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