Nonlinearities in modified gravity cosmology. II. Impacts of modified gravity on the halo properties

The statistics of dark matter halos is an essential component of understanding the nonlinear evolution in modified gravity cosmology. Based on a series of modified gravity N-body simulations, we investigate the halo mass function, concentration and bias. We model the impact of modified gravity by a single parameter \zeta, which determines the enhancement of particle acceleration with respect to GR, given the identical mass distribution (\zeta=1 in GR). We select snapshot redshifts such that the linear matter power spectra of different gravity models are identical, in order to isolate the impact of gravity beyond modifying the linear growth rate. At the baseline redshift corresponding to z_S=1.2 in the standard \Lambda CDM, for a 10% deviation from GR(|\zeta-1|=0.1), the measured halo mass function can differ by about 5-10%, the halo concentration by about 10-20%, while the halo bias differs significantly less. These results demonstrate that the halo mass function and/or the halo concentration are sensitive to the nature of gravity and may be used to make interesting constraints along this line.Comment: 8 pages, 7 figures, accepted for publication in Physical Review

Identification of Hemodynamically Optimal Coronary Stent Designs Based on Vessel Caliber

Coronary stent design influences local patterns of wall shear stress (WSS) that are associated with neointimal growth, restenosis, and the endothelialization of stent struts. The number of circumferentially repeating crowns NC for a given stent de- sign is often modified depending on the target vessel caliber, but the hemodynamic implications of altering NC have not previously been studied. In this investigation, we analyzed the relationship between vessel diameter and the hemodynamically optimal NC using a derivative-free optimization algorithm coupled with computational fluid dynamics. The algorithm computed the optimal vessel diameter, defined as minimizing the area of stent-induced low WSS, for various configurations (i.e., NC) of a generic slotted-tube design and designs that resemble commercially available stents. Stents were modeled in idealized coronary arteries with a vessel diameter that was allowed to vary between 2 and 5 mm. The results indicate that the optimal vessel diameter increases for stent configurations with greater NC, and the designs of current commercial stents incorporate a greater NC than hemodynamically optimal stent designs. This finding suggests that reducing the NC of current stents may improve the hemodynamic environment within stented arteries and reduce the likelihood of excessive neointimal growth and thrombus formation

Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization

Instance segmentation on point clouds is crucially important for 3D scene understanding. Distance clustering is commonly used in state-of-the-art methods (SOTAs), which is typically effective but does not perform well in segmenting adjacent objects with the same semantic label (especially when they share neighboring points). Due to the uneven distribution of offset points, these existing methods can hardly cluster all instance points. To this end, we design a novel divide and conquer strategy and propose an end-to-end network named PBNet that binarizes each point and clusters them separately to segment instances. PBNet divides offset instance points into two categories: high and low density points (HPs vs.LPs), which are then conquered separately. Adjacent objects can be clearly separated by removing LPs, and then be completed and refined by assigning LPs via a neighbor voting method. To further reduce clustering errors, we develop an iterative merging algorithm based on mean size to aggregate fragment instances. Experiments on ScanNetV2 and S3DIS datasets indicate the superiority of our model. In particular, PBNet achieves so far the best AP50 and AP25 on the ScanNetV2 official benchmark challenge (Validation Set) while demonstrating high efficiency

From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion

We consider the problem of Multi-view 3D Face Reconstruction (MVR) with weakly supervised learning that leverages a limited number of 2D face images (e.g. 3) to generate a high-quality 3D face model with very light annotation. Despite their encouraging performance, present MVR methods simply concatenate multi-view image features and pay less attention to critical areas (e.g. eye, brow, nose and mouth). To this end, we propose a novel model called Deep Fusion MVR (DF-MVR) and design a multi-view encoding to a single decoding framework with skip connections, able to extract, integrate, and compensate deep features with attention from multi-view images. In addition, we develop a multi-view face parse network to learn, identify, and emphasize the critical common face area. Finally, though our model is trained with a few 2D images, it can reconstruct an accurate 3D model even if one single 2D image is input. We conduct extensive experiments to evaluate various multi-view 3D face reconstruction methods. Our proposed model attains superior performance, leading to 11.4% RMSE improvement over the existing best weakly supervised MVRs. Source codes are available in the supplementary materials

Rapid Preparation of Spherical Granules via the Melt Centrifugal Atomization Technique

Granules with superior fluidity and low moisture absorption are ideal for tableting and capsule filling. Melt granulation as a solvent-free technology has attracted increasing interest for the granulation of moisture-sensitive drugs. The objective of the present study was to develop a solvent-less and high throughput melt granulation method via the melt centrifugal atomization (MCA) technique. The granule formability of various drugs and excipients via MCA and their dissolution properties were studied. It was found that the yield, fluidity, and moisture resistance of the granules were affected by the drug and excipient types, operation temperature, and collector diameter. The drugs were in an amorphous state in pure drug granules, or were highly dispersed in excipients as solid dispersions. The granules produced via MCA showed an improved drug dissolution. The present study demonstrated that the solvent-free, one-step, and high-throughput MCA approach can be used to produce spherical granules with superior fluidity and immediate drug release characteristics for poorly water-soluble and moisture-sensitive therapeutics