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Aging is a problem that has to be faced in the process of social progress. How to protect people’s health through appropriate medical insurance composition is an important factor in the reform of China’s medical insurance system. Germany’s medical insurance has a special division system, especially the support for old-age care reflects the adequacy and systematization of social security. Through the exploration of the German division system, we can provide some favorable suggestions for China’s medical system development and pension model reform in line with its national conditions.

All the data are named with the figure number, where it is used in the manuscript.

Through the analysis of the construction of the Heidelberg Center for Transcultural Studies of Heidelberg University, this paper investigates how this innovative collaborative research institute assembles different faculties as well as international scholars and students from different disciplines and promotes interdisciplinary cooperation and further development of “interdisciplinary clusters” within universities on the micro-level, the experience of HCTS was introduced in terms of diversified international enrollment models, scholar teams of diversified academic backgrounds and multi/interdisciplinary course structures, the creation of interdisciplinary “academic communities”, overseas research and the construction of its funding guarantee system and digital research platform. This paper aims to provide a reference for the development path of “Country and Areas Studies” in China.

An interesting generalization of list coloring is so called DP-coloring (named after Dvo\v{r}\'ak and Postle). We study $(i,j)$-defective DP-colorings of simple graphs. Define $g_{DP}(i,j,n)$ to be the minimum number of edges in an $n$-vertex DP-$(i,j)$-critical graph. We prove sharp bounds on $g_{DP}(i,j,n)$ for $i=1,2$ and $j\geq 2i$ for infinitely many $n$., Comment: 19 pages, 1 figure. arXiv admin note: text overlap with arXiv:2006.10244

Being a powerful tool for linear time-invariant (LTI) systems, system response analysis can also be applied to the so-called linear space-invariant (LSI) but time-varying systems, which is a dual of the conventional LTI problems. In this paper, we propose a system response analysis method for LSI problems by conducting Fourier transform of the field distribution on the space instead of time coordinate. Specifically, input and output signals can be expressed in the wavenumber (spatial frequency) domain. In this way, the system function in wavenumber domain can also be obtained for LSI systems. Given an arbitrary input and temporal profile of the medium, the output can be easily predicted using the system function. Moreover, for a complex temporal system, the proposed method allows for decomposing it into multiple simpler subsystems that appear in sequence in time. The system function of the whole system can be efficiently calculated by multiplying those of the individual subsystems., This article has been withdrawn due to an unresolvable internal author dispute

Additional file3. Table S1: Six immune checkpoint inhibitor genes and 15 m6A regulatory genes.

Additional file 3: Table S1. Primers used for qPCR analysis and siRNA synthesis of CPR63.

Additional file1. Fig S1: The flow chart of this study. HCC, hepatocellular carcinoma. TMB, tumor mutation burden. TME, tumor microenvironment. GSVA, gene set variation analysis. GO, Gene Ontology. KEGG, Kyoto Encyclopedia.

Additional file 1.

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Deep neural networks (DNNs) have become a key part of many modern software applications. After training and validating, the DNN is deployed as an irrevocable component and applied in real-world scenarios. Although most DNNs are built meticulously with huge volumes of training data, data in the real world still remain unknown to the DNN model, which leads to the crucial requirement of runtime out-of-distribution (OOD) detection. However, many existing approaches 1) need OOD data for classifier training or parameter tuning, or 2) simply combine the scores of each hidden layer as an ensemble of features for OOD detection. In this paper, we present a novel outlook on in-distribution data in a generative manner, which takes their latent features generated from each hidden layer as a joint distribution across representation spaces. Since only the in-distribution latent features are comprehensively understood in representation space, the internal difference between in-distribution and OOD data can be naturally revealed without the intervention of any OOD data. Specifically, We construct a generative model, called Latent Sequential Gaussian Mixture (LSGM), to depict how the in-distribution latent features are generated in terms of the trace of DNN inference across representation spaces. We first construct the Gaussian Mixture Model (GMM) based on in-distribution latent features for each hidden layer, and then connect GMMs via the transition probabilities of the inference traces. Experimental evaluations on popular benchmark OOD datasets and models validate the superiority of the proposed method over the state-of-the-art methods in OOD detection., Comment: Submitted to a conference of computer vision

Generating diverse and natural human motion is one of the long-standing goals for creating intelligent characters in the animated world. In this paper, we propose a self-supervised method for generating long-range, diverse and plausible behaviors to achieve a specific goal location. Our proposed method learns to model the motion of human by decomposing a long-range generation task in a hierarchical manner. Given the starting and ending states, a memory bank is used to retrieve motion references as source material for short-range clip generation. We first propose to explicitly disentangle the provided motion material into style and content counterparts via bi-linear transformation modelling, where diverse synthesis is achieved by free-form combination of these two components. The short-range clips are then connected to form a long-range motion sequence. Without ground truth annotation, we propose a parameterized bi-directional interpolation scheme to guarantee the physical validity and visual naturalness of generated results. On large-scale skeleton dataset, we show that the proposed method is able to synthesise long-range, diverse and plausible motion, which is also generalizable to unseen motion data during testing. Moreover, we demonstrate the generated sequences are useful as subgoals for actual physical execution in the animated world., ECCV 2020, Project Page:\

Follow-up serves an important role in the management of pulmonary nodules for lung cancer. Imaging diagnostic guidelines with expert consensus have been made to help radiologists make clinical decision for each patient. However, tumor growth is such a complicated process that it is difficult to stratify high-risk nodules from low-risk ones based on morphologic characteristics. On the other hand, recent deep learning studies using convolutional neural networks (CNNs) to predict the malignancy score of nodules, only provides clinicians with black-box predictions. To this end, we propose a unified framework, named Nodule Follow-Up Prediction Network (NoFoNet), which predicts the growth of pulmonary nodules with high-quality visual appearances and accurate quantitative results, given any time interval from baseline observations. It is achieved by predicting future displacement field of each voxel with a WarpNet. A TextureNet is further developed to refine textural details of WarpNet outputs. We also introduce techniques including Temporal Encoding Module and Warp Segmentation Loss to encourage time-aware and shape-aware representation learning. We build an in-house follow-up dataset from two medical centers to validate the effectiveness of the proposed method. NoFoNet significantly outperforms direct prediction by a U-Net in terms of visual quality; more importantly, it demonstrates accurate differentiating performance between high- and low-risk nodules. Our promising results suggest the potentials in computer aided intervention for lung nodule management., MICCAI 2020

In video prediction tasks, one major challenge is to capture the multi-modal nature of future contents and dynamics. In this work, we propose a simple yet effective framework that can efficiently predict plausible future states. The key insight is that the potential distribution of a sequence could be approximated with analogous ones in a repertoire of training pool, namely, expert examples. By further incorporating a novel optimization scheme into the training procedure, plausible predictions can be sampled efficiently from distribution constructed from the retrieved examples. Meanwhile, our method could be seamlessly integrated with existing stochastic predictive models; significant enhancement is observed with comprehensive experiments in both quantitative and qualitative aspects. We also demonstrate the generalization ability to predict the motion of unseen class, i.e., without access to corresponding data during training phase., Comment: Project Page: https://sites.google.com/view/vpeg-supp/home

The glycoprotein uromodulin (UMOD) is the most abundant protein in human urine and forms filamentous homopolymers that encapsulate and aggregate uropathogens, promoting pathogen clearance by urine excretion. Despite its critical role in the innate immune response against urinary tract infections, the structural basis and mechanism of UMOD polymerization remained unknown. Here, we present the cryo-EM structure of the UMOD filament core at 3.5 Å resolution, comprised of the bipartite zona pellucida (ZP) module in a helical arrangement with a rise of ~65 Å and a twist of ~180°. The immunoglobulin-like ZPN and ZPC subdomains of each monomer are separated by a long linker that interacts with the preceding ZPC and following ZPN subdomains by β-sheet complementation. The unique filament architecture suggests an assembly mechanism in which subunit incorporation could be synchronized with proteolytic cleavage of the C-terminal pro-peptide that anchors assembly-incompetent UMOD precursors to the membrane. ISSN:2050-084X

This technical report presents a solution for the 2020 Traffic4Cast Challenge. We consider the traffic forecasting problem as a future frame prediction task with relatively weak temporal dependencies (might be due to stochastic urban traffic dynamics) and strong prior knowledge, \textit{i.e.}, the roadmaps of the cities. For these reasons, we use the U-Net as the backbone model, and we propose a roadmap generation method to make the predicted traffic flows more rational. Meanwhile, we use a fine-tuning strategy based on the validation set to prevent overfitting, which effectively improves the prediction results. At the end of this report, we further discuss several approaches that we have considered or could be explored in future work: (1) harnessing inherent data patterns, such as seasonality; (2) distilling and transferring common knowledge between different cities. We also analyze the validity of the evaluation metric., Comment: Code is available at \

This paper addresses a fundamental challenge in 3D medical image processing: how to deal with imaging thickness. For anisotropic medical volumes, there is a significant performance gap between thin-slice (mostly 1mm) and thick-slice (mostly 5mm) volumes. Prior arts tend to use 3D approaches for the thin-slice and 2D approaches for the thick-slice, respectively. We aim at a unified approach for both thin- and thick-slice medical volumes. Inspired by recent advances in video analysis, we propose AlignShift, a novel parameter-free operator to convert theoretically any 2D pretrained network into thickness-aware 3D network. Remarkably, the converted networks behave like 3D for the thin-slice, nevertheless degenerate to 2D for the thick-slice adaptively. The unified thickness-aware representation learning is achieved by shifting and fusing aligned "virtual slices" as per the input imaging thickness. Extensive experiments on public large-scale DeepLesion benchmark, consisting of 32K lesions for universal lesion detection, validate the effectiveness of our method, which outperforms previous state of the art by considerable margins without whistles and bells. More importantly, to our knowledge, this is the first method that bridges the performance gap between thin- and thick-slice volumes by a unified framework. To improve research reproducibility, our code in PyTorch is open source at https://github.com/M3DV/AlignShift., Comment: MICCAI 2020 (early accepted). Camera ready version. Code is available at https://github.com/M3DV/AlignShift

The carrier frequencies of array elements in a Frequency Diverse Array (FDA) radar are slightly distinguished, leading to a range-angle-time-dependent transmit beampattern. Thus, an FDA radar carries additional information in a certain range and provides more flexibility in signal processing and new technical issues. FDA is covered by scope of the general waveform diversity concept. This paper overviews the state-ofthe-art FDA technology and its radar applications. From the viewpoint of the general radar system theory, we mainly introduce the coherent FDA and orthogonal FDA frameworks. The orthogonal FDA is also referred to as Multiple-Input Multiple-Output (MIMO) radar using FDA or FDA-MIMO radar. Key applications in antijamming and issues related with range ambiguity are addressed. We also outline the challenges in FDA radar applications and several interesting research topics.

The ground clutter of sidelooking Multiple-Input Multiple-Output (MIMO) bistatic radar has severe problems of range-dependence and clutter spectrum spreading. In this paper, the clutter characteristics of bistatic airborne radars are analyzed and a method of range-ambiguous clutter suppression is proposed. This method employs the range-dependent phase term in the traditional MIMO bistatic radar to provide extra degrees of freedom. The range information of the phase term enables clutter separation with different ranges and consequent suppression. Thus, a range-ambiguous clutter suppression for bistatic airborne early warning radars is achieved. The simulation results show the accuracy of the method. By a comparison with the existing Doppler Warping (DW) and clutter suppression method with the Improved Factor (IF) line, the results showed that the method has better performance, which proved the effectiveness of ambiguous clutter suppression in MIMO bistatic radars.

This work make the first attempt to generate articulated human motion sequence from a single image. On the one hand, we utilize paired inputs including human skeleton information as motion embedding and a single human image as appearance reference, to generate novel motion frames, based on the conditional GAN infrastructure. On the other hand, a triplet loss is employed to pursue appearance-smoothness between consecutive frames. As the proposed framework is capable of jointly exploiting the image appearance space and articulated/kinematic motion space, it generates realistic articulated motion sequence, in contrast to most previous video generation methods which yield blurred motion effects. We test our model on two human action datasets including KTH and Human3.6M, and the proposed framework generates very promising results on both datasets., ACM MM 2017

Space-time coding radar has been recently proposed and investigated. It is a radar framework which can perform transmit beamforming at the receiver. However, the range resolution decreases when the number of the transmit element increases. A subarray-based space-time coding (sub-STC) radar is explored to alleviate the range resolution reduction. For the proposed radar configuration, an identical waveform is transmitted and it introduces a small time offset in different subarrays. The multidimensional ambiguity function of sub-STC radar is defined by considering resolutions in multiple domains including the range, Doppler, angle and probing direction. Analyses on properties of the multi-dimensional ambiguity function of the sub-STC radar with regard to the spatial coverage, resolution performance and low sidelobes are also given. Results reveal that the range resolution and low sidelobes performance are improved with the proposed approach.

Pinus thunbergii is one of the main tree species of the coastal forest in Shandong peninsula,China,and plays important roles in resisting natural perturbation and maintaining its ecological functions.Adapted to the sandy ocean coast habitats and the windy conditions,the species has developed shrubby crown due to compensatory response,an important mechanism to adapt the stress environments,induced by the insect boring wounds through still unknown mechanisms.For the purpose of sustainable management of P.thunbergii forests,this study elucidated the mechanism of compensatory response and its role in the formation process of crown architecture by comparing growth characteristics of the pre-and post-boring treatments.We randomly sampled 20 trees from the coastal pine forests for monitoring the morphological changes of buds and the crown dimension during March 2009 and May 2010,and identified the shoots into three age categories according to the morphological responses to the stressing perturbation.The results showed that 1) the current and next year shoots clearly increased by 1.56 and 1.4 times after the boring,while the treatment effect on the current shoots was higher than that of the next year.It indicated that the compensatory response was persistent but gradually decreased.The compensatory response of leaf module showed similar trend but was statistically insignificant among shoots of different ages.2) A close relationship between the compensatory growth and the position of one year shoots was found,and the branch length and leaf module near the top was increased 1.75 and 1.43 times in average,respectively,after the boring,but the compensatory growth near the bottom was not tested significant.3) The process of adapted architecture begun with the change of bud module quantity and forms.The mortality of bud increased by 5.4 times which could be explained by density effect,and the number of vegetative bud increase after boring.The production of vegetative bud increased by 1.55 times on average,but the production of generative bud had no significant difference.These results suggested that the boring induced compensatory response stimulates invest in vegetative growth.4) The shoot leaders stopped growing after the boring and the number of bifurcation was increased resulting in a 25% improvement in the box-counting dimension(Db) of shoot.There was a post-peak functional relationship between the crown dimension and damage rate of shoot: the crown dimension reached the maximum while the damage rate was also increased to about 70%.This compensatory response might enhance the competitive ability of the species for space.5) The total shoot and leaf biomass did not have significant differences between pre-and post treatment indicating an equal-compensatory growth.This study revealed that apical dominance removal and the consequent compensatory responses to the boring wounds resulted in some drastic changes in crown architecture of Pinus thunbergii in the stressed coastal environments.The morphological changes of bud and their deviating normal development,as well as the crown dimension could be used as the indicators of this compensatory growth.Further studies should focus on the physiological mechanisms of the stress responses of the species.

Based on the research of the plots in coastal areas in Jiaonan city, Shandong Province, the indices of richness, evenness and diversity of different community types are analyzed. The results show that: The richness indices indicates that arbor layers are close to shrub layers, and both are lower than herbage layers; the ranges among communities vary a little except those of the arbor ones. Mostly the biodiversity indices reveal that herbage, the arbor, the shrub; Biodiversity of communities has great influences on microclimate to a certain extent and is under the control of regional environment.

RFID authentication plays a significant role in protecting the security and privacy of Internet of Things, whereas RFID tags' low resource feature limits its applications. We propose a low-cost mutual authentication protocol based on cryptoGPS scheme, with an efficient flexible key management scheme and a coupons injection protocol. A new low-cost multiplier is designed to save resource. The simulation by Synopsys Design Complier with Smic 0.25amu;m library, result shows that the tag just needs 3076 equivalent gates and the maximum time of single step is 7.35ms at 500kHz. It's clear that this protocol can meet the demand of RFID tag well.

Above all, we pointed out that the basis of the calibration algorithm based on the grid points is the matrix, which indicates the mapping relationship between the target surface and the image surface. But to our disappointment, it is the correspondence problem of the calibration points that exists in the current way to calculate it. Therefore, the fiducial marker system is used for reference here, which includes the definition of selection criteria of fiducial markers, analysis of their recognition rate from the environment, and the wrong recognition rate from each other. Next, a improved method is brought up including the extraction of the calibration points, the calculation of their image coordinates, and a group of related formulas. In the end, we consider our algorithm is very proper for indoor environments which can be controlled easily, and proved it with great efficiency and liability by the experiments as well.

With the rapid advances of wearable sensors and wireless networks. There is a growing research interest in building Body Area Sensor Network (BSN) systems to support applications such as human activity recognition and daily health monitoring. The main challenges of building a BSN system include: First, the wearable sensor nodes are highly constrained in resources; Second, the sensor nodes are prong to failures; Third, the applications requires the sensors to work with high sampling rate which results in the heavy load on each sensor node. Beside the technical challenges, the usability of the system is also a critical issue when deployed into the the real-world environment.We present in this paper the lessons we learnt while building a real-world BSN system which aims at supporting real-time human activity recognition. We analyzed the challenges and problems in depth and proposed possible solutions. The conclusions we made in this paper can serve as the guidelines for building a BSN system in the future.

From August 2008 to August 2009, the project ‘Off-Shore Wind Energy Resource Assessment and Feasibility Study of Off-Shore Wind Farm Development in China’ was carried out by China Meteorological Administration (CMA), which was funded by the EU-China Energy and Environment Programme (EEP). As one part of the project, off-shore wind energy resource in China was assessed with QuikSCAT Satellite data and SAR Satellite Images. In this paper, the results from these two ways were introduced.

This thesis proposes a theory-based workflow model. It reviews workflow management basic concepts and discusses workflow generic constructs. It uses BWW Ontology as theoretical foundation and introduces an ontological process model. It derives a set of construct mapping rules that map ontological constructs to workflow management constructs, by extending PMLBWWP construct mapping rules which lead to the ontological workflow model by incorporating the concept of a controller and ontological analysis of workflow management using BWW process modeling. The model focuses on tasks, events and exceptions. An ontological workflow model algorithm provides a procedure on how to transform a process model into a workflow model by investigating each task and each event following a recursive procedure. An example is used to illustrate the way in which the algorithm can be applied. Finally, the thesis examines the contributions and limitations of the research work, and provides suggestions for nature research.

The glycoprotein uromodulin (UMOD) is the most abundant protein in human urine and forms filamentous homopolymers that encapsulate and aggregate uropathogens, promoting pathogen clearance by urine excretion. Despite its critical role in the innate immune response against urinary tract infections, the structural basis and mechanism of UMOD polymerization remained unknown. Here, we present the cryo-EM structure of the UMOD filament core at 3.5 Å resolution, comprised of the bipartite zona pellucida (ZP) module in a helical arrangement with a rise of ~65 Å and a twist of ~180°. The immunoglobulin-like ZPN and ZPC subdomains of each monomer are separated by a long linker that interacts with the preceding ZPC and following ZPN subdomains by β-sheet complementation. The unique filament architecture suggests an assembly mechanism in which subunit incorporation could be synchronized with proteolytic cleavage of the C-terminal pro-peptide that anchors assembly-incompetent UMOD precursors to the membrane., eLife, 9, ISSN:2050-084X