3D MR Ventricle Segmentation in Pre-term Infants with Post-Hemorrhagic Ventricle Dilation

Intraventricular hemorrhage (IVH) or bleed within the brain is a common condition among pre-term infants that occurs in very low birth weight preterm neonates. The prognosis is further worsened by the development of progressive ventricular dilatation, i.e., post-hemorrhagic ventricle dilation (PHVD), which occurs in 10-30% of IVH patients. In practice, predicting PHVD accurately and determining if that specific patient with ventricular dilatation requires the ability to measure accurately ventricular volume. While monitoring of PHVD in infants is typically done by repeated US and not MRI, once the patient has been treated, the follow-up over the lifetime of the patient is done by MRI. While manual segmentation is still seen as a gold standard, it is extremely time consuming, and therefore not feasible in a clinical context, and it also has a large inter-and intra-observer variability. This paper proposes an segmentation algorithm to extract the cerebral ventricles from 3D T1-weighted MR images of pre-term infants with PHVD. The proposed segmentation algorithm makes use of the convex optimization technique combined with the learned priors of image intensities and label probabilistic map, which is built from a multi-atlas registration scheme. The leave-one-out cross validation using 7 PHVD patient T1 weighted MR images showed that the proposed method yielded a mean DSC of 89.7% +/- 4.2%, a MAD of 2.6 +/- 1.1 mm, a MAXD of 17.8 +/- 6.2 mm, and a VD of 11.6% +/- 5.9%, suggesting a good agreement with manual segmentations

Rendering Secure and Trustworthy Edge Intelligence in 5G-Enabled IIoT using Proof of Learning Consensus Protocol

Industrial Internet of Things (IIoT) and fifth generation (5G) network have fueled the development of Industry 4.0 by providing an unparalleled connectivity and intelligence to ensure timely (or real time) and optimal decision making. Under this umbrella, the edge intelligence is ready to propel another ripple in the industrial growth by ensuring the next generation of connectivity and performance. With the recent proliferation of blockchain, edge intelligence enters a new era, where each edge trains the local learning model, then interconnecting the whole learning models in a distributed blockchain manner, known as blockchain-assisted federated learning. However, it is quiet challenging task to provide secure edge intelligence in 5G-enabled IIoT environment alongside ensuring latency and throughput. In this paper, we propose a Proof-of-Learning (PoL) consensus protocol that considers the reputation opinion for edge blockchain to ensure secure and trustworthy edge intelligence in IIoT. This protocol fetches each edge's reputation opinion by executing a smart contract, and partly adopts the winner's learning model according to its reputation opinion. By quantitative performance analysis and simulation experiments, the proposed scheme demonstrates the superior performance in contrast to the traditional counterparts

Data Processing Pipeline for Pointing Observations of Lunar-based Ultraviolet Telescope

We describe the data processing pipeline developed to reduce the pointing observation data of Lunar-based Ultraviolet Telescope (LUT), which belongs to the Chang'e-3 mission of the Chinese Lunar Exploration Program. The pointing observation program of LUT is dedicated to monitor variable objects in a near-ultraviolet (245-345 nm) band. LUT works in lunar daytime for sufficient power supply, so some special data processing strategies have been developed for the pipeline. The procedures of the pipeline include stray light removing, astrometry, flat fielding employing superflat technique, source extraction and cosmic rays rejection, aperture and PSF photometry, aperture correction, and catalogues archiving, etc. It has been intensively tested and works smoothly with observation data. The photometric accuracy is typically ~0.02 mag for LUT 10 mag stars (30 s exposure), with errors come from background noises, residuals of stray light removing, and flat fielding related errors. The accuracy degrades to be ~0.2 mag for stars of 13.5 mag which is the 5{\sigma} detection limit of LUT.Comment: 10 pages, 7 figures, 4 tables. Minor changes and some expounding words added. Version accepted for publication in Astrophysics and Space Science (Ap&SS

Temporal comparison of nonthermal flare emission and magnetic-flux change rates

To test the standard flare model (CSHKP-model), we measured the magnetic-flux change rate in five flare events of different GOES classes using chromospheric/photospheric observations and compared its progression with observed nonthermal flare emission. We calculated the cumulated positive and negative magnetic flux participating in the reconnection process, as well as the total reconnection flux. Finally, we investigated the relations between the total reconnection flux, the GOES class of the events, and the linear velocity of the flare-associated CMEs. Using high-cadence H-alpha and TRACE 1600 A image time-series data and MDI/SOHO magnetograms, we measured the required observables (newly brightened flare area and magnetic-field strength inside this area). RHESSI and INTEGRAL hard X-ray time profiles in nonthermal energy bands were used as observable proxies for the flare-energy release rate. We detected strong temporal correlations between the derived magnetic-flux change rate and the observed nonthermal emission of all events. The cumulated positive and negative fluxes, with flux ratios of between 0.64 and 1.35, were almost equivalent to each other. Total reconnection fluxes ranged between 1.8 x 10^21 Mx for the weakest event (GOES class B9.5) and 15.5 x 10^21 Mx for the most energetic one (GOES class X17.2). The amount of magnetic flux participating in the reconnection process was higher in more energetic events than in weaker ones. Flares with more reconnection flux were associated with faster CMEs.Comment: 12 pages, 13 figure