Agglomerative Clustering of Growing Squares

We study an agglomerative clustering problem motivated by interactive glyphs in geo-visualization. Consider a set of disjoint square glyphs on an interactive map. When the user zooms out, the glyphs grow in size relative to the map, possibly with different speeds. When two glyphs intersect, we wish to replace them by a new glyph that captures the information of the intersecting glyphs. We present a fully dynamic kinetic data structure that maintains a set of $n$ disjoint growing squares. Our data structure uses $O(n (\log n \log\log n)^2)$ space, supports queries in worst case $O(\log^3 n)$ time, and updates in $O(\log^7 n)$ amortized time. This leads to an $O(n\alpha(n)\log^7 n)$ time algorithm to solve the agglomerative clustering problem. This is a significant improvement over the current best $O(n^2)$ time algorithms.Comment: 14 pages, 7 figure

Quantitative Validation: An Overview and Framework for PD Backtesting and Benchmarking.

The aim of credit risk models is to identify and quantify future outcomes of a set of risk measurements. In other words, the model's purpose is to provide as good an approximation as possible of what constitutes the true underlying risk relationship between a set of inputs and a target variable. These parameters are used for regulatory capital calculations to determine the capital needed that serves a buffer to protect depositors in adverse economic conditions. In order to manage model risk, financial institutions need to set up validation processes so as to monitor the quality of the models on an ongoing basis. Validation is important to inform all stakeholders (e.g. board of directors, senior management, regulators, investors, borrowers, …) and as such allow them to make better decisions. Validation can be considered from both a quantitative and qualitative point of view. Backtesting and benchmarking are key quantitative validation tools. In backtesting, the predicted risk measurements (PD, LGD, CCF) will be contrasted with observed measurements using a workbench of available test statistics to evaluate the calibration, discrimination and stability of the model. A timely detection of reduced performance is crucial since it directly impacts profitability and risk management strategies. The aim of benchmarking is to compare internal risk measurements with external risk measurements so to allow to better gauge the quality of the internal rating system. This paper will focus on the quantitative PD validation process within a Basel II context. We will set forth a traffic light indicator approach that employs all relevant statistical tests to quantitatively validate the used PD model, and document this complete approach with a reallife case-study.Framework; Benchmarking; Credit; Credit scoring; Control;

Decrease of a specific biomarker of collagen degradation in osteoarthritis, Coll2-1, by treatment with highly bioavailable curcumin during an exploratory clinical trial

BACKGROUND: The management of osteoarthritis (OA) remains a challenge. There is a need not only for safe and efficient treatments but also for accurate and reliable biomarkers that would help diagnosis and monitoring both disease activity and treatment efficacy. Curcumin is basically a spice that is known for its anti-inflammatory properties. In vitro studies suggest that curcumin could be beneficial for cartilage in OA. The aim of this exploratory, non-controlled clinical trial was to evaluate the effects of bio-optimized curcumin in knee OA patients on the serum levels of specific biomarkers of OA and on the evaluation of pain. METHODS: Twenty two patients with knee OA were asked to take 2x3 caps/day of bio-optimized curcumin (Flexofytol®) for 3 months. They were monitored after 7, 14, 28 and 84 days of treatment. Pain over the last 24 hours and global assessment of disease activity by the patient were evaluated using a visual analog scale (100 mm). The serum levels of Coll-2-1, Coll-2-1NO(2), Fib3-1, Fib3-2, CRP, CTX-II and MPO were determined before and after 14 and 84 days of treatment. RESULTS: The treatment with curcumin was globally well tolerated. It significantly reduced the serum level of Coll2-1 (p < 0.002) and tended to decrease CRP. No other significant difference was observed with the other biomarkers. In addition, curcumin significantly reduced the global assessment of disease activity by the patient. CONCLUSION: This study highlighted the potential effect of curcumin in knee OA patient. This effect was reflected by the variation of a cartilage specific biomarker, Coll2-1 that was rapidly affected by the treatment. These results are encouraging for the qualification of Coll2-1 as a biomarker for the evaluation of curcumin in OA treatment. TRIAL REGISTRATION: NCT01909037 at clinicaltrials.go

In moratorio: het recht op nakoming

Coherent privaatrech

From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation

Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy

Sensitivity of the IceCube Detector to Astrophysical Sources of High Energy Muon Neutrinos

We present the results of a Monte-Carlo study of the sensitivity of the planned IceCube detector to predicted fluxes of muon neutrinos at TeV to PeV energies. A complete simulation of the detector and data analysis is used to study the detector's capability to search for muon neutrinos from sources such as active galaxies and gamma-ray bursts. We study the effective area and the angular resolution of the detector as a function of muon energy and angle of incidence. We present detailed calculations of the sensitivity of the detector to both diffuse and pointlike neutrino emissions, including an assessment of the sensitivity to neutrinos detected in coincidence with gamma-ray burst observations. After three years of datataking, IceCube will have been able to detect a point source flux of E^2*dN/dE = 7*10^-9 cm^-2s^-1GeV at a 5-sigma significance, or, in the absence of a signal, place a 90% c.l. limit at a level E^2*dN/dE = 2*10^-9 cm^-2s^-1GeV. A diffuse E-2 flux would be detectable at a minimum strength of E^2*dN/dE = 1*10^-8 cm^-2s^-1sr^-1GeV. A gamma-ray burst model following the formulation of Waxman and Bahcall would result in a 5-sigma effect after the observation of 200 bursts in coincidence with satellite observations of the gamma-rays.Comment: 33 pages, 13 figures, 6 table

Limits on diffuse fluxes of high energy extraterrestrial neutrinos with the AMANDA-B10 detector

Data from the AMANDA-B10 detector taken during the austral winter of 1997 have been searched for a diffuse flux of high energy extraterrestrial muon-neutrinos, as predicted from, e.g., the sum of all active galaxies in the universe. This search yielded no excess events above those expected from the background atmospheric neutrinos, leading to upper limits on the extraterrestrial neutrino flux. For an assumed E^-2 spectrum, a 90% classical confidence level upper limit has been placed at a level E^2 Phi(E) = 8.4 x 10^-7 GeV cm^-2 s^-1 sr^-1 (for a predominant neutrino energy range 6-1000 TeV) which is the most restrictive bound placed by any neutrino detector. When specific predicted spectral forms are considered, it is found that some are excluded.Comment: Submitted to Physical Review Letter