Velocity Estimation in Mixtures using Tomography

In oil production a lot of water is usually pumped up together with the oil. For many reasons the reduction of the water production is a very important issue. The method presented in this paper is meant to provide a necessary tool for this. Most drilling wells consist of a network of bore holes. Some of them may produce water, others oil or a mixture. At the moment the net flow of all bore holes together is brought to the surface. It is desirable to be able to detect how much water a specific bore hole contributes. If this amount surpasses a critical value one could then consider to close that bore hole. This leads to the question how the composition of the flow in a pipe can be determined in situ. In this paper we analyze how tomography techniques, well-known from medical applications, can be applied in the case of a bore hole. These techniques allow to measure instantaneously the mass distribution over a cross section of the pipe. For velocity estimation, the idea is to detect the mass distributions at two neighbouring cross sections at successive times. Correlating the obtained time series, one might be able to estimate the local velocity profile. The basic idea was already mentioned in literature before, but it was believed that the number of correlations to be evaluated is so huge, that the approach would fail in practice. In this paper we describe the mathematical details of the method and conclude that the number of time consuming calculations is not necessarily a limiting factor. In addition, suggestions are made to facilitate the use of tomography for velocity estimation

BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent

Matrix tri-factorization subject to binary constraints is a versatile and powerful framework for the simultaneous clustering of observations and features, also known as biclustering. Applications for biclustering encompass the clustering of high-dimensional data and explorative data mining, where the selection of the most important features is relevant. Unfortunately, due to the lack of suitable methods for the optimization subject to binary constraints, the powerful framework of biclustering is typically constrained to clusterings which partition the set of observations or features. As a result, overlap between clusters cannot be modelled and every item, even outliers in the data, have to be assigned to exactly one cluster. In this paper we propose Broccoli, an optimization scheme for matrix factorization subject to binary constraints, which is based on the theoretically well-founded optimization scheme of proximal stochastic gradient descent. Thereby, we do not impose any restrictions on the obtained clusters. Our experimental evaluation, performed on both synthetic and real-world data, and against 6 competitor algorithms, show reliable and competitive performance, even in presence of a high amount of noise in the data. Moreover, a qualitative analysis of the identified clusters shows that Broccoli may provide meaningful and interpretable clustering structures

Cache as ca$h can

In this contribution several caching strategies for the World Wide Weba re studied. Special attention is paid to the so-called proxy placement, i.e. placing of caches on carefully selected nodes in the network near to the end users

Multidirectional Subspace Expansion for One-Parameter and Multiparameter Tikhonov Regularization

Tikhonov regularization is a popular method to approximate solutions of linear discrete ill-posed problems when the observed or measured data is contaminated by noise. Multiparameter Tikhonov regularization may improve the quality of the computed approximate solutions. We propose a new iterative method for large-scale multiparameter Tikhonov regularization with general regularization operators based on a multidirectional subspace expansion. The multidirectional subspace expansion may be combined with subspace truncation to avoid excessive growth of the search space. Furthermore, we introduce a simple and effective parameter selection strategy based on the discrepancy principle and related to perturbation results

The Euro Diffusion Project

From 1st January 2002 we have the unique possibility to follow the spread of national euro coins over the different European countries. We model and analyse this movement and estimate the time it will take before on average half the coins in our wallet will be foreign

De politiek van buy-to-let

Mounting concerns exist that small private investors exacerbate the urban housing crisis, by purchasing dwellings to rent out so-called ‘buy-to-let’ purchases. By buying up property, they may drive up house prices and exclude regular house-seekers. In this paper, we show that buy-to-let purchases constitute an increasing share of all purchases on the Dutch housing market, and especially so in larger cities and university cities. We argue these local trends do not emerge out of thin air and are not a ‘natural’ market process but should be considered the product of both global economic developments and national policies supporting these changes. Global developments include the increased mobility and ample availability of capital, exemplified by a prolonged low interest environment and a growing scarcity of high quality collateral and investment opportunities, making housing attractive for storing capital. Dutch housing policies have increasingly restricted access to social rent to low-income groups, while blowing up house prices fuelled with mortgage debts. As a consequence, a growing number of households falls in-between these two tenures: they have to resort to private rent. Private investors respond to and accommodate this demand through buy-to-let investments. Furthermore, the Dutch national government has made steps to relax regulation on the private-rental market and weakened tenant rights. In so doing, it sets the scene for amplifying social and spatial inequalities between the property rich and the property poor