151,009 research outputs found
Caustic Skeleton & Cosmic Web
We present a general formalism for identifying the caustic structure of an
evolving mass distribution in an arbitrary dimensional space. For the class of
Hamiltonian fluids the identification corresponds to the classification of
singularities in Lagrangian catastrophe theory. Based on this we develop a
theoretical framework for the formation of the cosmic web, and specifically
those aspects that characterize its unique nature: its complex topological
connectivity and multiscale spinal structure of sheetlike membranes, elongated
filaments and compact cluster nodes. The present work represents an extension
of the work by Arnol'd et al., who classified the caustics for the 1- and
2-dimensional Zel'dovich approximation. His seminal work established the role
of emerging singularities in the formation of nonlinear structures in the
universe. At the transition from the linear to nonlinear structure evolution,
the first complex features emerge at locations where different fluid elements
cross to establish multistream regions. The classification and characterization
of these mass element foldings can be encapsulated in caustic conditions on the
eigenvalue and eigenvector fields of the deformation tensor field. We introduce
an alternative and transparent proof for Lagrangian catastrophe theory, and
derive the caustic conditions for general Lagrangian fluids, with arbitrary
dynamics, including dissipative terms and vorticity. The new proof allows us to
describe the full 3-dimensional complexity of the gravitationally evolving
cosmic matter field. One of our key findings is the significance of the
eigenvector field of the deformation field for outlining the spatial structure
of the caustic skeleton. We consider the caustic conditions for the
3-dimensional Zel'dovich approximation, extending earlier work on those for 1-
and 2-dimensional fluids towards the full spatial richness of the cosmic web
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Sensor, Signal, and Imaging Informatics in 2017.
Objective To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.Methods PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.Results The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201
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A survey of simulation techniques in commerce and defence
Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches
Investability and Firm Value
We study how investability, or openness to foreign equity investors, affects firm value in a sample of over 1,400 firms from 26 emerging markets. We find that, on average, investability is associated with a 9% valuation premium (as measured by Tobin's q). However, in firm-fixed effects regressions this valuation premium disappears, suggesting that investability does not have a causal effect on firm value. Analysis of the components of Tobin's q shows that firms that become investable experience significant increases in both market values and physical investment. These effects are strongest for firms that face country-level or firm-level financial constraints prior to becoming investableFinancial liberalization; Investability; Foreign investors; Tobin's q
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