On Matrices, Automata, and Double Counting

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables M, with the same constraint defined by a finite-state automaton A on each row of M and a global cardinality constraint gcc on each column of M. We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the gcc constraints from the automaton A. The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We evaluate the impact of our methods on a large set of nurse rostering problem instances

Domain k-Wise Consistency Made as Simple as Generalized Arc Consistency

Abstract. In Constraint Programming (CP), Generalized Arc Consistency (GAC) is the central property used for making inferences when solving Constraint Satisfaction Problems (CSPs). Developing simple and practical filtering algorithms based on consistencies stronger than GAC is a challenge for the CP community. In this paper, we propose to combine k-Wise Consistency (kWC) with GAC, where kWC states that every tuple in a constraint can be extended to every set of k − 1 additional constraints. Our contribution is as follows. First, we derive a domain-filtering consistency, called Domain k-Wise Consistency (DkWC), from the combination of kWC and GAC. Roughly speaking, this property corresponds to the pruning of values of GAC, when enforced on a CSP previously made kWC. Second, we propose a procedure to enforce DkWC, relying on an encoding of kWC to generate a modified CSP called k-interleaved CSP. Formally, we prove that enforcing GAC on the k-interleaved CSP corresponds to enforcing DkWC on the initial CSP. Consequently, we show that the strong DkWC can be enforced very easily in constraint solvers since the k-interleaved CSP is rather immediate to generate and only existing GAC propagators are required: in a nutshell, DkWC is made as simple and practical as GAC. Our experimental results show the benefits of our approach on a variety of benchmarks.

The Groundwater Drought Initiative (GDI): analysing and understanding groundwater drought across Europe

In Europe, it is estimated that around 65 % of drinking water is extracted from groundwater. Worryingly, groundwater drought events (defined as below normal groundwater levels) pose a threat to water security. Groundwater droughts are caused by seasonal to multi-seasonal or even multi-annual episodes of meteorological drought during which the drought propagates through the river catchment into the groundwater system by mechanisms of pooling, lagging, and lengthening of the drought signals. Recent European drought events in 2010–2012, 2015 and 2017–2018 exhibited spatial coherence across large areas, thus demonstrating the need for transboundary monitoring and analysis of groundwater level fluctuations. However, such monitoring and analysis of groundwater drought at a pan-European scale is currently lacking, and so represents a gap in drought research as well as in water management capability. To address this gap, the European Groundwater Drought Initiative (GDI), a pan-European collaboration, is undertaking a large-scale data synthesis of European groundwater level data. This is being facilitated by the establishment of a new network to co-ordinate groundwater drought research across Europe. This research will deliver the first assessment of spatio-temporal changes in groundwater drought status from ∼1960 to present, and a series of case studies on groundwater drought impacts in selected temperate and semi-arid environments across Europe. Here, we describe the methods used to undertake the continental-scale status assessment, which are more widely applicable to transboundary or large-scale groundwater level analyses also in regions beyond Europe, thereby enhancing groundwater management decisions and securing water supply

Automatic discovery and exploitation of promising subproblems for tabulation

The performance of a constraint model can often be improved by converting a subproblem into a single table constraint. In this paper we study heuristics for identifying promising subproblems. We propose a small set of heuristics to identify common cases such as expressions that will propagate weakly. The process of discovering promising subproblems and tabulating them is entirely automated in the tool Savile Row. A cache is implemented to avoid tabulating equivalent subproblems many times. We give a simple algorithm to generate table constraints directly from a constraint expression in Savile Row. We demonstrate good performance on the benchmark problems used in earlier work on tabulation, and also for several new problem classes

Discriminating instance generation for automated constraint model selection

One approach to automated constraint modelling is to generate, and then select from, a set of candidate models. This method is used by the automated modelling system Conjure. To select a preferred model or set of models for a problem class from the candidates Conjure produces, we use a set of training instances drawn from the target class. It is important that the training instances are discriminating. If all models solve a given instance in a trivial amount of time, or if no models solve it in the time available, then the instance is not useful for model selection. This paper addresses the task of generating small sets of discriminating training instances automatically. The instance space is determined by the parameters of the associated problem class. We develop a number of methods of finding parameter configurations that give discriminating training instances, some of them leveraging existing parameter-tuning techniques. Our experimental results confirm the success of our approach in reducing a large set of input models to a small set that we can expect to perform well for the given problem class

Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters

In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception–action loop, based on probabilistic modeling and Bayesian inference, which we call the Bayesian Action–Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using Bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). We present computer simulations of each of these cognitive tasks, and discuss experimental predictions and theoretical developments

LUNEX5: A French FEL Test Facility Light Source Proposal

http://accelconf.web.cern.ch/AccelConf/IPAC2012/papers/tuppp005.pdfInternational audienceLUNEX5 is a new Free Electron Laser (FEL) source project aimed at delivering short and coherent X-ray pulses to probe ultrafast phenomena at the femto-second scale, to investigate extremely low density samples as well as to image individual nm scale objects

Effect of [OH-] linkages on luminescent properties of ZnO nanoparticles

Optical properties of ZnO nanoparticles prepared from a simple chemical method using sodium zincate bath show strong white light emission. X-ray absorption fine structure studies reveal a completely different local environment around Zn in these ZnO nanoparticles. The observed luminescence properties and local structural changes have been explained on the basis of a linkage between Zn and OH- ions in the surface layers of ZnO nanoparticles.Comment: J. Phys. Chem. C. (2011) (in print

Being there: a preliminary study examining the role of presence in Internet Gaming Disorder

Internet Gaming Disorder (IGD) has been introduced as an emerging mental health condition requiring further study. Associations between IGD and gaming presence (i.e., absorption in the virtual environment) have been implied. The aim of the present study was twofold: (a) to evaluate the extent to which presence contributes to IGD severity and, (b) to examine longitudinal differences in IGD according to the initial level of presence experienced. The participants comprising 125 emerging adults aged 18 to 29 years completed either: (i) three face-to-face assessments (one month apart, over three months) or (ii) a cross-sectional, online assessment. IGD was assessed with the nine-item IGD Scale Short Form and presence was assessed using the Presence Questionnaire. Regression and latent growth modelling analyses were conducted. Findings demonstrated that the level of gaming presence related to IGD severity but not to linear change in severity over a three-month period. The study shows that emergent adults who play internet games may be at a high risk of IGD given a more salient sense of being present within the gaming environment. Clinical implications considering prevention and intervention initiatives are discussed