Long-term outcome of ten children with opsoclonus-myoclonus syndrome

Opsoclonus-myoclonus syndrome (OMS) in children is a rare neurological condition with opsoclonus, myoclonus, ataxia and irritability in the first 2 years of life. It can be idiopathic, parainfectious, or paraneoplastic with tumours of the neural crest. Few studies of long-term follow-up after OMS have been published. We investigated the motor, cognitive and behavioural outcome of ten patients (eight girls and two boys) seen between 1987 and 2002. We reviewed the records and reassessed the patients. A ganglioneuroma was found in one patient and a neuroblastoma in another. Tumour resection did not influence the OMS. The age at diagnosis was 10-24months and the follow-up period 1-17years (average 6.5years). The interval between the first signs and symptoms and starting treatment was 2-12weeks: treatment consisted of different immunosupressants. Remission was achieved within 5months in seven, and relapses were present in seven of ten. At follow-up, only one child had mild ataxia. IQ testing was performed in nine with scores below 75 in four and above 85 in four. Attention deficit and visuomotor difficulties led to school problems with special needs, also in those three children with normal IQs. Only two children were attending regular schools. Behavioural problems were reported in seven, and speech difficulties were present in five. In conclusion, the long-term outcome in our patients with OMS was dominated by cognitive and behavioural problems and not by ataxia. Compared with previous reports, our patients were treated earlier. Larger studies and uniform treatment protocols are needed to demonstrate whether early and prolonged immunosupressant therapy has a favourable influence on outcom

Good corporate social performance may lead to higher credit ratings

But only for firms whose countries share the same values, find Christian Klein, Christoph Stellner and Bernhard Zwerge

On the Non-Associativity of Analog Computations

The energy efficiency of analog forms of computing makes it one of the most promising candidates to deploy resource-hungry machine learning tasks on resource-constrained system such as mobile or embedded devices. However, it is well known that for analog computations the safety net of discretization is missing, thus all analog computations are exposed to a variety of imperfections of corresponding implementations. Examples include non-linearities, saturation effect and various forms of noise. In this work, we observe that the ordering of input operands of an analog operation also has an impact on the output result, which essentially makes analog computations non-associative, even though the underlying operation might be mathematically associative. We conduct a simple test by creating a model of a real analog processor which captures such ordering effects. With this model we assess the importance of ordering by comparing the test accuracy of a neural network for keyword spotting, which is trained based either on an ordered model, on a non-ordered variant, and on real hardware. The results prove the existence of ordering effects as well as their high impact, as neglecting ordering results in substantial accuracy drops.Comment: Published at the ECML PKDD Conference 2023, at the 4th Workshop on IoT, Edge, and Mobile for Embedded Machine Learnin

Approximating Intersections and Differences Between Linear Statistical Shape Models Using Markov Chain Monte Carlo

To date, the comparison of Statistical Shape Models (SSMs) is often solely performance-based, carried out by means of simplistic metrics such as compactness, generalization, or specificity. Any similarities or differences between the actual shape spaces can neither be visualized nor quantified. In this paper, we present a new method to qualitatively compare two linear SSMs in dense correspondence by computing approximate intersection spaces and set-theoretic differences between the (hyper-ellipsoidal) allowable shape domains spanned by the models. To this end, we approximate the distribution of shapes lying in the intersection space using Markov chain Monte Carlo and subsequently apply Principal Component Analysis (PCA) to the posterior samples, eventually yielding a new SSM of the intersection space. We estimate differences between linear SSMs in a similar manner; here, however, the resulting spaces are no longer convex and we do not apply PCA but instead use the posterior samples for visualization. We showcase the proposed algorithm qualitatively by computing and analyzing intersection spaces and differences between publicly available face models, focusing on gender-specific male and female as well as identity and expression models. Our quantitative evaluation based on SSMs built from synthetic and real-world data sets provides detailed evidence that the introduced method is able to recover ground-truth intersection spaces and differences accurately.Comment: Accepted to WACV'2

Individual investment advice is key for sustainable investment behaviour

Given the global environmental and societal challenges of the 21st century, the concept of sustainability is becoming increasingly important in the financial markets. It seems necessary to involve private individuals in the transition to a sustainable economy. However, investment preferences are very heterogeneous, so that investment products customised to "averaged" preferences often fail to achieve the goal of promoting sustainable [...

Computational Urban Planning: Using the Value Lab as Control Center

Urban planning involves many aspects and various disciplines, demanding an asynchronous planning approach. The level of complexity rises with each aspect to be considered and makes it difficult to find universally satisfactory solutions. To improve this situation we propose a new approach, which complement traditional design methods with a computational urban plan- ning method that can fulfil formalizable design requirements automatically. Based on this approach we present a design space exploration framework for complex urban planning projects. For a better understanding of the idea of design space exploration, we introduce the concept of a digital scout which guides planners through the design space and assists them in their creative explorations. The scout can support planners during manual design by informing them about potential im- pacts or by suggesting different solutions that fulfill predefined quality requirements. The planner can change flexibly between a manually controlled and a completely automated design process. The developed system is presented using an exemplary urban planning scenario on two levels from the street layout to the placement of building volumes. Based on Self-Organizing Maps we implemented a method which makes it possible to visualize the multi-dimensional solution space in an easily analysable and comprehensible form

Einflussfaktoren auf die Wahl einer Markteintrittsstrategie: Eine meta-analytische Untersuchung der Entscheidung zwischen Tochtergesellschaft und Kooperation

Zusammenfassung: Die Wahl einer Markteintrittsstrategie, eine der wichtigsten Entscheidungen im Kontext der Internationalisierung, wurde in zahlreichen empirischen Untersuchungen analysiert. Die vorliegenden Ergebnisse sind vielfältig, teilweise kontrovers und nur noch schwer zu überschauen. In der vorliegenden Meta-Analyse werden empirische Ergebnisse zur Entscheidung zwischen den Alternativen Tochtergesellschaft und Kooperation integriert. Auf Basis von 61 empirischen Untersuchungen wird analysiert, welche Einflussfaktoren auf die Entscheidung bislang betrachtet wurden und welche bei der Kombination der bisherigen Studienergebnisse einen signifikanten Einfluss aufweisen. Zugleich wird ein Bezug zu unterschiedlichen theoretischen Ansätzen, welche einen Erklärungsbeitrag zur Wahl der Markteintrittsentscheidung liefern, hergestellt. Als Ergebnisse können zusammenfassend signifikante Einflüsse der Mitarbeiterzahl des Unternehmens, der Machtabstandstoleranz im Stammland, der landesspezifischen internationalen Erfahrung, der Werbe- und Exportintensität, der internationalen Produktdiversifikation, des Marktwachstums, der Ressourcen-Intensität der Auslandseinheit, der Marktgröße, der staatlichen Restriktionen im Gastland und des Länderrisikos festgestellt werden. Nicht-signifikante Ergebnisse werden für eine Vielzahl von Variablen erzielt, so für die Faktorspezifität, die F&E-Intensität, die Größe der Tochtergesellschaft, das Vermögen und den Umsatz des Unternehmen