656 research outputs found
Unsupervised cryo-EM data clustering through adaptively constrained K-means algorithm
In single-particle cryo-electron microscopy (cryo-EM), K-means clustering
algorithm is widely used in unsupervised 2D classification of projection images
of biological macromolecules. 3D ab initio reconstruction requires accurate
unsupervised classification in order to separate molecular projections of
distinct orientations. Due to background noise in single-particle images and
uncertainty of molecular orientations, traditional K-means clustering algorithm
may classify images into wrong classes and produce classes with a large
variation in membership. Overcoming these limitations requires further
development on clustering algorithms for cryo-EM data analysis. We propose a
novel unsupervised data clustering method building upon the traditional K-means
algorithm. By introducing an adaptive constraint term in the objective
function, our algorithm not only avoids a large variation in class sizes but
also produces more accurate data clustering. Applications of this approach to
both simulated and experimental cryo-EM data demonstrate that our algorithm is
a significantly improved alterative to the traditional K-means algorithm in
single-particle cryo-EM analysis.Comment: 35 pages, 14 figure
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
Residential Segregation and Unequal Access to Schools
Socio-spatial inequality and school inequality are strongly related. Where people live affects the opportunities individuals have in life, such as the opportunity to send your children to a good school. The level of urbanisation is related to the number of options people have to choose good schools, so more urbanised areas likely offer more options for good schools. However, the families that can choose good schools are likely families with high income or education levels. Data for this study come from two waves of the Taiwan Youth Project (N = 2,893), which consists of two cohorts of students from 162 classrooms in 40 junior high schools in northern Taiwan. When school quality is proxied by socioeconomic status (SES), the results show that, in general, students from the most urbanised areas, wealthier parents, and higher-educated parents, are more likely to go to higher SES schools. However, the strongest effects are for higher income and higher-educated parents in the most urbanised areas. This suggests that in the most urbanised areas, families have the most options regarding school choice, and richer and more educated families are better able to circumvent school catchment areas, either because they can afford an address in a better catchment area or because they understand the importance of school choice
VMap: An Interactive Rectangular Space-filling Visualization for Map-like Vertex-centric Graph Exploration
We present VMap, a map-like rectangular space-filling visualization, to
perform vertex-centric graph exploration. Existing visualizations have limited
support for quality optimization among rectangular aspect ratios, vertex-edge
intersection, and data encoding accuracy. To tackle this problem, VMap
integrates three novel components: (1) a desired-aspect-ratio (DAR) rectangular
partitioning algorithm, (2) a two-stage rectangle adjustment algorithm, and (3)
a simulated annealing based heuristic optimizer. First, to generate a
rectangular space-filling layout of an input graph, we subdivide the 2D
embedding of the graph into rectangles with optimization of rectangles' aspect
ratios toward a desired aspect ratio. Second, to route graph edges between
rectangles without vertex-edge occlusion, we devise a two-stage algorithm to
adjust a rectangular layout to insert border space between rectangles. Third,
to produce and arrange rectangles by considering multiple visual criteria, we
design a simulated annealing based heuristic optimization to adjust vertices'
2D embedding to support trade-offs among aspect ratio quality and the encoding
accuracy of vertices' weights and adjacency. We evaluated the effectiveness of
VMap on both synthetic and application datasets. The resulting rectangular
layout has better aspect ratio quality on synthetic data compared with the
existing method for the rectangular partitioning of 2D points. On three
real-world datasets, VMap achieved better encoding accuracy and attained faster
generation speed compared with existing methods on graphs' rectangular layout
generation. We further illustrate the usefulness of VMap for vertex-centric
graph exploration through three case studies on visualizing social networks,
representing academic communities, and displaying geographic information.Comment: Submitted to IEEE Visualization Conference (IEEE VIS) 2019 and 202
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