Miracle-dz/UniFAR-FTUs huggingface dataset:
A randomized controlled trial to evaluate the effectiveness of a brief motivational intervention to improve exclusive breastfeeding rates AS PER PDF: Study protocol AS PER PDF

AIMS To estimate the effectiveness of a brief motivational intervention (BMI) in increasing the duration of exclusive breastfeeding in the first 6 months postpartum. A complementary aim was to examine a potential mediating role of breastfeeding self-efficacy in the effectiveness of the BMI. BACKGROUND Breastfeeding is associated with benefits for babies and mothers' health. Among the pool of techniques used to encourage healthy behaviours, BMI is highlighted based on the principles of motivational interviewing. One of the main components of these interventions is the promotion of self-efficacy, which, in fact, is a key factor for breastfeeding success. DESIGN A multi-centre randomized controlled clinical trial of parallel groups. METHODS Women who begin to breastfeed in the first hour after birth will be randomly assigned to the intervention group (receiving a BMI at immediate postpartum plus a telephone booster at the 1st and 3rd month postpartum) or the control group (receiving standard breastfeeding education at the same time). Outcome measures include the following: breastfeeding, breastfeeding self-efficacy, general self-efficacy, and postnatal depression. Data will be collected before the intervention and at the 1st, 3rd, and 6th month after birth. The study protocol has been approved by Badajoz Ethics Committee of Clinical Research in October 2017. DISCUSSION This study will identify the effectiveness of BMI in improving exclusive breastfeeding rates. The findings will give useful evidence to health professionals about how to support breastfeeding. IMPACT This study will address the low-exclusive breastfeeding rates, that in our country are far lower than World Health Organization's recommendation.
Targeting Inflammation With Nanosized Drug Delivery Platforms in Cardiovascular Diseases: Immune Cell Modulation in Atherosclerosis

Atherosclerosis (AS) is a disorder of large and medium-sized arteries; it consists in the formation of lipid-rich plaques in the intima and inner media, whose pathophysiology is mostly driven by inflammation. Currently available interventions and therapies for treating atherosclerosis are not always completely effective; side effects associated with treatments, mainly caused by immunodepression for anti-inflammatory molecules, limit the systemic administration of these and other drugs. Given the high degree of freedom in the design of nanoconstructs, in the last decades researchers have put high effort in the development of nanoparticles (NPs) formulations specifically designed for either drug delivery, visualization of atherosclerotic plaques, or possibly the combination of both these and other functionalities. Here we will present the state of the art of these subjects, the knowledge of which is necessary to rationally address the use of NPs for prevention, diagnosis, and/or treatment of AS. We will analyse the work that has been done on: (a) understanding the role of the immune system and inflammation in cardiovascular diseases, (b) the pathological and biochemical principles in atherosclerotic plaque formation, (c) the latest advances in the use of NPs for the recognition and treatment of cardiovascular diseases, (d) the cellular and animal models useful to study the interactions of NPs with the immune system cells.
Profiles and Predictors of Smart Home Technology Adoption by Older Adults

The commercial popularity of smart home (SH) technology has broadened the scope of aging-in-place and home health occupational therapy. The objective of this article is to examine ownership of SH technology by older adults, their readiness to adopt SH technology, and identify the client factors relating to the adoption. A survey of older adults aged 60 and above living in the community was conducted. Respondents (N = 445) who were women; in the age group of 60 to 70 years; living in a two-level home, with a body function impairment; with a fall history; and experienced in information and communication technology (ICT) were significantly likely to be “brisk adopters” of SH (p < .05). Stepwise regression model identified marital status, home security, and overall ICT ownership as the predictors of SH ownership, whereas being female, concern over home security, and perceived independence contributed to SH readiness (p < .05). Consideration of the identified client profiles, health, and personal factors will strengthen SH integration for aging-in-place.
Automated development of fast transforms

The fast multipole method (FMM) proposed by Greengard and Rokhlin is a well-recognized and effective approach to the design of fast and accurate algorithms for large-scale computational problems. In this thesis we investigate the systematic development, implementation, and optimization of FMM-based algorithms for an important class of computational problems. We also present SOFIA, an automation system aimed at facilitating prototyping, validation, code generation, and overall performance optimization of such FMM-based algorithms. Many large-scale computational problems can be cast as discrete transforms, or equivalently as dense matrix-vector products with the matrices representing discrete or discretized transform operators. The FMM has enabled successful computations of many transforms that were previously intractable with traditional methods. A FMM-based algorithm has low arithmetic complexity within required accuracy. But, computational efficiency is also indisputably affected by the data latency imposed on contemporary computer systems. We recognize the mutual dependence problem between reduction in arithmetic complexity and reduction in data latency, which may offset benefits from each other. The demand for algorithm improvements and efficient implementations grows steadily due to the constant extension in scientific inquiries and the constant change in computer systems. Unfortunately, efforts to meet this demand have been largely confined to and impeded by manual development. We establish a model for systematic development of FMM-based algorithms. We explore and exploit a unified algorithm representation in terms of matrix computations. Based on the unified representation, we extract a set of basic operations upon which various transform algorithms can be systematically built. The model attempts to orthogonalize the relationship between complexity reduction and data latency reduction in the sense that both mathematical properties and performance tuning techniques can be explored and exploited to the effect of mutual enhancement. The SOFIA system implements the model in the following stages. Provided with an approximation of a transform kernel in scalar form, SOFIA generates a decomposition of the corresponding transform matrix in symbolic form. The structure of each matrix component in the decomposition is identified. The system then associates matrix components with basic program modules and produces a transform code as an assembly of these modules. Furthermore, SOFIA provides means for performance optimization via both algorithm transformations at the matrix representation level and via program transformations at the code generation level. The SOFIA design incorporates mathematical operations on matrices and efficient computation techniques in addition to languages for formal representation, language translations, and code generation in a target language. Finally, we show the effectiveness of SOFIA with model problems.
Parameter estimation of a new energy spot model from futures prices

An important issue in the energy market as in the financial market is the parameter estimation of the models representing the dynamics of the spot and the futures. Because one is dealing with unobservable factors, a popular estimation method is the maximum likelihood estimation (MLE), under the assumption that observations are corrupted with additive Gaussian noise. In this framework, the state space representation is used together with the Kalman filtering techniques, and the parameter estimates are obtained through maximization of a likelihood functional. The additional noise in the observation is interpreted to take into account bid-ask spreads, price limits, etc. The argument is clearly forced and unconvincing. The problem is that there is no feedback of the observation noise to the spot price; this leads to a model that is not arbitrage free anymore. The goal of this thesis is two folds: Starting from the two factor model of Schwartz-Smith (2000), we formulate and implement a new arbitrage free model for the futures prices of energy which can be used in a mathematically sound way when estimating the parameter of the model, using the method of maximum-likelihood. In our setup and by using a reverse engineering concept, a new model is developed by perturbing the futures price given by Schwartz-Smith by extra term that takes into account the uncertainties in both the time, and time of maturity, of the term structure of the futures. We ensure that the new model is arbitrage free. As a result of this formulation, the added measurement noise is built in within the model, and the model parameters can be calibrated through the derived likelihood functional without any ad hoc observation noise. The second goal is to estimate the parameters of the new model without any modification to the nonlinear payoff of the futures. For that, we use the particlefiltering algorithm to estimate the parameters. On the empirical side, we identify the parameters of the model using real data from the European energy market.
Named Entity Recognition Modeling for the Thai Language from a Disjointedly Labeled Corpus

In the Thai language, named entity can be used with or without a prefix or an indication of word. This may cause confusion between named entity and other types of noun. However, a named entity is likely to be used in adjacent to verbs or prepositions. This means that the adjacent verbs or prepositions to a noun can be as a good feature to determine the type of named entity. There are some studies on named entity recognition (NER) task in other languages such as Indonesian showing that combination of word embedding and part-of-speech (POS) tag can improve the performance of the NER model. In this paper, we investigate the Thai Named Entity Recognition task using Bi-LSTM model with word embedding and POS embedding for dealing with the relatively small and disjointedly labeled corpus. We compare our model with the one without POS tag, and the baseline model of CRF with the similar set of feature. The experiment results show that our proposed model outperforms the other two in all F1-score measures. Especially, in the case of location file, the F1-score is increased by 14 percent.
Improving the Accuracy of Rain Rate Estimates Using X-Band Phased-Array Weather Radar Network

To improve rain rate estimation accuracy of the networked phased-array weather radars (PAWRs) installed in Osaka and Kobe, Japan, we employ a rain rate estimation method using a sequentially varying radar reflectivity and rain rate (<inline-formula> <tex-math notation="LaTeX">${Z}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula>) relationship. To calculate the time variation in the <inline-formula> <tex-math notation="LaTeX">${Z}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> relationship, the rain rate of the existing X-band radar network comprising four dual-polarization radars and the radar reflectivity factor of the PAWRs are used. The proposed method is compared with estimation methods using the Marshall–Palmer relationship and the attenuation coefficient (k). From the comparisons between the rain rate with the three different estimation methods and the rain rate of the rain gauges installed in an analyzed area, we discuss the estimation accuracy. From the comparisons, the proposed method shows significantly better performance than do the other two methods even when weather conditions include heavy or violent rain.
Tukey-Inspired Video Object Segmentation

We investigate the problem of strictly unsupervised video object segmentation, i.e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset. We find foreground objects in low-level vision data using a John Tukey-inspired measure of "outlierness. " This Tukey-inspired measure also estimates the reliability of each data source as video characteristics change (e.g., a camera starts moving). The proposed method achieves state-of-the-art results for strictly unsupervised video object segmentation on the challenging DAVIS dataset. Finally, we use a variant of the Tukey-inspired measure to combine the output of multiple segmentation methods, including those using supervision during training, runtime, or both. This collectively more robust method of segmentation improves the Jaccard measure of its constituent methods by as much as 28%.
Differences between Men and Women in Mortality and the Health Dimensions of the Morbidity Process.

BACKGROUND Do men have worse health than women? This question is addressed by examining sex differences in mortality and the health dimensions of the morbidity process that characterize health change with age. We also discuss health differences across historical time and between countries. CONTENT Results from national-level surveys and data systems are used to identify male/female differences in mortality rates, prevalence of diseases, physical functioning, and indicators of physiological status. Male/female differences in health outcomes depend on epidemiological and social circumstances and behaviors, and many are not consistent across historical time and between countries. In all countries, male life expectancy is now lower than female life expectancy, but this was not true in the past. In most countries, women have more problems performing instrumental activities of daily living, and men do better in measured performance of functioning. Men tend to have more cardiovascular diseases; women, more inflammatory-related diseases. Sex differences in major cardiovascular risk factors vary between countries-men tend to have more hypertension; women, more raised lipids. Indicators of physiological dysregulation indicate greater inflammatory activity for women and generally higher cardiovascular risk for men, although women have higher or similar cardiovascular risk in some markers depending on the historical time and country. SUMMARY In some aspects of health, men do worse; in others, women do worse. The lack of consistency across historical times and between countries in sex differences in health points to the complexity and the substantial challenges in extrapolating future trends in sex differences.
Directed Circular Polarized Antenna

The goal of proposed research is to construct the circular polarized narrow-band directed antenna using low-loss dielectric and high relative permittivity material for the substrate. Presented first part contains the simulation of such antenna using the software which based on Finite Element Method for the purpose to fined optimal parameters of such antenna before its construction. Using numerical experiment we suppose to determine best parameters of antenna, to achieve desired radiation parameters: well matched to the open space; circular polarization of radiated field; narrow-beam; wide-ban. It was designed 2.49 GHz and 2.4 GHz circular polarization radiation patch antennas, which was archived by cutting edges from square patch antenna. For simulation it was used 6.8 relative permittivity material for the substrate.
Efficient Video Understanding via Layered Multi Frame-Rate Analysis

One of the greatest challenges in the design of a real-time perception system for autonomous driving vehicles and drones is the conflicting requirement of safety (high prediction accuracy) and efficiency. Traditional approaches use a single frame rate for the entire system. Motivated by the observation that the lack of robustness against environmental factors is the major weakness of compact ConvNet architectures, we propose a dual frame-rate system that brings in the best of both worlds: A modulator stream that executes an expensive models robust to environmental factors at a low frame rate to extract slowly changing features describing the environment, and a prediction stream that executes a light-weight model at real-time to extract transient signals that describes particularities of the current frame. The advantage of our design is validated by our extensive empirical study, showing that our solution leads to consistent improvements using a variety of backbone architecture choice and input resolutions. These findings suggest multiple frame-rate systems as a promising direction in designing efficient perception for autonomous agents.
Neural network ensemble and support vector machine classifiers for the analysis of remotely sensed data: a comparison

This paper presents a comparative evaluation between a classification strategy based on the combination of the outputs of a neural (NN) ensemble and the application of Support Vector Machine (SVM) classifiers in the analysis of remotely sensed data. Two sets of experiments have been carried out on a benchmark data set. The first set concerns the application of linear and non linear techniques to the combination of the outputs of a Multilayer Perceptron (MLP) neural network ensemble. In particular, the Bayesian and the error correlation matrix approaches are used for coefficient selection in the linear combination of the network's outputs. A MLP module is used for the non linear outputs combination. The results of linear and non linear combination schemes are compared and discussed versus the performance of SVM classifiers. The comparative analysis evidences that the nonlinear, MLP based, combination provides the best results among the different combination schemes. On the other hand, better performance can be obtained with SVM classifiers. However, the complexity of the SVM training procedure can be considered a limitation for SVMs application to real-world problems.
Autonomous and Collaborating Cyber-Physical Systems

The term “autonomy” comes from the Greek roots “autos” and “nomos”, which mean “self” and “law” respectively. This survey paper investigates the concept of autonomy in connection with the development of cyber-physical systems. The concept of autonomy refers to a form of selfregulation or self-government of individual or collaborating systems. An autonomously operating system will need to be able to recognise and differentiate itself among other systems to be able to function on different levels. To be able to increase systems’ autonomy, they need self-configuring, self-optimising, self-healing, and self-protecting capabilities. By developing such capabilities, systems will be able to adapt to changes that emerge from within them or in their environment. Networking these systems subsequently allows them to communicate and interact with each other, ultimately enabling them to jointly make decisions. This kind of joint decision making potentially amplifies the autonomy of the whole of interconnected systems in the performance of joint activities.
Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion

This paper presents a refinement framework of WaveNet vocoders for variational autoencoder (VAE) based voice conversion (VC), which reduces the quality distortion caused by the mismatch between the training data and testing data. Conventional WaveNet vocoders are trained with natural acoustic features but conditioned on the converted features in the conversion stage for VC, and such a mismatch often causes significant quality and similarity degradation. In this work, we take advantage of the particular structure of VAEs to refine WaveNet vocoders with the self-reconstructed features generated by VAE, which are of similar characteristics with the converted features while having the same temporal structure with the target natural features. We analyze these features and show that the self-reconstructed features are similar to the converted features. Objective and subjective experimental results demonstrate the effectiveness of our proposed framework.
Performance Optimization of Multithreaded 2D Fast Fourier Transform on Multicore Processors Using Load Imbalancing Parallel Computing Method

Fast Fourier transform (FFT) is a key routine employed in application domains such as molecular dynamics, computational fluid dynamics, signal processing, image processing, and condition monitoring systems. Its performance on modern multicore platforms is therefore of paramount concern to the high-performance computing community. The inherent complexities in these platforms such as severe resource contention and non-uniform memory access, however, pose formidable challenges. We study the performance profiles of multithreaded 2D FFTs provided in three highly optimized packages, FFTW-2.1. 5, FFTW-3.3. 7, and Intel Math Kernel Library (Intel MKL) FFT, on a modern Intel Haswell multicore processor consisting of 36 cores. We show that all the three routines exhibit drastic performance variations, and hence, their average performances are considerably lower than their peak performances. The ratios of average-to-peak performance for the 2D FFT routines from the three packages are 40%, 30%, and 24%. We conclude that improving the average performance of 2D FFT on modern multicore processors by the removal of performance variations constitutes a tremendous research challenge. To address this challenge, we propose two novel optimization methods, PFFT-FPM and PFFT-FPM-PAD, specifically designed and implemented for 2D FFT. The methods employ model-based parallel computing using a load-imbalancing technique. They take as inputs, the discrete 3D functions of the performance of the processors against problem size, compute 2D DFT of a complex signal matrix of size <inline-formula> <tex-math notation="LaTeX">$N \times N$ </tex-math></inline-formula> using <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> abstract processors, and output the transformed signal matrix. Based on our experiments on a modern Intel Haswell multicore server consisting of 36 physical cores, the average and maximum speedups observed for PFFT-FPM using FFTW-3.3. 7 are <inline-formula> <tex-math notation="LaTeX">$1.9\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$6.8\times $ </tex-math></inline-formula>, and the average and maximum speedups observed using Intel MKL FFT are <inline-formula> <tex-math notation="LaTeX">$1.3\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula>. The average and maximum speedups observed for PFFT-FPM-PAD using FFTW-3.3. 7 are <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$9.4\times $ </tex-math></inline-formula>, and the average and maximum speedups observed using Intel MKL FFT are <inline-formula> <tex-math notation="LaTeX">$1.4\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$5.9\times $ </tex-math></inline-formula>.