevented the prevalence and epidemiological status of VAD from being established. This study aimed to analyze vitamin A nutritional status in women receiving prenatal care at a reference center in northeastern Brazil. A cross-sectional study was conducted with a sample of 676 women. Serum retinol was measured by high-performance liquid chromatography. Subclinical infection was detected by measuring C-reactive protein (CRP). The World Health Organization criteria were used in the prevalence analysis, VAD classification level, and CRP effect evaluation. The prevalence of VAD (serum retinol <0.70 μmol/L) was 6.2% (95% confidence interval 4.5–8.3). In the univariate analysis, the variables significantly associated with VAD (p < 0.05) were having <12 years of schooling, being in the third trimester of pregnancy, and anemia. In the final multivariate model, the variables that remained significantly associated (p < 0.05) were being in the third trimester of pregnancy and anemia. VAD constituted a mild public health problem in this sample of pregnant women and was associated with the third trimester of pregnancy and maternal anemia.
Emotion Recognition from Variable-Length Speech Segments Using Deep Learning on Spectrograms

In this work, an approach of emotion recognition is proposed for variable-length speech segments by applying deep neutral network to spectrograms directly. The spectrogram carries comprehensive para-lingual information that are useful for emotion recognition. We tried to extract such information from spectrograms and accomplish the emotion recognition task by combining Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). To handle the variable-length speech segments, we proposed a specially designed neural network structure that accepts variable-length speech sentences directly as input. Compared to the traditional methods that split the sentence into smaller ﬁxed-length segments, our method can solve the problem of accuracy degradation introduced by the speech segmentation process. We evaluated the emotion recognition model on the IEMOCAP dataset over four emotions. Experimental results demonstrate that the proposed method outperforms the ﬁxed-length neural network on both weighted accuracy (WA) and unweighted accuracy (UA).
Alarming and Threatening Signals from Health Centers About Multi Drug Resistance Staphylococcus haemolyticus.

OBJECTIVES Nowadays, due to the biofilm formation among coagulase-negative staphylococci (CoNS), acquisition of antibiotic resistance and virulence genes could be noted. These organisms resemble other staphylococcus that harbor mecA gene, which encode methicillin resistance but the diversity in CoNS is higher than other species. Based on increasing antibiotic resistance in Staphylococcus haemolyticus, analyzing of phenotypic and genotypic biofilm formation, antibiotic resistance and genes involved in this process, SCCmec and ACME typing were the aims of the current study. METHODS 256 clinical CoNS isolates were collected that 49 isolates were identified as S. haemolyticus. For evaluation the antibiotic resistance patterns, disk agar diffusion method was applied, and also biofilm assay carried out among methicillin resistant S. haemolyticus and SCCmec and ACME typing. RESULTS The results of antibiotic susceptibility indicated that the highest resistance was found for cotrimoxazole and erythromycin (86.6%) and the frequency of blaA (96.5%) and msrA (79.3%) genes was much higher than others. Among MRSH isolates 58.6 % showed a weak biofilm phenotype and 41.4% demonstrated a moderate biofilm density. Also, among the biofilm correlated genes, IS 256 (79.31%) was the most frequent. The SCCmec typing of MRSH isolates indicated that the type V was dominant. CONCLUSION Our findings indicated that the correlation between high rates of existence Is 256 gene and high prevalence of weak biofilm phenotype was among MRSH isolates. The current study revealed that multiple antibiotic resistance existed in S. haemolyticus isolates that is a warning for public health.
Bottom-Up Attention Guidance for Recurrent Image Recognition

This paper presents a recurrent neural network architecture, guided by the bottom-up attention, for the recognition task. The proposed architecture processes an input image as a sequence of selectively chosen patches. The patches are chosen from the salient regions of the input image. Using human driven saliency maps from gaze, the benefit of such a selection process is first shown. Next, the performance of computational models of bottom-up attention are assessed as alternative to human attention.
The $\epsilon$-error Capacity of Symmetric PIR with Byzantine Adversaries

The capacity of symmetric private information retrieval with $K$ messages, $N$ servers (out of which any $T$ may collude), and an omniscient Byzantine adversary (who can corrupt any $B$ answers) is shown to be $1 - \frac{T+2B}{N}$ [1], under the requirement of zero probability of error. In this work, we show that by weakening the adversary slightly (either providing secret low rate channels between the servers and the user, or limiting the observation of the adversary), and allowing vanishing probability of error, the capacity increases to $1 - \frac{T+B}{N}$.
Aktualisierte S3-Leitlinie Colitis ulcerosa der Deutschen Gesellschaft für Gastroenterologie, Verdauungs- und Stoffwechselkrankheiten (DGVS)

Zusammenfassung Die neue S3-Leitlinie Colitis stellt aktuelle und evidenzbasierte Empfehlungen zur Behandlung der Colitis ulcerosa zur Verfügung. Sie ersetzt damit die Vorläuferversion von 2011. Neben den neuesten Erkenntnissen zu Diagnostik und Therapie werden insbesondere infektiologische Probleme, chirurgische und Ernährungsmaßnahmen aufgegriffen. Unter der Federführung der DGVS wurde die Leitlinie gemeinsam mit 10 weiteren Fachgesellschaften und Patientenvertretern erarbeitet mit dem Ziel, eine optimale interdisziplinäre Versorgung der Patienten zu gewährleisten.
Information, Motivation, and Self-Efficacy Among Men Who Have Sex With Men and Transgender Women in the State of Maharashtra, India

Background. India’s National AIDS Control Programme calls for, among other things, targeted behavioral interventions that address determinants contributing to new infections among men who have sex with men (MSM) and hijras (transgender women). Aim. To determine if the information–motivation–behavioral skills model was transferable to an Indian context. Methods. We recruited 442 MSM and 7 hijras into an online cross-sectional study that asked questions about condom use knowledge, motivation, self-efficacy, and sexual behavior. Structural equation modeling was used to test the overall information–motivation–behavioral skills structure. Results. Condom use and HIV risk knowledge did not significantly predict condom use self-efficacy or having condomless anal sex with more than one male partner in the past 3 months, and knowledge was not significantly correlated with attitudes toward barebacking. Despite this, the final structural model explained 14.4% of the variance in condom use self-efficacy and 20.0% of the variance in risky sexual behavior. Overall fit of the model was good: χ2(201, N = 447) = 445.002, p < . 0001, root mean square error of approximation = . 052, 90% CI [. 046, . 059], comparative fit index = . 989, Tucker–Lewis Index = . 987. Conclusion. The public health response to India’s HIV epidemic is currently different than in the West due to differences in medication access and public health infrastructure. As Indian MSM and hijras learn more about the effectiveness of viral suppression and hopefully have greater access to preexposure prophylaxis, researchers should monitor for changing attitudes toward condoms and be prepared to collaborate with the public health infrastructure in order to develop interventions that reinforce positive attitudes toward condom use.
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting

Machine Learning on graph-structured data is an important and omnipresent task for a vast variety of applications including anomaly detection and dynamic network analysis. In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary graph. In contrast to all learning formulations in the area of discriminative pattern recognition, we propose a scalable generative optimization/algorithm theoretically proved to capture distributions at the nodes of a graph. Our model is able to generate samples from the probability densities learned at each node. This probabilistic data generation model, i.e. convolutional graph auto-encoder (CGAE), is devised based on the localized first-order approximation of spectral graph convolutions, deep learning, and the variational Bayesian inference. We apply our CGAE to a new problem, the spatio-temporal probabilistic solar irradiance prediction. Multiple solar radiation measurement sites in a wide area in northern states of the US are modeled as an undirected graph. Using our proposed model, the distribution of future irradiance given historical radiation observations is estimated for every site/node. Numerical results on the National Solar Radiation Database show state-of-the-art performance for probabilistic radiation prediction on geographically distributed irradiance data in terms of reliability, sharpness, and continuous ranked probability score.
Detection of Glottal Closure Instants from Speech Signals: A Convolutional Neural Network Based Method

Most conventional methods to detect glottal closure instants (GCI) are based on signal processing technologies and different GCI candidate selection methods. This paper proposes a classification method to detect glottal closure instants from speech waveforms using convolutional neural network (CNN). The procedure is divided into two successive steps. Firstly, a low-pass filtered signal is computed, whose negative peaks are taken as candidates for GCI placement. Secondly, a CNN-based classification model determines for each peak whether it corresponds to a GCI or not. The method is compared with three existing GCI detection algorithms on two publicly available databases. For the proposed method, the detection accuracy in terms of F1-score is 98.23%. Additional experiment indicates that the model can perform better after trained with the speech data from the speakers who are the same as those in the test set.
Trained Perceptual Transform for Quality Assessment of High Dynamic Range Images and Video

In this paper, we propose a trained perceptually transform for quality assessment of high dynamic range (HDR) images and video. The transform is used to convert absolute luminance values found in HDR images into perceptually uniform units, which can be used with any standard-dynamic-range metric. The new transform is derived by fitting the parameters of a previously proposed perceptual encoding function to 4 different HDR subjective quality assessment datasets using Bayesian optimization. The new transform combined with a simple peak signal-to-noise ratio measure achieves better prediction performance in cross-dataset validation than existing transforms. We provide Matlab code for our metric11https://github.com/ynyCL/T-PT-metric.
Matrix Exponential Learning Schemes With Low Informational Exchange

We consider a distributed resource allocation problem in networks where each transmitter–receiver pair aims at maximizing its local utility function by adjusting its action matrix, which belongs to a given feasible set. This problem has been addressed recently by applying a matrix exponential learning (MXL) algorithm, which has a very appealing convergence rate. In this learning algorithm, however, each transmitter must know an estimate of the gradient matrix of the local utility. The knowledge of the gradient matrix at the transmitters incurs a high signaling overhead, especially that the matrix size increases with the dimension of the action matrix. In this paper, we therefore, investigate two strategies in order to decrease the informational exchange per iteration of the algorithm. In the first strategy, each receiver sends at each iteration part of the elements of the gradient matrix with respect to a certain probability. In the second strategy, each receiver feeds back “sporadically” the whole gradient matrix. We focus on the analysis of the convergence of the MXL algorithm to optimum under these two strategies. We prove that the algorithm can still converge to optimum almost surely. Upper bounds of the average convergence rate are also derived in both situations with general step-size setting, from which we can clearly see the impact of the incompleteness of the feedback information. The proposed algorithms are applied to solve the energy efficiency maximization problem in a multicarrier multiuser multiple-input and multiple-output (MIMO) network. Simulation results further corroborate our claim.
Investigating Objective Intelligibility in Real-Time EMG-to-Speech Conversion

This paper presents an analysis of the influence of various system parameters on the output quality of our neural network based real-time EMG-to-Speech conversion system. This EMG-to-Speech system allows for the direct conversion of facial surface electromyographic signals into audible speech in real time, allowing for a closed-loop setup where users get direct audio feedback. Such a setup opens new avenues for research and applications through co-adaptation approaches. In this paper, we evaluate the influence of several parameters on the output quality, such as time context, EMG-Audio delay, network-, training data- and Mel spectrogram size. The resulting output quality is evaluated based on the objective output quality measure STOI.
Towards Quantifying Uncertainty in Data Analysis & Exploration

In the age big data, uncertainty in data constantly grows with its volume, variety, and velocity. Data is noisy, biased and error-prone; blindly applying even the most advanced data analysis techniques can easily mislead users to incorrect analytical conclusions. Also, compounding the problem of uncertain data is the uncertainty in data analysis and exploration. A typical end-to-end data analysis pipeline involves cleaning and processing the data, summarizing different characteristics and running more complex machine learning algorithms to extract interesting insights. The problem is that each step in the pipeline is error-prone and imperfect. From the input to the output, the uncertainty propagates and compounds. This paper discusses the challenges in dealing with various forms of uncertainty in data analysis and provides an overview of our work on Quantifying the Uncertainty in Data Exploration (QUDE), a toolset for safe and reliable data analysis.
On the Fusion of RGB and Depth Information for Hand Pose Estimation

Recent advances in deep learning have spurred 3D hand pose estimation, as convolutional network (ConvNet) based methods outperformed random forests. However, in the state of the art, ConvNet based methods employ only depth images of the hand without leveraging color and texture information from the RGB domain. In this paper, we investigate whether ConvNets can learn more rich and discriminative em-beddings, by combining RGB and depth information. To answer this question, we propose the fusion of RGB and depth information in a double-stream architecture. More specifically, RGB and depth images are fed into two separate networks by extracting features, which are subsequently fused at an intermediate layer of the ConvNet, implementing input-level fusion, feature-level fusion and score-level fusion. The double-stream scheme is coupled with a deep ConvNet, contrary to the shallow networks that are mostly proposed in the literature. Experimental results show that while the depth of the network is crucial for hand pose estimation, the double-stream nets perform very similarly with the net trained only with depth images. This may suggest that training double-stream architectures purely with supervision may be insufficient for hand pose estimation with RGB-D fusion.
Improving Attention Based Sequence-to-Sequence Models for End-to-End English Conversational Speech Recognition

In this work, we propose two improvements to attention based sequence-to-sequence models for end-to-end speech recognition systems. For the ﬁrst improvement, we propose to use an input-feeding architecture which feeds not only the previous context vector but also the previous decoder hidden state information as inputs to the decoder. The second improvement is based on a better hypothesis generation scheme for sequential minimum Bayes risk (MBR) training of sequence-to-sequence models where we introduce softmax smoothing into N-best generation during MBR training. We conduct the experiments on both Switchboard-300hrs and Switchboard+Fisher-2000hrs datasets and observe signiﬁcant gains from both proposed improvements. Together with other training strategies such as dropout and scheduled sampling, our best model achieved WERs of 8.3%/15.5% on the Switchboard/CallHome subsets of Eval2000 without any external language models which is highly competitive among state-of-the-art English conversational speech recognition systems.
Reducing Uncertainty of Schema Matching via Crowdsourcing with Accuracy Rates

Schema matching is a central challenge for data integration systems. Inspired by the popularity and the success of crowdsourcing platforms, we explore the use of crowdsourcing to reduce the uncertainty of schema matching. Since crowdsourcing platforms are most effective for simple questions, we assume that each Correspondence Correctness Question (CCQ) asks the crowd to decide whether a given correspondence should exist in the correct matching. Furthermore, members of a crowd may sometimes return incorrect answers with different probabilities. Accuracy rates of individual crowd workers can be attributes of CCQs as well as evaluations of individual workers. We prove that uncertainty reduction equals to entropy of answers minus entropy of crowds and show how to obtain lower and upper bounds for it. We propose frameworks and efficient algorithms to dynamically manage the CCQs to maximize the uncertainty reduction within a limited budget of questions. We develop two novel approaches, namely “Single CCQ” and “Multiple CCQ”, which adaptively select, publish, and manage questions. We verify the value of our solutions with simulation and real implementation.
Speaker Adaptation and Adaptive Training for Jointly Optimised Tandem Systems

© 2018 International Speech Communication Association. All rights reserved. Speaker independent (SI) Tandem systems trained by joint optimisation of bottleneck (BN) deep neural networks (DNNs) and Gaussian mixture models (GMMs) have been found to produce similar word error rates (WERs) to Hybrid DNN systems. A key advantage of using GMMs is that existing speaker adaptation methods, such as maximum likelihood linear regression (MLLR), can be used which to account for diverse speaker variations and improve system robustness. This paper investigates speaker adaptation and adaptive training (SAT) schemes for jointly optimised Tandem systems. Adaptation techniques investigated include constrained MLLR (CMLLR) transforms based on BN features for SAT as well as MLLR and parameterised sigmoid functions for unsupervised test-time adaptation. Experiments using English multi-genre broadcast (MGB3) data show that CMLLR SAT yields a 4% relative WER reduction over jointly trained Tandem and Hybrid SI systems, and further reductions in WER are obtained by system combination.
Deep Learning for Acoustic Echo Cancellation in Noisy and Double-Talk Scenarios

Traditional acoustic echo cancellation (AEC) works by identifying an acoustic impulse response using adaptive algorithms. We formulate AEC as a supervised speech separation problem, which separates the loudspeaker signal and the near-end signal so that only the latter is transmitted to the far end. A recurrent neural network with bidirectional long short-term memory (BLSTM) is trained to estimate the ideal ratio mask from features extracted from the mixtures of near-end and far-end signals. A BLSTM estimated mask is then applied to separate and suppress the far-end signal, hence removing the echo. Experimental results show the effectiveness of the proposed method for echo removal in double-talk, background noise, and nonlinear distortion scenarios. In addition, the proposed method can be generalized to untrained speakers.
Task Specific Sentence Embeddings for ASR Error Detection

This paper presents a study on the modeling of automatic speech recognition errors at the sentence level. We aim in this study to compensate certain phenomena highlighted by the analysis of outputs generated by an ASR error detection system we previously proposed. We investigated three different approaches, that are based respectively on the use of sentence embeddings dedicated to ASR error detection task, on a probabilistic contextual model, and on a bidirectional long short-term memory (BLSTM) architecture. An approach to build task-speciﬁc sentence embeddings is proposed and compared to the Doc2vec approach. Experiments are performed on transcriptions generated by the LIUM ASR system applied to the French ETAPE corpus. They show that the proposed sentence embeddings dedicated to ASR error detection achieve better results than generic sentence embeddings, and that the integration of task-speciﬁc embeddings in our system achieves better results than the probabilistic contextual model and BLSTM models.
Causes and prevention of postoperative myocardial injury

Over the past few years non-cardiac surgery has been recognised as a serious circulatory stress test which may trigger cardiovascular events such as myocardial infarction, in particular in patients at high risk. Detection of these postoperative cardiovascular events is difficult as clinical symptoms often go unnoticed. To improve detection, guidelines advise to perform routine postoperative assessment of cardiac troponin. Troponin elevation – or postoperative myocardial injury – can be caused by myocardial infarction. However, also non-coronary causes, such as cardiac arrhythmias, sepsis and pulmonary embolism, may play a role in a considerable number of patients with postoperative myocardial injury. It is crucial to acquire more knowledge about the underlying mechanisms of postoperative myocardial injury because effective prevention and treatment options are lacking. Preoperative administration of beta-blockers, aspirin, statins, clonidine, angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, and preoperative revascularisation have all been investigated as preventive options. Of these, only statins should be considered as the initiation or reload of statins may reduce the risk of postoperative myocardial injury. There is also not enough evidence for intraoperative measures such blood pressure optimisation or intensified medical therapy once patients have developed postoperative myocardial injury. Given the impact, better preoperative identification of patients at risk of postoperative myocardial injury, for example using preoperatively measured biomarkers, would be helpful to improve cardiac optimisation.