53,695 research outputs found
Compositional uniformity, domain patterning and the mechanism underlying nano-chessboard arrays
We propose that systems exhibiting compositional patterning at the nanoscale,
so far assumed to be due to some kind of ordered phase segregation, can be
understood instead in terms of coherent, single phase ordering of minority
motifs, caused by some constrained drive for uniformity. The essential features
of this type of arrangements can be reproduced using a superspace construction
typical of uniformity-driven orderings, which only requires the knowledge of
the modulation vectors observed in the diffraction patterns. The idea is
discussed in terms of a simple two dimensional lattice-gas model that simulates
a binary system in which the dilution of the minority component is favored.
This simple model already exhibits a hierarchy of arrangements similar to the
experimentally observed nano-chessboard and nano-diamond patterns, which are
described as occupational modulated structures with two independent modulation
wave vectors and simple step-like occupation modulation functions.Comment: Preprint. 11 pages, 11 figure
Anatomy of a Spin: The Information-Theoretic Structure of Classical Spin Systems
Collective organization in matter plays a significant role in its expressed
physical properties. Typically, it is detected via an order parameter,
appropriately defined for each given system's observed emergent patterns.
Recent developments in information theory, however, suggest quantifying
collective organization in a system- and phenomenon-agnostic way: decompose the
system's thermodynamic entropy density into a localized entropy, that solely
contained in the dynamics at a single location, and a bound entropy, that
stored in space as domains, clusters, excitations, or other emergent
structures. We compute this decomposition and related quantities explicitly for
the nearest-neighbor Ising model on the 1D chain, the Bethe lattice with
coordination number k=3, and the 2D square lattice, illustrating its generality
and the functional insights it gives near and away from phase transitions. In
particular, we consider the roles that different spin motifs play (in cluster
bulk, cluster edges, and the like) and how these affect the dependencies
between spins.Comment: 12 pages, 8 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/ising_bmu.ht
Sparse approaches for the exact distribution of patterns in long state sequences generated by a Markov source
We present two novel approaches for the computation of the exact distribution
of a pattern in a long sequence. Both approaches take into account the sparse
structure of the problem and are two-part algorithms. The first approach relies
on a partial recursion after a fast computation of the second largest
eigenvalue of the transition matrix of a Markov chain embedding. The second
approach uses fast Taylor expansions of an exact bivariate rational
reconstruction of the distribution. We illustrate the interest of both
approaches on a simple toy-example and two biological applications: the
transcription factors of the Human Chromosome 5 and the PROSITE signatures of
functional motifs in proteins. On these example our methods demonstrate their
complementarity and their hability to extend the domain of feasibility for
exact computations in pattern problems to a new level
Structural Properties, Order-Disorder Phenomena and Phase Stability of Orotic Acid Crystal Forms
Orotic acid (OTA) is reported to exist in the anhydrous (AH), monohydrate (Hy1) and dimethylsulfoxide monosolvate (SDMSO) forms. In this study we investigate the (de)hydration/desolvation behavior, aiming at an understanding of the elusive structural features of anhydrous OTA by a combination of experimental and computational techniques, namely, thermal analytical methods, gravimetric moisture (de)sorption studies, water activity measurements, X-ray powder diffraction, spectroscopy (vibrational, solid-state NMR), crystal energy landscape and chemical shift calculations. The Hy1 is a highly stable hydrate, which dissociates above 135°C and loses only a small part of the water when stored over desiccants (25°C) for more than one year. In Hy1, orotic acid and water molecules are linked by strong hydrogen bonds in nearly perfectly planar arranged stacked layers. The layers are spaced by 3.1 Å and not linked via hydrogen-bonds. Upon dehydration the X-ray powder diffraction and solid-state NMR peaks become broader indicating some disorder in the anhydrous form. The Hy1 stacking reflection (122) is maintained, suggesting that the OTA molecules are still arranged in stacked layers in the dehydration product. Desolvation of SDMSO, a non-layer structure, results in the same AH phase as observed upon dehydrating Hy1. Depending on the desolvation conditions different levels of order-disorder of layers present in anhydrous OTA are observed, which is also suggested by the computed low energy crystal structures. These structures provide models for stacking faults as intergrowth of different layers is possible. The variability in anhydrate crystals is of practical concern as it affects the moisture dependent stability of AH with respect to hydration
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
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