Out-of-body experiences: false climbs in a supine position?

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Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

Out-of-body experiences (OBEs) are characterized by the subjective feeling of being located outside one's physical body and perceiving one's own body from an elevated perspective looking downwards. OBEs have been correlated with abnormal integration of bod ...

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

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 ...

Technology supporting meditation is a multimillion-dollar market that continues to grow. There is also strong academic interest to understand and improve the impact technology can have for the user experience of practitioners. However, little work investig ...

We recently put forward an argument, the Unfolding Argument (UA), that integrated information theory (IIT) and other causal structure theories are either already falsified or unfalsifiable, which provoked significant criticism. It seems that we and the cri ...

The concept of novelty is central to questions of creativity, innovation, and discovery. Despite the prominence in scientific inquiry and everyday discourse, there is a chronic ambiguity over its meaning and a surprising variety of empirical measures, whic ...

This thesis is devoted to the construction, analysis, and implementation of two types of hierarchical Markov Chain Monte Carlo (MCMC) methods for the solution of large-scale Bayesian Inverse Problems (BIP). The first hierarchical method we present is base ...

Since the birth of Information Theory, researchers have defined and exploited various information measures, as well as endowed them with operational meanings. Some were born as a "solution to a problem", like Shannon's Entropy and Mutual Information. Other ...

We present a Bayesian inference for a three-dimensional hydrodynamic model of Lake Geneva with stochastic weather forcing and high-frequency observational datasets. This is achieved by coupling a Bayesian inference package, SPUX, with a hydrodynamics packa ...

The Customer Experience Management at the Swiss Federal Railways (SBB) experiments with new technologies and methods to capture experiences and perceptions of passengers to design safe and customer friendly environments. First, a new methodological approac ...

We study the computations that Bayesian agents undertake when exchanging opinions over a network. The agents act repeatedly on their private information and take myopic actions that maximize their expected utility according to a fully rational posterior be ...

Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...

Familiarity is an elusive concept, capturing what we know intimately and what we only recognize from having seen before. This article aims to disambiguate these interpretations by proposing a sociological conceptualization of familiarity as a dynamic relat ...

Temporal point-processes are often used for mathematical modeling of sequences of discrete events with asynchronous timestamps. We focus on a class of temporal point-process models called multivariate Wold processes (MWP). These processes are well suited t ...

The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the admittance matrix, capturing the topology and line param ...

Recently, a new adaptive path interpolation method has been developed as a simple and versatile scheme to calculate exactly the asymptotic mutual information of Bayesian inference problems defined on dense factor graphs. These include random linear and gen ...

Understanding the diffusion patterns of sequences of interdependent events is a central question for a variety of disciplines. Temporal point processes are a class of elegant and powerful models of such sequences; these processes have become popular across ...

This thesis focuses on two kinds of statistical inference problems in signal processing and data science. The first problem is the estimation of a structured informative tensor from the observation of a noisy tensor in which it is buried. The structure com ...

In this paper, we attempt to show some consequences of bringing the body back into higher education, through the use of performing arts in the curricular context of scientific programs. We start by arguing that dominant traditions in higher education repro ...

Out-of-body experiences: false climbs in a supine position?

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ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Out-of-body illusion induced by visual-vestibular stimulation

Robust Outlier Rejection for 3D Registration with Variational Bayes

An Unbiased Method of Measuring the Ratio of Two Data Sets

Adapting Haptic Feedback for Guided Meditation

First-person experience cannot rescue causal structure theories from the unfolding argument

Nothing New Under the Sun: Novelty Constructs and Measures in Social Studies

HIERARCHICAL MARKOV CHAIN MONTE CARLO METHODS FOR BAYESIAN INVERSE PROBLEMS

A Functional Perspective on Information Measures

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Assessing Experiences and Visual Perception in Train Station Environments with a Mobile Application and Eye Tracking

Bayesian Decision Making in Groups is Hard

Learning in Volatile Environments With the Bayes Factor Surprise

Familiarity as a Practical Sense of Place

A Variational Inference Approach to Learning Multivariate Wold Processes

Bayesian Methods for the Identification of Distribution Networks

Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model

Learning Self-Exciting Temporal Point Processes Under Noisy Observations

Statistical limits of high-dimensional inference problems

The Dimension of the Body in Higher Education: Matrix of Meanings in Students’ Diaries