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Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, ...
This paper presents a novel probabilistic framework for localizing multiple speakers with a microphone array. In this framework, the generalized cross correlation function (GCC) of each microphone pair is interpreted as a probability distribution of the ti ...
Extreme events can be statistically characterised as excesses of a high threshold. Inference in this case has to account for dependence between excesses. The peaks over threshold approach suggests pre-processing the series by defining clusters of successiv ...
Super-resolution localization microscopy relies on sparse activation of photo-switchable probes. Such activation, however, introduces limited temporal resolution. High-density imaging overcomes this limitation by allowing several neighboring probes to be a ...
We describe a failure of standard extremal models to account for a catastrophic rainfall event in the coastal regions of Venezuela on 14-16 December 1999, due both to inaccurate tail modelling and to an inadequate treatment of clusters of rare events. We i ...
To improve hydro-chemical modeling and forecasting, there is a need to better understand flood-induced variability in water chemistry and the processes controlling it in watersheds. In the literature, assumptions are often made, for instance, that stream c ...
Microsimulation of urban transportation and land use evolution require base year, individual characteristics and disaggregate locations of the households and persons living in the study area. On the other hand, mainly due to privacy reasons, at best the ce ...
We present a new approach to address the problem of large sequence mining from big data. The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suff ...
We present a theoretical investigation into the use of normalised artificial neural network (ANN) outputs in the context of hidden Markov models (HMMs). The work is motivated by the pursuit of a more theoretically rigorous understanding of the Kullback-Lie ...
The need for forecasting the direct and indirect effects of land use and transport policies on society, the environment, and the local economy has led to the development of integrated land use and transport (LUTI) models. The land use and transport policy ...
