9,990 research outputs found
Restructuring Health Insurance Markets
Examines six possible structural changes to the health insurance market to expand coverage, including rate compression, high-risk pools, and an insurance exchange. Outlines their benefits and the most effective way to structure and implement them
A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation
In this paper, we adopt 3D Convolutional Neural Networks to segment
volumetric medical images. Although deep neural networks have been proven to be
very effective on many 2D vision tasks, it is still challenging to apply them
to 3D tasks due to the limited amount of annotated 3D data and limited
computational resources. We propose a novel 3D-based coarse-to-fine framework
to effectively and efficiently tackle these challenges. The proposed 3D-based
framework outperforms the 2D counterpart to a large margin since it can
leverage the rich spatial infor- mation along all three axes. We conduct
experiments on two datasets which include healthy and pathological pancreases
respectively, and achieve the current state-of-the-art in terms of
Dice-S{\o}rensen Coefficient (DSC). On the NIH pancreas segmentation dataset,
we outperform the previous best by an average of over 2%, and the worst case is
improved by 7% to reach almost 70%, which indicates the reliability of our
framework in clinical applications.Comment: 9 pages, 4 figures, Accepted to 3D
The Arabidopsis JAGGED gene encodes a zinc finger protein that promotes leaf tissue development
Important goals in understanding leaf development are to identify genes involved in pattern specification, and also genes that translate this information into cell types and tissue structure. Loss-of-function mutations at the JAGGED (JAG) locus result in Arabidopsis plants with abnormally shaped lateral organs including serrated leaves, narrow floral organs, and petals that contain fewer but more elongate cells. jag mutations also suppress bract formation in leafy, apetala1 and apetala2 mutant backgrounds. The JAG gene was identified by map-based cloning to be a member of the zinc finger family of plant transcription factors and encodes a protein similar in structure to SUPERMAN with a single C2H2-type zinc finger, a proline-rich motif and a short leucine-rich repressor motif. JAG mRNA is localized to lateral organ primordia throughout the plant but is not found in the shoot apical meristem. Misexpression of JAG results in leaf fusion and the development of ectopic leaf-like outgrowth from both vegetative and floral tissues. Thus, JAG is necessary for proper lateral organ shape and is sufficient to induce the proliferation of lateral organ tissue
The effects of peer influence on adolescent pedestrian road-crossing decisions
Objective: Adolescence is a high-risk period for pedestrian injury. It is also a time of heightened susceptibility to peer influence. The aim of this research was to examine the effects of peer influence on the pedestrian road-crossing decisions of adolescents.
Methods: Using 10 videos of road-crossing sites, 80 16- to 18-year-olds were asked to make pedestrian road-crossing decisions. Participants were assigned to one of 4 experimental conditions: negative peer (influencing unsafe decisions), positive peer (influencing cautious decisions), silent peer (who observed but did not comment), and no peer (the participant completed the task alone). Peers from the adolescent’s own friendship group were recruited to influence either an unsafe or a cautious decision.
Results: Statistically significant differences were found between peer conditions. Participants least often identified safe road-crossing
sites when accompanied by a negative peer and more frequently identified dangerous road-crossing sites when accompanied by a positive peer. Both cautious and unsafe comments from a peer influenced adolescent pedestrians’ decisions.
Conclusions: These findings showed that road-crossing decisions of adolescents were influenced by both unsafe and cautious comments from their peers. The discussion highlighted the role that peers can play in both increasing and reducing adolescent risk-taking
Stocator: A High Performance Object Store Connector for Spark
We present Stocator, a high performance object store connector for Apache
Spark, that takes advantage of object store semantics. Previous connectors have
assumed file system semantics, in particular, achieving fault tolerance and
allowing speculative execution by creating temporary files to avoid
interference between worker threads executing the same task and then renaming
these files. Rename is not a native object store operation; not only is it not
atomic, but it is implemented using a costly copy operation and a delete.
Instead our connector leverages the inherent atomicity of object creation, and
by avoiding the rename paradigm it greatly decreases the number of operations
on the object store as well as enabling a much simpler approach to dealing with
the eventually consistent semantics typical of object stores. We have
implemented Stocator and shared it in open source. Performance testing shows
that it is as much as 18 times faster for write intensive workloads and
performs as much as 30 times fewer operations on the object store than the
legacy Hadoop connectors, reducing costs both for the client and the object
storage service provider
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used prediction from the first (coarse) stage to indicate a smaller input
region for the second (fine) stage. Despite its effectiveness, this algorithm
dealt with two stages individually, which lacked optimizing a global energy
function, and limited its ability to incorporate multi-stage visual cues.
Missing contextual information led to unsatisfying convergence in iterations,
and that the fine stage sometimes produced even lower segmentation accuracy
than the coarse stage.
This paper presents a Recurrent Saliency Transformation Network. The key
innovation is a saliency transformation module, which repeatedly converts the
segmentation probability map from the previous iteration as spatial weights and
applies these weights to the current iteration. This brings us two-fold
benefits. In training, it allows joint optimization over the deep networks
dealing with different input scales. In testing, it propagates multi-stage
visual information throughout iterations to improve segmentation accuracy.
Experiments in the NIH pancreas segmentation dataset demonstrate the
state-of-the-art accuracy, which outperforms the previous best by an average of
over 2%. Much higher accuracies are also reported on several small organs in a
larger dataset collected by ourselves. In addition, our approach enjoys better
convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures
- …