3,050 research outputs found
Efficient Learning of a One-dimensional Density Functional Theory
Density functional theory underlies the most successful and widely used
numerical methods for electronic structure prediction of solids. However, it
has the fundamental shortcoming that the universal density functional is
unknown. In addition, the computational result---energy and charge density
distribution of the ground state---is useful for electronic properties of
solids mostly when reduced to a band structure interpretation based on the
Kohn-Sham approach. Here, we demonstrate how machine learning algorithms can
help to free density functional theory from these limitations. We study a
theory of spinless fermions on a one-dimensional lattice. The density
functional is implicitly represented by a neural network, which predicts,
besides the ground-state energy and density distribution, density-density
correlation functions. At no point do we require a band structure
interpretation. The training data, obtained via exact diagonalization, feeds
into a learning scheme inspired by active learning, which minimizes the
computational costs for data generation. We show that the network results are
of high quantitative accuracy and, despite learning on random potentials,
capture both symmetry-breaking and topological phase transitions correctly.Comment: 5 pages, 3 figures; 4+ pages appendi
Multi-camera Realtime 3D Tracking of Multiple Flying Animals
Automated tracking of animal movement allows analyses that would not
otherwise be possible by providing great quantities of data. The additional
capability of tracking in realtime - with minimal latency - opens up the
experimental possibility of manipulating sensory feedback, thus allowing
detailed explorations of the neural basis for control of behavior. Here we
describe a new system capable of tracking the position and body orientation of
animals such as flies and birds. The system operates with less than 40 msec
latency and can track multiple animals simultaneously. To achieve these
results, a multi target tracking algorithm was developed based on the Extended
Kalman Filter and the Nearest Neighbor Standard Filter data association
algorithm. In one implementation, an eleven camera system is capable of
tracking three flies simultaneously at 60 frames per second using a gigabit
network of nine standard Intel Pentium 4 and Core 2 Duo computers. This
manuscript presents the rationale and details of the algorithms employed and
shows three implementations of the system. An experiment was performed using
the tracking system to measure the effect of visual contrast on the flight
speed of Drosophila melanogaster. At low contrasts, speed is more variable and
faster on average than at high contrasts. Thus, the system is already a useful
tool to study the neurobiology and behavior of freely flying animals. If
combined with other techniques, such as `virtual reality'-type computer
graphics or genetic manipulation, the tracking system would offer a powerful
new way to investigate the biology of flying animals.Comment: pdfTeX using libpoppler 3.141592-1.40.3-2.2 (Web2C 7.5.6), 18 pages
with 9 figure
Legislation and Administration: Labor Law -- Contract-Bar -- Recent NLRB Decisions Significantly Alter Contract-Bar Doctrine
Legislation and Administration: Labor Law -- Contract-Bar -- Recent NLRB Decisions Significantly Alter Contract-Bar Doctrine
A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data
Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified
single-cell genomes, and metagenomes has enabled investigation of a wide range
of organisms and ecosystems. However, sampling variation in short-read data
sets and high sequencing error rates of modern sequencers present many new
computational challenges in data interpretation. These challenges have led to
the development of new classes of mapping tools and {\em de novo} assemblers.
These algorithms are challenged by the continued improvement in sequencing
throughput. We here describe digital normalization, a single-pass computational
algorithm that systematizes coverage in shotgun sequencing data sets, thereby
decreasing sampling variation, discarding redundant data, and removing the
majority of errors. Digital normalization substantially reduces the size of
shotgun data sets and decreases the memory and time requirements for {\em de
novo} sequence assembly, all without significantly impacting content of the
generated contigs. We apply digital normalization to the assembly of microbial
genomic data, amplified single-cell genomic data, and transcriptomic data. Our
implementation is freely available for use and modification
Arbuscular mycorrhizae of Mojave Desert plants
Roots of 15 Mojave Desert plant species were assessed for arbuscular mycorrhizal (AM) colonization in spring and autumn 1999. Another 19 species were assessed in autumn only. Perennial species were AM colonized, whereas annual species contained very low levels of AM hyphae or were nonmycorrhizal. The nonnative tree Tamarix ramosissima was also nonmycorrhizal. Levels of hyphae changed over the season in 2 species, arbuscle levels increased in 1 species, and vesicles decreased in 7 species. Mycorrhizal inoculum potential (MIP) was assessed in the soils associated with spring-collected plants. All soils were found to contain MIP. MIP values were not correlated with AM root colonization
A novel platform for two-dimensional chiral topological superconductivity
We show that the surface of an -wave superconductor decorated with a
two-dimensional lattice of magnetic impurities can exhibit chiral topological
superconductivity. If impurities order ferromagnetically and the
superconducting surface supports a sufficiently strong Rashba-type spin-orbit
coupling, Shiba sub-gap states at impurity locations can hybridize into
Bogoliubov bands with non-vanishing, sometimes large, Chern number . This
topological superconductor supports chiral Majorana edge modes. We
construct phase diagrams for model two-dimensional superconductors, accessing
the dilute and dense magnetic impurity limits analytically and the intermediate
regime numerically. To address potential experimental systems, we identify
stable configurations of ferromagnetic iron atoms on the Pb (111) surface and
conclude that ferromagnetic adatoms on Pb surfaces can provide a versatile
platform for two-dimensional topological superconductivity
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