An approach to predicting bowing control parameter contours in violin performance

We present a machine learning approach to modeling bowing control parametercontours in violin performance. Using accurate sensing techniqueswe obtain relevant timbre-related bowing control parameters such as bowtransversal velocity, bow pressing force, and bow-bridge distance of eachperformed note. Each performed note is represented by a curve parametervector and a number of note classes are defined. The principal componentsof the data represented by the set of curve parameter vectors are obtainedfor each class. Once curve parameter vectors are expressed in the new spacedefined by the principal components, we train a model based on inductivelogic programming, able to predict curve parameter vectors used for renderingbowing controls. We evaluate the prediction results and show the potentialof the model by predicting bowing control parameter contours from anannotated input score.

Statistical modeling of bowing control applied to violin sound synthesis

Excitation-continuous music instrument control patterns are often not explicitly represented in current sound synthesis techniques when applied to automatic performance. Both physical model-based and sample-based synthesis paradigmswould benefit from a flexible and accurate instrument control model, enabling the improvement of naturalness and realism. Wepresent a framework for modeling bowing control parameters inviolin performance. Nearly non-intrusive sensing techniques allow for accurate acquisition of relevant timbre-related bowing control parameter signals.We model the temporal contour of bow velocity, bow pressing force, and bow-bridge distance as sequences of short Bézier cubic curve segments. Considering different articulations, dynamics, and performance contexts, a number of note classes are defined. Contours of bowing parameters in a performance database are analyzed at note-level by following a predefined grammar that dictates characteristics of curve segment sequences for each of the classes in consideration. As a result, contour analysis of bowing parameters of each note yields an optimal representation vector that is sufficient for reconstructing original contours with significant fidelity. From the resulting representation vectors, we construct a statistical model based on Gaussian mixtures suitable for both the analysis and synthesis of bowing parameter contours. By using the estimated models, synthetic contours can be generated through a bow planning algorithm able to reproduce possible constraints caused by the finite length of the bow. Rendered contours are successfully used in two preliminary synthesis frameworks: digital waveguide-based bowed stringphysical modeling and sample-based spectral-domain synthesis.

Susceptibilitat genètica al càncer gàstric: caracterització de les interaccions de segon ordre entre gens i variants identificades en l’estudi EPIC‐EURGAST

En aquest Treball de Final de Grau s’exposen els resultats de l’anàlisi de les dades genètiques del projecte EurGast2 "Genetic susceptibility, environmental exposure and gastric cancer risk in an European population”, estudi cas‐control niat a la cohort europea EPIC “European Prospective lnvestigation into Cancer and Nutrition”, que té per objectiu l’estudi dels factors genètics i ambientals associats amb el risc de desenvolupar càncer gàstric (CG). A partir de les dades resultants de l’estudi EurGast2, en el què es van analitzar 1.294 SNPs en 365 casos de càncer gàstric i 1.284 controls en l’anàlisi Single SNP previ, la hipòtesi de partida del present Treball de Final de Grau és que algunes variants amb un efecte marginal molt feble, però que conjuntament amb altres variants estarien associades al risc de CG, podrien no haver‐se detectat. Així doncs, l’objectiu principal del projecte és la identificació d’interaccions de segon ordre entre variants genètiques de gens candidats implicades en la carcinogènesi de càncer gàstric. L’anàlisi de les interaccions s’ha dut a terme aplicant el mètode estadístic Model‐based Multifactor Dimensionality Reduction Method (MB‐MDR), desenvolupat per Calle et al. l’any 2008 i s’han aplicat dues metodologies de filtratge per seleccionar les interaccions que s’exploraran: 1) filtratge d’interaccions amb un SNP significatiu en el Single SNP analysis i 2) filtratge d’interaccions segons la mesura Sinèrgia. Els resultats del projecte han identificat 5 interaccions de segon ordre entre SNPs associades significativament amb un major risc de desenvolupar càncer gàstric, amb p‐valor inferior a 10‐4. Les interaccions identificades corresponen a interaccions entre els gens MPO i CDH1, XRCC1 i GAS6, ADH1B i NR5A2 i IL4R i IL1RN (que s’ha validat en les dues metodologies de filtratge). Excepte CDH1, cap altre d’aquests gens s’havia associat significativament amb el CG o prioritzat en les anàlisis prèvies, el que confirma l’interès d’analitzar les interaccions genètiques de segon ordre. Aquestes poden ser un punt de partida per altres anàlisis destinades a confirmar gens putatius i a estudiar a nivell biològic i molecular els mecanismes de carcinogènesi, i orientades a la recerca de noves dianes terapèutiques i mètodes de diagnosi i pronòstic més eficients.

The estimation of PMP and PMF on alpine basins in Switzerland

This paper presents a very fine grid hydrological model based on the spatiotemporal repartition of precipitation and on the topography. The goal is to estimate the flood on a catchment area, using a Probable Maximum Precipitation (PMP) leading to a Probable Maximum Flood (PMF). The spatiotemporal distribution of the precipitation was realized using six clouds modeled by the advection-diffusion equation. The equation shows the movement of the clouds over the terrain and also gives the evolution of the rain intensity in time. This hydrological modeling is followed by a hydraulic modeling of the surface and subterranean flows, done considering the factors that contribute to the hydrological cycle, such as the infiltration, the exfiltration and the snowmelt. This model was applied to several Swiss basins using measured rain, with results showing a good correlation between the simulated and observed flows. This good correlation proves that the model is valid and gives us the confidence that the results can be extrapolated to phenomena of extreme rainfall of PMP type. In this article we present some results obtained using a PMP rainfall and the developed model.

Frontshear and backshear instabilities of the mean longshore current

An analytical model based on Bowen and Holman [1989] is used to prove the existence of instabilities due to the presence of a second extremum of the background vorticity at the front side of the longshore current. The growth rate of the so-called frontshear waves depends primarily upon the frontshear but also upon the backshear and the maximum and the width of the current. Depending on the values of these parameters, either the frontshear or the backshear instabilities may dominate. Both types of waves have a cross-shore extension of the order of the width of the current, but the frontshear modes are localized closer to the coast than are the backshear modes. Moreover, under certain conditions both unstable waves have similar growth rates with close wave numbers and angular frequencies, leading to the possibility of having modulated shear waves in the alongshore direction. Numerical analysis performed on realistic current profiles confirm the behavior anticipated by the analytical model. The theory has been applied to a current profile fitted to data measured during the 1980 Nearshore Sediment Transport Studies experiment at Leadbetter Beach that has an extremum of background vorticity at the front side of the current. In this case and in agreement with field observations, the model predicts instability, whereas the theory based only on backshear instability fai led to do so.

Self-organization mechanisms for the formation on nearshore crescentic and transverse sand bars

The formation and development of transverse and crescentic sand bars in the coastal marine environment has been investigated by means of a nonlinear numerical model based on the shallow-water equations and on a simpli ed sediment transport parameterization. By assuming normally approaching waves and a saturated surf zone, rhythmic patterns develop from a planar slope where random perturbations of small amplitude have been superimposed. Two types of bedforms appear: one is a crescentic bar pattern centred around the breakpoint and the other, herein modelled for the rst time, is a transverse bar pattern. The feedback mechanism related to the formation and development of the patterns can be explained by coupling the water and sediment conservation equations. Basically, the waves stir up the sediment and keep it in suspension with a certain cross-shore distribution of depth-averaged concentration. Then, a current flowing with (against) the gradient of sediment concentration produces erosion (deposition). It is shown that inside the surf zone, these currents may occur due to the wave refraction and to the redistribution of wave breaking produced by the growing bedforms. Numerical simulations have been performed in order to understand the sensitivity of the pattern formation to the parameterization and to relate the hydro-morphodynamic input conditions to which of the patterns develops. It is suggested that crescentic bar growth would be favoured by high-energy conditions and ne sediment while transverse bars would grow for milder waves and coarser sediment. In intermediate conditions mixed patterns may occur.

Outcome evaluation of ankle osteoarthritis treatments using spatio-temporal gait parameters and plantar pressure during unconstrained long distance walking : P25

Introduction: Ankle arthrodesis (AD) and total ankle replacement (TAR) are typical treatments for ankle osteoarthritis (AO). Despite clinical interest, there is a lack of their outcome evaluation using objective criteria. Gait analysis and plantar pressure assessment are appropriate to detect pathologies in orthopaedics but they are mostly used in lab with few gait cycles. In this study, we propose an ambulatory device based on inertial and plantar pressure sensors to compare the gait during long-distance trials between healthy subjects (H) and patients with AO or treated by AD and TAR. Methods: Our study included four groups: 11 patients with AO, 9 treated by TAR, 7 treated by AD and 6 control subjects. An ambulatory system (Physilog®, CH) was used for gait analysis; plantar pressure measurements were done using a portable insole (Pedar®-X, DE). The subjects were asked to walk 50 meters in two trials. Mean value and coefficient of variation of spatio-temporal gait parameters were calculated for each trial. Pressure distribution was analyzed in ten subregions of foot. All parameters were compared among the four groups using multi-level model-based statistical analysis. Results: Significant difference (p <0.05) with control was noticed for AO patients in maximum force in medial hindfoot and forefoot and in central forefoot. These differences were no longer significant in TAR and AD groups. Cadence and speed of all pathologic groups showed significant difference with control. Both treatments showed a significant improvement in double support and stance. TAR decreased variability in speed, stride length and knee ROM. Conclusions: In spite of a small sample size, this study showed that ankle function after AO treatments can be evaluated objectively based on plantar pressure and spatio-temporal gait parameters measured during unconstrained walking outside the lab. The combination of these two ambulatory techniques provides a promising way to evaluate foot function in clinics.

Statistical modeling of violin bowing parameter contours

We present a framework for modeling right-hand gestures in bowed-string instrument playing, applied to violin. Nearly non-intrusive sensing techniques allow for accurate acquisition of relevant timbre-related bowing gesture parameter cues. We model the temporal contour of bow transversal velocity, bow pressing force, and bow-bridge distance as sequences of short segments, in particular B´ezier cubic curve segments. Considering different articulations, dynamics, andcontexts, a number of note classes is defined. Gesture parameter contours of a performance database are analyzed at note-level by following a predefined grammar that dictatescharacteristics of curve segment sequences for each of the classes into consideration. Based on dynamic programming, gesture parameter contour analysis provides an optimal curve parameter vector for each note. The informationpresent in such parameter vector is enough for reconstructing original gesture parameter contours with significant fidelity. From the resulting representation vectors, weconstruct a statistical model based on Gaussian mixtures, suitable for both analysis and synthesis of bowing gesture parameter contours. We show the potential of the modelby synthesizing bowing gesture parameter contours from an annotated input score. Finally, we point out promising applicationsand developments.

Systemic exposure to tobramycin after local antibiotic treatment with calcium sulphate as carrier material.

INTRODUCTION: Osteoset(®) T is a calcium sulphate void filler containing 4% tobramycin sulphate, used to treat bone and soft tissue infections. Despite systemic exposure to the antibiotic, there are no pharmacokinetic studies in humans published so far. Based on the observations made in our patients, a model predicting tobramycin serum levels and evaluating their toxicity potential is presented. METHODS: Following implantation of Osteoset(®) T, tobramycin serum concentrations were monitored systematically. A pharmacokinetic analysis was performed using a non-linear mixed effects model based on a one compartment model with first-degree absorption. RESULTS: Data from 12 patients treated between October 2006 and March 2008 were analysed. Concentration profiles were consistent with the first-order slow release and single-compartment kinetics, whilst showing important variability. Predicted tobramycin serum concentrations depended clearly on both implanted drug amount and renal function. DISCUSSION AND CONCLUSION: Despite the popularity of aminoglycosides for local antibiotic therapy, pharmacokinetic data for this indication are scarce, and not available for calcium sulphate as carrier material. Systemic exposure to tobramycin after implantation of Osteoset(®) T appears reassuring regarding toxicity potential, except in case of markedly impaired renal function. We recommend in adapting the dosage to the estimated creatinine clearance rather than solely to the patient's weight.

Ink dating using thermal desorption and gas chromatography / mass spectrometry: comparison of results obtained in two laboratories

Recent ink dating methods focused mainly on changes in solvent amounts occurring over time. A promising method was developed at the Landeskriminalamt of Munich using thermal desorption (TD) followed by gas chromatography / mass spectrometry (GC/MS) analysis. Sequential extractions of the phenoxyethanol present in ballpoint pen ink entries were carried out at two different temperatures. This method is applied in forensic practice and is currently implemented in several laboratories participating to the InCID group (International Collaboration on Ink Dating). However, harmonization of the method between the laboratories proved to be a particularly sensitive and time consuming task. The main aim of this work was therefore to implement the TD-GC/MS method at the Bundeskriminalamt (Wiesbaden, Germany) in order to evaluate if results were comparable to those obtained in Munich. At first validation criteria such as limits of reliable measurements, linearity and repeatability were determined. Samples were prepared in three different laboratories using the same inks and analyzed using two TDS-GC/MS instruments (one in Munich and one in Wiesbaden). The inter- and intra-laboratory variability of the ageing parameter was determined and ageing curves were compared. While inks stored in similar conditions yielded comparable ageing curves, it was observed that significantly different storage conditions had an influence on the resulting ageing curves. Finally, interpretation models, such as thresholds and trend tests, were evaluated and discussed in view of the obtained results. Trend tests were considered more suitable than threshold models. As both approaches showed limitations, an alternative model, based on the slopes of the ageing curves, was also proposed.

Meiotic drive favors Robertsonian metacentric chromosomes in the common shrew (Sorex araneus, Insectivora, mammalia).

Meiotic drive has attracted much interest because it concerns the robustness of Mendelian segregation and its genetic and evolutionary stability. We studied chromosomal meiotic drive in the common shrew (Sorex araneus, Insectivora, Mammalia), which exhibits one of the most remarkable chromosomal polymorphisms within mammalian species. The open question of the evolutionary success of metacentric chromosomes (Robertsonian fusions) versus acrocentrics in the common shrew prompted us to test whether a segregation distortion in favor of metacentrics is present in female and/or male meiosis. Performing crosses under controlled laboratory conditions with animals from natural populations, we found a clear trend toward a segregation distortion in favor of metacentrics during male meiosis, two chromosome combinations (gm and jl) being significantly preferred over their acrocentric homologs. Apart for one Robertsonian fusion (hi), this trend was absent in female meiosis. We propose a model based on recombination events between twin acrocentrics to explain the difference in transmission ratios of the same metacentric in different sexes and unequal drive of particular metacentrics in the same sex. Pooled data for female and male meiosis revealed a trend toward stronger segregation distortion for larger metacentrics. This is partially in agreement with the frequency of metacentrics occurring in natural populations of a chromosome race showing a high degree of chromosomal polymorphism.

Finite mixture analysis of beauty-contest data using generalised beta distributions

This paper introduces a mixture model based on the beta distribution, without preestablishedmeans and variances, to analyze a large set of Beauty-Contest data obtainedfrom diverse groups of experiments (Bosch-Domenech et al. 2002). This model gives a bettert of the experimental data, and more precision to the hypothesis that a large proportionof individuals follow a common pattern of reasoning, described as iterated best reply (degenerate),than mixture models based on the normal distribution. The analysis shows thatthe means of the distributions across the groups of experiments are pretty stable, while theproportions of choices at dierent levels of reasoning vary across groups.

Aggregation invariance in general clustering approaches

General clustering deals with weighted objects and fuzzy memberships. We investigate the group- or object-aggregation-invariance properties possessed by the relevant functionals (effective number of groups or objects, centroids, dispersion, mutual object-group information, etc.). The classical squared Euclidean case can be generalized to non-Euclidean distances, as well as to non-linear transformations of the memberships, yielding the c-means clustering algorithm as well as two presumably new procedures, the convex and pairwise convex clustering. Cluster stability and aggregation-invariance of the optimal memberships associated to the various clustering schemes are examined as well.

Crystal size distribution of periclase in contact metamorphic dolomite marbles from the southern Adamello Massif, Italy

Crystal size distributions (CSD) of periclase in contact metamorphic dolomite marbles are presented for two profiles near the Cima Uzza summit in the southern Adamello Massif (Italy). The database was combined with geochemical and petrological information to deduce the controls on the periclase-forming reaction. The contact metamorphic dolomite marbles are exposed at the contact of mafic intrusive rocks and are partially surrounded by them. Brucite is retrograde and pseudomorphs spherical periclase crystals. Prograde periclase growth is the consequence of limited infiltration of water-rich fluid at T near 605C. Stable isotope data show depletion in (13)C and (18)O over a narrow region (40 cm) near the magmatic contact, whereas the periclase-forming reaction front extends up to 4 m from the contact. CSD analyses along the two profiles show that the median grain size of the periclase crystals does not change, but that there is a progressively greater distribution of grain sizes, including a greater proportion of larger grains, with increasing distance from the contact. A qualitative model, based on the textural and geochemical data, attributes these variations in grain size to changing reaction affinities along a kinetically dispersed infiltration front. This study highlights the need to invoke disequilibrium processes for metamorphic mineral growth and expands the use of CSDs to systems of mineral formation driven by fluid infiltration.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.