Information Splitting for Big Data Analytics

Many statistical models require an estimation of unknown (co)-variance parameter(s) in a model. The estimation usually obtained by maximizing a log-likelihood which involves log determinant terms. In principle, one requires the \emph{observed information}--the negative Hessian matrix or the second derivative of the log-likelihood---to obtain an accurate maximum likelihood estimator according to the Newton method. When one uses the \emph{Fisher information}, the expect value of the observed information, a simpler algorithm than the Newton method is obtained as the Fisher scoring algorithm. With the advance in high-throughput technologies in the biological sciences, recommendation systems and social networks, the sizes of data sets---and the corresponding statistical models---have suddenly increased by several orders of magnitude. Neither the observed information nor the Fisher information is easy to obtained for these big data sets. This paper introduces an information splitting technique to simplify the computation. After splitting the mean of the observed information and the Fisher information, an simpler approximate Hessian matrix for the log-likelihood can be obtained. This approximated Hessian matrix can significantly reduce computations, and makes the linear mixed model applicable for big data sets. Such a spitting and simpler formulas heavily depends on matrix algebra transforms, and applicable to large scale breeding model, genetics wide association analysis.Comment: arXiv admin note: text overlap with arXiv:1605.0764

AutoAMG(θ\theta): An Auto-tuned AMG Method Based on Deep Learning for Strong Threshold

Algebraic Multigrid (AMG) is one of the most used iterative algorithms for solving large sparse linear equations $Ax=b$. In AMG, the coarse grid is a key component that affects the efficiency of the algorithm, the construction of which relies on the strong threshold parameter $\theta$. This parameter is generally chosen empirically, with a default value in many current AMG solvers of 0.25 for 2D problems and 0.5 for 3D problems. However, for many practical problems, the quality of the coarse grid and the efficiency of the AMG algorithm are sensitive to $\theta$; the default value is rarely optimal, and sometimes is far from it. Therefore, how to choose a better $\theta$ is an important question. In this paper, we propose a deep learning based auto-tuning method, AutoAMG($\theta$) for multiscale sparse linear equations, which are widely used in practical problems. The method uses Graph Neural Networks (GNNs) to extract matrix features, and a Multilayer Perceptron (MLP) to build the mapping between matrix features and the optimal $\theta$, which can adaptively output $\theta$ values for different matrices. Numerical experiments show that AutoAMG($\theta$) can achieve significant speedup compared to the default $\theta$ value

Analysis of the Mixed Control System of Damper and TMD

Conference Name:International Conference on Advanced Engineering Materials and Technology (AEMT2011). Conference Address: Sanya, PEOPLES R CHINA. Time:JUL 29-31, 2011.Many new control techniques and energy dissipation systems which can decrease the response of wind vibration and earthquake. But there is less research on the mixed control system of Damper and TMD. In order to improve the shortcomings of Damper system and TMD system, combining their respective advantages reasonably, the mixed control system of Damper and TMD were analyzed in this paper. A 20-storey Benchmark model was used to compare the effects of various control systems under 2-directional earthquake. Taking the displacement reducing coefficient (DRF) as the objective function, Damper system, TMD system, and the mixed control system of Damper and TMD are optimally designed based on Genetic Algorithm (GA). Numerical results show that the mixed control system of dampers and TMD proposed in the paper can work in coordination and complement each other to achieve better control effect

Dynamic response of damped outrigger system for frame-core tube structure under earthquake loads

Conference Name:1st International Conference on Civil Engineering, Architecture and Building Materials, CEABM 2011. Conference Address: Haikou, China. Time:June 18, 2011 - June 20, 2011.Hainan University, College of Civil Engineering and Architecture; Guizhou University, College of Civil and Architecture Engineering; Hainan Society of Theoretical and Applied MechanicsThe novel passive energy dissipation system named Damped Outrigger System for frame-core tube structure is introduced in recent years, in which the outrigger and perimeter columns are separate, and the vertically acting fluid-viscous dampers connect the end of each of the outrigger walls to the adjacent perimeter column. In this paper, a new simplified model of this structure is studied by considering the damping force and shear stiffness of the core tube and lateral stiffness of the frame with finite element method. The shear correction factor is also employed to consider the shape of the core tube cross section. The numerical example shows that the displacement and the inter-story drift of the structure are reduced effectively under earthquake loads. It means that the damped outrigger is an innovative solution to resisting earthquake loads for frame-core tube structure. ? (2011) Trans Tech Publications

Study on Mechanical Properties and Deformation Mechanism of TWIP Stainless Steel

In this study, based on the sensitivity of the chemical composition fluctuation to the thermodynamic parameter, which controls the level of the stacking fault energy (SFE), a series of high Cr–Mn–N twinning-induced plasticity (TWIP) stainless steels are designed by using a sublattice model, and their mechanical properties and micro deformation mechanism are analyzed The formation of mechanical twins (Mts) during the deformation makes the test steel show a perfect combination of strength and ductility after different solution treatments. Among them, after a solution treatment at 950 °C and 1050 °C, the 19Cr–0.7N and 19CrSi–0.7N samples have the maximum value with the product of the strength and plasticity reaching 60.7% and 64.6%, and 12Cr–CN has the maximum value after the solution treatment at 1200 °C, reaching 81.3%. The SFE values of the 19Cr–0.7N and 19CrSi–0.7N samples were relatively high, 48 mJ·m−2 and 45 mJ·m−2, respectively. The SFE of 12Cr–CN is 37 mJ·m−2, and the Mts grow rapidly during the deformation and maintain the highest twinning density under the same strain conditions. The characterization of the tensile samples occurs under different deformations by electron backscattered diffraction (EBSD) and transmission electron microscope (TEM). The results of the EBSD local misorientation difference angle analysis showed the Silicon element addition with a good Mts saturation rate. It is observed from the TEM that the nucleation process of the Mts with a high SFE is difficult, and the Mts emit and grow inward along the grain boundary during the tensile process and present a cross shape with the increase in strain. The contribution of the grain boundary strengthening (σ0), dislocation strengthening (σf), and twinning strengthening effect (σt) under dynamic micro-refinement to stress were calculated. It is known that under a certain amount of strain, the ratio of σt and σf changes with increasing, and when the contribution of the twinning deformation to the stress exceeds about 25%, the reinforcement of the plastic deformation is dominated by the plane of σf

Wide Bandgap Semiconductors for Ultraviolet Photodetectors: Approaches, Applications, and Prospects

Ultraviolet (UV) light, invisible to the human eye, possesses both benefits and risks. To harness its potential, UV photodetectors (PDs) have been engineered. These devices can convert UV photons into detectable signals, such as electrical impulses or visible light, enabling their application in diverse fields like environmental monitoring, healthcare, and aerospace. Wide bandgap semiconductors, with their high-efficiency UV light absorption and stable opto-electronic properties, stand out as ideal materials for UV PDs. This review comprehensively summarizes recent advancements in both traditional and emerging wide bandgap-based UV PDs, highlighting their roles in UV imaging, communication, and alarming. Moreover, it examines methods employed to enhance UV PD performance, delving into the advantages, challenges, and future research prospects in this area. By doing so, this review aims to spark innovation and guide the future development and application of UV PDs

A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks

Although many prior efforts found that road networks significantly affect landscape fragmentation, the spatially heterogeneous effects of road networks on urban ecoenvironments remain poorly understood. A new remote-sensing-based ecological index (RSEI) is proposed to calculate the ecoenvironmental quality, and a local model (geographically weighted regression, GWR) was applied to explore the spatial variations in the relationship between kernel density of roads (KDR) and ecoenvironmental quality and understand the coupling mechanism of road networks and ecoenvironments. The average effect of KDR on the variables of normalized difference vegetation index (NDVI), land surface moisture (LSM), and RSEI was negative, while it was positively associated with the soil index (SI), normalized differential build-up and bare soil index (NDBSI), index-based built-up index (IBI), and land surface temperature (LST). This study shows that rivers and the landscape pattern along rivers exacerbate the impact of road networks on urban ecoenvironments. Moreover, spatial variation in the relationship between road network and ecoenvironment is mainly controlled by the relationship of the road network with vegetation and bare soil. This research can help in better understanding the diversified relationships between road networks and ecoenvironments and offers guidance for urban planners to avoid or mitigate the negative impacts of roads on urban ecoenvironments

A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks

Although many prior efforts found that road networks significantly affect landscape fragmentation, the spatially heterogeneous effects of road networks on urban ecoenvironments remain poorly understood. A new remote-sensing-based ecological index (RSEI) is proposed to calculate the ecoenvironmental quality, and a local model (geographically weighted regression, GWR) was applied to explore the spatial variations in the relationship between kernel density of roads (KDR) and ecoenvironmental quality and understand the coupling mechanism of road networks and ecoenvironments. The average effect of KDR on the variables of normalized difference vegetation index (NDVI), land surface moisture (LSM), and RSEI was negative, while it was positively associated with the soil index (SI), normalized differential build-up and bare soil index (NDBSI), index-based built-up index (IBI), and land surface temperature (LST). This study shows that rivers and the landscape pattern along rivers exacerbate the impact of road networks on urban ecoenvironments. Moreover, spatial variation in the relationship between road network and ecoenvironment is mainly controlled by the relationship of the road network with vegetation and bare soil. This research can help in better understanding the diversified relationships between road networks and ecoenvironments and offers guidance for urban planners to avoid or mitigate the negative impacts of roads on urban ecoenvironments

Small Extracellular Vesicles Secreted by iPSC-Derived MSCs Ameliorate Pulmonary Inflammation and Lung Injury Induced by Sepsis through Delivery of miR-125b-5p

Background. Sepsis-induced acute lung injury is a common critical illness in intensive care units with no effective treatment is currently available. Small extracellular vesicles, secreted by mesenchymal stem cells (MSCs), derived from human-induced pluripotent stem cells (iMSC-sEV), possess striking advantages when incorporated MSCs and iPSCs, which are considered extremely promising cell-free therapeutic agents. However, no studies have yet been conducted to systemically examine the effects and underlying mechanisms of iMSC-sEV application on attenuated lung injury under sepsis conditions. Method. iMSC-sEV were intraperitoneally administered in a rat septic lung injury model induced by cecal ligation and puncture (CLP). The efficacy of iMSC-sEV was assessed by histology, immunohistochemistry, and pro-inflammatory cytokines of bronchoalveolar lavage fluid. We also evaluated the in vitro effects of iMSC-sEV on the activation of the inflammatory response in alveolar macrophages (AMs). Small RNA sequencing was utilized to detect changes in the miRNA expression profile in lipopolysaccharide (LPS)-treated AMs after iMSC-sEV administration. The effects of miR-125b-5p on the function of AMs were studied. Results. iMSC-sEV were able to attenuate pulmonary inflammation and lung injury following CLP-induced lung injury. iMSC-sEV were internalized by AMs and alleviated the release of inflammatory factors by inactivating the NF-κB signaling pathway. Moreover, miR-125b-5p showed a fold-change in LPS-treated AMs after iMSC-sEV administration and was enriched in iMSC-sEV. Mechanistically, iMSC-sEV transmitted miR-125b-5p into LPS-treated AMs to target TRAF6. Conclusion. Our findings demonstrated that iMSC-sEV treatment protects against septic lung injury and exerts anti-inflammatory effects on AMs at least partially through miR-125b-5p, suggesting that iMSC-sEV may provide a novel cell-free strategy for the treatment of septic lung injury