Field-regularised factorization machines for mining the maintenance logs of equipment

© Springer Nature Switzerland AG 2018. Failure prediction is very important for railway infrastructure. Traditionally, data from various sensors are collected for this task. Value of maintenance logs is often neglected. Maintenance records of equipment usually indicate equipment status. They could be valuable for prediction of equipment faults. In this paper, we propose Field-regularised Factorization Machines (FrFMs) to predict failures of railway points with maintenance logs. Factorization Machine (FM) and its variants are state-of-the-art algorithms designed for sparse data. They are widely used in click-through rate prediction and recommendation systems. Categorical variables are converted to binary features through one-hot encoding and then fed into these models. However, field information is ignored in this process. We propose Field-regularised Factorization Machines to incorporate such valuable information. Experiments on data set from railway maintenance logs and another public data set show the effectiveness of our methods

An early evaluation of the 2050 Calculator international outreach programme

This paper presents the findings of an early evaluation of the UK Department of Energy and Climate Change’s 2050 Calculator International Outreach Programme. The programme supported eleven countries to develop their own versions of the 2050 Calculator. Drawing on interviews with stakeholders who were involved directly and indirectly in the development of the 2050 Calculators, this paper evaluates the process of developing these tools in different national contexts and discusses the lessons learnt so far. The findings discussed include the original motivations for involvement and how these evolved through the project, and the process of stakeholder engagement. The latter was expected to be a key benefit of the Calculator, and one which would open up debate about long term energy futures. While the teams developing the Calculators faced challenges, including data availability, political buy-in, and defining scenario trajectories, a flexible approach enabled countries to develop Calculators that were tailored to their national objectives and political environments. Overall, the 2050 Calculators have led to a wide range of benefits and there is ongoing commitment to develop new iterations and applications to use these Calculators to support planning of, and debate on, future energy and emissions trajectories

Railway Infrastructure Defects Recognition using Fine-grained Deep Convolutional Neural Networks

© 2018 IEEE. Railway power supply infrastructure is one of the most important components of railway transportation. As the key step of railway maintenance system, power supply infrastructure defects recognition plays a vital role in the whole defects inspection sub-system. Traditional defects recognition task is performed manually, which is time-consuming and high-labor costing. Inspired by the great success of deep neural networks in dealing with different vision tasks, this paper presents an end-to-end deep network to solve the railway infrastructure defects detection problem. More importantly, this paper is the first work that adopts the idea of deep fine-grained classification to do railway defects detection. We propose a new bilinear deep network named Spatial Transformer And Bilinear Low-Rank (STABLR) model and apply it to railway infrastructure defects detection. The experimental results demonstrate that the proposed method outperforms both hand-craft features based machine learning methods and classic deep neural network methods

Endoscopic Treatment of Vesicoureteral Reflux with Dextranomer/Hyaluronic Acid in Children

Purpose. The goal of this review is to present current indications, injectable agents, techniques, success rates, complications, and potential future applications of endoscopic treatment for vesicoureteral reflux (VUR) in children. Materials and Methods. The endoscopic method currently achieving one of the highest success rates is the double hydrodistention-implantation technique (HIT). This method employs dextranomer/hyaluronic acid copolymer, which has been used in pediatric urology for over 10 years and may be at present the first choice injectable agent due to its safety and efficacy. Results. While most contemporary series report cure rates of greater than 85% for primary VUR, success rates of complicated cases of VUR may be, depending on the case, significantly lower. Endoscopic treatment offers major advantages to patients while avoiding potentially complicated open surgery. As the HIT method continues to be applied to complex cases of VUR and more outcome data become available, the indication for endoscopic treatment may exceed the scope of primary VUR. Conclusions. Endoscopic injection is emerging as the treatment of choice for VUR in children

Concepts, Developments and Advanced Applications of the PAX Toolkit

The Physics Analysis eXpert (PAX) is an open source toolkit for high energy physics analysis. The C++ class collection provided by PAX is deployed in a number of analyses with complex event topologies at Tevatron and LHC. In this article, we summarize basic concepts and class structure of the PAX kernel. We report about the most recent developments of the kernel and introduce two new PAX accessories. The PaxFactory, that provides a class collection to facilitate event hypothesis evolution, and VisualPax, a Graphical User Interface for PAX objects

Ink dating using thermal desorption and gas chromatography / mass spectrometry: comparison of results obtained in two laboratories

Recent ink dating methods focused mainly on changes in solvent amounts occurring over time. A promising method was developed at the Landeskriminalamt of Munich using thermal desorption (TD) followed by gas chromatography / mass spectrometry (GC/MS) analysis. Sequential extractions of the phenoxyethanol present in ballpoint pen ink entries were carried out at two different temperatures. This method is applied in forensic practice and is currently implemented in several laboratories participating to the InCID group (International Collaboration on Ink Dating). However, harmonization of the method between the laboratories proved to be a particularly sensitive and time consuming task. The main aim of this work was therefore to implement the TD-GC/MS method at the Bundeskriminalamt (Wiesbaden, Germany) in order to evaluate if results were comparable to those obtained in Munich. At first validation criteria such as limits of reliable measurements, linearity and repeatability were determined. Samples were prepared in three different laboratories using the same inks and analyzed using two TDS-GC/MS instruments (one in Munich and one in Wiesbaden). The inter- and intra-laboratory variability of the ageing parameter was determined and ageing curves were compared. While inks stored in similar conditions yielded comparable ageing curves, it was observed that significantly different storage conditions had an influence on the resulting ageing curves. Finally, interpretation models, such as thresholds and trend tests, were evaluated and discussed in view of the obtained results. Trend tests were considered more suitable than threshold models. As both approaches showed limitations, an alternative model, based on the slopes of the ageing curves, was also proposed

A Bayesian Approach to Inverse Quantum Statistics

A nonparametric Bayesian approach is developed to determine quantum potentials from empirical data for quantum systems at finite temperature. The approach combines the likelihood model of quantum mechanics with a priori information over potentials implemented in form of stochastic processes. Its specific advantages are the possibilities to deal with heterogeneous data and to express a priori information explicitly, i.e., directly in terms of the potential of interest. A numerical solution in maximum a posteriori approximation was feasible for one--dimensional problems. Using correct a priori information turned out to be essential.Comment: 4 pages, 6 figures, revte

Lyapunov exponent and natural invariant density determination of chaotic maps: An iterative maximum entropy ansatz

We apply the maximum entropy principle to construct the natural invariant density and Lyapunov exponent of one-dimensional chaotic maps. Using a novel function reconstruction technique that is based on the solution of Hausdorff moment problem via maximizing Shannon entropy, we estimate the invariant density and the Lyapunov exponent of nonlinear maps in one-dimension from a knowledge of finite number of moments. The accuracy and the stability of the algorithm are illustrated by comparing our results to a number of nonlinear maps for which the exact analytical results are available. Furthermore, we also consider a very complex example for which no exact analytical result for invariant density is available. A comparison of our results to those available in the literature is also discussed.Comment: 16 pages including 6 figure