Unsupervised learning of visual taxonomies

As more images and categories become available, organizing them becomes crucial. We present a novel statistical method for organizing a collection of images into a treeshaped hierarchy. The method employs a non-parametric Bayesian model and is completely unsupervised. Each image is associated with a path through a tree. Similar images share initial segments of their paths and therefore have a smaller distance from each other. Each internal node in the hierarchy represents information that is common to images whose paths pass through that node, thus providing a compact image representation. Our experiments show that a disorganized collection of images will be organized into an intuitive taxonomy. Furthermore, we find that the taxonomy allows good image categorization and, in this respect, is superior to the popular LDA model

Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation

Nonparametric Bayesian approaches to clustering, information retrieval, language modeling and object recognition have recently shown great promise as a new paradigm for unsupervised data analysis. Most contributions have focused on the Dirichlet process mixture models or extensions thereof for which efficient Gibbs samplers exist. In this paper we explore Gibbs samplers for infinite complexity mixture models in the stick breaking representation. The advantage of this representation is improved modeling flexibility. For instance, one can design the prior distribution over cluster sizes or couple multiple infinite mixture models (e.g. over time) at the level of their parameters (i.e. the dependent Dirichlet process model). However, Gibbs samplers for infinite mixture models (as recently introduced in the statistics literature) seem to mix poorly over cluster labels. Among others issues, this can have the adverse effect that labels for the same cluster in coupled mixture models are mixed up. We introduce additional moves in these samplers to improve mixing over cluster labels and to bring clusters into correspondence. An application to modeling of storm trajectories is used to illustrate these ideas.Comment: Appears in Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI2006

Machine-Learned Temporal Brand Scores for Video Ads

A machine learning system infers a “brand score” curve of a video across the run time for the video. The system uses a ground truth score obtained using user surveys, audio transcription of words spoken, video transcription of words displayed, type of music being played, and computer vision signals to learn a model for inferring the brand score. A given video is segmented, and a piecewise brand score for each segment is generated using the model

Agent-Based Virtual Urban Environments for Population Health Applications

Agent-based computational models are gaining traction as a means for modelling the complexities of designing and implementing health interventions in our rapidly-changing society. When such models are integrated with an interactive virtual environment they o er a way to investigate complex conditions including social and environmental de- terminants, while also facilitating participation and interaction from re- search users and policy-makers. Here we present a prototype Agent-Based Virtual Environment which features an early-stage model of obesity in- tended to support planners and local authority members in the develop- ment of environments that encourage healthy diets and higher physical exertion. We illustrate the construction of the model and its intended role in raising awareness of the role of the built environment in prevent- ing obesity. We also describe future extensions and ways to extend this framework to other areas of concern in public health

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

Alzheimer's Disease Genes Are Associated with Measures of Cognitive Ageing in the Lothian Birth Cohorts of 1921 and 1936

Alzheimer's disease patients have deficits in specific cognitive domains, and susceptibility genes for this disease may influence human cognition in nondemented individuals. To evaluate the role of Alzheimer's disease-linked genetic variation on cognition and normal cognitive ageing, we investigated two Scottish cohorts for which assessments in major cognitive domains are available: the Lothian Birth Cohort of 1921 and the Lothian Birth Cohort of 1936, consisting of 505 and 998 individuals, respectively. 158 SNPs from eleven genes were evaluated. Single SNP analyses did not reveal any statistical association after correction for multiple testing. One haplotype from TRAPPC6A was associated with nonverbal reasoning in both cohorts and combined data sets. This haplotype explains a small proportion of the phenotypic variability (1.8%). These findings warrant further investigation as biological modifiers of cognitive ageing

Measurement of H₂O₂ within living drosophila during aging using a ratiometric mass spectrometry probe targeted to the mitochondrial matrix

Hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) is central to mitochondrial oxidative damage and redox signaling, but its roles are poorly understood due to the difficulty of measuring mitochondrial H<sub>2</sub>O<sub>2</sub> in vivo. Here we report a ratiometric mass spectrometry probe approach to assess mitochondrial matrix H<sub>2</sub>O<sub>2</sub> levels in vivo. The probe, MitoB, comprises a triphenylphosphonium (TPP) cation driving its accumulation within mitochondria, conjugated to an arylboronic acid that reacts with H<sub>2</sub>O<sub>2</sub> to form a phenol, MitoP. Quantifying the MitoP/MitoB ratio by liquid chromatography-tandem mass spectrometry enabled measurement of a weighted average of mitochondrial H<sub>2</sub>O<sub>2</sub> that predominantly reports on thoracic muscle mitochondria within living flies. There was an increase in mitochondrial H<sub>2</sub>O<sub>2</sub> with age in flies, which was not coordinately altered by interventions that modulated life span. Our findings provide approaches to investigate mitochondrial ROS in vivo and suggest that while an increase in overall mitochondrial H<sub>2</sub>O<sub>2</sub> correlates with aging, it may not be causative

Investigating the relationship between DNA methylation age acceleration and risk factors for Alzheimer’s disease

This work was supported by a Alzheimer's Research UK Major Project grant (ARUK-PG2017B-10). Generation Scotland received core funding from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). We are grateful to all the families who took part, the general practitioners and the Scottish School of Primary Care for their help in recruiting them, and the whole Generation Scotland team that includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, health-care assistants and nurses. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” [STRADL];104036/Z/14/Z). DNA methylation data collection was funded by the Wellcome Trust Strategic Award (10436/Z/14/Z). The research was conducted in the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1); funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged. CCACE supports I.J.D. with some additional support from the Dementias Platform UK (MR/L015382/1). A.M.M. and H.C.W. have received support from the Sackler Institute.Peer reviewedPublisher PD

Using a knowledge exchange event to assess study participants’ attitudes to research in a rapidly evolving research context

Grant information: DJP, IJD and AMM are supported by Wellcome Trust Grant 104036. IJD, DJP, JPB and AMM, IB, EJK and SFW are supported by MRC Mental Health Data Pathfinder Grant MC_PC_17209. AMM and SML are supported by MRC Grant MC_PC_MR/R01910X/1. AMM is supported by MRC Grant MR/S035818/1. Theirworld Edinburgh Birth Cohort is funded by the charity Theirworld (www.theirworld.org), and is undertaken in the MRC Centre for Reproductive Health, which is funded by MRC Centre Grant (G1002033). CB and DJP are supported by Health Data Research UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities.Peer reviewedPublisher PD