A Framework of Multivariate Utility Optimization with General Benchmarks

Benchmarks in the utility function have various interpretations, including performance guarantees and risk constraints in fund contracts and reference levels in cumulative prospect theory. In most literature, benchmarks are a deterministic constant or a fraction of the underlying wealth; as such, the utility is still a univariate function of the wealth. In this paper, we propose a framework of multivariate utility optimization with general benchmark variables, which include stochastic reference levels as typical examples. The utility is state-dependent and the objective is no longer distribution-invariant. We provide the optimal solution(s) and fully investigate the issues of well-posedness, feasibility, finiteness and attainability. The discussion does not require many classic conditions and assumptions, e.g., the Lagrange multiplier always exists. Moreover, several surprising phenomena and technical difficulties may appear: (i) non-uniqueness of the optimal solutions, (ii) various reasons for non-existence of the Lagrangian multiplier and corresponding results on the optimal solution, (iii) measurability issues of the concavification of a multivariate utility and the selection of the optimal solutions, and (iv) existence of an optimal solution not decreasing with respect to the pricing kernel. These issues are thoroughly addressed, rigorously proved, completely summarized and insightfully visualized. As an application, the framework is adopted to model and solve a constraint utility optimization problem with state-dependent performance and risk benchmarks.Comment: 52 pages, 8figure

High-performance Self-lubricating Ceramic Composites with Laminated-graded Structure

High-performance ceramic composites are potential candidates for the application of wear-resistance components because of their excellent properties. Nevertheless, many problems, such as high friction coefficient of ceramic material and poor mechanical properties of ceramic-matrix self-lubricating composites, limit a wider range of applications of these composites in tribological areas. Therefore, improving high-toughness ceramic-matrix self-lubricating materials for practical applications is significant. This study proposes a new design for ceramic self-lubricating composites to overcome the conflict between their mechanical and tribological properties. Complying with the design principle of bionic and graded composites, two kinds of self-lubricating ceramic composites with laminated-graded structure were prepared, and their mechanical and tribological properties were studied. The results show that this newly developed ceramic composite has achieved satisfactory strength and tribological properties compared with the traditional ceramic self-lubricating composites. The bending strength reached the same level as the properties of general monolithic ceramics. In the temperature range of 25-800 °C, the friction coefficient of composites was less than 0.55, which was about half of that of monolithic ceramics

Robo360: A 3D Omnispective Multi-Material Robotic Manipulation Dataset

Building robots that can automate labor-intensive tasks has long been the core motivation behind the advancements in computer vision and the robotics community. Recent interest in leveraging 3D algorithms, particularly neural fields, has led to advancements in robot perception and physical understanding in manipulation scenarios. However, the real world's complexity poses significant challenges. To tackle these challenges, we present Robo360, a dataset that features robotic manipulation with a dense view coverage, which enables high-quality 3D neural representation learning, and a diverse set of objects with various physical and optical properties and facilitates research in various object manipulation and physical world modeling tasks. We confirm the effectiveness of our dataset using existing dynamic NeRF and evaluate its potential in learning multi-view policies. We hope that Robo360 can open new research directions yet to be explored at the intersection of understanding the physical world in 3D and robot control

Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization

Multimodal summarization with multimodal output (MSMO) generates a summary with both textual and visual content. Multimodal news report contains heterogeneous contents, which makes MSMO nontrivial. Moreover, it is observed that different modalities of data in the news report correlate hierarchically. Traditional MSMO methods indistinguishably handle different modalities of data by learning a representation for the whole data, which is not directly adaptable to the heterogeneous contents and hierarchical correlation. In this paper, we propose a hierarchical cross-modality semantic correlation learning model (HCSCL) to learn the intra- and inter-modal correlation existing in the multimodal data. HCSCL adopts a graph network to encode the intra-modal correlation. Then, a hierarchical fusion framework is proposed to learn the hierarchical correlation between text and images. Furthermore, we construct a new dataset with relevant image annotation and image object label information to provide the supervision information for the learning procedure. Extensive experiments on the dataset show that HCSCL significantly outperforms the baseline methods in automatic summarization metrics and fine-grained diversity tests

The role of pathogenic microorganisms in the pathogenesis of scleroderma

Systemic sclerosis (SSc) is an autoimmune connective tissue disease characterized by localized or widespread sclerosis of the skin and progressive sclerosis in the internal organs. The pathogenesis of this disease has not been completely elucidated. However, it is considered to be associated with environmental factors, epigenetic mechanisms, and disorders of the immune system. This article reviews the research progress on the role of pathogenic microorganisms in the pathogenesis of scleroderma. It has been shown that scleroderma can be induced by infections with viruses, bacteria, mycoplasma, parasites, and other pathogenic microorganisms. Human herpesvirus and viruses such as B19V and HBV can cause pathological changes such as endothelial dysfunction and fibroblast activation. Alterations in the microbiota inside and outside the body are also associated with SSc. Treatments of pathogenic microorganisms improve SSc associated with infections by pathogenic microorganisms such as C burnetii, Mycoplasma and parasites, providing evidence of new insights in the pathogenesis of SSc, and early diagnosis, intervention and the treatment of this disease

Surface nanocrystallization of Cu-Cr alloy by a high power density continuous laser beam

A nanostructured surface layer of similar to 300 mu m thickness was fabricated on Cu-30Cr (wt%) hypereutectic alloy by a continuous laser beam with high power density (1.08 x 10(7) W/cm(2)). The average grain size of Cr-rich particles was refined to similar to 40 nm, and the solid solubility limit of Cr in Cu was extended to 1.96 at. %. Experimental results show that the dispersion of nano-sized Cr-rich spheroids in Cu-rich matrix was attributed to the Brownian motion of Cr-rich spheroids, and the high cooling rate (5.75 x 10(6) K/s) during liquid phase separation which inhibits the collisions between Cr-rich spheroids. (C) 2018 Published by Elsevier B.V

Refractory high-entropy alloys fabricated using laser technologies: a concrete review

Refractory high-entropy alloys (RHEAs) have attracted widespread attention in recent years as multi-component alloys applied to high-temperature fields. High melting point elements endow special microstructures and properties to RHEAs, which differ from those of conventional alloys and pose a challenge to conventional fabricating technologies. Laser fabrication technologies are attractive in fabricating RHEAs since a high-power density laser beam can be used as a controllable heat source to quickly melt refractory elements and then followed by rapid cooling and solidification to optimize the dependent properties. This paper reviews recent research progress in the fabricating process and the influence of processing on microstructural evolution and phase formation of laser-fabricated RHEAs, aiming to address the use of laser technologies for improving room temperature and high-temperature properties of RHEAs, thereby providing a reference for research community. The current methods of laser fabricating RHEAs, namely selective laser melting, laser metal deposition and laser cladding, are first introduced, and then the relationships between chemical composition, microstructure and properties of RHEAs are analyzed from experimental and simulation perspectives. In addition, the microhardness, oxidation resistance, wear resistance, corrosion resistance, irradiation resistance, and biocompatibility of laser fabricated RHEAs are discussed. Finally, the critical challenges and opportunities for laser fabricating RHEAs in the research field are highlighted, based on the research perspective of this topic