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