Flexible Multi-layer Sparse Approximations of Matrices and Applications

The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors. This paper introduces an algorithm aimed at reducing the complexity of applying linear operators in high dimension by approximately factorizing the corresponding matrix into few sparse factors. The approach relies on recent advances in non-convex optimization. It is first explained and analyzed in details and then demonstrated experimentally on various problems including dictionary learning for image denoising, and the approximation of large matrices arising in inverse problems

Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

On systematic approaches for interpreted information transfer of inspection data from bridge models to structural analysis

In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

The AFLOW Fleet for Materials Discovery

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.Comment: 14 pages, 8 figure

Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels

Forensic analysis of digital photo provenance relies on intrinsic traces left in the photograph at the time of its acquisition. Such analysis becomes unreliable after heavy post-processing, such as down-sampling and re-compression applied upon distribution in the Web. This paper explores end-to-end optimization of the entire image acquisition and distribution workflow to facilitate reliable forensic analysis at the end of the distribution channel. We demonstrate that neural imaging pipelines can be trained to replace the internals of digital cameras, and jointly optimized for high-fidelity photo development and reliable provenance analysis. In our experiments, the proposed approach increased image manipulation detection accuracy from 45% to over 90%. The findings encourage further research towards building more reliable imaging pipelines with explicit provenance-guaranteeing properties.Comment: Camera ready + supplement, CVPR'1

Accelerating Materials Discovery with Machine Learning

As we enter the data age, ever-increasing amounts of human knowledge are being recorded in machine-readable formats. This has opened up new opportunities to leverage data to accelerate scientific discovery. This thesis focuses on how we can use historical and computational data to aid the discovery and development of new materials. We begin by looking at a traditional materials informatics task -- elucidating the structure-function relationships of high-temperature cuprate superconductors. One of the most significant challenges for materials informatics is the limited availability of relevant data. We propose a simple calibration-based approach to estimate the apical and in-plane copper-oxygen distances from more readily available lattice parameter data to address this challenge for cuprate superconductors. Our investigation uncovers a large, unexplored region of materials space that may yield cuprates with higher critical temperatures. We propose two experimental avenues that may enable this region to be accessed. Computational materials exploration is bottle-necked by our ability to provide input structures to feed our workflows. Whilst \textit{ab-intio} structure identification is possible, it is computationally burdensome and we lack design rules for deciding where to target searches in high-throughput setups. To address this, there is a need to develop tools that suggest promising candidates, enabling automated deployment and increased efficiency. Machine learning models are well suited to this task, however, current approaches typically use hand-engineered inputs. This means that their performance is circumscribed by the intuitions reflected in the chosen inputs. We propose a novel way to formulate the machine learning task as a set regression problem over the elements in a material. We show that our approach leads to higher sample efficiency than other well-established composition-based approaches. Having demonstrated the ability of machine learning to aid in the selection of promising compound compositions, we next explore how useful machine learning might be for identifying fabrication routes. Using a recently released data-mined data set of solid-state synthesis reactions, we design a two-stage model to predict the products of inorganic reactions. We critically explore the performance of this model, showing that whilst the predictions fall short of the accuracy required to be chemically discriminative, the model provides valuable insights into understanding inorganic reactions. Through careful investigation of the model's failure modes, we explore the challenges that remain in the construction of forward inorganic reaction prediction models and suggest some pathways to tackle the identified issues. One of the principal ways that material scientists understand and categorise materials is in terms of their symmetries. Crystal structure prototypes are assigned based on the presence of symmetrically equivalent sites known as Wyckoff positions. We show that a powerful coarse-grained representation of materials structures can be constructed from the Wyckoff positions by discarding information about their coordinates within crystal structures. One of the strengths of this representation is that it maintains the ability of structure-based methods to distinguish polymorphs whilst also allowing combinatorial enumeration akin to composition-based approaches. We construct an end-to-end differentiable model that takes our proposed Wyckoff representation as input. The performance of this approach is examined on a suite of materials discovery experiments showing that it leads to strong levels of enrichment in materials discovery tasks. The research presented in this thesis highlights the promise of applying data-driven workflows and machine learning in materials discovery and development. This thesis concludes by speculating about promising research directions for applying machine learning within materials discovery