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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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      <rdf:Seq>	<rdf:li rdf:resource="http://arxiv.org/abs/1410.3353"/>
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  </channel><item rdf:about="http://arxiv.org/abs/1410.3353">
    <title>[1410.3353] Ab-Initio Molecular Dynamics Acceleration Scheme with an Adaptive Machine Learning Framework</title>
    <dc:date>2015-12-08T14:37:33+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.3353</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Quantum mechanics based ab-initio molecular dynamics (MD) simulation schemes offer an accurate and direct means to monitor the time-evolution of materials. Nevertheless, the expensive and repetitive energy and force computations required in such simulations lead to significant bottlenecks. Here, we lay the foundations for such an accelerated ab-initio MD approach integrated with a machine learning framework. The proposed algorithm learns from previously visited configurations in a continuous and adaptive manner on-the-fly, and predicts (with chemical accuracy) the energies and atomic forces of a new configuration at a minuscule fraction of the time taken by conventional ab-initio methods. Key elements of this new accelerated ab-initio MD paradigm include representations of atomic configurations by numerical fingerprints, the learning algorithm, a decision engine that guides the choice of the prediction scheme, and requisite amount of ab-initio data. The performance of each aspect of the proposed scheme is critically evaluated for Al in several different chemical environments. This work can readily be extended to address non-elemental compounds, and has enormous implications beyond ab-initio MD acceleration. It can also lead to accelerated structure and property prediction schemes, and accurate force-fields.
]]></description>
<dc:subject>modeling rather-interesting model-fusion metamodeling simulation algorithms heuristics nudge-targets approximation introspection</dc:subject>
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<dc:identifier>https://pinboard.in/u:Vaguery/b:ab294d866ce3/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1408.6520">
    <title>[1408.6520] Knowledge Engineering for Planning-Based Hypothesis Generation</title>
    <dc:date>2014-12-27T13:45:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1408.6520</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we address the knowledge engineering problems for hypothesis generation motivated by applications that require timely exploration of hypotheses under unreliable observations. We looked at two applications: malware detection and intensive care delivery. In intensive care, the goal is to generate plausible hypotheses about the condition of the patient from clinical observations and further refine these hypotheses to create a recovery plan for the patient. Similarly, preventing malware spread within a corporate network involves generating hypotheses from network traffic data and selecting preventive actions. To this end, building on the already established characterization and use of AI planning for similar problems, we propose use of planning for the hypothesis generation problem. However, to deal with uncertainty, incomplete model description and unreliable observations, we need to use a planner capable of generating multiple high-quality plans. To capture the model description we propose a language called LTS++ and a web-based tool that enables the specification of the LTS++ model and a set of observations. We also proposed a 9-step process that helps provide guidance to the domain expert in specifying the LTS++ model. The hypotheses are then generated by running a planner on the translated LTS++ model and the provided trace. The hypotheses can be visualized and shown to the analyst or can be further investigated automatically.
]]></description>
<dc:subject>planning artificial-intelligence diagnosis rather-interesting representation the-other-paradigm modeling metamodeling nudge-targets consider:bridge-building</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:267a47255048/</dc:identifier>
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<item rdf:about="http://blog.abegong.com/2014/03/therbligs-for-data-science.html">
    <title>Therbligs for data science: A nuts and bolts framework for accelerating data work</title>
    <dc:date>2014-07-03T11:39:41+00:00</dc:date>
    <link>http://blog.abegong.com/2014/03/therbligs-for-data-science.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[To my mind, this is about the right level of specificity for data science workflows. I've done each of these therbligs many times. When I map out daily to-do lists and dependencies for projects, these are the task divisions that I use naturally. If one of these tasks becomes a pain point, I can imagine ways to fix it. Fledging data scientists who want to improving their "wax on, wax off" can focus on these skills one at a time---and mentors can provide coaching.
]]></description>
<dc:subject>data-analysis cultural-dynamics engineering-philosophy modeling metamodeling to-cite</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:840f00ccf2e2/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1010.4735">
    <title>[1010.4735] Exploring the Energy Landscapes of Protein Folding Simulations with Bayesian Computation</title>
    <dc:date>2012-01-05T13:38:06+00:00</dc:date>
    <link>http://arxiv.org/abs/1010.4735</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Nested sampling is a Bayesian sampling technique developed to explore probability distributions lo- calised in an exponentially small area of the parameter space. The algorithm provides both posterior samples and an estimate of the evidence (marginal likelihood) of the model. The nested sampling algo- rithm also provides an efficient way to calculate free energies and the expectation value of thermodynamic observables at any temperature, through a simple post-processing of the output. Previous applications of the algorithm have yielded large efficiency gains over other sampling techniques, including parallel tempering (replica exchange). In this paper we describe a parallel implementation of the nested sampling algorithm and its application to the problem of protein folding in a Go-type force field of empirical potentials that were designed to stabilize secondary structure elements in room-temperature simulations. We demonstrate the method by conducting folding simulations on a number of small proteins which are commonly used for testing protein folding procedures: protein G, the SH3 domain of Src tyrosine kinase and chymotrypsin inhibitor 2. A topological analysis of the posterior samples is performed to produce energy landscape charts, which give a high level description of the potential energy surface for the protein folding simulations. These charts provide qualitative insights into both the folding process and the nature of the model and force field used.]]></description>
<dc:subject>structural-biology biochemistry modeling algorithms statistics metamodeling</dc:subject>
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