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    <title>Pinboard (Vaguery)</title>
    <link>https://pinboard.in/u:Vaguery/public/</link>
    <description>recent bookmarks from Vaguery</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://arxiv.org/abs/1810.03972"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1107.4030"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1105.0095"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1509.03280"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1307.1266"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1203.3353"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1102.2359"/>
	<rdf:li rdf:resource="http://precedings.nature.com/documents/1490/version/1"/>
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  </channel><item rdf:about="https://arxiv.org/abs/1810.03972">
    <title>[1810.03972] Machine learning clustering technique applied to powder X-ray diffraction patterns to distinguish alloy substitutions</title>
    <dc:date>2021-07-21T21:40:07+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.03972</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We applied the clustering technique using DTW (dynamic time wrapping) analysis to XRD (X-ray diffraction) spectrum patterns in order to identify the microscopic structures of substituents introduced in the main phase of magnetic alloys. The clustering is found to perform well to identify the concentrations of the substituents with successful rates (around 90%). The sufficient performance is attributed to the nature of DTW processing to filter out irrelevant informations such as the peak intensities (due to the incontrollability of diffraction conditions in polycrystalline samples) and the uniform shift of peak positions (due to the thermal expansions of lattices).
]]></description>
<dc:subject>crystallography machine-learning clustering rather-interesting spectra to-write-about synthetic-data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6dcee0ddaeab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crystallography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:spectra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:synthetic-data"/>
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<item rdf:about="https://arxiv.org/abs/1107.4030">
    <title>[1107.4030] Three dimensional structure from intensity correlations</title>
    <dc:date>2020-01-19T14:39:02+00:00</dc:date>
    <link>https://arxiv.org/abs/1107.4030</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We develop the analysis of x-ray intensity correlations from dilute ensembles of identical particles in a number of ways. First, we show that the 3D particle structure can be determined if the particles can be aligned with respect to a single axis having a known angle with respect to the incident beam. Second, we clarify the phase problem in this setting and introduce a data reduction scheme that assesses the integrity of the data even before the particle reconstruction is attempted. Finally, we describe an algorithm that reconstructs intensity and particle density simultaneously, thereby making maximal use of the available constraints.
]]></description>
<dc:subject>signal-processing crystallography inverse-problems rather-interesting diffraction numerical-methods to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:903517046ee1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:signal-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crystallography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diffraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
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<item rdf:about="https://arxiv.org/abs/1105.0095">
    <title>[1105.0095] Kinematic Diffraction from a Mathematical Viewpoint</title>
    <dc:date>2019-09-08T12:18:06+00:00</dc:date>
    <link>https://arxiv.org/abs/1105.0095</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Mathematical diffraction theory is concerned with the analysis of the diffraction image of a given structure and the corresponding inverse problem of structure determination. In recent years, the understanding of systems with continuous and mixed spectra has improved considerably. Simultaneously, their relevance has grown in practice as well. In this context, the phenomenon of homometry shows various unexpected new facets. This is particularly so for systems with stochastic components. After the introduction to the mathematical tools, we briefly discuss pure point spectra, based on the Poisson summation formula for lattice Dirac combs. This provides an elegant approach to the diffraction formulas of infinite crystals and quasicrystals. We continue by considering classic deterministic examples with singular or absolutely continuous diffraction spectra. In particular, we recall an isospectral family of structures with continuously varying entropy. We close with a summary of more recent results on the diffraction of dynamical systems of algebraic or stochastic origin.
]]></description>
<dc:subject>review idealizations inverse-problems rather-interesting spectra rewriting-systems fractals crystallography to-write-about to-simulate mathematical-physics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c5b45c2f237c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:review"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:idealizations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:spectra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rewriting-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fractals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crystallography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-physics"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1509.03280">
    <title>[1509.03280] Statistical Topology of Perturbed Two-Dimensional Lattices</title>
    <dc:date>2015-09-12T20:55:59+00:00</dc:date>
    <link>http://arxiv.org/abs/1509.03280</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The Voronoi cell of any atom in a lattice is identical. If atoms are perturbed from their lattice coordinates, then the topologies of the Voronoi cells of the atoms will change. We consider the distribution of Voronoi cell topologies in two-dimensional perturbed systems. These systems can be thought of as simple models of finite-temperature crystals. We give analytical results for the distribution of Voronoi topologies of points in two-dimensional Bravais lattices under infinitesimal perturbations and present a discussion with numerical results for finite perturbations.
]]></description>
<dc:subject>tiling crystallography physics computational-geometry rather-interesting nudge-targets consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:84aeaa7f071c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tiling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crystallography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1307.1266">
    <title>[1307.1266] How to represent crystal structures for machine learning: towards fast prediction of electronic properties</title>
    <dc:date>2014-10-07T11:09:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1307.1266</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[High-throughput density-functional calculations of solids are extremely time consuming. As an alternative, we here propose a machine learning approach for the fast prediction of solid-state properties. To achieve this, LSDA calculations are used as training set. We focus on predicting metallic vs. insulating behavior, and on predicting the value of the density of electronic states at the Fermi energy. We find that conventional representations of the input data, such as the Coulomb matrix, are not suitable for the training of learning machines in the case of periodic solids. We propose a novel crystal structure representation for which learning and competitive prediction accuracies become possible within an unrestricted class of spd systems. Due to magnetic phenomena learning on d systems is found more difficult than in pure sp systems.
]]></description>
<dc:subject>crystallography materials-science machine-learning representation nudge-targets rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6bcff4463f62/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crystallography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:materials-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
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<item rdf:about="http://arxiv.org/abs/1203.3353">
    <title>[1203.3353] Solving Structure with Sparse, Randomly-Oriented X-ray Data</title>
    <dc:date>2012-03-18T10:21:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1203.3353</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Single-particle imaging experiments of biomolecules at x-ray free-electron lasers (XFELs) require processing of hundreds of thousands (or more) of images that contain very few x-rays. Each low-flux image of the diffraction pattern is produced by a single, randomly oriented particle, such as a protein. We demonstrate the feasibility of collecting data at these extremes, averaging only 2.5 photons per frame, where it seems doubtful there could be information about the state of rotation, let alone the image contrast. This is accomplished with an expectation maximization algorithm that processes the low-flux data in aggregate, and without any prior knowledge of the object or its orientation. The versatility of the method promises, more generally, to redefine what measurement scenarios can provide useful signal in the high-noise regime."]]></description>
<dc:subject>structural-biology image-analysis crystallography algorithms inverse-problems nudge-targets statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:66baf5a1cb02/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crystallography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
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<item rdf:about="http://arxiv.org/abs/1102.2359">
    <title>[1102.2359] A Phyllotactic Approach to the Structure of Collagen Fibrils</title>
    <dc:date>2011-04-02T12:48:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1102.2359</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["… We examine here how the algorithm of phyllotaxis could contribute to the analysis of the structure of collagen fibrils. Such an algorithm indeed leads to organizations giving to each element of the assembly the most homogeneous and isotropic dense environment in a situation of cylindrical symmetry. The scattered intensity expected from a phyllotactic distribution of triple helices in collagen fibrils well agrees with the major features observed along the equatorial direction of their X ray patterns. Following this approach, the aggregation of triple helices in fibrils should be considered within the frame of soft condensed matter studies rather than that of molecular crystal studies."]]></description>
<dc:subject>self-assembly nanotechnology molecular-design molecular-machinery theoretical-biology structural-biology crystallography condensed-matter</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4e0746d267c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assembly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crystallography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:condensed-matter"/>
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</item>
<item rdf:about="http://precedings.nature.com/documents/1490/version/1">
    <title>Understanding Hydrogen-Bond Patterns in Proteins using a Novel Statistical Model</title>
    <dc:date>2008-01-06T14:45:38+00:00</dc:date>
    <link>http://precedings.nature.com/documents/1490/version/1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Bond motifs.
]]></description>
<dc:subject>protein-folding crystallography machine-learning pattern-discovery data-mining bioinformatics structural-biology</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:aee500c2adfa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
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