Analysing Temporal Relations – Beyond Windows, Frames and Predicates

This article proposes an approach to rely on the standard operators of relational algebra (including grouping and ag- gregation) for processing complex event without requiring window specifications. In this way the approach can pro- cess complex event queries of the kind encountered in appli- cations such as emergency management in metro networks. This article presents Temporal Stream Algebra (TSA) which combines the operators of relational algebra with an analy- sis of temporal relations at compile time. This analysis de- termines which relational algebra queries can be evaluated against data streams, i. e. the analysis is able to distinguish valid from invalid stream queries. Furthermore the analysis derives functions similar to the pass, propagation and keep invariants in Tucker's et al. \Exploiting Punctuation Seman- tics in Continuous Data Streams". These functions enable the incremental evaluation of TSA queries, the propagation of punctuations, and garbage collection. The evaluation of TSA queries combines bulk-wise and out-of-order processing which makes it tolerant to workload bursts as they typically occur in emergency management. The approach has been conceived for efficiently processing complex event queries on top of a relational database system. It has been deployed and tested on MonetDB

Organic Crowding Out? - A Study of Danish Organic Food Demand

Only a handful of studies have estimated organic food demand. These all focus on specific food sub-markets assuming separability from other food consumption. However, consumers typically associate attributes such as e.g. healthiness and environment friendliness with organic variants of most types of food. If such general organic attributes are important for consumer behaviour then separability may not hold and what could be termed organic crowding out might result. In this paper we utilize a unique Danish micro panel where all food demand is registered on a disaggregated level with an organic/non-organic indicator to estimate a general food demand system with organic variants. We clearly reject the usual separability assumption and find that our data is consistent with organic crowding out in the Danish food market. In addition estimation of a general demand system makes calculation of economy wide organic price elasticities and other insights into the structure of organic food demand possible

Optimal measurement of visual motion across spatial and temporal scales

Sensory systems use limited resources to mediate the perception of a great variety of objects and events. Here a normative framework is presented for exploring how the problem of efficient allocation of resources can be solved in visual perception. Starting with a basic property of every measurement, captured by Gabor's uncertainty relation about the location and frequency content of signals, prescriptions are developed for optimal allocation of sensors for reliable perception of visual motion. This study reveals that a large-scale characteristic of human vision (the spatiotemporal contrast sensitivity function) is similar to the optimal prescription, and it suggests that some previously puzzling phenomena of visual sensitivity, adaptation, and perceptual organization have simple principled explanations.Comment: 28 pages, 10 figures, 2 appendices; in press in Favorskaya MN and Jain LC (Eds), Computer Vision in Advanced Control Systems using Conventional and Intelligent Paradigms, Intelligent Systems Reference Library, Springer-Verlag, Berli

Reckoning Inter-group Poverty Differentials in the Measurement of Aggregate Poverty

group poverty profile, group Lorenz profile, externality, group affiliation

Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information from IoT devices open up tremendous opportunities for managing smart city infrastructures. The privacy-enhancing aggregation of distributed sensor data, such as residential energy consumption or traffic information, is the research focus of this paper. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared as opposed to sharing data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario. The proposed system is evaluated using real-world data from two smart city pilot projects. Privacy under grouping increases, while preserving the accuracy of the baseline scenario. Intra-group influences of privacy by one group member on the other ones are measured and fairness on privacy is found to be maximized between group members with similar privacy choices. Several grouping strategies are compared. Grouping by proximity of privacy choices provides the highest privacy gains. The implications of the strategy on the design of incentives mechanisms are discussed

A computer vision model for visual-object-based attention and eye movements

This is the post-print version of the final paper published in Computer Vision and Image Understanding. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.This paper presents a new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach. Attention operates at multiple levels of visual selection by space, feature, object and group depending on the nature of targets and visual tasks. Attentional shifts and gaze shifts are constructed upon their common process circuits and control mechanisms but also separated from their different function roles, working together to fulfil flexible visual selection tasks in complicated visual environments. The framework integrates the important aspects of human visual attention and eye movements resulting in sophisticated performance in complicated natural scenes. The proposed approach aims at exploring a useful visual selection system for computer vision, especially for usage in cluttered natural visual environments.National Natural Science of Founda- tion of Chin

A multi-coloured survey of NGC 253 with XMM-Newton: testing the methods used for creating luminosity functions from low-count data

NGC 253 is a local, star-bursting spiral galaxy with strong X-ray emission from hot gas, as well as many point sources. We have conducted a spectral survey of the X-ray population of NGC 253 using a deep XMM-Newton observation.NGC 253 only accounts for ~20% of the XMM-Newton EPIC field of view, allowing us to identify ~100 X-ray sources that are unlikely to be associated with NGC\thinspace 253. Hence we were able to make a direct estimate of contamination from e.g. foreground stars and background galaxies. X-ray luminosity functions (XLFs) of galaxy populations are often used to characterise their properties. There are several methods for estimating the luminosities of X-ray sources with few photons. We have obtained spectral fits for the brightest 140 sources in the 2003 XMM-Newton observation of NGC 253, and compare the best fit luminosities of those 69 non-nuclear sources associated with NGC 253 with luminosities derived using other methods. We find the luminosities obtained from these various methods to vary systematically by a factor of up to three for the same data; this is largely due to differences in absorption. We therefore conclude that assuming Galactic absorption is probably unwise; rather, one should measure the absorption for the population. A remarkable correlation has been reported between the XLFs of galaxies and their star formation rates. However, the XLFs used in that study were obtained using several different methods. If the sample galaxies were revisited and a single method were applied, then this correlation may become stronger still.Comment: Accepted for publication in the Monthly Notices of the Royal Astronomical Society (MNRAS). 17 pages, 7 figure

Nonparametric regression with homogeneous group testing data

We introduce new nonparametric predictors for homogeneous pooled data in the context of group testing for rare abnormalities and show that they achieve optimal rates of convergence. In particular, when the level of pooling is moderate, then despite the cost savings, the method enjoys the same convergence rate as in the case of no pooling. In the setting of "over-pooling" the convergence rate differs from that of an optimal estimator by no more than a logarithmic factor. Our approach improves on the random-pooling nonparametric predictor, which is currently the only nonparametric method available, unless there is no pooling, in which case the two approaches are identical.Comment: Published in at http://dx.doi.org/10.1214/11-AOS952 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org