Adversarial Sparse-View CBCT Artifact Reduction

We present an effective post-processing method to reduce the artifacts from sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based on the state-of-the-art, image-to-image generative models with a perceptual loss as regulation. Unlike the traditional CT artifact-reduction approaches, our method is trained in an adversarial fashion that yields more perceptually realistic outputs while preserving the anatomical structures. To address the streak artifacts that are inherently local and appear across various scales, we further propose a novel discriminator architecture based on feature pyramid networks and a differentially modulated focus map to induce the adversarial training. Our experimental results show that the proposed method can greatly correct the cone-beam artifacts from clinical CBCT images reconstructed using 1/3 projections, and outperforms strong baseline methods both quantitatively and qualitatively

International Laboratory Comparison of Influenza Microneutralization Assays for A(H1N1) pdm09, A(H3N2), and A(H5N1) Influenza Viruses by CONSISE

The microneutralization assay is commonly used to detect antibodies to influenza virus, and multiple protocols are used worldwide. These protocols differ in the incubation time of the assay as well as in the order of specific steps, and even within protocols there are often further adjustments in individual laboratories. The impact these protocol variations have on influenza serology data is unclear. Thus, a laboratory comparison of the 2-day enzyme-linked immunosorbent assay (ELISA) and 3-day hemagglutination (HA) microneutralization (MN) protocols, using A(H1N1)pdm09, A(H3N2), and A(H5N1) viruses, was performed by the CONSISE Laboratory Working Group. Individual laboratories performed both assay protocols, on multiple occasions, using different serum panels. Thirteen laboratories from around the world participated. Within each laboratory, serum sample titers for the different assay protocols were compared between assays to determine the sensitivity of each assay and were compared between replicates to assess the reproducibility of each protocol for each laboratory. There was good correlation of the results obtained using the two assay protocols in most laboratories, indicating that these assays may be interchangeable for detecting antibodies to the influenza A viruses included in this study. Importantly, participating laboratories have aligned their methodologies to the CONSISE consensus 2-day ELISA and 3-day HA MN assay protocols to enable better correlation of these assays in the future

Inferring Population Preferences via Mixtures of Spatial Voting Models

Understanding political phenomena requires measuring the political preferences of society. We introduce a model based on mixtures of spatial voting models that infers the underlying distribution of political preferences of voters with only voting records of the population and political positions of candidates in an election. Beyond offering a cost-effective alternative to surveys, this method projects the political preferences of voters and candidates into a shared latent preference space. This projection allows us to directly compare the preferences of the two groups, which is desirable for political science but difficult with traditional survey methods. After validating the aggregated-level inferences of this model against results of related work and on simple prediction tasks, we apply the model to better understand the phenomenon of political polarization in the Texas, New York, and Ohio electorates. Taken at face value, inferences drawn from our model indicate that the electorates in these states may be less bimodal than the distribution of candidates, but that the electorates are comparatively more extreme in their variance. We conclude with a discussion of limitations of our method and potential future directions for research.Comment: To be published in the 8th International Conference on Social Informatics (SocInfo) 201

SL(2,R)SL(2,R) symmetry and quasi-normal modes in the BTZ black hole

With the help of two new intrinsic tensor fields associated with the $SL(2,R)$ quadratic Casimir of Killing fields, we uncover the $SL(2,R)$ symmetry satisfied by the solutions to the equations of motion for various fields in the BTZ black hole in a uniform way by performing tensor and spinor analysis without resorting to any specific coordinate system. Then with the standard algebraic method developed recently, we determine the quasi-normal modes for various fields in the BTZ black hole. As a result, the quasi-normal modes are given by the infinite tower of descendants of the chiral highest weight mode, which is in good agreement with the previous analytic result obtained by exactly solving equations of motion instead.Comment: JHEP style, 1+13 pages, version to appear in JHE

Unusual Formation of Point-Defect Complexes in the Ultrawide-Band-Gap Semiconductor β-Ga2 O3

Understanding the unique properties of ultra-wide band gap semiconductors requires detailed information about the exact nature of point defects and their role in determining the properties. Here, we report the first direct microscopic observation of an unusual formation of point defect complexes within the atomic-scale structure of β-Ga2O3 using high resolution scanning transmission electron microscopy (STEM). Each complex involves one cation interstitial atom paired with two cation vacancies. These divacancy-interstitial complexes correlate directly with structures obtained by density functional theory, which predicts them to be compensating acceptors in β-Ga2O3. This prediction is confirmed by a comparison between STEM data and deep level optical spectroscopy results, which reveals that these complexes correspond to a deep trap within the band gap, and that the development of the complexes is facilitated by Sn doping through increased vacancy concentration. These findings provide new insight on this emerging material's unique response to the incorporation of impurities that can critically influence their properties

The mass area of jets

We introduce a new characteristic of jets called mass area. It is defined so as to measure the susceptibility of the jet's mass to contamination from soft background. The mass area is a close relative of the recently introduced catchment area of jets. We define it also in two variants: passive and active. As a preparatory step, we generalise the results for passive and active areas of two-particle jets to the case where the two constituent particles have arbitrary transverse momenta. As a main part of our study, we use the mass area to analyse a range of modern jet algorithms acting on simple one and two-particle systems. We find a whole variety of behaviours of passive and active mass areas depending on the algorithm, relative hardness of particles or their separation. We also study mass areas of jets from Monte Carlo simulations as well as give an example of how the concept of mass area can be used to correct jets for contamination from pileup. Our results show that the information provided by the mass area can be very useful in a range of jet-based analyses.Comment: 36 pages, 12 figures; v2: improved quality of two plots, added entry in acknowledgments, nicer form of formulae in appendix A; v3: added section with MC study and pileup correction, version accepted by JHE

Transient Characteristics of a Hydraulically Interconnected Suspension System

This paper describes vehicle dynamic models that capture the large amplitude transient characteristics of a passive Hydraulically Interconnected Suspension (HIS) system. Accurate mathematical models are developed to represent pressure-flow characteristics, fluid properties, damper valves, accumulators and nonlinear coupling between mechanical and fluid systems. The vehicle is modeled as a lumped mass system with half- and fullcar configurations. The transient performance is demonstrated by numerical integration of the second order nonlinear differential equations. The stiffness and damping characteristics corresponding to vehicle bounce, roll and pitch motions are extracted from the transient simulation. Simulation results clearly demonstrate the superiority of the HIS system during vehicle handling and stability by providing additional roll stiffness and reduced articulation stiffness

Adversarial training and dilated convolutions for brain MRI segmentation

Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to distinguish from real images. In this study we use an adversarial training approach to improve CNN-based brain MRI segmentation. To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations. During training, this loss function is optimised together with the conventional average per-voxel cross entropy loss. The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi

Different reactions to adverse neighborhoods in games of cooperation

In social dilemmas, cooperation among randomly interacting individuals is often difficult to achieve. The situation changes if interactions take place in a network where the network structure jointly evolves with the behavioral strategies of the interacting individuals. In particular, cooperation can be stabilized if individuals tend to cut interaction links when facing adverse neighborhoods. Here we consider two different types of reaction to adverse neighborhoods, and all possible mixtures between these reactions. When faced with a gloomy outlook, players can either choose to cut and rewire some of their links to other individuals, or they can migrate to another location and establish new links in the new local neighborhood. We find that in general local rewiring is more favorable for the evolution of cooperation than emigration from adverse neighborhoods. Rewiring helps to maintain the diversity in the degree distribution of players and favors the spontaneous emergence of cooperative clusters. Both properties are known to favor the evolution of cooperation on networks. Interestingly, a mixture of migration and rewiring is even more favorable for the evolution of cooperation than rewiring on its own. While most models only consider a single type of reaction to adverse neighborhoods, the coexistence of several such reactions may actually be an optimal setting for the evolution of cooperation.Comment: 12 pages, 5 figures; accepted for publication in PLoS ON