923 research outputs found

    Products of Random Matrices

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    We derive analytic expressions for infinite products of random 2x2 matrices. The determinant of the target matrix is log-normally distributed, whereas the remainder is a surprisingly complicated function of a parameter characterizing the norm of the matrix and a parameter characterizing its skewness. The distribution may have importance as an uncommitted prior in statistical image analysis.Comment: 9 pages, 1 figur

    Nutritional Rehabilitation: Practical Guidelines for Refeeding the Anorectic Patient

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    Weight restoration is crucial for successful treatment of anorexia nervosa. Without it, patients may face serious or even fatal medical complications of severe starvation. However, the process of nutritional rehabilitation can also be risky to the patient. The refeeding syndrome, a problem of electrolyte and fluid shifts, can cause permanent disability or even death. It is essential to identify at-risk patients, to monitor them carefully, and to initiate a nutritional rehabilitation program that aims to avoid the refeeding syndrome. A judicious, slow initiation of caloric intake, requires daily management to respond to entities such as liver inflammation and hypoglycemia that can complicate the body's conversion from a catabolic to an anabolic state. In addition, nutritional rehabilitation should take into account clinical characteristics unique to these patients, such as gastroparesis and slowed colonic transit, so that measures can be taken to ameliorate the physical discomforts of weight restoration. Adjunct methods of refeeding such as the use of enteral or parenteral nutrition may play a small but important role in a select patient group who cannot tolerate oral nutritional rehabilitation alone

    Architektur eines Open Source BI Systems mit Geo-Erweiterung

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    In diesem Paper wird die Kombination von einer Open Source BI Lösung mit geo-bezogener Analyse und Visualisierung adressiert. Im Fokus stehen dabei die Architektur des Gesamt-systems sowie die Kommunikation der BI und Geo-Visualisierungsbezogenen Komponenten. Ziel ist es, eine Widget-basierte Architektur als Best-Practice Ansatz vorzustellen und aufzuzei-gen, welche Vorteile diese in der Nutzung bietet. Die Evaluierung des vorgestellten Ansatzes erfolgt in einem Anwendungsfall aus dem Gesundheitswesen

    Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification

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    Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classification improves with the addition of eye pose information? We answer these questions by evaluating data drawn from an on-road study of 40 drivers. The main insight of the paper is conveyed through the analogy of an "owl" and "lizard" which describes the degree to which the eyes and the head move when shifting gaze. When the head moves a lot ("owl"), not much classification improvement is attained by estimating eye pose on top of head pose. On the other hand, when the head stays still and only the eyes move ("lizard"), classification accuracy increases significantly from adding in eye pose. We characterize how that accuracy varies between people, gaze strategies, and gaze regions.Comment: Accepted for Publication in IET Computer Vision. arXiv admin note: text overlap with arXiv:1507.0476

    Major flaws in conflict prevention policies towards Africa : the conceptual deficits of international actors’ approaches and how to overcome them

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    Current thinking on African conflicts suffers from misinterpretations oversimplification, lack of focus, lack of conceptual clarity, state-centrism and lack of vision). The paper analyses a variety of the dominant explanations of major international actors and donors, showing how these frequently do not distinguish with sufficient clarity between the ‘root causes’ of a conflict, its aggravating factors and its triggers. Specifically, a correct assessment of conflict prolonging (or sustaining) factors is of vital importance in Africa’s lingering confrontations. Broader approaches (e.g. “structural stability”) offer a better analytical framework than familiar one-dimensional explanations. Moreover, for explaining and dealing with violent conflicts a shift of attention from the nation-state towards the local and sub-regional level is needed.Aktuelle Analysen afrikanischer Gewaltkonflikte sind hĂ€ufig voller Fehlinterpretationen (Mangel an Differenzierung, Genauigkeit und konzeptioneller Klarheit, Staatszentriertheit, fehlende mittelfristige Zielvorstellungen). Breitere AnsĂ€tze (z. B. das Modell der Strukturellen StabilitĂ€t) könnten die Grundlage fĂŒr bessere Analyseraster und Politiken sein als eindimensionale ErklĂ€rungen. hĂ€ufig differenzieren ErklĂ€rungsansĂ€tze nicht mit ausreichender Klarheit zwischen Ursachen, verschĂ€rfenden und auslösenden Faktoren. Insbesondere die richtige Einordnung konfliktverlĂ€ngernder Faktoren ist in den jahrzehntelangen gewaltsamen Auseinandersetzungen in Afrika von zentraler Bedeutung. Das Diskussionspapier stellt die große Variationsbreite dominanter ErklĂ€rungsmuster der wichtigsten internationalen Geber und Akteure gegenĂŒber und fordert einen Perspektivenwechsel zum Einbezug der lokalen und der subregionalen Ebene fĂŒr die ErklĂ€rung und Bearbeitung gewaltsamer Konflikte

    A New Distribution-Sensitive Secure Sketch and Popularity-Proportional Hashing

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    Motivated by typo correction in password authentication, we investigate cryptographic error-correction of secrets in settings where the distribution of secrets is a priori (approximately) known. We refer to this as the distribution-sensitive setting. We design a new secure sketch called the layer-hiding hash (LHH) that offers the best security to date. Roughly speaking, we show that LHH saves an additional log H_0(W) bits of entropy compared to the recent layered sketch construction due to Fuller, Reyzin, and Smith (FRS). Here H_0(W) is the size of the support of the distribution W. When supports are large, as with passwords, our new construction offers a substantial security improvement. We provide two new constructions of typo-tolerant password-based authentication schemes. The first combines a LHH or FRS sketch with a standard slow-to-compute hash function, and the second avoids secure sketches entirely, correcting typos instead by checking all nearby passwords. Unlike the previous such brute-force-checking construction, due to Chatterjee et al., our new construction uses a hash function whose run-time is proportional to the popularity of the password (forcing a longer hashing time on more popular, lower entropy passwords). We refer to this as popularity-proportional hashing (PPH). We then introduce a frame-work for comparing different typo-tolerant authentication approaches. We show that PPH always offers a better time / security trade-off than the LHH and FRS constructions, and for certain distributions outperforms the Chatterjee et al. construction. Elsewhere, this latter construction offers the best trade-off. In aggregate our results suggest that the best known secure sketches are still inferior to simpler brute-force based approaches

    Systematic Overestimation of Machine Learning Performance in Neuroimaging Studies of Depression

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    We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls. Drawing upon a balanced sample of N=1,868N = 1,868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes (N=4N=4 to N=150N=150) from the population and showed a strong risk of overestimation. Specifically, for small sample sizes (N=20N=20), we observe accuracies of up to 95%. For medium sample sizes (N=100N=100) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance overestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets
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