Search results for "Ensembl"

GPU accelerated Monte Carlo simulations of lattice spin models

2011

We consider Monte Carlo simulations of classical spin models of statistical mechanics using the massively parallel architecture provided by graphics processing units (GPUs). We discuss simulations of models with discrete and continuous variables, and using an array of algorithms ranging from single-spin flip Metropolis updates over cluster algorithms to multicanonical and Wang-Landau techniques to judge the scope and limitations of GPU accelerated computation in this field. For most simulations discussed, we find significant speed-ups by two to three orders of magnitude as compared to single-threaded CPU implementations.

Aural-Based Detection and Assessment of Real Versus Artificially Synchronized String Quartet Performance

2013

In a musical ensemble musicians can influence each other’s performance in terms not only of timing but also in other aspects of the performance such as dynamics, intonation, and timbre. The goal of this work is to test whether this influence can be perceived by a listener from an audio recording solely. We utilize a set of string quartet recordings where every piece is recorded in two experimental conditions: the solo condition, where each musician performs alone; and the ensemble condition, where the musicians perform together after a brief rehearsal. Using state-of-the-art audio analysis/synthesis methods, we artificially synchronize the record-ings in the solo condition note-by-note, thu…

Ensemble deep clustering analysis for time window determination of event-related potentials

2023

Objective Cluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering methods. Methods We designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clusterin…

Unstable feature relevance in classification tasks

2011

Climate and extreme rainfall events in the Mono river basin (West Africa): investigating future changes with Regional Climate Models.

2020

This study characterizes the future changes in extreme rainfall and air temperature in the Mono river basin where the main economic activity is weather dependent and local populations are highly vulnerable to natural hazards, including flood inundations. Daily precipitation and temperature from observational datasets and Regional Climate Models (RCMs) output from REMO, RegCM, HadRM3, and RCA were used to analyze climatic variations in space and time, and fit a GEV model to investigate the extreme rainfalls and their return periods. The results indicate that the realism of the simulated climate in this domain is mainly controlled by the choice of the RCMs. These RCMs projected a 1 to 1.5 &deg

Inducing Rules of Ensemble Music Performance : A Machine Learning Approach

2013

Previous research in expressive music performance has described how solo musicians intuitively shape each note in relation to local/global score contexts. However, expression in ensemble performances, where each individual voice is played simultaneously with other voices, has been little explored. We present an exploratory study in which the performance of a string quartet is recorded and analysed by a computer. We use contact microphones to acquire four audio signals from which a set of audio descriptors is extracted individually for each musician. Moreover, we use motion capture to extract bowing descriptors (bow velocity/force) from each of the four performers. The gathered multimodal da…

Experimentelle Untersuchungen zur Reproduzierbarkeit von Ensemble-gemittelten elektromyographischen Ganganalysedaten im Bereich der experimentellen u…

2004

With suitable application and signal processing methods, surface electromyography is a comparatively simple instrument for investigating the temporal pattern of the muscular activity of a walking subject. The influence of changes both in the external experimental conditions (e.g. orthopedic shoe design) and in the human locomotor system (due to disease or therapy) on the individual muscular gait characteristics can be documented in this way. The usefulness of this kind of investigation is basically limited by the reproducibility of the gait analytical findings of the subject, who is examined at different times with unchanged bodily state and under identical experimental conditions unchanged…

An EEMD Aided Comparison of Time Histories and Its Application in Vehicle Safety

2017

In the context of signal processing, the comparison of time histories is required for different purposes, especially for the model validation of vehicle safety. Most of the existing metrics focus on the mathematical value only. Therefore, they suffer the measuring errors, disturbance, and uncertainties and can hardly achieve a stable result with a clear physical interpretation. This paper proposes a novel scheme of time histories comparison to be used in vehicle safety analysis. More specifically, each signal for comparison is decomposed into a trend signal and several intrinsic mode functions (IMFs) by ensemble empirical mode decomposition. The trend signals reflect the general variation a…

The role of polaronic states in the enhancement of CO oxidation by single-atom Pt/CeO2

2023

Single Atom Catalysts (SACs) have shown that the miniaturization of the active site implies new phenomena like dynamic charge transfer between isolated metal atoms and the oxide. To obtain direct proof of this character is challenging, as many experimental techniques provide averaged properties or have limitations with poorly conductive materials, leaving kinetic measurements from catalytic testing as the only reliable reference. Here we present an integrated Density Functional Theory-Microkinetic model including ground and metastable states to address the reactivity of Pt1/CeO2 for CO oxidation. Our results agree with experimentally available kinetic data in the literature and show that CO…

ENSEMBLE METHODS FOR RANKING DATA

2017

The last years have seen a remarkable flowering of works about the use of decision trees for ranking data. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures, as ensemble methods, in order to find which predictors are able to explain the preference structure. In this work ensemble methods as BAGGING and Random Forest are proposed, from both a theoretical and computational point of view, for deriving classification trees when ranking data are observed. The advantages of these procedures are shown through an example on the SUSHI data set.