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Concept

Multivariate statistics

Related publications (31)

Search for the lepton flavour violating decays B0→e±μ∓ and B0s→e±μ∓ with LHCb Run 2 data

Sebastian Schulte

A large variety of new physics models suggest that the rates for lepton flavour violating

b

-hadron decays may be much higher than predicted in the Standard Model, which leads to a high interest in the search for such decays. This thesis presents the se ...

EPFL2024

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Mathieu Salzmann, Zheng Dang

In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches have shown remarkable success on synthetic datasets, we have observed them to fail in the presence of real-world data. We ...

Ieee Computer Soc2024

Multivariate geometric anisotropic Cox processes

Sofia Charlotta Olhede

This paper introduces a new modeling and inference framework for multivariate and anisotropic point processes. Building on recent innovations in multivariate spatial statistics, we propose a new family of multivariate anisotropic random fields, and from th ...

WILEY2023

Roles of Clinical Features and Chest CT in Predicting the Outcomes of Hospitalized Patients with COVID-19 Developing AKI

Ali Falsafi

This research aimed to evaluate the clinical features and computed tomography (CT) scans associated with poor outcomes in COVID-19 patients with acute kidney injury (AKI). A total of 351 COVID-19 patients (100 AKI, 251 non-AKI) hospitalized at Imam Hossein ...

IRANIAN SOC NEPHROLGY2023

An Unbiased Method of Measuring the Ratio of Two Data Sets

Jean-Paul Richard Kneib, Huanyuan Shan

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

IOP Publishing Ltd2023

Surrogate modelling for seismic collapse risk assessment of steel moment resisting frames

Dimitrios Lignos, Andronikos Skiadopoulos, Nenad Bijelic

With the overall goal of reducing the numerical burden of uncertainty quantification involved in seismic risk assessments, this paper examines opportunities for assessment of seismic collapse capacity and risk using data-driven surrogates as a complement t ...

2023

Cortical alterations after very preterm birth and the association with socio-emotional abilities from childhood to early adolescence

Jean-Philippe Thiran, Elda Fischi Gomez, Vanessa Siffredi

Very preterm birth (VPT;

2023

Modeling Object Dissimilarity for Deep Saliency Prediction

Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Seungryong Kim, Deblina Bhattacharjee, Bahar Aydemir

Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level ones, such as attention and gaze direction for entire objects. Despite ...

Journal of Machine Learning Research Inc.2022

The Debiased Spatial Whittle likelihood

Sofia Charlotta Olhede

We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle l ...

WILEY2022

Fragment ion-functional group relationships in organic aerosols using aerosol mass spectrometry and mid-infrared spectroscopy

Satoshi Takahama, Nikunj Dudani, Amir Yazdani

Aerosol mass spectrometry (AMS) and mid-infrared spectroscopy (MIR) are two analytical methods for characterizing the chemical composition of OM. While AMS provides high-temporal-resolution bulk measurements, the extensive fragmentation during the electron ...

2022

Impact of adherence to a lifestyle-integrated programme on physical function and behavioural complexity in young older adults at risk of functional decline: a multicentre RCT secondary analysis

Kamiar Aminian, Anisoara Ionescu

Context Long-term adherence to physical activity (PA) interventions is challenging. The Lifestyle-integrated Functional Exercise programmes were adapted Lifestyle-integrated Functional Exercise (aLiFE) to include more challenging activities and a behaviour ...

BMJ PUBLISHING GROUP2022

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Vasiliki Tileli, Reinis Ignatans

Analysis of the temperature- and stimulus-dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature-induced phase transition in BaTiO3 is explored using the machine learning ana ...

WILEY-V C H VERLAG GMBH2022

Covariance matrices for variance-suppressed simulations

Cheng Zhao

Cosmological N-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistica ...

OXFORD UNIV PRESS2022

SporTran: A code to estimate transport coefficients from the cepstral analysis of (multivariate) current time series

Loris Ercole

SporTran is a Python utility designed to estimate generic transport coefficients in extended systems, based on the Green-Kubo theory of linear response and the recently introduced cepstral analysis of the current time series generated by molecular dynamics ...

ELSEVIER2022

Advanced Phenomenology for Indirect New Physics Searches

Siyu Chen

Searching indirectly for physics beyond the standard model requires careful investigation of collider data and methodological advances that are the subject of this thesis. In the first part, we develop a multivariate analysis tool to compare data with the ...

EPFL2022

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Parviz Ghaderi

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of dens ...

FRONTIERS MEDIA SA2022

Cosmic void baryon acoustic oscillation measurement: evaluation of sensitivity to selection effects

Jean-Paul Richard Kneib, Cheng Zhao, Daniel Felipe Forero Sanchez, Amélie Tamone, Andrei Variu

Cosmic voids defined as a subset of Delaunay triangulation (DT) circumspheres have been used to measure the baryon acoustic oscillations (BAO) scale; providing tighter constraints on cosmological parameters when combined with matter tracers. These voids ar ...

OXFORD UNIV PRESS2022

Learning Finance with Games: An Empirical Study

Sandy Ingram, Rania Islambouli

This paper presents an empirical case study on applying game-based learning in an undergraduate finance course. The paper describes the experimental study context, protocol, and results. Using multivariate regression analysis, a significant game effect on ...

IEEE2021

Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models

Giovanni De Micheli, Sandro Carrara, Francesca Criscuolo, Mandresy Ivan Ny Hanitra

One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental da ...

2021

Numerical investigation on the volute cutwater for pumps running in turbine mode

Elena Vagnoni, Alessandro Morabito

The hydropower sector has recently raised the interest in pump as turbine (PaT) that can be a valid trade-off between capital cost and performance in micro-scale installations. Nevertheless, the modest efficiency of PaTs often restricts their exploitation. ...

Pergamon-Elsevier Science Ltd2021