1,475 research outputs found
Polarimetric variations of binary stars. II. Numerical simulations for circular and eccentric binaries in Mie scattering envelopes
We present numerical simulations of the periodic polarimetric variations
produced by a binary star placed at the center of an empty spherical cavity
inside a circumbinary ellipsoidal and optically thin envelope made of dust
grains. Mie single-scattering is considered along with pre- and post-scattering
extinction factors which produce a time-varying optical depth and affect the
morphology of the periodic variations. We are interested in the effects that
various parameters will have on the average polarization, the amplitude of the
polarimetric variations, and the morphology of the variability. We show that
the absolute amplitudes of the variations are smaller for Mie scattering than
for Thomson scattering. Among the four grain types that we have studied, the
highest polarizations are produced by grains with sizes in the range 0.1-0.2
micron. In general, the variations are seen twice per orbit. In some cases,
because spherical dust grains have an asymmetric scattering function, the
polarimetric curves produced also show variations seen once per orbit.
Circumstellar disks produce polarimetric variations of greater amplitude than
circumbinary envelopes.
Another goal of these simulations is to see if the 1978 BME (Brown, McLean, &
Emslie, ApJ, 68, 415) formalism, which uses a Fourier analysis of the
polarimetric variations to find the orbital inclination for Thomson-scattering
envelopes, can still be used for Mie scattering. We find that this is the case,
if the amplitude of the variations is sufficient and the true inclinations is
i_true > 45 deg. For eccentric orbits, the first-order coefficients of the
Fourier fit, instead of second-order ones, can be used to find almost all
inclinations.Comment: 23 pages, 5 figures, to be published in Astronomical Journa
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Users may strive to formulate an adequate textual query for their information
need. Search engines assist the users by presenting query suggestions. To
preserve the original search intent, suggestions should be context-aware and
account for the previous queries issued by the user. Achieving context
awareness is challenging due to data sparsity. We present a probabilistic
suggestion model that is able to account for sequences of previous queries of
arbitrary lengths. Our novel hierarchical recurrent encoder-decoder
architecture allows the model to be sensitive to the order of queries in the
context while avoiding data sparsity. Additionally, our model can suggest for
rare, or long-tail, queries. The produced suggestions are synthetic and are
sampled one word at a time, using computationally cheap decoding techniques.
This is in contrast to current synthetic suggestion models relying upon machine
learning pipelines and hand-engineered feature sets. Results show that it
outperforms existing context-aware approaches in a next query prediction
setting. In addition to query suggestion, our model is general enough to be
used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management
(CIKM) 201
Ring Formation in Magnetically Subcritical Clouds and Multiple Star Formation
We study numerically the ambipolar diffusion-driven evolution of
non-rotating, magnetically subcritical, disk-like molecular clouds, assuming
axisymmetry. Previous similar studies have concentrated on the formation of
single magnetically supercritical cores at the cloud center, which collapse to
form isolated stars. We show that, for a cloud with many Jeans masses and a
relatively flat mass distribution near the center, a magnetically supercritical
ring is produced instead. The supercritical ring contains a mass well above the
Jeans limit. It is expected to break up, through both gravitational and
possibly magnetic interchange instabilities, into a number of supercritical
dense cores, whose dynamic collapse may give rise to a burst of star formation.
Non-axisymmetric calculations are needed to follow in detail the expected ring
fragmentation into multiple cores and the subsequent core evolution.
Implications of our results on multiple star formation in general and the
northwestern cluster of protostars in the Serpens molecular cloud core in
particular are discussed.Comment: 25 pages, 4 figures, to appear in Ap
A reduced-order, rotation-based model for thin hard-magnetic plates
We develop a reduced-order model for thin plates made of hard
magnetorheological elastomers (hard-MREs), which are materials composed of
hard-magnetic particles embedded in a polymeric matrix. First, we propose a new
magnetic potential, as an alternative to an existing torque-based 3D continuum
theory of hard-MREs, obtained by reformulating the remnant magnetization of a
deformed hard-MRE body. Specifically, the magnetizations in the initial and
current configurations are related by the rotation tensor decomposed from the
deformation gradient, independently of stretching deformation. This description
is motivated by recently reported observations in microscopic homogenization
simulations. Then, we derive a 2D plate model through the dimensional reduction
of our proposed rotation-based 3D theory. For comparison, we also provide a
second plate model derived from the existing 3D theory. Finally, we perform
precision experiments to thoroughly evaluate the proposed 3D and 2D models on
hard-magnetic plates under various magnetic and mechanical loading conditions.
We demonstrate that our rotation-based modification of the magnetic potential
is crucial in correctly capturing the behavior of plates subjected to an
applied field aligned with the magnetization, and undergoing in-plane
stretching. In all the tested cases, our rotation-based 3D and 2D models yield
predictions in excellent quantitative agreement with the experiments and can
thus serve as predictive tools for the rational design of hard-magnetic plate
structures
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
Energy disaggregation estimates appliance-by-appliance electricity
consumption from a single meter that measures the whole home's electricity
demand. Recently, deep neural networks have driven remarkable improvements in
classification performance in neighbouring machine learning fields such as
image classification and automatic speech recognition. In this paper, we adapt
three deep neural network architectures to energy disaggregation: 1) a form of
recurrent neural network called `long short-term memory' (LSTM); 2) denoising
autoencoders; and 3) a network which regresses the start time, end time and
average power demand of each appliance activation. We use seven metrics to test
the performance of these algorithms on real aggregate power data from five
appliances. Tests are performed against a house not seen during training and
against houses seen during training. We find that all three neural nets achieve
better F1 scores (averaged over all five appliances) than either combinatorial
optimisation or factorial hidden Markov models and that our neural net
algorithms generalise well to an unseen house.Comment: To appear in ACM BuildSys'15, November 4--5, 2015, Seou
Designing a braille reader using the snap buckling of bistable magnetic shells
A design concept is introduced for the building block, a dot, of programmable
braille readers utilizing bistable shell buckling, magnetic actuation, and
pneumatic loading. The design process is guided by Finite Element simulations,
which are initially validated through precision experiments conducted on a
scaled-up, single-shell model system. Then, the simulations are leveraged to
systematically explore the design space, adhering to the standardized geometric
and physical specifications of braille systems. The findings demonstrate the
feasibility of selecting design parameters that satisfy both geometric
requirements and blocking forces under moderate magnetic fields, facilitated by
pneumatic loading to switch between the two stable states. The advantages of
the proposed design include the reversible bistability of the actuators and
fast state-switching via a transient magnetic field. While the study is focused
on experimentally validated numerical simulations, several manufacturing
challenges that need to be resolved for future physical implementations are
identified
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