A Gaussian Bayesian model to identify spatio-temporal causalities for air pollution based on urban big data

Identifying the causalities for air pollutants and answering questions, such as, where do Beijing's air pollutants come from, are crucial to inform government decision-making. In this paper, we identify the spatio-temporal (ST) causalities among air pollutants at different locations by mining the urban big data. This is challenging for two reasons: 1) since air pollutants can be generated locally or dispersed from the neighborhood, we need to discover the causes in the ST space from many candidate locations with time efficiency; 2) the cause-and-effect relations between air pollutants are further affected by confounding variables like meteorology. To tackle these problems, we propose a coupled Gaussian Bayesian model with two components: 1) a Gaussian Bayesian Network (GBN) to represent the cause-and-effect relations among air pollutants, with an entropy-based algorithm to efficiently locate the causes in the ST space; 2) a coupled model that combines cause-and-effect relations with meteorology to better learn the parameters while eliminating the impact of confounding. The proposed model is verified using air quality and meteorological data from 52 cities over the period Jun 1st 2013 to May 1st 2015. Results show superiority of our model beyond baseline causality learning methods, in both time efficiency and prediction accuracy. © 2016 IEEE.postprintLink_to_subscribed_fulltex

Approaches to the cortical analysis of auditory objects

We describe work that addresses the cortical basis for the analysis of auditory objects using ‘generic’ sounds that do not correspond to any particular events or sources (like vowels or voices) that have semantic association. The experiments involve the manipulation of synthetic sounds to produce systematic changes of stimulus features, such as spectral envelope. Conventional analyses of normal functional imaging data demonstrate that the analysis of spectral envelope and perceived timbral change involves a network consisting of planum temporale (PT) bilaterally and the right superior temporal sulcus (STS). Further analysis of imaging data using dynamic causal modelling (DCM) and Bayesian model selection was carried out in the right hemisphere areas to determine the effective connectivity between these auditory areas. Specifically, the objective was to determine if the analysis of spectral envelope in the network is done in a serial fashion (that is from HG to PT to STS) or parallel fashion (that is PT and STS receives input from HG simultaneously). Two families of models, serial and parallel (16 in total) that represent different hypotheses about the connectivity between HG, PT and STS were selected. The models within a family differ with respect to the pathway that is modulated by the analysis of spectral envelope. After the models are identified, Bayesian model selection procedure is then used to select the ‘optimal’ model from the specified models. The data strongly support a particular serial model containing modulation of the HG to PT effective connectivity during spectral envelope variation. Parallel work in neurological subjects addresses the effect of lesions to different parts of this network. We have recently studied in detail subjects with ‘dystimbria’: an alteration in the perceived quality of auditory objects distinct from pitch or loudness change. The subjects have lesions of the normal network described above with normal perception of pitch strength but abnormal perception of the analysis of spectral envelope change

Learning to Address Health Inequality in the United States with a Bayesian Decision Network

Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. There is a growing concern about a further widening of healthcare inequality caused by Artificial Intelligence (AI) due to differential access to AI-driven services. Hence, it is imperative to explore and exploit the potential of AI for illuminating biases and enabling transparent policy decisions for positive social and health impact. In this work, we reveal actionable interventions for decreasing the longevity-gap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features. We learn an ensemble-averaged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the impact of diversity, preventive-care quality and stable-families within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our reported findings and to explore the impact of ones beyond reported in this work.Comment: 8 pages, 4 figures, 1 table (excluding the supplementary material), accepted for publication in AAAI 201

A probabilistic model for information and sensor validation

This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is only apparent since it may be that the estimated value is itself based on faulty data. The theory extends our understanding of when it is possible to isolate real faults from potential faults and supports the development of an algorithm that is capable of isolating real faults without deferring the problem to the use of expert provided domain-specific rules. To enable practical adoption for real-time processes, an any time version of the algorithm is developed, that, unlike most other algorithms, is capable of returning improving assessments of the validity of the sensors as it accumulates more evidence with time. The developed model is tested by applying it to the validation of temperature sensors during the start-up phase of a gas turbine when conditions are not stable; a problem that is known to be challenging. The paper concludes with a discussion of the practical applicability and scalability of the model