74,216 research outputs found

    A data-centric framework for crystal structure identification in atomistic simulations using machine learning

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    Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have historically been challenging to construct, requiring significant intuition and effort. Here we propose an alternative framework for a fundamental structural characterization task: classifying atoms according to the crystal structure to which they belong. Our approach is data-centric and favors the employment of Machine Learning over heuristic rules of classification. A group of data-science tools and simple local descriptors of atomic structure are employed together with an efficient synthetic training set. We also introduce the first standard and publicly available benchmark data set for evaluation of algorithms for crystal-structure classification. It is demonstrated that our data-centric framework outperforms all of the most popular heuristic methods -- especially at high temperatures when lattices are the most distorted -- while introducing a systematic route for generalization to new crystal structures. Moreover, through the use of outlier detection algorithms our approach is capable of discerning between amorphous atomic motifs (i.e., noncrystalline phases) and unknown crystal structures, making it uniquely suited for exploratory materials synthesis simulations.Comment: 16 pages, 7 figure

    Human-Centric Program Synthesis

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    Program synthesis techniques offer significant new capabilities in searching for programs that satisfy high-level specifications. While synthesis has been thoroughly explored for input/output pair specifications (programming-by-example), this paper asks: what does program synthesis look like beyond examples? What actual issues in day-to-day development would stand to benefit the most from synthesis? How can a human-centric perspective inform the exploration of alternative specification languages for synthesis? I sketch a human-centric vision for program synthesis where programmers explore and learn languages and APIs aided by a synthesis tool

    Designing a novel virtual collaborative environment to support collaboration in design review meetings

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    Project review meetings are part of the project management process and are organised to assess progress and resolve any design conflicts to avoid delays in construction. One of the key challenges during a project review meeting is to bring the stakeholders together and use this time effectively to address design issues as quickly as possible. At present, current technology solutions based on BIM or CAD are information-centric and do not allow project teams to collectively explore the design from a range of perspectives and brainstorm ideas when design conflicts are encountered. This paper presents a system architecture that can be used to support multi-functional team collaboration more effectively during such design review meetings. The proposed architecture illustrates how information-centric BIM or CAD systems can be made human- and team-centric to enhance team communication and problem solving. An implementation of the proposed system architecture has been tested for its utility, likability and usefulness during design review meetings. The evaluation results suggest that the collaboration platform has the potential to enhance collaboration among multi-functional teams

    Document Classification Systems in Heterogeneous Computing Environments

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    Datacenter workloads demand high throughput, low cost and power efficient solutions. In most data centers the operating costs dominates the infrastructure cost. The ever growing amounts of data and the critical need for higher throughput, more energy efficient document classification solutions motivated us to investigate alternatives to the traditional homogeneous CPU based implementations of document classification systems. Several heterogeneous systems were investigated in the past where CPUs were combined with GPUs and FPGAs as system accelerators. The increasing complexity of FPGAs made them an interesting device in the heterogeneous computing environments and on the other hand difficult to program using Hardware Description languages. We explore the trade-offs when using high level synthesis and low level synthesis when programming FPGAs. Using low level synthesis results in less hardware resource usage on FPGAs and also offers the higher throughput compared to using HLS tool. While using HLS tool different heterogeneous computing devices such as multicore CPU and GPU targeted. Through our implementation experience and empirical results for data centric applications, we conclude that we can achieve power efficient results for these set of applications by either using low level synthesis or high level synthesis for programming FPGAs

    An Ontological Basis for Design Methods

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    This paper presents a view of design methods as process artefacts that can be represented using the function-behaviour-structure (FBS) ontology. This view allows identifying five fundamental approaches to methods: black-box, procedural, artefact-centric, formal and managerial approaches. They all describe method structure but emphasise different aspects of it. Capturing these differences addresses common terminological confusions relating to methods. The paper provides an overview of the use of the fundamental method approaches for different purposes in designing. In addition, the FBS ontology is used for developing a notion of prescriptiveness of design methods as an aggregate construct defined along four dimensions: certainty, granularity, flexibility and authority. The work presented in this paper provides an ontological basis for describing, understanding and managing design methods throughout their life cycle. Keywords: Design Methods; Function-Behaviour-Structure (FBS) Ontology; Prescriptive Design Knowledge</p

    A planning approach to the automated synthesis of template-based process models

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    The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiment

    A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data

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    DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes
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