Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting therapeutic improvement early in therapy: validation of the SCORE-15 index of family functioning and change

The SCORE index of family functioning and change is an established measure, with strong psychometric properties, of the quality of family life. We report the sensitivity to therapeutic change of the short form, the SCORE-15. Data are reported from 584 participants aged above 11 years, representing 239 families. All couples and families had been referred for systemic couples and family therapy, completing the form at start of the first session and close to the fourth. The SCORE-15 is shown to be acceptable with strong consistency and reliability. Change over only three sessions was highly statistically significant. Further validation is provided by improvements in quantified scores correlating significantly with independent measures provided by family members and by their therapists. The SCORE-15 is a proven measure of therapy and of therapeutic change in family functioning. It is therefore a routinely usable tool applicable to service evaluation, quality improvement, and to support clinical practice. Practitioner Points •The SCORE provides practitioners with brief descriptions of varied aspects of family interaction that have proven significance for many families who present for therapy. •SCORE-15 can be used with confidence to monitor and report proven indicators of progress in systemic therapy. •Because SCORE identifies clinically significant issues of family interaction it has many potential uses in therapy. •There are many new possibilities for therapists to undertake collaborative research.
PREVENTING CHRONIC DISEASE

Introduction Studies have investigated the relationships between chronic systemic and dental conditions, but it remains unclear how such knowledge can be used in clinical practice. In this article, we provide an overview of existing systematic reviews, identifying and evaluating the most frequently reported dental–chronic disease correlations and common risk factors. Methods We conducted a systematic review of existing systematic reviews (umbrella review) published between 1995 and 2017 and indexed in 4 databases. We focused on the 3 most prevalent dental conditions and 10 chronic systemic diseases with the highest burden of disease in Germany. Two independent reviewers assessed all articles for eligibility and methodologic quality using the AMSTAR criteria and extracted data from the included studies.
A Note on Our Submission to Track 4 of iDASH 2019

iDASH is a competition soliciting implementations of cryptographic schemes of interest in the context of biology. In 2019, one track asked for multi-party computation implementations of training of a machine learning model suitable for two datasets from cancer research. In this note, we describe our solution submitted to the competition. We found that the training can be run on three AWS c5.9xlarge instances in less then one minute using MPC tolerating one semi-honest corruption, and less than ten seconds at a slightly lower accuracy.
Parallel Wavegan: A Fast Waveform Generation Model Based on Generative Adversarial Networks with Multi-Resolution Spectrogram

We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network. In the proposed method, a non-autoregressive WaveNet is trained by jointly optimizing multi-resolution spectrogram and adversarial loss functions, which can effectively capture the time-frequency distribution of the realistic speech waveform. As our method does not require density distillation used in the conventional teacher-student framework, the entire model can be easily trained. Furthermore, our model is able to generate high-fidelity speech even with its compact architecture. In particular, the proposed Parallel WaveGAN has only 1.44 M parameters and can generate 24 kHz speech waveform 28.68 times faster than real-time on a single GPU environment. Perceptual listening test results verify that our proposed method achieves 4.16 mean opinion score within a Transformer-based text-to-speech framework, which is comparative to the best distillation-based Parallel WaveNet system.
Bayesian Modeling of Random Walker for Community Detection in Networks

We propose a generative model to detect globally optimal community structures in networks by utilizing random walks. Sophisticated parameter optimization algorithms are developed based on the Markov chain Monte Carlo methods to overcome limitations of the EM algorithm, which has been used in previous works but is sometimes trapped in local optima depending on initial conditions. We apply the algorithms to synthetic and real-world networks to examine their performance in terms of precision and robustness of detected communities. It is found that the Gibbs samplers outperform the previous approaches especially in detecting overlapping communities. The Markovian dynamics of random walkers is crucial to robustly detect the optimal community structures.
Retraction of Soft Growing Robots Without Buckling

Tip-extending soft robots that “grow” via pneumatic eversion of their body material have demonstrated applications in exploration of cluttered environments. During growth, the motion and force of the robot tip can be controlled in three degrees of freedom using actuators that direct the tip in combination with extension. However, when reversal of the growth process is attempted by retracting the internal body material from the base, the robot body often responds by buckling rather than inverting the body material, making control of tip motion and force impossible. We present and validate a model to predict when buckling occurs instead of inversion, and introduce an electromechanical device that can be added to a tip-extending soft robot to prevent buckling during retraction, restoring the ability of steering actuators to control the robot's motion and force during inversion. Using our retraction device, we demonstrate three previously impossible tasks: exploring different branches of a forking path, reversing growth while applying minimal force on the environment, and bringing back environment samples to the base without disturbing the environment.
Dirt-cheap Gas Scaling Relations: Using Dust Absorption, Metallicity, and Galaxy Size to Predict Gas Masses for Large Samples of Galaxies

We apply novel survival analysis techniques to investigate the relationship between a number of the properties of galaxies and their atomic (MH i) and molecular ( ) gas mass, with the aim of devising efficient, effective empirical estimators of the cold gas content in galaxies that can be applied to large optical galaxy surveys. We find that dust attenuation, AV, of both the continuum and nebular emission, shows significant partial correlations with , after controlling for the effect of star formation rate (SFR). The partial correlation between AV and MH i, however, is weak. This is expected because in poorly dust-shielded regions molecular hydrogen is dissociated by far-ultraviolet photons. We also find that the stellar half-light radius, R50, shows significant partial correlations with both and MH i. This hints at the importance of environment (e.g., galactocentric distance) on the gas content of galaxies and the interplay between gas and SFR. We fit multiple regression to summarize the median, mean, and the 0.15/0.85 quantile multivariate relationships among , AV, metallicity, and/or R50. A linear combination of AV and metallicity (inferred from stellar mass) or AV and R50, can estimate molecular gas masses within ∼2.5–3 times the observed masses. If SFR is used in addition, can be predicted to within a factor ≲2. In this case, AV and R50 are the two best secondary parameters that improve the primary relation between and SFR. Likewise, MH i can be predicted to within a factor ≲3 using R50 and SFR.
Toward an Automatic System for Computer-Aided Assessment in Facial Palsy

Importance: Machine learning (ML) approaches to facial landmark localization carry great clinical potential for quantitative assessment of facial function as they enable high-throughput automated quantification of relevant facial metrics from photographs. However, translation from research settings to clinical applications requires important improvements. Objective: To develop an ML algorithm for accurate facial landmarks localization in photographs of facial palsy patients, and use it as part of an automated computer-aided diagnosis system. Design, Setting, and Participants: Facial landmarks were manually localized in portrait photographs of eight expressions obtained from 200 facial palsy patients and 10 controls. A novel ML model for automated facial landmark localization was trained using this disease-specific database. Model output was compared to manual annotations and the output of a model trained using a larger database consisting only of healthy subjects. Model accuracy was evaluated by the normalized root mean square error (NRMSE) between algorithms' prediction and manual annotations. Results: Publicly available algorithms provide poor results when applied to patients compared to healthy controls (NRMSE, 8.56 +/- 2.16 vs. 7.09 +/- 2.34, p << 0.01). We found significant improvement in facial landmark localization accuracy for the clinical population when using a model trained with a relatively small number patients' photographs (1440) compared to a model trained using several thousand more images of healthy faces (NRMSE, 6.03 +/- 2.43 vs. 8.56 +/- 2.16, p << 0.01). Conclusions: Retraining a landmark detection model with a small number of clinical images significantly improved landmark detection performance in frontal view photographs of the clinical population. These results represent the first steps towards an automatic system for computer-aided assessment in facial palsy.
Contextual Imagined Goals for Self-Supervised Robotic Learning

While reinforcement learning provides an appealing formalism for learning individual skills, a general-purpose robotic system must be able to master an extensive repertoire of behaviors. Instead of learning a large collection of skills individually, can we instead enable a robot to propose and practice its own behaviors automatically, learning about the affordances and behaviors that it can perform in its environment, such that it can then repurpose this knowledge once a new task is commanded by the user? In this paper, we study this question in the context of self-supervised goal-conditioned reinforcement learning. A central challenge in this learning regime is the problem of goal setting: in order to practice useful skills, the robot must be able to autonomously set goals that are feasible but diverse. When the robot's environment and available objects vary, as they do in most open-world settings, the robot must propose to itself only those goals that it can accomplish in its present setting with the objects that are at hand. Previous work only studies self-supervised goal-conditioned RL in a single-environment setting, where goal proposals come from the robot's past experience or a generative model are sufficient. In more diverse settings, this frequently leads to impossible goals and, as we show experimentally, prevents effective learning. We propose a conditional goal-setting model that aims to propose goals that are feasible from the robot's current state. We demonstrate that this enables self-supervised goal-conditioned off-policy learning with raw image observations in the real world, enabling a robot to manipulate a variety of objects and generalize to new objects that were not seen during training.
The covariate-adjusted residual estimator and its use in both randomized trials and observational settings

We often seek to estimate the causal effect of an exposure on a particular outcome in both randomized and observational settings. One such estimation method is the covariate-adjusted residuals estimator, which was designed for individually or cluster randomized trials. In this manuscript, we study the properties of this estimator and develop a new estimator that utilizes both covariate adjustment and inverse probability weighting We support our theoretical results with a simulation study and an application in an infectious disease setting. The covariate-adjusted residuals estimator is an efficient and unbiased estimator of the average treatment effect in randomized trials; however, it is not guaranteed to be unbiased in observational studies. Our novel estimator, the covariate-adjusted residuals estimator with inverse probability weighting, is unbiased in randomized and observational settings, under a reasonable set of assumptions. Furthermore, when these assumptions hold, it provides efficiency gains over inverse probability weighting in observational studies. The covariate-adjusted residuals estimator is valid for use in randomized trials, but should not be used in observational studies. The covariate-adjusted residuals estimator with inverse probability weighting provides an efficient alternative for use in randomized and observational settings.
Evaluation of Sentence Representations in Polish

Methods for learning sentence representations have been actively developed in recent years. However, the lack of pre-trained models and datasets annotated at the sentence level has been a problem for low-resource languages such as Polish which led to less interest in applying these methods to language-specific tasks. In this study, we introduce two new Polish datasets for evaluating sentence embeddings and provide a comprehensive evaluation of eight sentence representation methods including Polish and multilingual models. We consider classic word embedding models, recently developed contextual embeddings and multilingual sentence encoders, showing strengths and weaknesses of specific approaches. We also examine different methods of aggregating word vectors into a single sentence vector.
Online Gaussian LDA for Unsupervised Pattern Mining from Utility Usage Data

Non-intrusive load monitoring (NILM) aims at separating a whole-home energy signal into its appliance components. Such method can be harnessed to provide various services to better manage and control energy consumption (optimal planning and saving). NILM has been traditionally approached from signal processing and electrical engineering perspectives. Recently, machine learning has started to play an important role in NILM. While most work has focused on supervised algorithms, unsupervised approaches can be more interesting and of practical use in real case scenarios. Specifically, they do not require labelled training data to be acquired from individual appliances and the algorithm can be deployed to operate on the measured aggregate data directly. In this paper, we propose a fully unsupervised NILM framework based on Bayesian hierarchical mixture models. In particular, we develop a new method based on Gaussian Latent Dirichlet Allocation (GLDA) in order to extract global components that summarise the energy signal. These components provide a representation of the consumption patterns. Designed to cope with big data, our algorithm, unlike existing NILM ones, does not focus on appliance recognition. To handle this massive data, GLDA works online. Another novelty of this work compared to the existing NILM is that the data involves different utilities (e.g, electricity, water and gas) as well as some sensors measurements. Finally, we propose different evaluation methods to analyse the results which show that our algorithm finds useful patterns.
Fast Structured Decoding for Sequence Models

Autoregressive sequence models achieve state-of-the-art performance in domains like machine translation. However, due to the autoregressive factorization nature, these models suffer from heavy latency during inference. Recently, non-autoregressive sequence models were proposed to reduce the inference time. However, these models assume that the decoding process of each token is conditionally independent of others. Such a generation process sometimes makes the output sentence inconsistent, and thus the learned non-autoregressive models could only achieve inferior accuracy compared to their autoregressive counterparts. To improve then decoding consistency and reduce the inference cost at the same time, we propose to incorporate a structured inference module into the non-autoregressive models. Specifically, we design an efficient approximation for Conditional Random Fields (CRF) for non-autoregressive sequence models, and further propose a dynamic transition technique to model positional contexts in the CRF. Experiments in machine translation show that while increasing little latency (8~14ms), our model could achieve significantly better translation performance than previous non-autoregressive models on different translation datasets. In particular, for the WMT14 En-De dataset, our model obtains a BLEU score of 26.80, which largely outperforms the previous non-autoregressive baselines and is only 0.61 lower in BLEU than purely autoregressive models.
Recycling of the used automotive lubricating oil by ionizing radiation process

The recycling process of the used mineral oils has been gaining a very important gap in the context of environmental protection. Among mineral oils from petroleum, the lubricating oils are not entirely consumed during their use; therefore, it is necessary to apply a treatment for recuperation seeking their reuse. Moreover, the environmental legislation of countries does not allow their discard in any type of soils, rivers, lakes, oceans or sewerage systems. The conventional treatment has shown certain difficulties in the recuperation process for used oils. The ionizing radiation process is renowned in the industrial effluents treatments due to its high efficiency in the degradation of organic compounds and in the removal of metals by the action of OH, H and eaq radicals. In this work, used automotive lubricating oil was treated by the ionizing radiation process for metal removal and degradation of organic compounds. The samples were irradiated with 100 and 200 kGy irradiation doses. Determination of the elements Mg, Al, P, S, Cl, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Se, Mo, Nb, Cd, Sn, Ba, Bi and Pb, before and after the irradiation, was done by X-ray fluorescence technique and the organic profile was obtained by infrared spectroscopy. r 2007 Elsevier Ltd. All rights reserved.
Distributed consensus control in vehicle

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
Mobile Social Networking Aided Content Dissemination in Heterogeneous Networks

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THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING ACCURACY IN IMAGE PROCESSING WITH CONVOLUTIONAL NEURAL NETWORKS ON TRUENORTH PROCESSOR SHAMBHAVI GUPTA SPRING 2017 A thesis submitted in partial fulfillment of the requirements for a baccalaureat

Convolutional Neural Networks(CNNs) have been widely used for image processing and have demonstrated high accuracy in recognition and classification tasks [4]. In particular, convolutional neural networks have been able to classify the images from the CIFAR-10 dataset with high accuracy [9]. However, the CIFAR-10 dataset images are of a fixed size of 32X32X3 pixels. In this research, we explore changes to the CNN’s classification accuracy and loss when the CIFAR-10 input images are interpolated to sizes other than 32X32X3 pixels to understand the effects of change in input data transmission size. We demonstrate that Convolutional Neural Networks can classify the interpolated images with reasonable accuracy of the base size image. The minor deviation in accuracy can be traded off for a major reduction in the data transmission size in the case of smaller images which can significantly improve performance in the data intensive applications of image processing. The efficiency of the classification process can be also improved by employing a fast scanning technique where smaller images can be used to reduce regions of interest and narrow the scope of classification. CNN’s ability to retain significant classification accuracy over interpolated input images provides an advantage in application performance.
Analysis of Herbal-drug-associated Adverse Drug Reactions Using Data from Spontaneous Reporting System in Electronic Medical Records

Objectives: The purpose of this study was to understand the status of reporting and characteristics of adverse drug reactions (ADRs) induced by herbal drugs and to make a suggestion for the domestic pharmacovigilance system on herbal medicine. Methods: We carried out a hospital-based observational study at Dongguk University Ilsan Oriental Hospital from April 2012 to December 2014. We reviewed all the herbal-drug-associated ADRs reports registered to the spontaneous ADR reporting system in electronic medical records of the hospital in the period. Results: We found out 101 reports including 163 herbal-drug-associated ADRs from 97 patients. Females (69.3%) outnumbered males and the most frequent age group was the 50s (44, 27.0%). No serious adverse event was observed. The most commonly reported ADR was gastro-intestinal system disorders (68, 41.5%) followed by skin-related disorders (42, 25.8%). Diarrhea (29, 17.8%) was the most frequently referred clinical manifestation. Most ADRs were induced by internal medicines (160, 98.2%) including manufactured (36, 22.1%) and self-prepared decoction (160, 76.1%). The pairs of Igi-hwan-diarrhea, gamiboa-tang-vomiting, and Magnoliae Flos-gastro-intestinal-system-related ADRs were observed twice each and the others appeared only once. Conclusions: We propose Korean government to take an initiative in national pharmacovigilance system for herbal medicine. To perform the surveillance on herbal drugs, the Association of Korean Medicine (AKOM) should set up a nationwide network by designating centers connecting the Korean medical hospitals, local Korean medicine clinics, and the public health centers. The government and AKOM should also educate and encourage them to understand the pharmacovigilance system and report the ADRs actively.
The American Psychiatric Press Textbook of Psychiatry

This Textbook of Psychiatry , edited by John A. Talbott, Robert E. Hales, and Stuart C. Yudofsky, provides in one large volume an authoritative and up-to-date review of modern psychiatry that will be of great value to medical students, general physicians, and psychiatric residents, as well as practicing psychiatrists. It is neither a comprehensive textbook covering all of psychiatry nor a reference text with detailed coverage of a few areas. Instead of inadequate coverage in multiple brief chapters, the editors have chosen to identify and emphasize what they believe to be the areas most worthy of inclusion. Good writing and editing are evident throughout. With frequent citation of research data, including some 1987 references, contributors present controversial and important issues, and make clear statements of the limits of current knowledge. The major sections are "Theoretical Foundations," "Assessment," "Psychiatric Disorders," "Psychiatric Treatments," and "Special Topics" ("Suicide," "Violence," "Psychiatry and the Law," "Ethics