7,077 research outputs found
Non-Local Compressive Sensing Based SAR Tomography
Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse
reconstruction problem and, hence, can be solved using compressive sensing (CS)
algorithms. This paper proposes solutions for two notorious problems in this
field: 1) TomoSAR requires a high number of data sets, which makes the
technique expensive. However, it can be shown that the number of acquisitions
and the signal-to-noise ratio (SNR) can be traded off against each other,
because it is asymptotically only the product of the number of acquisitions and
SNR that determines the reconstruction quality. We propose to increase SNR by
integrating non-local estimation into the inversion and show that a reasonable
reconstruction of buildings from only seven interferograms is feasible. 2)
CS-based inversion is computationally expensive and therefore barely suitable
for large-scale applications. We introduce a new fast and accurate algorithm
for solving the non-local L1-L2-minimization problem, central to CS-based
reconstruction algorithms. The applicability of the algorithm is demonstrated
using simulated data and TerraSAR-X high-resolution spotlight images over an
area in Munich, Germany.Comment: 10 page
A geometric and structural approach to the analysis and design of biological circuit dynamics: a theory tailored for synthetic biology
Much of the progress in developing our ability to successfully design genetic circuits with predictable dynamics has followed the strategy of molding biological systems to fit into conceptual frameworks used in other disciplines, most notably the engineering sciences. Because biological systems have fundamental differences from systems in these other disciplines, this approach is challenging and the insights obtained from such analyses are often not framed in a biologically-intuitive way. Here, we present a new theoretical framework for analyzing the dynamics of genetic circuits that is tailored towards the unique properties associated with biological systems and experiments. Our framework approximates a complex circuit as a set of simpler circuits, which the system can transition between by saturating its various internal components. These approximations are connected to the intrinsic structure of the system, so this representation allows the analysis of dynamics which emerge solely from the system's structure. Using our framework, we analyze the presence of structural bistability in a leaky autoactivation motif and the presence of structural oscillations in the Repressilator
A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR
regularization is used for finding sparse solutions to an
underdetermined linear system. As sparse signals are widely expected in remote
sensing, this type of regularization scheme and its extensions have been widely
employed in many remote sensing problems, such as image fusion, target
detection, image super-resolution, and others and have led to promising
results. However, solving such sparse reconstruction problems is
computationally expensive and has limitations in its practical use. In this
paper, we proposed a novel efficient algorithm for solving the complex-valued
regularized least squares problem. Taking the high-dimensional
tomographic synthetic aperture radar (TomoSAR) as a practical example, we
carried out extensive experiments, both with simulation data and real data, to
demonstrate that the proposed approach can retain the accuracy of second order
methods while dramatically speeding up the processing by one or two orders.
Although we have chosen TomoSAR as the example, the proposed method can be
generally applied to any spectral estimation problems.Comment: 11 pages, IEEE Transactions on Geoscience and Remote Sensin
Compressive Sensing for PAN-Sharpening
Based on compressive sensing framework and sparse reconstruction technology, a new pan-sharpening method, named Sparse Fusion of Images (SparseFI, pronounced as sparsify), is proposed in [1]. In this paper, the proposed SparseFI algorithm is validated using UltraCam and WorldView-2 data. Visual and statistic analysis show superior performance of SparseFI compared to the existing conventional pan-sharpening methods in general, i.e. rich in spatial information and less spectral distortion. Moreover, popular quality assessment metrics are employed to explore the dependency on regularization parameters and evaluate the efficiency of various sparse reconstruction toolboxes
On the Reproducibility and Generalisation of the Linear Transformation of Word Embeddings
Linear transformation is a way to learn a linear relationship between two word embeddings, such that words in the two different embedding spaces can be semantically related. In this paper, we examine the reproducibility and generalisation of the linear transformation of word embeddings. Linear transformation is particularly useful when translating word embedding models in different languages, since it can capture the semantic relationships between two models. We first reproduce two linear transformation approaches, a recent one using orthogonal transformation and the original one using simple matrix transformation. Previous findings on a machine translation task are re-examined, validating that linear transformation is indeed an effective way to transform word embedding models in different languages. In particular, we show that the orthogonal transformation can better relate the different embedding models. Following the verification of previous findings, we then study the generalisation of linear transformation in a multi-language Twitter election classification task. We observe that the orthogonal transformation outperforms the matrix transformation. In particular, it significantly outperforms the random classifier by at least 10% under the F1 metric across English and Spanish datasets. In addition, we also provide best practices when using linear transformation for multi-language Twitter election classification
Simulation Tools for Detector and Instrument Design
The high performance requirements at the European Spallation Source have been
driving the technological advances on the neutron detector front. Now more than
ever is it important to optimize the design of detectors and instruments, to
fully exploit the ESS source brilliance. Most of the simulation tools the
neutron scattering community has at their disposal target the instrument
optimization until the sample position, with little focus on detectors. The ESS
Detector Group has extended the capabilities of existing detector simulation
tools to bridge this gap. An extensive software framework has been developed,
enabling efficient and collaborative developments of required simulations and
analyses -- based on the use of the Geant4 Monte Carlo toolkit, but with
extended physics capabilities where relevant (like for Bragg diffraction of
thermal neutrons in crystals). Furthermore, the MCPL (Monte Carlo Particle
Lists) particle data exchange file format, currently supported for the primary
Monte Carlo tools of the community (McStas, Geant4 and MCNP), facilitates the
integration of detector simulations with existing simulations of instruments
using these software packages. These means offer a powerful set of tools to
tailor the detector and instrument design to the instrument application
WHAT CAN SLA LEARN FROM CONTRASTIVE CORPUS LINGUISTICS? THE CASE OF PASSIVE CONSTRUCTIONS IN CHINESE LEARNER ENGLISH
This article seeks to demonstrate the predictive and diagnostic power of the integrated approach that combines contrastive corpus linguistics with interlanguage analysis in second language acquisition research, via a case study of passive constructions in Chinese learner English. The type of corpora used in contrastive corpus linguistics is first discussed, which is followed by a summary of the findings from a published contrastive study of passive constructions in English and Chinese based on comparable corpora of the two languages. These findings are in turn used to predict and diagnose the performance of Chinese learners of English in their use of English passives as mirrored in a sizeable Chinese learner English corpus in comparison with a comparable native English corpus. Keywords: contrastive analysis, corpus, learner English, passive construction, ChineseÂ
Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching
Fog computing is a promising architecture to
provide economical and low latency data services for future
Internet of Things (IoT)-based network systems. Fog computing
relies on a set of low-power fog nodes (FNs) that are located
close to the end users to offload the services originally targeting
at cloud data centers. In this paper, we consider a specific
fog computing network consisting of a set of data service operators
(DSOs) each of which controls a set of FNs to provide the
required data service to a set of data service subscribers (DSSs).
How to allocate the limited computing resources of FNs to all
the DSSs to achieve an optimal and stable performance is an
important problem. Therefore, we propose a joint optimization
framework for all FNs, DSOs, and DSSs to achieve the optimal
resource allocation schemes in a distributed fashion. In the
framework, we first formulate a Stackelberg game to analyze
the pricing problem for the DSOs as well as the resource allocation
problem for the DSSs. Under the scenarios that the DSOs
can know the expected amount of resource purchased by the
DSSs, a many-to-many matching game is applied to investigate
the pairing problem between DSOs and FNs. Finally, within the
same DSO, we apply another layer of many-to-many matching
between each of the paired FNs and serving DSSs to solve
the FN-DSS pairing problem. Simulation results show that our
proposed framework can significantly improve the performance
of the IoT-based network systems
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