Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning a Multimodal Prior Distribution for Generative Adversarial Nets

Generative adversarial nets (GANs) have shown their potential in various tasks like image generation, 3D object generation, image super-resolution, and video prediction. Nevertheless, they are still considered as highly unstable to train and are endangered to miss modes. One problem is that real data is usually discontinuous, whereas the prior distribution is continuous. This circumstance can lead to non-convergence of the GAN and makes it hard for the generator to generate fair results. In this paper, we introduce an approach to directly learn modes in the prior distribution which map to the modes in the real data by changing the training procedure of GANs. Our empirical results show that this extension stabilizes the training of GANs, and it captures discrete uniform distributions fairer. We use the score of the earth mover’s distance as an evaluation metric to underline this effect.
Tuning of Parameters for a Vibrotactile Kinaesthetic Feedback System Utilizing Tactile Illusions

Kinaesthetic and haptic somatosensory feedback is an integral part of the natural movement feedback loop. Afflictions like spinal cord injury potentially disrupt both efferent and afferent pathways, and thus neuroprosthesis research must address both control and feedback. Artificial somatosensory feedback has great potential to inform the user in an intuitive way and facilitate the integration of a prosthesis into their body image. Our aim is to provide kinaesthetic feedback of arm movements via a sparse grid of vibrating actuators, manipulating actuator intensities in such a way that tactile illusions of temporally and spatially continuous movement are evoked. To this end, we examine parameter spaces of apparent tactile motion and phantom sensations, in order to design a comprehensive feedback system.
Swlda Offers a Valuable Trade-off between interpretability and accuracy for rehabilitative BCIs

Interpretability, accuracy and a solid neurophysiological basis can be considered as the main requirements for the classification model to monitor motor imagery tasks in post-stroke motor recovery paradigms supported by the brain-computer interface technology. This study aimed at comparing the accuracy performance of different classification approaches applied on a dataset of 15 stroke patients. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performance. To this purpose, stepwise linear discriminant analysis (SWLDA), shrinkage linear discriminant analysis, logistic regression, support vector machine, multilayer perceptron, decision tree and random forest classifiers entered in the performance analysis. SWLDA statistically outperformed the classifiers commonly used in sensorimotor-BCI paradigms, achieving 80% in classification accuracy even in case of feature domain dimensionality reduction. The linearity, the interpretability and the accuracy of the SWLDA model, even just by means few EEG electrodes, yielded to consider SWLDA an optimal solution to fulfil the main requirements of the rehabilitation context.
Towards Automated Data Cleaning Workflows

The success of AI-based technologies depends crucially on trustful and clean data. Research in data cleaning has provided a variety of approaches to address diﬀerent data quality problems. Most of them require some prior knowledge about the dataset in order to select and conﬁgure the approach correctly. We argue that for unknown datasets, it is unrealistic to know the data quality problems upfront and to for-mulate all necessary quality constraints in one shot. Pragmatically, the user solves data quality problems by implementing an iterative cleaning process. This incremental approach poses the challenge of identifying the right sequence of cleaning routines and their conﬁgurations. In this paper, we highlight our work in progress towards building a cleaning workﬂow orchestrator that learns from cleaning tasks in the past and proposes promising cleaning workﬂows for a new dataset. To this end, we highlight new approaches for selecting the most promising error detection routines, aggregating their outputs, and explaining the ﬁnal results.
Systematic Review of the state-of-the-art in brain computer interface robotic walking in stroke

In stroke rehabilitation intelligent robots are required that can interpret walking intention accurately. This systematic review, guided by PRISMA and registered with PROSPERO (CRD42018112252), identifies current state-of-the-art in brain-computer interface (BCI) robotic walking in stroke using electroencephalogram (EEG). A detailed search strategy was applied across the following databases: PubMed (1949-2018), EMBASE (1947-2018), Web of Science (1945-2018), COMPENDEX (1967-2018), CINAHL (1982-2018), SPORTDiscus (1985-2018), ScienceDirect (1997-2018), Cochrane Library (1974-2018). Eligibility for inclusion was considered independently by two reviewers. From a total of 38895 publications, 2 studies comprising N=48 chronic stroke patients met inclusion criteria and rated moderate to strong in quality. Different exoskeleton devices were employed in studies (Ekso; H2 exoskeleton). No study closed the BCI loop. Longitudinal training was noted to increase classification accuracy. Improved frontoparietal connectivity in the affected side was observed during robotic gait training. BCI robotic gait training after stroke is not reported in the literature to date but shows promise with adequate training for classification.
Decoding SSVEP on time and frequency domain using Convolutional Neural Network

: A brain-computer interface (BCI) is an highly interdisciplinary research topic which involved psychology, signal processing, machine learning, medical engineering etc. A steady-state visually evoked potentials (SSVEP) paradigm-based Brain Computer Inter-face systems are the most promising and reliable communication systems for people with disabilities on clinical purposes. This paper proposes the use of Deep Neural Network models for decoding the data acquired through visual-based Electroencephalography (EEG). in this paper, we concentrate on two points with the Convolutional Neural Network The ﬁrst is decoding of the EEG data using the raw signal (time domain) and the extracted frequency features of the signal after Fourier analysis. The second point explored in this paper is the evaluation of the selection of electrodes on the performance of the Deep Neural Network models for raw signals (time domain data). We have also compared the performance of Canonical Correlation analysis of the data with the Deep Neural Network models.
Increasing cultural sensitivity: development of a tool kit

Issues and Objectives: Incorporating cultural considerations into the patient’s plan of care promotes patient safety and satisfaction (Smith, 2010). This was identified as an area of improvement for Carbonear General Hospital (CGH) therefore a Tool Kit was developed. Practicum Methods: A literature review was conducted to identify key elements of culturally competent care and to identify tools to promoting such care. In order to identify current practices, key issues, needs and available resources, consultations were conducted with three groups of key stakeholders: nurses and managers from CGH, support services and client relations. Results: Information collected led to the development of a Tool Kit, consisting of three sections, which will assist nurses from CGH in delivering culturally competent care. The Decision Tree outlines the steps to follow and identifies the cultural characteristics to assess. The Cultural Assessment Guide lists each cultural characteristic, reason to assess it, relevant assessment cues and questions, and interventions. The Resources and Strategies section summarizes relevant resources, suggested strategies and helpful tips that provide guidance and clarification regarding the cultural needs. Conclusion: The developed Increasing Cultural Sensitivity Tool Kit is ready for trial at CGH. After the completed trial period, revisions will be made as necessary and it will be determined if the Tool Kit can be adapted for regional implementation.
What Company Does My News Article Refer to? Tackling Multi Class Problems With Topic Modeling

While it is technically trivial to search for the company name to predict the company a new article refers to, it often leads to incorrect results. In this article, we compare the two approaches bag-of-words with k-nearest neighbors and Latent Dirichlet Allocation with k-nearest neighbor by assessing their applicability for predicting the S&P 500 company which is mentioned in a business news article or press release. Both approaches are evaluated on a corpus of 13k documents containing 84% news articles and 16% press releases. While the bag-of-words approach yields accurate predictions, it is highly inefficient due to its gigantic feature space. The Latent Dirichlet Allocation approach, on the other hand, manages to achieve roughly the same prediction accuracy (0.58 instead of 0.62) but reduces the feature space by a factor of seven.
Percentiles de peso y talla en niños con bajo peso al nacer. Una perspectiva para el Especialista en Ortodoncia

Introduccion: la evaluacion del peso y la talla constituyen indicadores de salud y nutricion importantes en pacientes de Ortodoncia. Un embarazo saludable con adecuada nutricion es esencial para un nacimiento de buen peso, mientras que un embarazo con trastornos puede provocar retardos en el crecimiento del nino perdurables durante toda la vida. El objetivo del presente estudio es ofrecer tablas donde se manifiestan los efectos de la malnutricion fetal sobre el crecimiento fisico reflejados en el peso y la talla desde el nacimiento a la edad adulta. Metodo: se realizo un estudio clinico, longitudinal en una muestra intencionada de 83 ninos, 38 nacidos con bajo peso y 45 con crecimiento adecuado, que sirvieron como grupo control seleccionados a partir de los nacimientos ocurridos en el Hospital Universitario Ginecoobstetrico “Mariana Grajales” de Santa Clara en el primer trimestre del ano 1987 y que habian sido evaluados al nacimiento, a los tres y seis meses, al ano, a los ocho, a los 15 y a los 20 anos de edad. Resultados: se caracterizo el crecimiento mediante las variables peso y talla por grupos y generos. Se compararon con los percentiles de la poblacion cubana. Resulto que los integrantes del grupo control se mantuvieron cerca del percentil 90, mientras que los malnutridos fetales se ubicaron por debajo del 50% en el peso y del 10% en la talla. Conclusiones: se constato la influencia del factor nutricional desde la etapa prenatal del desarrollo y su repercusion hasta la edad adulta, aspectos de gran importancia en la valoracion clinica y el diagnostico de pacientes con necesidades ortodonticas
IT-basierte Entscheidungsunterstützung für die Ersatzteilversorgung mit Additive Manufacturing

Additive Manufacturing (AM) ist eine Fertigungstechnologie, die zunehmend Bedeutung und Anwendung in unterschiedlichen Industriebranchen findet. Forschungsund Entwicklungsschwerpunkte liegen derzeit vermehrt im Bereich der Technologie selbst. Während AM zur Prototypenherstellung bereits in Unternehmen eingesetzt wird, gibt es Anwendungsbereiche, deren Potenzial erst in den letzten Jahren erkannt wurde, z. B. die ortsund zeitunabhängige Herstellung von Ersatzteilen. Das Ersatzteilwesen zählt zu den Haupteinnahmequellen in vielen Industrieunternehmen, v. a. bei Maschinenund Anlagenbauern. Nicht zuletzt wird dies durch immer länger werdende Produktlebenszyklen und neue Geschäftsmodelle, wie Pay-per-Use, verstärkt. Die Unternehmen stehen vor der Herausforderung, AM in die bestehenden Strukturen des Ersatzteilmanagements zu integrieren. Entscheidungen über den AM-Einsatz werden während des gesamten Produktlebenszyklus und über alle Funktionen des Unternehmens hinweg getroffen. Das hier skizzierte Forschungsprojekt verfolgt einen gestaltungsorientierten Ansatz zur Entwicklung einer IT-basierten Entscheidungsunterstützung. Der Beitrag zeigt die Ergebnisse einer explorativen Untersuchung und präsentiert die daraus abgeleiteten Anforderungen an eine IT-basierte Entscheidungsunterstützung zur Integration von AM in die Ersatzteilversorgung des Maschinenund Anlagenbaus.
Expectation mismatch during a tracking task: an EEG analysis

Understanding the dynamics of brain areas’ activation during BCI tasks is essential to improve BCIs performance and features selection when training a classifier. The role of the cerebellum in the sudden adaptation of the motor plan in response to unexpected perturbations in tasks with continuous motor control and continuous visual feedback has been well reported in the literature. We recorded EEG data of five subjects to study the cerebellar activation in tasks requiring a sudden motor plan update. Using sLORETA inverse source localization method, we observed activation in the cerebellum at around 200 and 400 ms after the deviation onset. Furthermore, EMG recording and analysis at the level of the neck could disprove that the activity observed was due to movement artifacts.
A Wearable SSVEP-based brain-Computer Interface with off-the-Shelf Components

: In this paper, a wearable Brain-Computer Interface is proposed. This BCI exploits a non-invasive single-channel electroencephalogram to measure steady-state visually evoked potentials (SSVEP) from the user’s scalp. Dry electrodes are employed. The visual stimuli for SSVEP elicitation are presented on the LCD display of Augmented Reality glasses, and each stimulus can be associated with a command to actuate. The brain signals processing is conducted by means of a simple power spectral density analysis based on an FFT algo-rithm. Then, the signal features are classiﬁed with a Support Vector Machine. The resulting algorithm requires a low computational burden, and it was easily implemented in Android. The ﬁnal BCI system is built with off-the-shelf components, and it is easily customizable. This work aims to give a contribution to the development of BCI systems for applications in daily life.
Are users' traits informative enough to predict/explain their mental-imagery based BCI performances?

: Mental-Imagery based Brain-Computer In-terfaces (MI-BCIs) make use of brain signals produced during mental imagery tasks to control a computerised system. The current unreliability of MI-BCIs could be due, at least in part, to the use of inappropriate user-training procedures. In order to improve these procedures, it is necessary ﬁrst to understand the mechanisms underlying MI-BCI user-training, notably through the identiﬁcation of the factors inﬂuencing it. Thus, this paper aims at creating a statistical model that could explain/predict the performances of MI-BCI users us-ing their traits (e.g., personality). We used the data of 42 participants (i.e., 180 MI-BCI sessions in total) collected from three different studies that were based on the same MI-BCI paradigm. We used machine learning re-gressions with a leave-one-subject-out cross validation to build different models. Our ﬁrst results showed that using the users’ traits only may enable the prediction of performances within one multiple-session experiment, but might not be sufﬁcient to reliably predict MI-BCI performances across experiments.
A Scoring-based Approach for Data Preparator Suggestion

Self-service data preparation enables end users to prepare data by themselves. However, the plethora of possible data preparation steps can overwhelm the user. We introduce a score-based preparator ranking approach to propose preparator candidates in a context-speciﬁc manner. To this end, we give scoring functions for a selected set of preparators and outline future work towards a full-ﬂedged data preparation system
A QUBO Formulation of the k-Medoids Problem

We are concerned with k-medoids clustering and propose a quadratic unconstrained binary optimization (QUBO) formulation of the problem of identifying k medoids among n data points without having to cluster the data. Given our QUBO formulation of this NP-hard problem, it should be possible to solve it on adiabatic quantum computers.
Electronic medical record-based cohort selection and direct-to-patient, targeted recruitment: early efficacy and lessons learned

OBJECTIVE The study sought to characterize institution-wide participation in secure messaging (SM) at a large academic health network, describe our experience with electronic medical record (EMR)-based cohort selection, and discuss the potential roles of SM for research recruitment. MATERIALS AND METHODS Study teams defined eligibility criteria to create a computable phenotype, structured EMR data, to identify and recruit participants. Patients with SM accounts matching this phenotype received recruitment messages. We compared demographic characteristics across SM users and the overall health system. We also tabulated SM activation and use, characteristics of individual studies, and efficacy of the recruitment methods. RESULTS Of the 1 308 820 patients in the health network, 40% had active SM accounts. SM users had a greater proportion of white and non-Hispanic patients than nonactive SM users id. Among the studies included (n = 13), 77% recruited participants with a specific disease or condition. All studies used demographic criteria for their phenotype, while 46% (n = 6) used demographic, disease, and healthcare utilization criteria. The average SM response rate was 2.9%, with higher rates among condition-specific (3.4%) vs general health (1.4%) studies. Those studies with a more inclusive comprehensive phenotype had a higher response rate. DISCUSSION Target population and EMR queries (computable phenotypes) affect recruitment efficacy and should be considered when designing an EMR-based recruitment strategy. CONCLUSIONS SM guided by EMR-based cohort selection is a promising approach to identify and enroll research participants. Efforts to increase the number of active SM users and response rate should be implemented to enhance the effectiveness of this recruitment strategy.
Parenting stress and its correlates in an infant mental health unit: a cross-sectional study

Abstract Background: An infant’s development is closely linked to the relationship they have with their parents. Parenting stress, affective disorder, and an upbringing with substance-abusing parents can affect parenting quality and increase the risk of children developing behavioral, mental and social problems. The overall aim of the study was to investigate how parents of children attending an outpatient Infant Mental Health (IMH) unit rate their own psychological health and parenting stress, and to explore predictors of parenting stress. Methods: The sample comprised 197 parents, 129 mothers and 68 fathers, referred with their infant/toddler to an outpatient IMH unit for interplay treatment. On admission, the parents completed self-report questionnaires concerning their own mental health problems and parenting stress. Results: The mothers reported significantly more psychiatric symptoms and parenting stress than the fathers. Fathers with substance-abusing parents had often experienced divorce in the family of origin, had a low level of education, and had often experienced trauma. Depression was a predictor for parenting stress for both mothers and fathers. Conclusion: The parents’ situation was strained, presenting a variety of psychiatric symptoms and high levels of parenting stress, making assessment of parental health before starting treatment important. The mothers’ situations were more serious compared with the fathers’, and for both parents depression was a significant predictor for parenting stress. To increase the chances of a positive treatment outcome for the child, both parents should be included in the treatment.
Phytostabilization as a phytoremediation strategy for mitigating water pollutants by the floating macrophyte Ludwigia stolonifera (Guill. & Perr.) P.H. Raven

Abstract The present study evaluated the phytoremediation potential of the floating macrophyte Ludwigia stolonifera for removing trace metals from contaminated water bodies. Forty quadrats, distributed equally in eight sites (six polluted two unpolluted sites) were selected seasonally for water, sediment and plant investigations. The leaf area, fresh and dry biomass, chlorophyll b and carotenoids contents of L. stolonifera were significantly reduced in polluted sites. L. stolonifera plants accumulated concentrations of the investigated trace metals in their roots higher than the shoots. The roots contributed to the highest concentrations of Al and Cu during spring; Fe, Mn and Ni during summer; Cd and Zn during autumn; and Cr and Pb during winter. Compared to the unpolluted sites, the below- and above-ground parts from the polluted sites accumulated higher concentrations of most investigated trace metals, except Fe. The below-ground parts of L. stolonifera had high seasonal potential for seasonal accumulation of Cd, Cu, Ni, Zn and Pb with a bioaccumulation factor that exceeded 1, the translocation factor of the investigated metals was <1. Therefore, the study species is suitable for metals phytostabilization and thus can be considered a potential phytoremediator of these metals.
Fetal transient tricuspid valve regurgitation: sonographic features and clinical evolution

Abstract Purpose To analyze the prevalence, the sonographic features, the clinical evolution and significance of fetal tricuspid valve regurgitation (TR). Study design This is a prospective study. Serial ultrasound examinations were performed at 20–23, at 26–29 and at 30–34 gestational weeks in 675 consecutive singleton pregnancies with fetal normal growth and normal cardiac anatomy. The fetal tricuspid valve regurgitation was classified according to its duration, to the peak of jet maximum velocity and to its maximum spatial extension. A clinical examination and echocardiography were performed in neonates after birth. Results During the first examination (20–23 weeks), 32 cases of tricuspid valve regurgitation were identified. The prevalence of tricuspid regurgitation was 4.74%. The large majority of TR cases were not-holosystolic (87.5%), with a maximum velocity below 2 m/sec (80–130 cm/sec in 84% cases and 180–200 cm/sec in 16% cases) and with a little spatial extension (type I or II in 87.5% cases). Following this hemodynamic phenomenon during the following weeks, we found that it disappeared around 29 weeks in all cases. Conclusions Tricuspid regurgitation observed during the second trimester can be considered a transient and functional hemodynamic phenomenon, without apparent pathological significance.