A class of second-order geometric quasilinear hyperbolic PDEs and their application in imaging science

In this paper, we study damped second-order dynamics, which are quasilinear hyperbolic partial differential equations (PDEs). This is inspired by the recent development of second-order damping systems for accelerating energy decay of gradient flows. We concentrate on two equations: one is a damped second-order total variation flow, which is primarily motivated by the application of image denoising; the other is a damped second-order mean curvature flow for level sets of scalar functions, which is related to a non-convex variational model capable of correcting displacement errors in image data (e.g. dejittering). For the former equation, we prove the existence and uniqueness of the solution. For the latter, we draw a connection between the equation and some second-order geometric PDEs evolving the hypersurfaces which are described by level sets of scalar functions, and show the existence and uniqueness of the solution for a regularized version of the equation. The latter is used in our algorithmic development. A general algorithm for numerical discretization of the two nonlinear PDEs is proposed and analyzed. Its efficiency is demonstrated by various numerical examples, where simulations on the behavior of solutions of the new equations and comparisons with first-order flows are also documented

A class of second-order geometric quasilinear hyperbolic PDEs and their application in imaging science

In this paper, we study damped second-order dynamics, which are quasilinear hyperbolic partial differential equations (PDEs). This is inspired by the recent development of second-order damping systems for accelerating energy decay of gradient flows. We concentrate on two equations: one is a damped second-order total variation flow, which is primarily motivated by the application of image denoising; the other is a damped second-order mean curvature flow for level sets of scalar functions, which is related to a non-convex variational model capable of correcting displacement errors in image data (e.g. dejittering). For the former equation, we prove the existence and uniqueness of the solution. For the latter, we draw a connection between the equation and some second-order geometric PDEs evolving the hypersurfaces which are described by level sets of scalar functions, and show the existence and uniqueness of the solution for a regularized version of the equation. The latter is used in our algorithmic development. A general algorithm for numerical discretization of the two nonlinear PDEs is proposed and analyzed. Its efficiency is demonstrated by various numerical examples, where simulations on the behavior of solutions of the new equations and comparisons with first-order flows are also documented

A class of second-order geometric quasilinear hyperbolic PDEs and their application in imaging science

In this paper, we study damped second-order dynamics, which are quasilinear hyperbolic partial differential equations (PDEs). This is inspired by the recent development of second-order damping systems for accelerating energy decay of gradient flows. We concentrate on two equations: one is a damped second-order total variation flow, which is primarily motivated by the application of image denoising; the other is a damped second-order mean curvature flow for level sets of scalar functions, which is related to a non-convex variational model capable of correcting displacement errors in image data (e.g. dejittering). For the former equation, we prove the existence and uniqueness of the solution. For the latter, we draw a connection between the equation and some second-order geometric PDEs evolving the hypersurfaces which are described by level sets of scalar functions, and show the existence and uniqueness of the solution for a regularized version of the equation. The latter is used in our algorithmic development. A general algorithm for numerical discretization of the two nonlinear PDEs is proposed and analyzed. Its efficiency is demonstrated by various numerical examples, where simulations on the behavior of solutions of the new equations and comparisons with first-order flows are also documented

EFFECT OF THE Si POWDER ADDITIONS ON THE PROPERTIES OF SiC COMPOSITES

By means of transient plastic phase process, the SiC silicon carbide kiln furniture materials were produced through adding Si powder to SiC materials. At the condition of the same additions of SiO2 powder, the effect of the Si powder additions on properties of silicon carbide materials after sintered at 1450°C for 3 h in air atmosphere was studied by means of SEM and other analysis methods. The results showed that silicon powder contributes to both sintering by liquid state and plastic phase combination to improve the strength of samples. When the Si powder additions is lower than 3.5 %, the density and strength of samples increase and porosity decrease with increasing Si powder additions. However when the Si powder additions is higher than 3.5 %, the density and strength of samples decrease and porosity increase with increasing Si powder additions. With increasing of Si additions, the residual strength of sample after thermal shocked increased and linear change rate decreased, and get to boundary value when Si additions is 4.5 %. The results also indicated that at the same sintering temperature, the sample with 3.5 % silicon powder has maximum strength

Integrate range shifter in immobilization for proton therapy: 3D printed materials characterization

3D printing is investigated for application in patient immobilization during proton therapy (PT). It potentially enables a merge of immobilization, range shifting and other functionality into one patient-specific structure. Beside minimizing the lateral beam spread due to the removal of air gap it could also reduce the collision risk and the treatment time compared to movable nozzle snouts. In this first study, 9 different 3D printed materials were characterized in detail. The resulting data (Table 1) will serve as input for the design of a printed immobilization structure. The printed test objects showed reduced geometric printing accuracy for 3 materials. Compression testing yielded Young’s moduli from 0.6 MPa to 3445 MPa, without deterioration after exposure to 100 Gy in a MV photon beam. Dual-energy CT methods were used to estimate the effective atomic number Zeff, the relative electron density e and the stopping power ratio SPR. Zeff ranged from 5.91 to 10.43. The SPR and e both ranged from 0.6 to 1.22. The measured photon attenuation coefficients at therapeutic energies scaled linearly with e. In a 62 MeV proton beam, good agreement was seen between the DECT estimated SPR and the measured range shift, except for the higher Zeff. As opposed to the photon attenuation, the proton range shifting was printing orientation dependent for certain materials. In conclusion printed materials exhibit a wide variation in structural and radiological properties. The quantification of these characteristics enables optimal material selection for the design of a multifunctional 3D printed immobilization structure for PT

Nonreciprocal ultrastrong magnon-photon coupling in the bandgap of photonic crystals

We observe a nonreciprocal ultrastrong magnon-photon coupling in the bandgap of photonic crystals by introducing a single crystal YIG cylinder into copper photonic crystals cavity as a point defect. The coupling strength reaches up to 1.18 GHz, which constitutes about 10.9% of the photon energy compared to the photon frequency around 10.8 GHz. It is fascinating that the coupling achieves unidirectional signal transmission in the whole bandgap. This study demonstrates the possibility of controlling nonreciprocal magnon-photon coupling by manipulating the structure of photonic crystals, providing new methods to investigate the influence of magnetic point defects on microwave signal transmission.Comment: 6 pages, 5 figure

Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution

Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education. Most existing works decoupled this problem into several independent sub-tasks of text spotting (text detection and recognition) and information extraction, which completely ignored the high correlation among them during optimization. In this paper, we propose a robust visual information extraction system (VIES) towards real-world scenarios, which is a unified end-to-end trainable framework for simultaneous text detection, recognition and information extraction by taking a single document image as input and outputting the structured information. Specifically, the information extraction branch collects abundant visual and semantic representations from text spotting for multimodal feature fusion and conversely, provides higher-level semantic clues to contribute to the optimization of text spotting. Moreover, regarding the shortage of public benchmarks, we construct a fully-annotated dataset called EPHOIE (https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for both text spotting and visual information extraction. EPHOIE consists of 1,494 images of examination paper head with complex layouts and background, including a total of 15,771 Chinese handwritten or printed text instances. Compared with the state-of-the-art methods, our VIES shows significant superior performance on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used SROIE dataset under the end-to-end scenario.Comment: 8 pages, 5 figures, to be published in AAAI 202

CCR2 expression correlates with prostate cancer progression

Although the primary role of chemokines and their receptors is controlling the trafficking of leukocytes during inflammatory responses, they also play pleoitropic roles in cancer development. There is emerging evidence that cancer cells produce chemokines that induce tumor cell proliferation or chemotaxis in various cancer types. We have previously reported that MCP-1 acts as a paracrine and autocrine factor for prostate cancer (PCa) growth and invasion. As the cellular effects of MCP-1 are mediated by CC chemokine receptor 2 (CCR2), we hypothesized that CCR2 may contribute PCa progression. Accordingly, we first determined CCR2 mRNA and protein expression in various cancer cell lines, including PCa and other cancer types. All cells expressed CCR2 mRNA and protein, but in PCa, more aggressive cancer cells such as C4-2B, DU145, and PC3 expressed a higher amount of CCR2 compared with the less aggressive cancer cells such as LNCaP or non-neoplastic PrEC and RWPE-1 cells. Further, we found a positive correlation between CCR2 expression and PCa progression by analyzing an ONCOMINE gene array database. We confirmed that CCR2 mRNA was highly expressed in PCa metastatic tissues compared with the localized PCa or benign prostate tissues by real-time RT-PCR. Finally, CCR2 protein expression was examined by immunohistochemical staining on tissue microarray specimens from 96 PCa patients and 31 benign tissue controls. We found that CCR2 expression correlated with Gleason score and clinical pathologic stages, whereas lower levels of CCR2 were expressed in normal prostate tissues. These results suggest that CCR2 may contribute to PCa development. J. Cell. Biochem. 101: 676–685, 2007. © 2007 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56008/1/21220_ftp.pd