Central limit theorem for reducible and irreducible open quantum walks

In this work we aim at proving central limit theorems for open quantum walks on \({\mathbb {Z}}^d\). We study the case when there are various classes of vertices in the network. In particular, we investigate two ways of distributing the vertex classes in the network. First, we assign the classes in a regular pattern. Secondly, we assign each vertex a random class with a transition invariant distribution. For each way of distributing vertex classes, we obtain an appropriate central limit theorem, illustrated by numerical examples. These theorems may have application in the study of complex systems in quantum biology and dissipative quantum computation.     

An assessment view to evaluate whether Spatial Data Infrastructures meet their goals

The motives for constructing Spatial Data Infrastructures (SDIs) are often based on their anticipated benefits for society, economy, and environment. According to those widely articulated but rarely proven benefits, SDI coordinators have been defining more specific objectives to be achieved by their SDIs. However, there is a limited number of assessment approaches that are able to demonstrate whether SDIs indeed realize the intended goals. In this article we develop, apply and evaluate an assessment view for evaluating the extent to which SDIs realize their goals. The assessment view has been developed stepwise using the Multi-view SDI assessment framework as a guideline. The application of the proposed view in the Dutch SDI demonstrates its potential. In addition, the evaluation of the proposed view by the potential users confirms to a certain extent its usability. The results also show that the ease of determining assessment indicators depends on the precision with which the SDI goals are formulated.     

Model Checking Games for the Quantitative μ-Calculus

We investigate quantitative extensions of modal logic and the modal μ-calculus, and study the question whether the tight connection between logic and games can be lifted from the qualitative logics to their quantitative counterparts. It turns out that, if the quantitative μ-calculus is defined in an appropriate way respecting the duality properties between the logical operators, then its model checking problem can indeed be characterised by a quantitative variant of parity games. However, these quantitative games have quite different properties than their classical counterparts, in particular they are, in general, not positionally determined. The correspondence between the logic and the games goes both ways: the value of a formula on a quantitative transition system coincides with the value of the associated quantitative game, and conversely, the values of quantitative parity games are definable in the quantitative μ-calculus.     

A Universal Randomized Packet Scheduling Algorithm

We give a memoryless scale-invariant randomized algorithm ReMix for Packet Scheduling that is e/(e?1)-competitive against an adaptive adversary. ReMix unifies most of previously known randomized algorithms, and its general analysis yields improved performance guarantees for several restricted variants, including the s-bounded instances. In particular, ReMix attains the optimum competitive ratio of 4/3 on 2-bounded instances. Our results are applicable to a more general problem, called Item Collection, in which only the relative order between packets’ deadlines is known. ReMix is the optimal memoryless randomized algorithm against adaptive adversary for that problem.     

A thermo-mechanical machining method for improving the accuracy and stability of the geometric shape of long low-rigidity shafts

Journal of Intelligent Manufacturing - The article presents a new thermo-mechanical machining method for the manufacture of long low-rigidity shafts which combines straightening and heat treatment...     

Machine Learning-Based Predictions of Customers’ Decisions in Car Insurance

Predicting customer decisions allows companies to obtain higher profits due to better resource management. The accuracy of those predictions can be currently boosted by the application of machine learning algorithms.

We propose a new method to predict a car driver’s decision about taking a replacement car after a vehicle accident happens. We use feature engineering to create attributes of high significance. The generated attributes are related to time (e.g., school holidays), place of collision (e.g., distance from home), time and conditions (e.g., weather), vehicles (e.g., vehicle value), addresses of both the victim and the perpetrator. Feature engineering involves external sources of data.

Five machine learning methods of classification are considered: decision trees, multi-layer perceptrons, AdaBoost, logistic regression and gradient boosting. Algorithms are tested on real data from a Polish insurance company. Over 80% accuracy of prediction is achieved. Significance of the attributes is calculated using the linear vector quantization method.

Presented work shows the applicability of machine learning in the car insurance market.     

Structural damage identification by adding virtual masses

This paper presents a method for damage identification by adding virtual masses to the structure in order to increase its sensitivity to local damages. The main concept is based on the Virtual Distortion Method (VDM), which is a fast structural reanalysis method that employs virtual distortions or pseudo loads to simulate structural modifications. In this paper, the structure with an added virtual mass is called the virtual structure. First, the acceleration frequency response of the virtual structure is constructed numerically by the VDM using local dynamic data measured only by a single excitation sensor and a single acceleration sensor. Second, the value of the additional mass is determined via sensitivity analysis of the constructed frequency responses of the virtual structure with respect to damage parameters; only the natural frequencies with high sensitivity are selected. This process is repeated for all the considered placements of the virtual mass. At last, the selected natural frequencies of all the virtual structures are used together for damage identification of the real structure. A finite element (FE) model of a plane frame is used to introduce and verify the proposed method. The damage can be identified precisely and effectively even under simulated 5 % Gaussian noise pollution.