3,846 research outputs found
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
Experimental quantum information processing with 43Ca+ ions
For quantum information processing (QIP) with trapped ions, the isotope 43Ca+
offers the combined advantages of a quantum memory with long coherence time, a
high fidelity read out and the possibility of performing two qubit gates on a
quadrupole transition with a narrow-band laser. Compared to other ions used for
quantum computing, 43Ca+ has a relatively complicated level structure. In this
paper we discuss how to meet the basic requirements for QIP and demonstrate
ground state cooling, robust state initialization and efficient read out for
the hyperfine qubit with a single 43Ca+ ion. A microwave field and a Raman
light field are used to drive qubit transitions, and the coherence times for
both fields are compared. Phase errors due to interferometric instabilities in
the Raman field generation do not limit the experiments on a time scale of 100
ms. We find a quantum information storage time of many seconds for the
hyperfine qubit.Comment: 9 pages, 10 figure
Time-and event-driven communication process for networked control systems: A survey
Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Data Multiplexing in Radio Interferometric Calibration
New and upcoming radio interferometers will produce unprecedented amounts of
data that demand extremely powerful computers for processing. This is a
limiting factor due to the large computational power and energy costs involved.
Such limitations restrict several key data processing steps in radio
interferometry. One such step is calibration where systematic errors in the
data are determined and corrected. Accurate calibration is an essential
component in reaching many scientific goals in radio astronomy and the use of
consensus optimization that exploits the continuity of systematic errors across
frequency significantly improves calibration accuracy. In order to reach full
consensus, data at all frequencies need to be calibrated simultaneously. In the
SKA regime, this can become intractable if the available compute agents do not
have the resources to process data from all frequency channels simultaneously.
In this paper, we propose a multiplexing scheme that is based on the
alternating direction method of multipliers (ADMM) with cyclic updates. With
this scheme, it is possible to simultaneously calibrate the full dataset using
far fewer compute agents than the number of frequencies at which data are
available. We give simulation results to show the feasibility of the proposed
multiplexing scheme in simultaneously calibrating a full dataset when a limited
number of compute agents are available.Comment: MNRAS Accepted 2017 November 28. Received 2017 November 28; in
original form 2017 July 0
Transport-Based Neural Style Transfer for Smoke Simulations
Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional
materials: http://www.byungsoo.me/project/neural-flow-styl
- …