On cheap entropy-sparsified regression learning.

Regression learning is one of the long-standing problems in statistics, machine learning, and deep learning (DL). We show that writing this problem as a probabilistic expectation over (unknown) feature probabilities - thus increasing the number of unknown parameters and seemingly making the problem more complex-actually leads to its simplification, and allows incorporating the physical principle of entropy maximization. It helps decompose a very general setting of this learning problem (including discretization, feature selection, and learning multiple piece-wise linear regressions) into an iterative sequence of simple substeps, which are either analytically solvable or cheaply computable through an efficient second-order numerical solver with a sublinear cost scaling. This leads to the computationally cheap and robust non-DL second-order Sparse Probabilistic Approximation for Regression Task Analysis (SPARTAn) algorithm, that can be efficiently applied to problems with millions of feature dimensions on a commodity laptop, when the state-of-the-art learning tools would require supercomputers. SPARTAn is compared to a range of commonly used regression learning tools on synthetic problems and on the prediction of the El Niño Southern Oscillation, the dominant interannual mode of tropical climate variability. The obtained SPARTAn learners provide more predictive, sparse, and physically explainable data descriptions, clearly discerning the important role of ocean temperature variability at the thermocline in the equatorial Pacific. SPARTAn provides an easily interpretable description of the timescales by which these thermocline temperature features evolve and eventually express at the surface, thereby enabling enhanced predictability of the key drivers of the interannual climate.

[The Need for Psychosocial Support of Parents of Children in Neonatal Care]. / Die Notwendigkeit der psychosozialen Begleitung von Eltern in der Neonatologie.

Background Advances in neonatal care have reduced mortality but increased morbidity in babies born pre-maturely or after high-risk pregnancies. However, this often increases the burden on the family and the parents in particular. A systematic review of the literature was conducted that demonstrated the importance of psychosocial support for parents of children in neonatal care. Methods A systematic search of Pubmed, Psyndex, CINAHI and medpilot was conducted. Reference lists of the included articles were also searched for relevant publications. A free-text search found further publications. Together, 78 publications (from 1975-2015) were included in our review. Results A shift from a biomedical model and child-centred treatment to family-centred care has already taken place in neonatal care. However, there is still a considerable gap between theory and practice. Although there is awareness of the need for psychosocial support of parents, the focus of day-to-day care is still on medical interventions and life-supporting treatment for the child. In particular, while the importance of an assessment of needs as a basis for family-centred psychosocial support appears to be well-known, validated screening instruments are rarely used. In addition, the demand for psychosocial support of parents is not just solely determined by the child's medical risk. Conclusions The results highlight the challenges of delivering individualised psychosocial support to families within a healthcare system of limited resources, with practitioners having to take into account the developing parent-child relationship as well as health economics. In future, psychosocial support should be based on evidence rather than intuition. Attachment theory and research, and health psychology can contribute to this development.

The face scan as a means for the visualization of complex prosthetic reconstructions.

The face scan can serve as a means for the visualization of planes relevant for extensive prosthetic reconstructions. The key prerequisite is the availability of data in stl format for further processing in different software programs. The method is described in this article by a patient case.

Zirconia crowns - the new standard for single-visit dentistry?

Zirconia crowns combine the advantages of metal restorations, such as minimally invasive tooth preparation and ease of cementation, with those of full ceramic crowns, such as low thermal conductivity and tooth color. With the introduction of a high-speed sintering procedure, it is possible to produce and cement zirconia crowns and small monolithic bridges in a Cerec Single Visit procedure. This new procedure is compared to established chairside methods.

[The living will. An analysis of state of knowledge of patients in the HELIOS hospital Leisnig]. / Die Patientenverfügung. Eine Wissensstandanalyse von Patienten im HELIOS Krankenhaus Leisnig.

On September 1, 2009, the Guardianship Law (Betreuungsgesetz) changed for the third time. Thus, the rights of the people living in Germany have been improved with reference to the preventive instruments. The aim of the work was to find about the subject to what extent patients understand the subject and how many of them have such a preventive document. The area of research has been examined on the basis of a standardized questionnaire drawn up by means of anonymous data collection in the quantitative research design. After the pretest, the survey was done in form of a documented Face-to-face-questioning in the HELIOS hospital Leisnig. The sample size comprised 139 patients from which 84 patients (37 female, 47 male) agreed with the survey. 25 percent of the questioned patients knew the law of living will (Patientenverfügungsgesetz) and 27 percent knew about the content of preventive instruments. 46.4 percent of the surveyed patients, aged between 70 and 75, had at least one preventive instrument. 63.1 percent of the patients believed that a living will has to be in a written form. 21.4 percent said that, in addition, the document has to be authenticated by a notary public. A study by van Oorschot claims that between ten and 15 percent of the people living in Germany have a living will (van Oorschot 2008, p. 443, Sahm & Schroeder 2009, p. 98). The results of this study show a much higher proportion of existing living wills. For the majority of respondents a preventive instrument plays still a minor role, but 46 percent of the population already use a preventive instrument to make provisions for themselves. 27 percent did express their will in theform of a living will. Nevertheless, the survey shows a lack of knowledge of the correct handling of the living will.

The international classification of functioning, disability and health in clinical practice, research findings and their impact on training and education.

At the ICF Research Institute (at MSH Medical School Hamburg) multiprofessional experts collaborate on various research projects with a focus on bio-psycho-social health and education. Initially, the main goal was monitoring and evaluating the implementation of the International Classification of Functioning, Disability and Health (ICF) in clinical practice. Over time and based on the initial findings, the research group started developing new approaches to support training and education of health professionals in the use of the ICF. As a result, substantial changes have recently been made in the curriculum and structure of several courses to improve and expand interprofessional teaching at the MSH Medical School Hamburg (MSH). Furthermore, creative didactic approaches in combination with interprofessional education have been developed.

Sparse Quadratic Approximation for Graph Learning.

Learning graphs represented by M-matrices via an l1-regularized Gaussian maximum-likelihood method is a popular approach, but also one that poses computational challenges for large scale datasets. Recently proposed methods cast this problem as a constrained optimization variant of precision matrix estimation. In this paper, we build on a state-of-the-art sparse precision matrix estimation method and introduce two algorithms that learn M-matrices, that can be subsequently used for the estimation of graph Laplacian matrices. In the first one, we propose an unconstrained method that follows a post processing approach in order to learn an M-matrix, and in the second one, we implement a constrained approach based on sequential quadratic programming. We also demonstrate the effectiveness, accuracy, and performance of both algorithms. Our numerical examples and comparative results with modern open-source packages reveal that the proposed methods can accelerate the learning of graphs by up to 3 orders of magnitude, while accurately retrieving the latent graphical structure of the data. Furthermore, we conduct large scale case studies for the clustering of COVID-19 daily cases and the classification of image datasets to highlight the applicability in real-world scenarios.

Multiway p-spectral graph cuts on Grassmann manifolds.

Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background. We present a novel direct multiway spectral clustering algorithm in the p-norm, for p ∈ ( 1 , 2 ] . The problem of computing multiple eigenvectors of the graph p-Laplacian, a nonlinear generalization of the standard graph Laplacian, is recasted as an unconstrained minimization problem on a Grassmann manifold. The value of p is reduced in a pseudocontinuous manner, promoting sparser solution vectors that correspond to optimal graph cuts as p approaches one. Monitoring the monotonic decrease of the balanced graph cuts guarantees that we obtain the best available solution from the p-levels considered. We demonstrate the effectiveness and accuracy of our algorithm in various artificial test-cases. Our numerical examples and comparative results with various state-of-the-art clustering methods indicate that the proposed method obtains high quality clusters both in terms of balanced graph cut metrics and in terms of the accuracy of the labelling assignment. Furthermore, we conduct studies for the classification of facial images and handwritten characters to demonstrate the applicability in real-world datasets.

Needles: Toward Large-Scale Genomic Prediction with Marker-by-Environment Interaction.

Genomic prediction relies on genotypic marker information to predict the agronomic performance of future hybrid breeds based on trial records. Because the effect of markers may vary substantially under the influence of different environmental conditions, marker-by-environment interaction effects have to be taken into account. However, this may lead to a dramatic increase in the computational resources needed for analyzing large-scale trial data. A high-performance computing solution, called Needles, is presented for handling such data sets. Needles is tailored to the particular properties of the underlying algebraic framework by exploiting a sparse matrix formalism where suited and by utilizing distributed computing techniques to enable the use of a dedicated computing cluster. It is demonstrated that large-scale analyses can be performed within reasonable time frames with this framework. Moreover, by analyzing simulated trial data, it is shown that the effects of markers with a high environmental interaction can be predicted more accurately when more records per environment are available in the training data. The availability of such data and their analysis with Needles also may lead to the discovery of highly contributing QTL in specific environmental conditions. Such a framework thus opens the path for plant breeders to select crops based on these QTL, resulting in hybrid lines with optimized agronomic performance in specific environmental conditions.