3,576 research outputs found
Alkaline Exospheres of Exoplanet Systems: Evaporative Transmission Spectra
Hydrostatic equilibrium is an excellent approximation for the dense layers of
planetary atmospheres where it has been canonically used to interpret
transmission spectra of exoplanets. Here we exploit the ability of
high-resolution spectrographs to probe tenuous layers of sodium and potassium
gas due to their formidable absorption cross-sections. We present an
atmosphere-exosphere degeneracy between optically thick and optically thin
mediums, raising the question of whether hydrostatic equilibrium is appropriate
for Na I lines observed at exoplanets. To this end we simulate three
non-hydrostatic, evaporative, density profiles: (i) escaping, (ii) exomoon, and
(iii) torus to examine their imprint on an alkaline exosphere in transmission.
By analyzing an evaporative curve of growth we find that equivalent widths of
mA are naturally driven by evaporation rates
kg/s of pure atomic Na. To break the degeneracy between
atmospheric and exospheric absorption, we suggest that if the line ratio is
the gas is optically thin on average and roughly
indicating a non-hydrostatic structure of the atmosphere/exosphere. We show
this is the case for Na I observations at hot Jupiters WASP-49b and HD189733b
and also simulate their K I spectra. Lastly, motivated by the slew of metal
detections at ultra-hot Jupiters, we suggest a toroidal atmosphere at WASP-76b
and WASP-121b is consistent with the Na I data at present.Comment: 23 pages, 21 figures, accepted by MNRA
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Active authentication refers to the process in which users are unobtrusively
monitored and authenticated continuously throughout their interactions with
mobile devices. Generally, an active authentication problem is modelled as a
one class classification problem due to the unavailability of data from the
impostor users. Normally, the enrolled user is considered as the target class
(genuine) and the unauthorized users are considered as unknown classes
(impostor). We propose a convolutional neural network (CNN) based approach for
one class classification in which a zero centered Gaussian noise and an
autoencoder are used to model the pseudo-negative class and to regularize the
network to learn meaningful feature representations for one class data,
respectively. The overall network is trained using a combination of the
cross-entropy and the reconstruction error losses. A key feature of the
proposed approach is that any pre-trained CNN can be used as the base network
for one class classification. Effectiveness of the proposed framework is
demonstrated using three publically available face-based active authentication
datasets and it is shown that the proposed method achieves superior performance
compared to the traditional one class classification methods. The source code
is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201
Antipolar ordering of topological defects in active liquid crystals
ATP-driven microtubule-kinesin bundles can self-assemble into two-dimensional
active liquid crystals (ALCs) that exhibit a rich creation and annihilation
dynamics of topological defects, reminiscent of particle-pair production
processes in quantum systems. This recent discovery has sparked considerable
interest but a quantitative theoretical description is still lacking. We
present and validate a minimal continuum theory for this new class of active
matter systems by generalizing the classical Landau-de Gennes free-energy to
account for the experimentally observed spontaneous buckling of motor-driven
extensile microtubule bundles. The resulting model agrees with recently
published data and predicts a regime of antipolar order. Our analysis implies
that ALCs are governed by the same generic ordering principles that determine
the non-equilibrium dynamics of dense bacterial suspensions and elastic bilayer
materials. Moreover, the theory manifests an energetic analogy with strongly
interacting quantum gases. Generally, our results suggest that complex
non-equilibrium pattern-formation phenomena might be predictable from a few
fundamental symmetry-breaking and scale-selection principles.Comment: final accepted journal version; SI text and movies available at
article on iop.or
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition
Models trained for classification often assume that all testing classes are
known while training. As a result, when presented with an unknown class during
testing, such closed-set assumption forces the model to classify it as one of
the known classes. However, in a real world scenario, classification models are
likely to encounter such examples. Hence, identifying those examples as unknown
becomes critical to model performance. A potential solution to overcome this
problem lies in a class of learning problems known as open-set recognition. It
refers to the problem of identifying the unknown classes during testing, while
maintaining performance on the known classes. In this paper, we propose an
open-set recognition algorithm using class conditioned auto-encoders with novel
training and testing methodology. In contrast to previous methods, training
procedure is divided in two sub-tasks, 1. closed-set classification and, 2.
open-set identification (i.e. identifying a class as known or unknown). Encoder
learns the first task following the closed-set classification training
pipeline, whereas decoder learns the second task by reconstructing conditioned
on class identity. Furthermore, we model reconstruction errors using the
Extreme Value Theory of statistical modeling to find the threshold for
identifying known/unknown class samples. Experiments performed on multiple
image classification datasets show proposed method performs significantly
better than state of the art.Comment: CVPR2019 (Oral
Lattices of hydrodynamically interacting flapping swimmers
Fish schools and bird flocks exhibit complex collective dynamics whose
self-organization principles are largely unknown. The influence of
hydrodynamics on such collectives has been relatively unexplored theoretically,
in part due to the difficulty in modeling the temporally long-lived
hydrodynamic interactions between many dynamic bodies. We address this through
a novel discrete-time dynamical system (iterated map) that describes the
hydrodynamic interactions between flapping swimmers arranged in one- and
two-dimensional lattice formations. Our 1D results exhibit good agreement with
previously published experimental data, in particular predicting the
bistability of schooling states and new instabilities that can be probed in
experimental settings. For 2D lattices, we determine the formations for which
swimmers optimally benefit from hydrodynamic interactions. We thus obtain the
following hierarchy: while a side-by-side single-row "phalanx" formation offers
a small improvement over a solitary swimmer, 1D in-line and 2D rectangular
lattice formations exhibit substantial improvements, with the 2D diamond
lattice offering the largest hydrodynamic benefit. Generally, our
self-consistent modeling framework may be broadly applicable to active systems
in which the collective dynamics is primarily driven by a fluid-mediated
memory
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