Decontextualized learning for interpretable hierarchical representations of visual patterns

Apart from discriminative models for classification and object detection tasks, the application of deep convolutional neural networks to basic research utilizing natural imaging data has been somewhat limited; particularly in cases where a set of interpretable features for downstream analysis is needed, a key requirement for many scientific investigations. We present an algorithm and training paradigm designed specifically to address this: decontextualized hierarchical representation learning (DHRL). By combining a generative model chaining procedure with a ladder network architecture and latent space regularization for inference, DHRL address the limitations of small datasets and encourages a disentangled set of hierarchically organized features. In addition to providing a tractable path for analyzing complex hierarchal patterns using variation inference, this approach is generative and can be directly combined with empirical and theoretical approaches. To highlight the extensibility and usefulness of DHRL, we demonstrate this method in application to a question from evolutionary biology

Changes from Classical Statistics to Modern Statistics and Data Science

A coordinate system is a foundation for every quantitative science, engineering, and medicine. Classical physics and statistics are based on the Cartesian coordinate system. The classical probability and hypothesis testing theory can only be applied to Euclidean data. However, modern data in the real world are from natural language processing, mathematical formulas, social networks, transportation and sensor networks, computer visions, automations, and biomedical measurements. The Euclidean assumption is not appropriate for non Euclidean data. This perspective addresses the urgent need to overcome those fundamental limitations and encourages extensions of classical probability theory and hypothesis testing , diffusion models and stochastic differential equations from Euclidean space to non Euclidean space. Artificial intelligence such as natural language processing, computer vision, graphical neural networks, manifold regression and inference theory, manifold learning, graph neural networks, compositional diffusion models for automatically compositional generations of concepts and demystifying machine learning systems, has been rapidly developed. Differential manifold theory is the mathematic foundations of deep learning and data science as well. We urgently need to shift the paradigm for data analysis from the classical Euclidean data analysis to both Euclidean and non Euclidean data analysis and develop more and more innovative methods for describing, estimating and inferring non Euclidean geometries of modern real datasets. A general framework for integrated analysis of both Euclidean and non Euclidean data, composite AI, decision intelligence and edge AI provide powerful innovative ideas and strategies for fundamentally advancing AI. We are expected to marry statistics with AI, develop a unified theory of modern statistics and drive next generation of AI and data science.Comment: 37 page

Recent Advances in Anomaly Detection Methods Applied to Aviation

International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance

Personality trait development in the context of daily experiences and close social relationships

Hoe veranderlijk zijn persoonlijkheidseigenschappen? Hoe ontwikkelen persoonlijkheidseigenschappen zich gedurende verschillende levensfases? En hebben alledaagse ervaringen invloed op onze persoonlijkheidseigenschappen? In mijn proefschrift heb ik geprobeerd antwoord te geven op deze vragen. Mijn onderzoek richtte zich op de veelgebruikte ‘Big Five’-persoonlijkheidseigenschappen: extraversie, vriendelijkheid, zorgvuldigheid, emotionele stabi-liteit en openheid. Ik heb gebruik gemaakt van bestaande data (de ‘RADAR’ dataset) over de zelf-gerapporteerde persoonlijkheidseigenschappen en alledaagse ervaringen van Nederlandse jongeren en hun broers en zussen, hun moeders en hun beste vrienden, die maximaal zeven jaar zijn gevold (N = 2.230 adolescenten en 483 moeders). Hoe stabiel zijn persoonlijkheidseigenschappen tijdens de adolescentie? Mijn onderzoek naar de zogenaamde rangschikking-stabiliteit van persoonlijkheidseigenschappen toonde aan dat de Big Five-eigenschappen al behoorlijk stabiel waren bij de aanvang van het onderzoek (leeftijd 12 jaar). De stabiliteit van de Big Five-eigenschappen nam tot de leeftijd van 18 jaar behoorlijk toe. In de periode van 18 tot 22 jaar nam de stabiliteit niet verder toe. Hoe veranderen mensen gemiddeld op persoonlijkheidseigenschappen? Tijdens de vroege adolescentie (leeftijd van 12 tot 15 jaar) lieten adolescenten gemiddeld een tijdelijke afname zien op sommige persoonlijkheidseigenschappen. In deze periode werden meisjes gemiddeld minder emotioneel stabiel en minder extravert en werden jongens minder zorgvuldig. In de leeftijdsperiode van 17 tot en met 22 jaar lieten deelnemers daarentegen vooral toenames zien: zowel jongens als meisjes werden gemiddeld zorgvuldiger, vriendelijker en extraverter, en meisjes werden in deze periode emotioneel stabieler. Moeders namen gemiddeld toe in hun niveau van zorgvuldigheid, extraversie, vriendelijkheid en emotionele stabiliteit. Wordt de persoonlijkheidsontwikkeling van adolescenten beïnvloed door persoonlijkheidseigenschappen van vrienden en broers en zussen? Ik heb geen bewijs gevonden voor sociale beïnvloeding tijdens de adolescentie. Ten eerste hing de persoonlijkheidsontwikkeling van adolescenten niet samen met de Big Five-eigenschappen van hun beste vrienden en broers of zussen. Ten tweede gingen de persoonlijkheidseigenschappen van adolescenten en hun vrienden, broers en zussen niet steeds meer op elkaar lijken. En ten derde vond ik geen bewijs dat vrienden, broers en zussen tijdens het onderzoek overeenkomsten vertoonden in hun ontwikkeling op persoonlijkheidseigenschappen. Hebben alledaagse ervaringen invloed op persoonlijkheidseigenschappen? De resultaten van mijn onderzoek suggereren dat alledaagse ervaringen onze persoonlijkheid beïnvloeden. Ten eerste vond ik dat moeders die in het dagelijkse leven relatief veel positieve emoties en relatiekwaliteit ervaarden, sterker toenamen in de Big Five-persoonlijkheidseigenschappen dan moeders die relatief weinig positieve emoties en relatie-kwaliteit ervaarden. Ten tweede vond ik dat adolescenten lager gingen scoren op de persoonlijkheidseigenschap emotionele stabiliteit nadat ze in een bepaald jaar meer negatieve gevoelens ervaarden dan normaal. Hoofdconclusie . Alledaagse sociale en emotionele ervaringen lijken een invloed te hebben op persoonlijkheidseigenschappen. Veel mensen willen graag toenemen in hun niveau van extraversie, vriendelijkheid, zorgvuldigheid, emotionele stabiliteit, en openheid. Mogelijk bevorderen we gewenste persoonlijkheidsontwikkeling bij onszelf en bij elkaar als we ervoor zorgen dat we vaker positieve emoties en meer relatiekwaliteit ervaren

Theorising Social Work Sense-Making: Developing a Model of Peer-Aided Judgement and Decision Making

This article addresses the challenges of sense making in social work practice and presents a descriptive model of peer-aided judgement to facilitate critical debate and knowledge creation. The model is founded in Hammond's Cognitive Continuum Theory and developed in direct application to social work practice. It seeks to expand currently available models of social work judgement and decision making to include processes and outcomes related to informal peer interaction. Building on empirical studies and multiple contemporary literatures, a model of peer-aided judgement is hypothesised, comprising four distinct and interacting elements. By modelling these fundamental aspects of the processes and outcomes of peer-aided judgement, this article provides a tool for illuminating the everyday unseen value of peer interaction in practice and a framework for critical debate of dilemmas and propositions for professional judgement in social work practice. This article concludes by examining some of the implications of the model and its potential use in the further development of theory, methodology and practice.Output Status: Forthcoming/Available Onlin

Differential geometric MCMC methods and applications

This thesis presents novel Markov chain Monte Carlo methodology that exploits the natural representation of a statistical model as a Riemannian manifold. The methods developed provide generalisations of the Metropolis-adjusted Langevin algorithm and the Hybrid Monte Carlo algorithm for Bayesian statistical inference, and resolve many shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlation structure. The performance of these Riemannian manifold Markov chain Monte Carlo algorithms is rigorously assessed by performing Bayesian inference on logistic regression models, log-Gaussian Cox point process models, stochastic volatility models, and both parameter and model level inference of dynamical systems described by nonlinear differential equations

Harmonic (Quantum) Neural Networks

Harmonic functions are abundant in nature, appearing in limiting cases of Maxwell's, Navier-Stokes equations, the heat and the wave equation. Consequently, there are many applications of harmonic functions from industrial process optimisation to robotic path planning and the calculation of first exit times of random walks. Despite their ubiquity and relevance, there have been few attempts to incorporate inductive biases towards harmonic functions in machine learning contexts. In this work, we demonstrate effective means of representing harmonic functions in neural networks and extend such results also to quantum neural networks to demonstrate the generality of our approach. We benchmark our approaches against (quantum) physics-informed neural networks, where we show favourable performance.Comment: 12 pages (main), 7 pages (supplementary), 7 figure