Directional Statistics for WIMP direct detection II: 2-d read-out

The direction dependence of the WIMP direct detection rate provides a powerful tool for distinguishing a WIMP signal from possible backgrounds. We study the the number of events required to discriminate a WIMP signal from an isotropic background for a detector with 2-d read-out using non-parametric circular statistics. We also examine the number of events needed to i) detect a deviation from rotational symmetry, due to flattening of the Milky Way halo and ii) detect a deviation in the mean direction due to a tidal stream. If the senses of the recoils are measured then of order 20-70 events (depending on the plane of the 2-d read out) will be sufficient to reject isotropy of the raw recoil angles at 90% confidence. If the senses can not be measured these number increase by roughly two orders of magnitude (compared with an increase of one order of magnitude for the case of full 3-d read-out). The distributions of the reduced angles, with the (time dependent) direction of solar motion subtracted, are far more anisotropic, however, and if the isotropy tests are applied to these angles then the numbers of events required are similar to the case of 3-d read-out. A deviation from rotational symmetry will only be detectable if the Milky Way halo is significantly flattened. The deviation in the mean direction due to a tidal stream is potentially detectable, however, depending on the density and direction of the stream.Comment: 11 pages, 4 figures, version to appear in Phys. Rev. D, added consideration of other detector locations and discussion of optimal plane for maximum anisotropy in signal, conclusions unchange

Machine learning accelerated likelihood-free event reconstruction in dark matter direct detection

Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detecting Dark Matter. Using the likelihood-free framework, a newalgorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presented. The algorithm uses the secondary scintillation light distribution (S2) obtained by simulating events using a waveform generator. To deal with the computational effort required by the likelihood-free approach, we employ the Bayesian Optimization for LikelihoodFree Inference (BOLFI) algorithm. Together with BOLFI, prior distributions for the parameters of interest (x; y; e) and highly informative discrepancy measures to performthe analyses are introduced. We evaluate the quality of the proposed algorithm by a comparison against the currently existing alternative methods using a large-scale simulation study. BOLFI provides a natural probabilistic uncertainty measure for the reconstruction and it improved the accuracy of the reconstruction over the next best algorithm by up to 15% when focusing on events at large radii (R > 30 cm, the outer 37% of the detector). In addition, BOLFI provides the smallest uncertainties among all the tested methods.Peer reviewe

Obesity dependent metabolic signatures associated with nonalcoholic fatty liver disease progression

Our understanding of the mechanisms by which nonalcoholic fatty liver disease (NAFLD) progresses from simple steatosis to steatohepatitis (NASH) is still very limited. Despite the growing number of studies linking the disease with altered serum metabolite levels, an obstacle to the development of metabolome-based NAFLD predictors has been the lack of large cohort data from biopsy-proven patients matched for key metabolic features such as obesity. We studied 467 biopsied individuals with normal liver histology (n=90) or diagnosed with NAFLD (steatosis, n=246; NASH, n=131), randomly divided into estimation (80% of all patients) and validation (20% of all patients) groups. Qualitative determinations of 540 serum metabolite variables were performed using ultra-performance liquid chromatography coupled to mass spectrometry (UPLCMS). The metabolic profile was dependent on patient body-mass index (BMI), suggesting that the NAFLD pathogenesis mechanism may be quite different depending on an individual’s level of obesity. A BMI-stratified multivariate model based on the NAFLD serum metabolic profile was used to separate patients with and without NASH. The area under the receiver operating characteristic curve was 0.87 in the estimation and 0.85 in the validation group. The cutoff (0.54) corresponding to maximum average diagnostic accuracy (0.82) predicted NASH with a sensitivity of 0.71 and a specificity of 0.92 (negative/positive predictive values = 0.82/0.84). The present data, indicating that a BMI-dependent serum metabolic profile may be able to reliably distinguish NASH from steatosis patients, have significant implications for the development of NASH biomarkers and potential novel targets for therapeutic intervention

Novel support vector machines for diverse learning paradigms

This dissertation introduces novel support vector machines (SVM) for the following traditional and non-traditional learning paradigms: Online classification, Multi-Target Regression, Multiple-Instance classification, and Data Stream classification. Three multi-target support vector regression (SVR) models are first presented. The first involves building independent, single-target SVR models for each target. The second builds an ensemble of randomly chained models using the first single-target method as a base model. The third calculates the targets\u27 correlations and forms a maximum correlation chain, which is used to build a single chained SVR model, improving the model\u27s prediction performance, while reducing computational complexity. Under the multi-instance paradigm, a novel SVM multiple-instance formulation and an algorithm with a bag-representative selector, named Multi-Instance Representative SVM (MIRSVM), are presented. The contribution trains the SVM based on bag-level information and is able to identify instances that highly impact classification, i.e. bag-representatives, for both positive and negative bags, while finding the optimal class separation hyperplane. Unlike other multi-instance SVM methods, this approach eliminates possible class imbalance issues by allowing both positive and negative bags to have at most one representative, which constitute as the most contributing instances to the model. Due to the shortcomings of current popular SVM solvers, especially in the context of large-scale learning, the third contribution presents a novel stochastic, i.e. online, learning algorithm for solving the L1-SVM problem in the primal domain, dubbed OnLine Learning Algorithm using Worst-Violators (OLLAWV). This algorithm, unlike other stochastic methods, provides a novel stopping criteria and eliminates the need for using a regularization term. It instead uses early stopping. Because of these characteristics, OLLAWV was proven to efficiently produce sparse models, while maintaining a competitive accuracy. OLLAWV\u27s online nature and success for traditional classification inspired its implementation, as well as its predecessor named OnLine Learning Algorithm - List 2 (OLLA-L2), under the batch data stream classification setting. Unlike other existing methods, these two algorithms were chosen because their properties are a natural remedy for the time and memory constraints that arise from the data stream problem. OLLA-L2\u27s low spacial complexity deals with memory constraints imposed by the data stream setting, and OLLAWV\u27s fast run time, early self-stopping capability, as well as the ability to produce sparse models, agrees with both memory and time constraints. The preliminary results for OLLAWV showed a superior performance to its predecessor and was chosen to be used in the final set of experiments against current popular data stream methods. Rigorous experimental studies and statistical analyses over various metrics and datasets were conducted in order to comprehensively compare the proposed solutions against modern, widely-used methods from all paradigms. The experimental studies and analyses confirm that the proposals achieve better performances and more scalable solutions than the methods compared, making them competitive in their respected fields

Predviđanje postoperacijske fibrilacije atrija korištenjem SVM klasifikatora.

In patients undergoing Coronary Artery Bypass G rafting (CABG) surgery postoperative atrial fibrillation (AF) occurs with prevalence of up to 40%. The highest incidence is between the second and third day after the operation. Following cardiac surgery AF causes various complications, hemodynamic instability, and can cause heart attack, cerebral and other thromboemolisms. AF increases morbidity, duration and expense of medical treatment. This study aims to identify patients at high risk of postoperative AF. An early prediction of AF would provide a timely prophylactic treatment and would reduce incidence of arrhythmia. Patients at low risk of postoperative AF could be excluded from the side effects of anti-arrhythmic drugs. The investigation included 50 patients in whom lead II electrocardiograms were continuously recorded for 48 hours following CABG. Univariate statistical analysis was used in the search of signal features that might predict AF. The most promising identified features were: P wave duration, RR interval duration and PQ segment level. On the basis of these a nonlinear multivariate prediction model was made deploying a Support Vector Machine (SVM) classifier. The prediction accuracy was found uprising over the time. At 48 hours following CABG; the measured best average sensitivity was 95 . 9% and specificity 93. 4% . The positive and negative predictive accuracy were 88. 9% and 98. 8% , respectively and the overall accuracy was 94. 6% . In regard to the prediction accuracy, the risk assessment and prediction of postoperative A F are optimal to be done in the period between 24 and 48 hours following CABG.Postoperacijska fibrilacija atrija (AF) pojavljuje se u oko 40% pacijenata podvrgnutih operaciji aortokoronarnog premoštenja (CABG), s najvećom učestalosti pojavljivanja oko trećeg dana nakon operacije. Postoperacijska AF može stvoriti brojne komplikacije poput hemodinamske nestabilnosti, srčanog udara, cerebralnih i drugih tromboembolija; povećava morbiditet, trajanje i troškove liječenja. S tudija ima za cilj rano otkrivanje pacijenta sa visokim rizikom razvoja postoperacijske AF, što bi osiguralo pravovremenu profilaktičku terapiju i smanjilo učestalost aritmije, dok bi pacijenti sa niskim rizikom razvoja postoperacijske AF bili pošteđeni nuspojava antiaritmičkih lijekova. Podatkovni skup uključuje 50 pacijenata, snimanih II standardnim odvodom elektrokardiografa, kontinuirano u razdoblju od 48 sati nakon operacije. Univarijatna statistička analiza korištena je za određivanje parametara signala koji bi mogli predvidjeti AF, te su kao najznačajniji određeni: trajanje P vala, trajanje RR intervala i razina PQ spojnice; na temelju kojih je izveden nelinearni multivarijatni predikcijski model zasnovan na SVM klasifikatoru. Ukupna predikcijska točnost modela povećava se s vremenom. U 48 . satu nakon operacije najbolje prosječne značajke iznosile su: osjetljivost 95 , 9%, specifičnost 93, 4% , pozitivna prediktivnost 88, 9% , negativna prediktivnost 98 , 8% te ukupna točnost 94, 6% . Prema rezultatima predikcijske točnosti, procjenu rizika i predikciju postoperacijske AF optimalno bilo bi načiniti u periodu između 24-tog i 48-og sata nakon operacije ugradnje aortokoronarnih premosnica

Incremental Market Behavior Classification in Presence of Recurring Concepts

In recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in the markets. Traditional systems are unable or slow to adapt to these changes. Ensemble-based systems are widely known for their good results predicting both cyclic and non-stationary data such as stock prices. In this work, we propose RCARF (Recurring Concepts Adaptive Random Forests), an ensemble tree-based online classifier that handles recurring concepts explicitly. The algorithm extends the capabilities of a version of Random Forest for evolving data streams, adding on top a mechanism to store and handle a shared collection of inactive trees, called concept history, which holds memories of the way market operators reacted in similar circumstances. This works in conjunction with a decision strategy that reacts to drift by replacing active trees with the best available alternative: either a previously stored tree from the concept history or a newly trained background tree. Both mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The experimental validation of the algorithm is based on the prediction of price movement directions one second ahead in the SPDR (Standard & Poor's Depositary Receipts) S&P 500 Exchange-Traded Fund. RCARF is benchmarked against other popular methods from the incremental online machine learning literature and is able to achieve competitive results.This research was funded by the Spanish Ministry of Economy and Competitiveness under grant number ENE2014-56126-C2-2-R

Evolutionary history of recombination on sex chromosomes and of sex-biased gene expression

Males and females within a species are often under different selection pressures, which can affect both gene sequence and expression. Sex differences in selection are predicted to precipitate sex chromosome formation, and the rate of recombination on sex chromosomes varies greatly across taxa. Examining the extent of recombination suppression on sex chromosomes across closely related species can offer insight into the forces shaping sex differences and sex chromosome evolution over time. Additionally, sex-biased genes are thought to encode sexually dimorphic traits and are therefore a useful way to examine the effect of sex-specific selection across the genome. In this thesis, I use whole genome and transcriptome sequencing data to characterise the structure and conservation of sex chromosome systems across related species. I combine this with patterns of sex-specific single nucleotide polymorphisms to uncover the degree of recombination suppression and divergence across poeciliid sex chromosomes, as well as explore the consequences of recombination arrest on gene expression patterns. Finally, I investigate the selective dynamics driving the expression and rate of sequence evolution of sex-biased genes