Theory of the critical state of low-dimensional spin glass

We analyse the critical region of finite-($d$)-dimensional Ising spin glass, in particular the limit of $d$ closely above the lower critical dimension $d_\ell$. At criticality the thermally active degrees of freedom are surfaces (of width zero) enclosing clusters of spins that may reverse with respect to their environment. The surfaces are organised in finite interacting structures. These may be called {\em protodroplets}\/, since in the off-critical limit they reduce to the Fisher and Huse droplets. This picture provides an explanation for the phenomenon of critical chaos discovered earlier. It also implies that the spin-spin and energy-energy correlation functions are multifractal and we present scaling laws that describe them. Several of our results should be verifiable in Monte Carlo studies at finite temperature in $d=3$.Comment: RevTeX, 33 pages + 1 PostScript figure (uuencoded). Uses german.sty and an input file def.tex, joined. Three additional figures may be requested from the author

Parents perceptions of the care their child receives in child care facilities in Barron County Wisconsin

Includes bibliographical references

Machine learning and deep learning approaches for multivariate time series prediction and anomaly detection

In many real-world applications today, it is critical to continuously record and monitor certain machine or system health indicators to discover malfunctions or other abnormal behavior at an early stage and prevent potential harm. The demand for such reliable monitoring systems is expected to increase in the coming years. Particularly in the industrial context, in the course of ongoing digitization, it is becoming increasingly important to analyze growing volumes of data in an automated manner using state-of-the-art algorithms. In many practical applications, one has to deal with temporal data in the form of data streams or time series. The problem of detecting unusual (or anomalous) behavior in time series is commonly referred to as time series anomaly detection. Anomalies are events observed in the data that do not conform to the normal or expected behavior when viewed in their temporal context.This thesis focuses on unsupervised machine learning algorithms for anomaly detection in time series. In an unsupervised learning setup, a model attempts to learn the normal behavior in a time series — which might already be contaminated with anomalies — without any external assistance. The model can then use its learned notion of normality to detect anomalous events. Algorithms and the Foundations of Software technolog

Requirements for Robotic Interpretation of Social Signals “in the Wild”: Insights from Diagnostic Criteria of Autism Spectrum Disorder

The last few decades have seen widespread advances in technological means to characterise observable aspects of human behaviour such as gaze or posture. Among others, these developments have also led to significant advances in social robotics. At the same time, however, social robots are still largely evaluated in idealised or laboratory conditions, and it remains unclear whether the technological progress is sufficient to let such robots move “into the wild”. In this paper, we characterise the problems that a social robot in the real world may face, and review the technological state of the art in terms of addressing these. We do this by considering what it would entail to automate the diagnosis of Autism Spectrum Disorder (ASD). Just as for social robotics, ASD diagnosis fundamentally requires the ability to characterise human behaviour from observable aspects. However, therapists provide clear criteria regarding what to look for. As such, ASD diagnosis is a situation that is both relevant to real-world social robotics and comes with clear metrics. Overall, we demonstrate that even with relatively clear therapist-provided criteria and current technological progress, the need to interpret covert behaviour cannot yet be fully addressed. Our discussions have clear implications for ASD diagnosis, but also for social robotics more generally. For ASD diagnosis, we provide a classification of criteria based on whether or not they depend on covert information and highlight present-day possibilities for supporting therapists in diagnosis through technological means. For social robotics, we highlight the fundamental role of covert behaviour, show that the current state-of-the-art is unable to characterise this, and emphasise that future research should tackle this explicitly in realistic settings

Sentence Processing: Linking Language to Motor Chains

A growing body of evidence in cognitive science and neuroscience points towards the existence of a deep interconnection between cognition, perception and action. According to this embodied perspective language is grounded in the sensorimotor system and language understanding is based on a mental simulation process (Jeannerod, 2007; Gallese, 2008; Barsalou, 2009). This means that during action words and sentence comprehension the same perception, action, and emotion mechanisms implied during interaction with objects are recruited. Among the neural underpinnings of this simulation process an important role is played by a sensorimotor matching system known as the mirror neuron system (Rizzolatti and Craighero, 2004). Despite a growing number of studies, the precise dynamics underlying the relation between language and action are not yet well understood. In fact, experimental studies are not always coherent as some report that language processing interferes with action execution while others find facilitation. In this work we present a detailed neural network model capable of reproducing experimentally observed influences of the processing of action-related sentences on the execution of motor sequences. The proposed model is based on three main points. The first is that the processing of action-related sentences causes the resonance of motor and mirror neurons encoding the corresponding actions. The second is that there exists a varying degree of crosstalk between neuronal populations depending on whether they encode the same motor act, the same effector or the same action-goal. The third is the fact that neuronal populations’ internal dynamics, which results from the combination of multiple processes taking place at different time scales, can facilitate or interfere with successive activations of the same or of partially overlapping pools

Chaos in a Two-Dimensional Ising Spin Glass

We study chaos in a two dimensional Ising spin glass by finite temperature Monte Carlo simulations. We are able to detect chaos with respect to temperature changes as well as chaos with respect to changing the bonds, and find that the chaos exponents for these two cases are equal. Our value for the exponent appears to be consistent with that obtained in studies at zero temperature.Comment: 4 pages, LaTeX, 4 postscript figures included. The analysis of the data is now done somewhat differently. The results are consistent with the chaos exponent found at zero temperature. Additional papers of PY can be obtained on-line at http://schubert.ucsc.edu/pete