Ruin Probabilities in the Mixed Claim Frequency Risk Models

We consider two mixed claim frequency risk models. Some important probabilistic properties are obtained by probability-theory methods. Some important results about ruin probabilities are obtained by martingale approach

On the special oxidation mechanism of a Mg-Y-Al alloy contained LPSO phase at high temperatures

This work investigated the oxidation of Mg-11Y-1Al alloy in Ar-20%O2 at 500{\deg}through multiscale characterization. The results show that the network-like long-period stacking ordered(LPSO) phase decomposed into a needle-like LPSO phase and a polygonal Mg24Y5 phase. The needle-like LPSO phase resulted in the formation of a high-dense of needle-like oxide at the oxidation front of the area initially occupied by the network-like LPSO phase. The further inward oxygen would diffuse along the needle-like oxide-matrix interfaces and react with Y in the surrounding Mg matrix, resulting in the lateral growth of these needle-like oxides. Finally, the discrete needle-like oxides were interconnected to form a thicker and continuous oxide scale which could be more effective in hindering the elemental diffusion. Meanwhile, Al could partially enter the Y2O3 oxide scale and formed a strengthened (Y,Al)O oxide scale which could show a greater resistance to cracking and debonding

Phase separation and enhanced wear resistance of Cu88Fe12 immiscible coating prepared by laser cladding

In order to eliminate the microstructure segregation of Cu–Fe immiscible alloys which characterized with a liquid miscible gap, the Cu88Fe12 (wt.%) immiscible coating was prepared by laser cladding. The phase separation characteristic and wear resistance of the Cu88Fe12 (wt.%) immiscible coating were also investigated. The results show that the size of the milled Cu88Fe12 composite powder is reduced comparing to that of the un-milled powder due to fracturing during mechanical milling. Moreover, the demixing or delamination disappears in the Cu88Fe12 immiscible coating and a large amount of face-centered-cubic (fcc) γ-Fe and body-centered-cubic (bcc) α-Fe particles are dispersed in the face-centered-cubic (fcc) ɛ-Cu matrix as a result of liquid phase separation. The size of Fe-rich particles presents an increasing tendency from the bottom to the top of the immiscible coating. As a result, the microhardness of the immiscible coating is improved compared with brass (∼138 HV0.2) due to the presence of high-hardness Fe-rich particles (∼191 HV0.2) and the solid solution strengthening effect of Fe in Cu-rich matrix. Furthermore, the width of ploughing, the width and height of wear scar on the surface of the immiscible coating are much less than those on the surface of brass. Therefore, the wear resistance of the immiscible coating is remarkably enhanced compared with brass

Adversarial Attacks on Video Object Segmentation with Hard Region Discovery

Video object segmentation has been applied to various computer vision tasks, such as video editing, autonomous driving, and human-robot interaction. However, the methods based on deep neural networks are vulnerable to adversarial examples, which are the inputs attacked by almost human-imperceptible perturbations, and the adversary (i.e., attacker) will fool the segmentation model to make incorrect pixel-level predictions. This will rise the security issues in highly-demanding tasks because small perturbations to the input video will result in potential attack risks. Though adversarial examples have been extensively used for classification, it is rarely studied in video object segmentation. Existing related methods in computer vision either require prior knowledge of categories or cannot be directly applied due to the special design for certain tasks, failing to consider the pixel-wise region attack. Hence, this work develops an object-agnostic adversary that has adversarial impacts on VOS by first-frame attacking via hard region discovery. Particularly, the gradients from the segmentation model are exploited to discover the easily confused region, in which it is difficult to identify the pixel-wise objects from the background in a frame. This provides a hardness map that helps to generate perturbations with a stronger adversarial power for attacking the first frame. Empirical studies on three benchmarks indicate that our attacker significantly degrades the performance of several state-of-the-art video object segmentation models

Spatial Representativeness of PM_(2.5) Concentrations Obtained Using Reduced Number of Network Stations

Haze has been a focused air pollution phenomenon in China, and its characterization is highly desired. Aerosol properties obtained from a single station are frequently used to represent the haze condition over a large domain, such as tens of kilometers, which could result in high uncertainties due to their spatial variation. Using a high resolution network observation over an urban city in North China from November 2015 to February 2016, this study examines the spatial representativeness of ground station observations of particulate matter with diameters less than 2.5 μm (PM_(2.5)). We developed a new method to determine the representative area of PM_(2.5) measurements from limited stations. The key idea is to determine the PM_(2.5) spatial representative area using its spatial variability and temporal correlation. We also determine stations with large representative area using two grid networks with different resolutions. Based on the high spatial resolution measurements, the representative area of PM_(2.5) at one station can be determined from the grids with high correlations and small differences of PM_(2.5). The representative area for a single station in the study period ranges from 0.25 to 16.25 km^2, but is less than 3 km^2 for more than half of the stations. The representative area varies with locations, and observation at 10 optimal stations would have a good representativeness of those obtained from 169 stations for the four-month time scale studied. Both evaluations with an empirical orthogonal function (EOF) analysis and with independent dataset corroborate the validity of the results found in this study

Adaptive transmission in heterogeneous networks

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166243/1/cmu2bf00018.pd