984 research outputs found
Image patch analysis and clustering of sunspots: a dimensionality reduction approach
Sunspots, as seen in white light or continuum images, are associated with
regions of high magnetic activity on the Sun, visible on magnetogram images.
Their complexity is correlated with explosive solar activity and so classifying
these active regions is useful for predicting future solar activity. Current
classification of sunspot groups is visually based and suffers from bias.
Supervised learning methods can reduce human bias but fail to optimally
capitalize on the information present in sunspot images. This paper uses two
image modalities (continuum and magnetogram) to characterize the spatial and
modal interactions of sunspot and magnetic active region images and presents a
new approach to cluster the images. Specifically, in the framework of image
patch analysis, we estimate the number of intrinsic parameters required to
describe the spatial and modal dependencies, the correlation between the two
modalities and the corresponding spatial patterns, and examine the phenomena at
different scales within the images. To do this, we use linear and nonlinear
intrinsic dimension estimators, canonical correlation analysis, and
multiresolution analysis of intrinsic dimension.Comment: 5 pages, 7 figures, accepted to ICIP 201
Quantum-inspired computational imaging
Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip
Chiral separation by enantioselective liquid–liquid extraction
The literature on enantioselective liquid–liquid extraction (ELLE) spans more than half a century of research. Nonetheless, a comprehensive overview has not appeared during the past few decades. Enantioselective liquid–liquid extraction is a technology of interest for a wide range of chemists and chemical engineers in the fields of fine chemicals, pharmaceuticals, agrochemicals, fragrances and foods. In this review the principles and advances of resolution through enantioselective liquid–liquid extraction are discussed, starting with an introduction on the principles of enantioselective liquid–liquid extraction including host–guest chemistry, extraction and phase transfer mechanisms, and multistage liquid–liquid extraction processing. Then the literature on enantioselective liquid–liquid extraction systems is reviewed, structured on extractant classes. The following extractant classes are considered: crown ether based extractants, metal complexes and metalloids, extractants based on tartrates, and a final section with all other types of chiral extractants.
Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization
Separating active regions that are quiet from potentially eruptive ones is a
key issue in Space Weather applications. Traditional classification schemes
such as Mount Wilson and McIntosh have been effective in relating an active
region large scale magnetic configuration to its ability to produce eruptive
events. However, their qualitative nature prevents systematic studies of an
active region's evolution for example. We introduce a new clustering of active
regions that is based on the local geometry observed in Line of Sight
magnetogram and continuum images. We use a reduced-dimension representation of
an active region that is obtained by factoring the corresponding data matrix
comprised of local image patches. Two factorizations can be compared via the
definition of appropriate metrics on the resulting factors. The distances
obtained from these metrics are then used to cluster the active regions. We
find that these metrics result in natural clusterings of active regions. The
clusterings are related to large scale descriptors of an active region such as
its size, its local magnetic field distribution, and its complexity as measured
by the Mount Wilson classification scheme. We also find that including data
focused on the neutral line of an active region can result in an increased
correspondence between our clustering results and other active region
descriptors such as the Mount Wilson classifications and the value. We
provide some recommendations for which metrics, matrix factorization
techniques, and regions of interest to use to study active regions.Comment: Accepted for publication in the Journal of Space Weather and Space
Climate (SWSC). 33 pages, 12 figure
Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis
The flare-productivity of an active region is observed to be related to its
spatial complexity. Mount Wilson or McIntosh sunspot classifications measure
such complexity but in a categorical way, and may therefore not use all the
information present in the observations. Moreover, such categorical schemes
hinder a systematic study of an active region's evolution for example. We
propose fine-scale quantitative descriptors for an active region's complexity
and relate them to the Mount Wilson classification. We analyze the local
correlation structure within continuum and magnetogram data, as well as the
cross-correlation between continuum and magnetogram data. We compute the
intrinsic dimension, partial correlation, and canonical correlation analysis
(CCA) of image patches of continuum and magnetogram active region images taken
from the SOHO-MDI instrument. We use masks of sunspots derived from continuum
as well as larger masks of magnetic active regions derived from the magnetogram
to analyze separately the core part of an active region from its surrounding
part. We find the relationship between complexity of an active region as
measured by Mount Wilson and the intrinsic dimension of its image patches.
Partial correlation patterns exhibit approximately a third-order Markov
structure. CCA reveals different patterns of correlation between continuum and
magnetogram within the sunspots and in the region surrounding the sunspots.
These results also pave the way for patch-based dictionary learning with a view
towards automatic clustering of active regions.Comment: Accepted for publication in the Journal of Space Weather and Space
Climate (SWSC). 23 pages, 11 figure
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