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Solving semiparametric models can be computationally challenging because the dimension of parameter space may grow large with increasing sample size. Classical Newton's method becomes quite slow and unstable with an intensive calculation of the large Hessian matrix and its inverse. Iterative methods separately updating parameters for the finite dimensional component and the infinite dimensional component have been developed to speed up single iterations, but they often take more steps until convergence or even sometimes sacrifice estimation precision due to sub-optimal update direction. We propose a computationally efficient implicit profiling algorithm that achieves simultaneously the fast iteration step in iterative methods and the optimal update direction in Newton's method by profiling out the infinite dimensional component as the function of the finite-dimensional component. We devise a first-order approximation when the profiling function has no explicit analytical form. We show that our implicit profiling method always solves any local quadratic programming problem in two steps. In two numerical experiments under semiparametric transformation models and GARCH-M models, as well as a real application using NTP data, we demonstrated the computational efficiency and statistical precision of our implicit profiling method. Finally, we implement the proposed implicit profiling method in the R package SemiEstimate [ABSTRACT FROM AUTHOR]

Accurate and timely assessment of post-disaster building damage is of great significance for national development and social security concerns. However, due to the high timeliness requirements of disaster emergency response and the conflict that sufficient computing resources are not easily available in harsh environments, and therefore the lightweight AI-driven post-disaster building damage assessment model is highly needed. In this paper, we introduced a knowledge distillation-based lightweight approach for assessing building damage from xBD high-resolution satellite images with the purpose of reducing the dependence on computing resources in disaster emergency response scenarios. Specifically, an ensemble Teacher-Student knowledge distillation method was designed and compared with the xBD baseline model. The result has shown that, the knowledge distillation reduces the parameter number of the original model by 30%, and the inference speed is increased by 30%-40%. In the building localization task, the accuracy of teacher and student model are 0.879 and 0.832 (IOU) respectively. In the damage classification task, the accuracy of teacher and student are 0.798 and 0.775 respectively. In addition, we proposed a dual-teacher-student knowledge distillation strategy, which cannot use the pre-training skills of curriculum learning in student model training, but achieve the same effect through more direct knowledge transfer. In the experiment, our dual-teacher-student method improves the knowledge distillation baseline by 3.7% with 30 epoch training. With only 70% parameters, our student model performs close to the teacher model at a degradation within 5%.This study verifies the effectiveness and prospect of knowledge distillation method in building damage assessment for disaster emergency. [ABSTRACT FROM AUTHOR]

A self-developed Ti-Al-V-Mo system titanium alloy flux-cored wire was used to join the Ti64 titanium alloy plate via laser welding with filler wire. The microstructure and properties of the obtained welded joint were investigated. The results showed the WM (welded metal) of the welded joint consisted mainly of acicular α' martensite, and the HAZ (heat affected zone) was comprised of a primary αp phase, Widmanstatten, and a few α' martensite and a residual β phase. The strength and elongation of the welded joint after breaking are equivalent to that of the BM (base metal). The tensile fracture, presenting as a microvoid coalescence ductile fracture, was encompassed by massive shear lips with deep and uniform dimples. The overall microhardness of the welded joint was sequenced as WM > HAZ > BM. In the WM, large-angle grain boundaries with intragranular misorientation greater than 15° accounted for about 84%. By XRD, it was discovered the welded joint was mainly composed of the α' martensite, with a modest amount of extremely weak multi-angle α phase diffraction peak. The test results showed the designed welding method of titanium flux-cored wire and laser wire filling is suitable for high-quality welding of titanium alloy plate. [ABSTRACT FROM AUTHOR]

The recently identified decoy receptor 3 (DcR3) inhibits FasL-induced apoptosis by binding to FasL, and it is considered to play a key role in the immune escape system of neoplastic cells. In order to examine the involvement of DcR3 in the immunologic tolerance of hepatocellular carcinoma (HCC), we investigated the amplification and expression of DcR3, FasL, and Fas in an HCC mice model using RT-PCR, western blotting, and ELISA, and analyzed the space-time relationship with various cytokines including the forkhead transcription factor forkhead/winged helix transcription factor gene (Foxp3), CTLA-4, TGF-β, IL-10, TNF-α, and IFN-γ. The RT-PCR results revealed that Fas expression preceded that of DcR3 during the early phases of tumorigenesis. Thereafter, the expression of DcR3 was up-regulated; however, the expression of Fas was down-regulated and eventually ceased. DcR3 and FasL were expressed and amplified simultaneously in muscle tumor. CTLA-4 expression was earlier than Foxp3, and both CTLA-4 and Foxp3 amplification and expression were consistent with that of DcR3. The results suggest that the elevated levels of DcR3, Foxp3, and CTLA-4 in tissue were positively correlated with tumor growth. The partial tumor immunoregulation inclined to negative modulation, and DcR3 may play an important role in inducing immunologic tolerance. [ABSTRACT FROM AUTHOR]

Most mainstream research on assessing building damage using satellite imagery is based on scattered datasets and lacks unified standards and methods to quantify and compare the performance of different models. To mitigate these problems, the present study develops a novel end-to-end benchmark model, termed the pyramid pooling module semi-Siamese network (PPM-SSNet), based on a large-scale xBD satellite imagery dataset. The high precision of the proposed model is achieved by adding residual blocks with dilated convolution and squeeze-and-excitation blocks into the network. Simultaneously, the highly automated process of satellite imagery input and damage classification result output is reached by employing concurrent learned attention mechanisms through a semi-Siamese network for end-to-end input and output purposes. Our proposed method achieves F1 scores of 0.90, 0.41, 0.65, and 0.70 for the undamaged, minor-damaged, major-damaged, and destroyed building classes, respectively. From the perspective of end-to-end methods, the ablation experiments and comparative analysis confirm the effectiveness and originality of the proposed method. Finally, the consistent prediction results of our model for data from the 2011 Great East Japan Earthquake verify the high performance of our model in terms of the domain shift problem, which implies that it is effective for evaluating future disasters. [ABSTRACT FROM AUTHOR]

Earth Observation satellite imaging helps building diagnosis during a disaster. Several models are put forward on the xBD dataset, which can be divided into two levels: the building level and the pixel level. Models from two levels evolve into several versions that will be reviewed in this paper. There are four key challenges hindering researchers from moving forward on this task, and this paper tries to give technical solutions. First, metrics on different levels could not be compared directly. We put forward a fairer metric and give a method to convert between metrics of two levels. Secondly, drone images may be another important source, but drone data may have only a post-disaster image. This paper shows and compares methods of directly detecting and generating. Thirdly, the class imbalance is a typical feature of the xBD dataset and leads to a bad F1 score for minor damage and major damage. This paper provides four specific data resampling strategies, which are Main-Label Over-Sampling (MLOS), Discrimination After Cropping (DAC), Dilation of Area with Minority (DAM) and Synthetic Minority Over-Sampling Technique (SMOTE), as well as cost-sensitive re-weighting schemes. Fourthly, faster prediction meets the need for a real-time situation. This paper recommends three specific methods, feature-map subtraction, parameter sharing, and knowledge distillation. Finally, we developed our AI-driven Damage Diagnose Platform (ADDP). This paper introduces the structure of ADDP and technical details. Customized settings, interface preview, and upload and download satellite images are major services our platform provides. [ABSTRACT FROM AUTHOR]