Step roughness on Ag(111) investigated by STM: a systematic study of tip influence

We have investigated monatomic steps on Ag(111) by STM at different temperatures. At room temperature, the rough appearance of these steps is usually attributed to thermal step fluctuations. We have investigated the influence of the tip systematically. Applying a new test, we demonstrate that even subtle influences can lead to wrong results in statistical analysis

The reliability and validity of the Sexual Violence Risk-20 (SVR-20): An International Review

This article reports on the current state of research about the most commonly used Structured Professional Judgement (SPJ) guidelines for sexual offender risk assessment, the Sexual Violence Risk-20 (SVR-20). After describing the general characteristics as well as frequently discussed strengths and weaknesses of this risk assessment approach, we give an international overview of the empirical results of the reliability and validity of the SVR-20. We conclude by describing briefly a convergent strategy for sexual offender risk assessment incorporating the SVR-20 and offer some future directions for international research on the SPJ approach

Hardware-aware block size tailoring on adaptive spacetree grids for shallow water waves.

Spacetrees are a popular formalism to describe dynamically adaptive Cartesian grids. Though they directly yield an adaptive spatial discretisation, i.e. a mesh, it is often more efficient to augment them by regular Cartesian blocks embedded into the spacetree leaves. This facilitates stencil kernels working efficiently on homogeneous data chunks. The choice of a proper block size, however, is delicate. While large block sizes foster simple loop parallelism, vectorisation, and lead to branch-free compute kernels, they bring along disadvantages. Large blocks restrict the granularity of adaptivity and hence increase the memory footprint and lower the numerical-accuracy-per-byte efficiency. Large block sizes also reduce the block-level concurrency that can be used for dynamic load balancing. In the present paper, we therefore propose a spacetree-block coupling that can dynamically tailor the block size to the compute characteristics. For that purpose, we allow different block sizes per spacetree node. Groups of blocks of the same size are identied automatically throughout the simulation iterations, and a predictor function triggers the replacement of these blocks by one huge, regularly rened block. This predictor can pick up hardware characteristics while the dynamic adaptivity of the fine grid mesh is not constrained. We study such characteristics with a state-of-the-art shallow water solver and examine proper block size choices on AMD Bulldozer and Intel Sandy Bridge processors

Aktuelle Entwicklungen und Perspektiven psychologisch fundierter Kriminalprognosen

Im forensisch-psychologischen Begutachtungsbereich haben kriminalprognostische Gutachten und Stellungnahmen einen hohen Stellenwert. Die Entwicklung der methodischen Zugänge zu kriminalprognostischen Einschätzungen wird beschrieben: vom (1) intuitiven Vorgehen zur (2) ersten statistisch-aktuarischen Prognose und (3) deren Weiterentwicklung um dynamische Risikofaktoren. Mit (4) der zusätzlichen Bereitstellung eines individuellen (idiografischen) Erklärungsmodells erfolgt eine Erweiterung von (3). Aktuelle Studienergebnisse mit Relevanz für die rechtspsychologische und forensisch-klinische Praxis werden referiert. Auf die Anwendung von Kriminalprognoseinstrumenten bei besonderen Gruppen (Sicherungsverwahrte, Patienten im Maßregelvollzug) wird ebenso eingegangen wie auf die kriminalprognostische Relevanz des Alters und klinischer Diagnosen wie sexuelle Präferenzstörungen oder Psychopathie. Abschließend wird auf die Bedeutung der Art und Weise der Risikokommunikation kriminalprognostischer Ergebnisse an den bzw. die Auftraggeber im Rahmen von Entscheidungen zur Verhängung, Aufrechterhaltung oder Beendigung freiheitsentziehender Maßnahmen eingegangen

Die Einschätzung der Gefährlichkeit bei extremistischer Gewalt und Terrorismus

Berichtet wird über neue Instrumente zur Gefährlichkeitseinschätzung. Die Entwicklung valider statistischer Prognoseinstrumente für Terrorismus und extremistische Gewalt ist aufgrund der kleinen Basisgruppen extrem aufwendig und deshalb kaum fortgeschritten. Prognosen mithilfe persönlicher Erfahrungen haben demgegenüber nur geringe Vorhersagekraft. Das Instrument VERA-2 wurde auf Grundlage einer Analyse von Fachliteratur und Experteninterviews in Kanada entwickelt: 28 Faktoren, darunter auch protektive und demographische Faktoren, werden in drei bzw. zwei Ausprägungen bewertet, woraus sich eine Einschätzung des Gesamtrisikos ergibt. Ergänzend wird die Erstellung einer individuellen Fallbeschreibung empfohlen. Das von einem amerikanischen Psychologen entwickelte Verfahren TRAP-18 unterscheidet zwei Gruppen von Indikatoren, deren Vorliegen Warnhinweise sind und eine genauere Untersuchung des Falls erfordern, ohne aus Zahlenwerten einen bestimmten Risikograd zu bilden. Beide Instrumente fordern vom Anwender großes Fachwissen. Erläutert wird zudem der Zusammenhang zwischen falsch-positiven und falsch- negativen Prognosen

Métodos de piano para niños de preescolar y frustración un estudio de caso comparativo

This thesis is based on the study of Huang (2007), which analyzes the quality of various methods of piano for children between 4 and 5 years and created a guide called Aspect-Fil-Lo-Musical. The assumption of Huang (2007) is that the methods more aligned with the principles of the guide are less likely to frustrate preschoolers. This thesis examined how and to what degree the preschool piano method that has greater similarity with the standards of the Fil-Lo-Musical- Aspect will cause in the child a lower frustration level than the method with lower similarity through case analysis. The application of two methods of piano analyzed by Huang (2007) in two children between 4-5 years was observed. Data was collected about their frustrating behaviors for 5 individual piano lessons for each child with TOF tool (Test Observation Form). These data were analyzed qualitatively when compared with the information gathered in the literature review about the frustration and the general and musical development of children between 4 and 5 years. The results show that the child's frustration level of the method with greater similarity to the guide created by Huang (2007) were lower than those of the child who was instructed by the method with less similarity.Esta tesis se basa en el estudio de Huang (2007), el cual analiza la calidad de diversos métodos de piano para niños entre 4 y 5 años y crea una guía llamada Aspecto- Fil-Lo- Musical. La suposición de Huang (2007) es que los métodos más alineados con los principios de su guía tienen una menor probabilidad de frustrar a los estudiantes de preescolar. Esta tesis examinó cómo y hasta qué punto el método de piano para preescolar que tiene una mayor similitud con los estándares del Aspecto Fil-Lo-Musical provocará en el niño un nivel de frustración más bajo que el método con una menor similitud a través de un análisis de caso. Se observó la aplicación de dos métodos de piano analizados por Huang (2007) en dos niños entre 4 a 5 años. Se recogió datos acerca de comportamientos frustrantes de ellos durante 5 clases de piano individuales para cada niño con la herramienta TOF (Test Observation Form). Estos datos fueron analizados de forma cualitativa al compararlos con la información recogida en la revisión de literatura acerca de la frustración y los aspectos generales y musicales del desarrollo de los niños entre 4 y 5 años. Los resultados demuestran que los niveles de frustración del niño del método con una mayor similitud con la guía creada por Huang (2007) fueron más bajos que los del niño que fue instruido con el método con una menor similitud. Más investigaciones son necesarias para descartar que estos resultados sean consecuencia de otros factores más allá de los métodos

Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels

Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical image recognition, as datasets are generally small-scale, challenging to obtain, expensive to label, and frequently heterogeneously labeled. Furthermore, heterogeneous labels are a challenge for supervised methods. If not all classes are labeled for an individual sample, supervised deep learning approaches can only learn on a subset of the dataset with common labels for each individual sample; consequently, biomedical image recognition engineers need to be frugal concerning their label and ground truth requirements. This paper discusses the effects of frugal labeling and proposes to train neural networks for multi-class semantic segmentation on heterogeneously labeled data based on a novel objective function. The objective function combines a class asymmetric loss with the Dice loss. The approach is demonstrated for training on the sparse ground truth of a heterogeneous labeled dataset, training within a transfer learning setting, and the use-case of merging multiple heterogeneously labeled datasets. For this purpose, a biomedical small-scale, multi-class semantic segmentation dataset is utilized. The heartSeg dataset is based on the medaka fish’s position as a cardiac model system. Automating image recognition and semantic segmentation enables high-throughput experiments and is essential for biomedical research. Our approach and analysis show competitive results in supervised training regimes and encourage frugal labeling within biomedical image recognition

How automation, machine learning, and DNA barcoding can accelerate species discovery in “dark taxa”: Robotics and AI

Robotics and artificial intelligence are two methods that are suitable for improving processes that are normally done manually. Therefore, these techniques also can be used when examining specimen-rich invertebrate samples, where traditional sorting methods are to slow and require expert knowledge. For that reason, we developed the DiversityScanner: a classification, sorting, and measurement robot for invertebrates. The 500 x 500 x 500 mm robot has three linear axes that enable a camera unit and an automated pipette to be moved over a square Petri dish, containing up to 150 specimens. After starting the DiversityScanner the image taken by an overview camera mounted directly above the Petri dish is utilized to calculate the position of the insects. Then the camera unit is moved over one specimen to capture high resolution detailed images. Convolutional neuronal networks (CNNs) are then used to classify the specimen into 14 different insect taxa (mostly families) and the specimen length and volume are estimated. In a final step, the specimen is moved into a microplate using an automated pipette. In this talk we show how the DiversityScanner uses automation and artificial intelligence to take advantage of previously nearly untapped resources in the study of specimen-rich invertebrate samples