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Zhengzhao Li,1,* Lianshan Guo,1,* Liqiao Lin,1 Tongting Wang,1 Yanqiu Jiang,1 Jin Song,1 Jihua Feng,1 Jianfeng Huang,2 Haoyu Li,2 Zhihao Bai,3 Wenqi Liu,4 Jianfeng Zhang1 1Department of Emergency, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, China; 2Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, China; 3College of Chemistry and Chemical Engineering, Guangxi University, Nanning, Guangxi, 530004, China; 4Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, China*These authors contributed equally to this workCorrespondence: Zhihao Bai, College of Chemistry and Chemical Engineering, Guangxi University, Nanning, Guangxi, 530004, China, Tel/Fax +86 771-3237584, Email zhihao_bai@163.com Jianfeng Zhang, Department of Emergency, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, China, Tel +86 771- 3277068, Fax +86 771- 3277277, Email zhangjianfeng@gxmu.edu.cnPurpose: Although the combined photo-thermal (PTT) and photodynamic therapy (PDT) of tumors have demonstrated promise as effective cancer therapy, the hypoxic and insufficient H2O2 supply of tumors seriously limits the efficacy of PDT, and the acidic environment reduces the catalytic activity of nanomaterial in the tumor microenvironment. To develop a platform for efficiently addressing these challenges, we constructed a nanomaterial of Aptamer@dox/GOD-MnO2-SiO2@HGNs-Fc@Ce6 (AMS) for combination tumor therapy. The treatment effects of AMS were evaluated both in vitro and in vivo.Methods: In this work, Ce6 and hemin were loaded on graphene (GO) through π-π conjugation, and Fc was connected to GO via amide bond. The HGNs-Fc@Ce6 was loaded into SiO2, and coated with dopamine. Then, MnO2 was modified on the SiO2. Finally, AS1411-aptamer@dox and GOD were fixed to gain AMS. We characterized the morphology, size, and zeta potential of AMS. The oxygen and reactive oxygen species (ROS) production properties of AMS were analyzed. The cytotoxicity of AMS was detected by MTT and calcein-AM/PI assays. The apoptosis of AMS to a tumor cell was estimated with a JC-1 probe, and the ROS level was detected with a 2’,7’-Dichlorodihydrofluorescein diacetate (DCFH-DA) probe. The anticancer efficacy in vivo was analyzed by the changes in the tumor size in different treatment groups.Results: AMS was targeted to the tumor cell and released doxorubicin. It decomposed glucose to produce H2O2 in the GOD-mediated reaction. The generated sufficient H2O2 was catalyzed by MnO2 and HGNs-Fc@Ce6 to produce O2 and free radicals (•OH), respectively. The increased oxygen content improved the hypoxic environment of the tumor and effectively reduced the resistance to PDT. The generated •OH enhanced the ROS treatment. Moreover, AMS depicted a good photo-thermal effect.Conclusion: The results revealed that AMS had an excellent enhanced therapy effect by combining synergistic PTT and PDT.Keywords: nanomaterial, photo-thermal therapy, photodynamic therapy, hypoxia

Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels. However, existing approaches usually adopt the models originally designed for other domains (e.g., computer vision) to encode the time series data and rely on strong assumptions to design learning objectives, which limits their ability to perform well. To deal with these problems, we propose a novel URL framework for multivariate time series by learning time-series-specific shapelet-based representation through a popular contrasting learning paradigm. To the best of our knowledge, this is the first work that explores the shapelet-based embedding in the unsupervised general-purpose representation learning. A unified shapelet-based encoder and a novel learning objective with multi-grained contrasting and multi-scale alignment are particularly designed to achieve our goal, and a data augmentation library is employed to improve the generalization. We conduct extensive experiments using tens of real-world datasets to assess the representation quality on many downstream tasks, including classification, clustering, and anomaly detection. The results demonstrate the superiority of our method against not only URL competitors, but also techniques specially designed for downstream tasks. Our code has been made publicly available at https://github.com/real2fish/CSL.

Seafloor observatories play a crucial role in acquiring continuous and precise submarine monitoring data, thereby holding significant implications for advancing major scientific advancements in marine science, particularly in the field of seafloor earthquake observation. This work mainly builds a dual-link observation system designed for observing seismic information on the seafloor based on a Zynq7000 system-on-chip and time synchronization module. The system is based on Zynq7000 SoC(MILIANKE; Changzhou, China) and AD7768(Analog Devices, Inc.; Norwood, MA, USA) to achieve eight-channel data (24 bit) synchronous acquisition, and the robustness of the system is improved by upgrading the link to full-duplex transmission and adding node data self-storage function. The P88 1588 PTP time synchronization single board(CoolShark; Beijing, China) is used to provide PPS (Pulse per second) signals for the system to realize microsecond timestamps to support subsequent seismic data inversion. An upper computer system based on the Qt framework is also developed to monitor the network condition in real time while visualizing the data transmission. For the acquisition of seismic signals, we employed triaxial seismic sensors. Additionally, a temperature and humidity monitoring module, along with an attitude detection module, was designed to enable real-time monitoring of the nodes. These modules not only facilitate the real-time monitoring of the nodes but also contribute to seismic data inversion. The experimental results indicate that the system provides a good synchronization of data acquisition, high accuracy, and reliability of inter-node transmission, which has good application prospects.

We examine how decentralized and simultaneous utility transfer promises affect collective decision-making by voting. Committee members with varying levels of support and opposition to an efficient reform can make enforceable promises before voting. An equilibrium requires stability and minimal total promises. Equilibria exist and are indeterminate, but do share several key characteristics. Equilibria result in enacting the reform and necessitate transfer promises from high to low-intensity members. When reform supporters lack sufficient voting power, promises must reach across the aisle. When reform supporters have enough voting power to enact the reform, promises must preclude reform opponents to entice the least enthusiastic reform supporters to become reform opponents. In that case, equilibrium promises do not need to reach across the aisle unless the weakest reform supporters have homogeneous intensities. We also discuss a finite sequence of promises that achieve an equilibrium that minimizes the ranges of intensities.

Introduction: The neurological impairment of survivors after ischemic stroke poses a serious risk to their quality of life and health. Effective therapeutic options are still lacking. Neural stem cells (NSCs) promote neurogenesis via secreted extracellular vesicles (NSC-EVs), which would be a potential therapeutic option, but the insufficient quantity of NSC-EVs in vivo restrains clinical application. Buyang Huanwu Decoction (BHD), a classic traditional Chinese medicine (TCM) decoction, is promising to alleviate neurological impairment after ischemic stroke. It was speculated that BHD might promote neurological recovery through the NSC-EVs.Methods: The medicated plasma of BHD (MP-BHD) was prepared to precondition NSCs and isolate EVs (BHD-NSC-EVs). Middle cerebral artery occlusion (MCAO) models and primary NSCs were administered to evaluate the therapeutic effect. Next-generation sequencing was performed to explore the mechanism.Results: The BHD-NSC-EVs more significantly accelerated neurological recovery after MCAO and promoted NSCs proliferation and differentiation than BHD and NSC-EVs alone. MP-BHD enhanced the largescale generation of BHD-NSC-EVs, which encapsulated functional miRNA and may play critical roles in neurogenesis.Discussion: In replacing BHD or NSCs, the preconditioned NSC-EVs present a more efficient therapeutic strategy for ischemic stroke. Based on the clinical efficacy of TCM, the preconditioning of NSC-derived EVs via the MP of TCM herbs would presents a newly promising therapeutic strategy for neurological diseases.

Although deep neural networks achieve tremendous success on various classification tasks, the generalization ability drops sheer when training datasets exhibit long-tailed distributions. One of the reasons is that the learned representations (i.e. features) from the imbalanced datasets are less effective than those from balanced datasets. Specifically, the learned representation under class-balanced distribution will present the Neural Collapse (NC) phenomena. NC indicates the features from the same category are close to each other and from different categories are maximally distant, showing an optimal linear separable state of classification. However, the pattern differs on imbalanced datasets and is partially responsible for the reduced performance of the model. In this work, we propose two explicit feature regularization terms to learn high-quality representation for class-imbalanced data. With the proposed regularization, NC phenomena will appear under the class-imbalanced distribution, and the generalization ability can be significantly improved. Our method is easily implemented, highly effective, and can be plugged into most existing methods. The extensive experimental results on widely-used benchmarks show the effectiveness of our method, accepted by AISTATS 2023

While substantial progresses have been made in automated 2D portrait stylization, admirable 3D portrait stylization from a single user photo remains to be an unresolved challenge. One primary obstacle here is the lack of high quality stylized 3D training data. In this paper, we propose a novel framework \emph{AgileGAN3D} that can produce 3D artistically appealing and personalized portraits with detailed geometry. New stylization can be obtained with just a few (around 20) unpaired 2D exemplars. We achieve this by first leveraging existing 2D stylization capabilities, \emph{style prior creation}, to produce a large amount of augmented 2D style exemplars. These augmented exemplars are generated with accurate camera pose labels, as well as paired real face images, which prove to be critical for the downstream 3D stylization task. Capitalizing on the recent advancement of 3D-aware GAN models, we perform \emph{guided transfer learning} on a pretrained 3D GAN generator to produce multi-view-consistent stylized renderings. In order to achieve 3D GAN inversion that can preserve subject's identity well, we incorporate \emph{multi-view consistency loss} in the training of our encoder. Our pipeline demonstrates strong capability in turning user photos into a diverse range of 3D artistic portraits. Both qualitative results and quantitative evaluations have been conducted to show the superior performance of our method. Code and pretrained models will be released for reproduction purpose.

For time inconsistent optimal control problems, a quite popular approach is the equilibrium approach, taken by the sophisticated agents. In this short note we construct a deterministic continuous time example where the unique equilibrium is dominated by another control. Therefore, in this situation it may not be wise to take the equilibrium strategy.

Recently, 3D object detection has attracted significant attention and achieved continuous improvement in real road scenarios. The environmental information is collected from a single sensor or multi-sensor fusion to detect interested objects. However, most of the current 3D object detection approaches focus on developing advanced network architectures to improve the detection precision of the object rather than considering the dynamic driving scenes, where data collected from sensors equipped in the vehicle contain various perturbation features. As a result, existing work cannot still tackle the perturbation issue. In order to solve this problem, we propose a group equivariant bird's eye view network (GeqBevNet) based on the group equivariant theory, which introduces the concept of group equivariant into the BEV fusion object detection network. The group equivariant network is embedded into the fused BEV feature map to facilitate the BEV-level rotational equivariant feature extraction, thus leading to lower average orientation error. In order to demonstrate the effectiveness of the GeqBevNet, the network is verified on the nuScenes validation dataset in which mAOE can be decreased to 0.325. Experimental results demonstrate that GeqBevNet can extract more rotational equivariant features in the 3D object detection of the actual road scene and improve the performance of object orientation prediction., Comment: 8 pages,3 figures

Searching for a universal trend by the same tuning method in different high-temperature superconductors with a similar crystal structure is a common strategy to find clues for a better understanding of the superconducting mechanism in a unified way. It is known that the hole-doped bismuth-oxide Ba1-xKxBiO3 possesses a similar perovskite structure to that of the hole-doped copper-oxide (cuprate) superconductors but also holds a comparatively high superconducting transition temperature. In this study, we report the first observation of the pressure-induced quantum phase transition (QPT) from superconducting to insulating states in a series of Ba1-xKxBiO3 single-crystal samples. A similar QPT has also been observed recently in the compressed cuprate superconductors1. Significantly, we found that the QPT observed in Ba1-xKxBiO3 is intriguingly associated with the valence change of the Bi ions in the material. These results lead us to propose that the pressure-induced valence change from Bi3+ to Bi5+ destroys the hole-doping effect on stabilizing the conductivity and corresponding superconductivity. By comparing the high-pressure behaviors observed in these two kinds of oxides, we identified another prominent feature shared by them - the more the hole-doping concentration, the higher the critical pressure required for driving the QPT., Comment: 20 pages and 4 figures

Lung cancer is a leading cause of death worldwide and early screening is critical for improving survival outcomes. In clinical practice, the contextual structure of nodules and the accumulated experience of radiologists are the two core elements related to the accuracy of identification of benign and malignant nodules. Contextual information provides comprehensive information about nodules such as location, shape, and peripheral vessels, and experienced radiologists can search for clues from previous cases as a reference to enrich the basis of decision-making. In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules. The context parsing module first segments the context structure of nodules and then aggregates contextual information for a more comprehensive understanding of the nodule. The prototype recalling module utilizes prototype-based learning to condense previously learned cases as prototypes for comparative analysis, which is updated online in a momentum way during training. Building on the two modules, our method leverages both the intrinsic characteristics of the nodules and the external knowledge accumulated from other nodules to achieve a sound diagnosis. To meet the needs of both low-dose and noncontrast screening, we collect a large-scale dataset of 12,852 and 4,029 nodules from low-dose and noncontrast CTs respectively, each with pathology- or follow-up-confirmed labels. Experiments on several datasets demonstrate that our method achieves advanced screening performance on both low-dose and noncontrast scenarios., Comment: MICCAI 2023

In this study, ultrafine WC@10Co powders were used for the fabrication of coarse-grained WC-10Co with almost full densification. The incorporation of WC@10Co resulted in a decreasing size of WC grain with a rounded shape, the mean free path of Co and the WC contiguity. Further, substantial Co with face-centered cubic structure dispersed uniformly in the cemented carbides with WC@10Co-contained intergranular fracture accompanied by a large number of plastic deformation tears of the Co phase at the fracture surface and crack bridges at the crack tip. Therefore, the hardness, fracture toughness and bending strength of cemented carbides with WC@10Co reached the highest levels of 1045.3 kgf m−2, 23.6 MPa·m1/2 and 2308 MPa, respectively. The ultrafine WC@10Co contracted size distribution of WC grain with a rounded shape.The uniformly dispersed Co with a high content of FCC structure was obtained.The as-obtained coarse-grained cemented carbides showed enhanced mechanical properties The ultrafine WC@10Co contracted size distribution of WC grain with a rounded shape. The uniformly dispersed Co with a high content of FCC structure was obtained. The as-obtained coarse-grained cemented carbides showed enhanced mechanical properties

Quasi-one-dimensional systems are fascinating as they can exhibit very rich and interesting physics. The spin chain compound Ba$_6$Cr$_2$S$_{10}$ has been synthesised experimentally under extreme conditions recently, which has shown interesting magnetic and toroidal properties due to dimerisation. Here we have performed first principles calculations to compute the electronic structure and magnetic properties of Ba$_6$Cr$_2$S$_{10}$, which are consistent with the experimental results for the magnetic structure and properties shown in [Zhang, et al, Adv. Mat. 34 (12), 2106728 (2022)]. Moreover, based on our calculations, we can find more interesting physics, including (i) the small size of the Hubbard $U$ parameter that implies the screening effect of surrounding Ba atoms, (ii) the dimerisation of Cr atoms mainly induced by the sulfur ligands, and (iii) the next-nearest-neighbouring anti-ferromagnetic interaction along the spin chain, which could bring forward spin frustration, thus spin liquid., Comment: 5 pages, 3 figures, author list corrected

We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses. To achieve such high level of disentangled control, we first explicitly define a novel semantic signed distance function (SDF) around a head geometry (FLAME) conditioned on the control parameters. This semantic SDF allows us to build a differentiable volumetric correspondence map from the observation space to a disentangled canonical space from all the control parameters. We then leverage the 3D-aware GAN framework (EG3D) to synthesize detailed shape and appearance of 3D full heads in the canonical space, followed by a volume rendering step guided by the volumetric correspondence map to output into the observation space. To ensure the control accuracy on the synthesized head shapes and expressions, we introduce a geometry prior loss to conform to head SDF and a control loss to conform to the expression code. Further, we enhance the temporal realism with dynamic details conditioned upon varying expressions and joint poses. Our model can synthesize more preferable identity-preserved 3D heads with compelling dynamic details compared to the state-of-the-art methods both qualitatively and quantitatively. We also provide an ablation study to justify many of our system design choices.

In an extended mean field game the vector field governing the flow of the population can be different from that of the individual player at some mean field equilibrium. This new class strictly includes the standard mean field games. It is well known that, without any monotonicity conditions, mean field games typically contain multiple mean field equilibria and the wellposedness of their corresponding master equations fails. In this paper, a partial order for the set of probability measure flows is proposed to compare different mean field equilibria. The minimal and maximal mean field equilibria under this partial order are constructed and satisfy the flow property. The corresponding value functions, however, are in general discontinuous. We thus introduce a notion of weak-viscosity solutions for the master equation and verify that the value functions are indeed weak-viscosity solutions. Moreover, a comparison principle for weak-viscosity semi-solutions is established and thus these two value functions serve as the minimal and maximal weak-viscosity solutions in appropriate sense. In particular, when these two value functions coincide, the value function becomes the unique weak-viscosity solution to the master equation. The novelties of the work persist even when restricted to the standard mean field games., Comment: 29 pages

Automatic parsing of human anatomies at instance-level from 3D computed tomography (CT) scans is a prerequisite step for many clinical applications. The presence of pathologies, broken structures or limited field-of-view (FOV) all can make anatomy parsing algorithms vulnerable. In this work, we explore how to exploit and conduct the prosperous detection-then-segmentation paradigm in 3D medical data, and propose a steerable, robust, and efficient computing framework for detection, identification, and segmentation of anatomies in CT scans. Considering complicated shapes, sizes and orientations of anatomies, without lose of generality, we present the nine degrees-of-freedom (9-DoF) pose estimation solution in full 3D space using a novel single-stage, non-hierarchical forward representation. Our whole framework is executed in a steerable manner where any anatomy of interest can be directly retrieved to further boost the inference efficiency. We have validated the proposed method on three medical imaging parsing tasks of ribs, spine, and abdominal organs. For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs. Extensive experiments on 9-DoF box detection and rib instance segmentation demonstrate the effectiveness of our framework (with the identification rate of 97.0% and the segmentation Dice score of 90.9%) in high efficiency, compared favorably against several strong baselines (e.g., CenterNet, FCOS, and nnU-Net). For spine identification and segmentation, our method achieves a new state-of-the-art result on the public CTSpine1K dataset. Last, we report highly competitive results in multi-organ segmentation at FLARE22 competition. Our annotations, code and models will be made publicly available at: https://github.com/alibaba-damo-academy/Med_Query., 13 pages, 12 figures

Background The assembly of the rhizomicrobiome, i.e., the microbiome in the soil adhering to the root, is influenced by soil conditions. Here, we investigated the core rhizomicrobiome of a wild plant species transplanted to an identical soil type with small differences in chemical factors and the impact of these soil chemistry differences on the core microbiome after long-term cultivation. We sampled three natural reserve populations of wild rice (i.e., in situ) and three populations of transplanted in situ wild rice grown ex situ for more than 40 years to determine the core wild rice rhizomicrobiome. Results Generalized joint attribute modeling (GJAM) identified a total of 44 amplicon sequence variants (ASVs) composing the core wild rice rhizomicrobiome, including 35 bacterial ASVs belonging to the phyla Actinobacteria, Chloroflexi, Firmicutes, and Nitrospirae and 9 fungal ASVs belonging to the phyla Ascomycota, Basidiomycota, and Rozellomycota. Nine core bacterial ASVs belonging to the genera Haliangium, Anaeromyxobacter, Bradyrhizobium, and Bacillus were more abundant in the rhizosphere of ex situ wild rice than in the rhizosphere of in situ wild rice. The main ecological functions of the core microbiome were nitrogen fixation, manganese oxidation, aerobic chemoheterotrophy, chemoheterotrophy, and iron respiration, suggesting roles of the core rhizomicrobiome in improving nutrient resource acquisition for rice growth. The function of the core rhizosphere bacterial community was significantly (p < 0.05) shaped by electrical conductivity, total nitrogen, and available phosphorus present in the soil adhering to the roots. Conclusion We discovered that nitrogen, manganese, iron, and carbon resource acquisition are potential functions of the core rhizomicrobiome of the wild rice Oryza rufipogon. Our findings suggest that further potential utilization of the core rhizomicrobiome should consider the effects of soil properties on the abundances of different genera.

Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM). However, existing event detection methods with fixed parameters may fail in coping with the inherent multi-timescale characteristics of events and their event detection accuracy is easily affected by the load fluctuation. In this regard, this paper extends our previously designed two-stage event detection framework, and proposes a novel multi-timescale event detection method based on the principle of minimum description length (MDL). Following the completion of step-like event detection in the first stage, a long-transient event detection scheme with variable-length sliding window is designed for the second stage, which is intended to provide the observation and characterization of the same event at different time scales. In that, the context information in the aggregated load data is mined by motif discovery, and then based on the MDL principle, the proper observation scales are selected for different events and the corresponding detection results are determined. In the post-processing step, a load fluctuation location method based on voice activity detection (VAD) is proposed to identify and remove the unreasonable events caused by fluctuations. Based on newly proposed evaluation metrics, the comparison tests on public and private datasets demonstrate that our method achieves higher detection accuracy and integrity for events of various appliances across different scenarios., 11 pages,16 figures

This paper analyzes the convergence of the finite population optimal stopping problem towards the corresponding mean field limit. Building on the viscosity solution characterization of the mean field optimal stopping problem of our previous papers [Talbi, Touzi & Zhang 2021 & 2022], we prove the convergence of the value functions by adapting the Barles-Souganidis [1991] monotone scheme method to our context. We next characterize the optimal stopping policies of the mean field problem by the accumulation points of the finite population optimal stopping strategies. In particular, if the limiting problem has a unique optimal stopping policy, then the finite population optimal stopping strategies do converge towards this solution. As a by-product of our analysis, we provide an extension of the standard propagation of chaos to the context of stopped McKean-Vlasov diffusions.

Accurate and robust abdominal multi-organ segmentation from CT imaging of different modalities is a challenging task due to complex inter- and intra-organ shape and appearance variations among abdominal organs. In this paper, we propose a probabilistic multi-organ segmentation network with hierarchical spatial-wise feature modulation to capture flexible organ semantic variants and inject the learnt variants into different scales of feature maps for guiding segmentation. More specifically, we design an input decomposition module via a conditional variational auto-encoder to learn organ-specific distributions on the low dimensional latent space and model richer organ semantic variations that is conditioned on input images.Then by integrating these learned variations into the V-Net decoder hierarchically via spatial feature transformation, which has the ability to convert the variations into conditional Affine transformation parameters for spatial-wise feature maps modulating and guiding the fine-scale segmentation. The proposed method is trained on the publicly available AbdomenCT-1K dataset and evaluated on two other open datasets, i.e., 100 challenging/pathological testing patient cases from AbdomenCT-1K fully-supervised abdominal organ segmentation benchmark and 90 cases from TCIA+&BTCV dataset. Highly competitive or superior quantitative segmentation results have been achieved using these datasets for four abdominal organs of liver, kidney, spleen and pancreas with reported Dice scores improved by 7.3% for kidneys and 9.7% for pancreas, while being ~7 times faster than two strong baseline segmentation methods(nnUNet and CoTr)., 12 pages, 6 figures

Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses. In this paper, we propose a novel self-supervised approach that allows us to explicitly generate 2D-3D pose pairs for augmenting supervision, through a self-enhancing dual-loop learning framework. This is made possible via introducing a reinforcement-learning-based imitator, which is learned jointly with a pose estimator alongside a pose hallucinator; the three components form two loops during the training process, complementing and strengthening one another. Specifically, the pose estimator transforms an input 2D pose sequence to a low-fidelity 3D output, which is then enhanced by the imitator that enforces physical constraints. The refined 3D poses are subsequently fed to the hallucinator for producing even more diverse data, which are, in turn, strengthened by the imitator and further utilized to train the pose estimator. Such a co-evolution scheme, in practice, enables training a pose estimator on self-generated motion data without relying on any given 3D data. Extensive experiments across various benchmarks demonstrate that our approach yields encouraging results significantly outperforming the state of the art and, in some cases, even on par with results of fully-supervised methods. Notably, it achieves 89.1% 3D PCK on MPI-INF-3DHP under self-supervised cross-dataset evaluation setup, improving upon the previous best self-supervised methods by 8.6%. Code can be found at: https://github.com/Garfield-kh/PoseTriplet, Comment: CVPR 2022 Oral Paper, code available: https://github.com/Garfield-kh/PoseTriplet

Integrated modular avionics is the core system of modern aircraft, which hosts almost all kinds of electrical functions. The performance of integrated modular avionics has an immediate influence on flight mission. Remaining useful life prediction is an effective manner to guarantee the safety and reliability of airplane. To satisfy the real-time requirement of integrated modular avionics, the prediction algorithm should have fast learning speed. This paper proposes an ensemble enhanced online sequential parallel extreme learning machine to predict the remaining useful life of integrated modular avionics. Firstly, a network with parallel hidden layers is designed to improve feature extraction. Secondly, to enhance the learning stability, the input weights of the network are determined by using extreme learning machine autoencoder. Thirdly, an updating method is developed for online prediction and an adaptive weight is designed to construct the ensemble online sequential prediction method. The effectiveness and superiority of the proposed method are verified through the standard datasets. Finally, this paper regards intermittent faults as the feature of integrated modular avionics and builds a degradation model by using Levy Process. The proposed method is applied to remaining useful life prediction of integrated modular avionics.

An unsplit-field higher order nearly perfectly matched layer (NPML) based on the auxiliary differential equation approach is introduced in three-dimensional finite-difference timedomain lattices. The proposed scheme has the advantage of both the NPML scheme and the higher order concept in terms of the improved absorbing performance and considerable computational efficiency. By incorporating with the generalized material independent concept, the proposed implementation is independent of the material's type. Thus, it has the advantages of terminating arbitrary media without changing the updated equations in the PML regions. Its effectiveness and efficiency is further demonstrated through numerical examples.

In the original article, we neglected to include some funding sources from the department who funds the research that relates to a wide range of natural science researches in Guangdong province. Grant number 2018A030310687 to Natural Science Foundation of Guangdong Province, and number 2019A1515012149 to Natural Science Foundation of Guangdong Province are now added to the Funding statement. The updated Funding statement can be found below.

Assessing the spatial variability of soil water content is important for precision agriculture. To measure the spatial variability of the soil water content and to determine the optimal number of sampling sites for predicting the mean soil water content at different stages of the irrigation cycle, field experiments were carried out in a potato field in northwestern China. The soil water content was measured in 2016 and 2017 at depths of 0–20 and 20–40 cm at 116 georeferenced locations. The average coefficient of variation of the soil water content was 20.79% before irrigation and was 16.44% after irrigation at a depth of 0–20 cm. The spatial structure of the soil water content at a depth of 20–40 cm was similar throughout the irrigation cycle, but at a depth of 0–20 cm a relatively greater portion of the variation in the soil water content was spatially structured before irrigation than after irrigation. The autocorrelation of soil water contents was influenced by irrigation only in the surface soil layer. To accurately predict mean soil moisture content, 40 and 20 random sampling sites should be chosen with errors of 5% and 10%, respectively.

It is well known that the monotonicity condition, either in Lasry-Lions sense or in displacement sense, is crucial for the global well-posedness of mean field game master equations, as well as for the uniqueness of mean field equilibria and solutions to mean field game systems. In the literature, the monotonicity conditions are always taken in a fixed direction. In this paper we propose a new type of monotonicity condition in the opposite direction, which we call the anti-monotonicity condition, and establish the global well-posedness for mean field game master equations with nonseparable Hamiltonians. Our anti-monotonicity condition allows our data to violate both the Lasry-Lions monotonicity and the displacement monotonicity conditions., 31 pages

Additional file 4: Figure S2. The principal coordinate analysis (PCoA) of the (A) bacterial and (B) fungal communities in the rhizosphere of wild rice populations of the in situ natural reserves (ZTI, Zhangtang; AJSI, Anjiashan; STSI, Shuitaoshu) and ex situ (ZT, Zhangtang; AJS, Anjiashan; STS, Shuitaoshu).

Additional file 1: Table S1. The antibody seroconversion and associated factors among PLWH sub-group.

Additional file 5: Figure S3. The wild rice sites. The three in situ natural reserve sites (A) AJSI, (B) STSI and (C) ZTI and three ex situ artificial protection nurseries (D) AJS, (E) STS and (F) ZT. (In Figure C, professor Dazhou Chen who is one of the initiators for Dongxiang wild rice conservation is observing the growth status of wild rice).

Additional file 1: Table S1. Statistics for the quality assessment of the sequencing data.

The principal agent problem in the standard literature is typically time inconsistent, in the sense that an optimal contract may not remain optimal if the principal reconsiders the problem at a later time. Such time inconsistency issue becomes highly relevant when one or both parties do not commit to the contract. In this paper we consider a model where the current agent can quit before the expiration date, and then the principal will hire a new agent from the market, possibly with a different type. Both parties are aware of the non-commitment of the agent when considering the contract in the beginning. We shall show that the new dynamic problem is time consistent in certain sense and we characterize the principal's optimal utility through an infinite dimensional system of HJB equations, parametrized with the agent's type. However, the dynamic value function of the principal could be discontinuous on the boundary, and thus it is characterized as the minimum solution of the HJB system. Two interesting observations are worth mentioning. First, we assume there are a family of agents with different types in the market (as often in reality). It is possible that the principal could hire a cheaper agent when the current one quits, and ends up with a larger optimal utility. So, the principal may have the incentive to design a contract to encourage the agent to quit before the expiration date. Next, we assume the agent (and the principal) will bear some cost when he quits. As a consequence, the principal will see only finitely many quittings within the finite contract period., Comment: 42 pages

This paper is a continuation of our accompanying paper [Talbi, Touzi and Zhang (2021)], where we characterized the mean field optimal stopping problem by an obstacle equation on the Wasserstein space of probability measures, provided that the value function is smooth. Our purpose here is to establish this characterization under weaker regularity requirements. We shall define a notion of viscosity solutions for such equation, and prove existence, stability, and comparison principle., Comment: 25 pages

Aiming at the problems existing in the method of using sensors to detect the rotor position of marine main propulsion motor at low speed, a sensorless rotor position identification method based on high-frequency pulse vibration signal is proposed. In this method, the low-pass filter is used to separate the high-frequency signal component, and the current component of d-axis to q-axis is demodulated. The control mode of PI action law is used to phase-locked processing of the demodulated signal, in which purpose is to obtain the rotor position signal. The design method is simulated and verified by MATLAB/Simulink software. The error between the estimated position and the actual position of the rotor in the simulation waveform is small, and the accuracy is high. The simulation results show that this method can achieve good results when applied to the ship electric propulsion system without position sensor, which provides a theoretical reference for the design of marine main propulsion motor control system.

Recent advances in generative adversarial networks (GANs) have demonstrated the capabilities of generating stunning photo-realistic portrait images. While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos. In this work, we propose PV3D, the first generative framework that can synthesize multi-view consistent portrait videos. Specifically, our method extends the recent static 3D-aware image GAN to the video domain by generalizing the 3D implicit neural representation to model the spatio-temporal space. To introduce motion dynamics to the generation process, we develop a motion generator by stacking multiple motion layers to generate motion features via modulated convolution. To alleviate motion ambiguities caused by camera/human motions, we propose a simple yet effective camera condition strategy for PV3D, enabling both temporal and multi-view consistent video generation. Moreover, PV3D introduces two discriminators for regularizing the spatial and temporal domains to ensure the plausibility of the generated portrait videos. These elaborated designs enable PV3D to generate 3D-aware motion-plausible portrait videos with high-quality appearance and geometry, significantly outperforming prior works. As a result, PV3D is able to support many downstream applications such as animating static portraits and view-consistent video motion editing. Code and models are released at https://showlab.github.io/pv3d., Comment: Accepted to ICLR2023, Project Page https://showlab.github.io/pv3d

Unsupervised generation of 3D-aware clothed humans with various appearances and controllable geometries is important for creating virtual human avatars and other AR/VR applications. Existing methods are either limited to rigid object modeling, or not generative and thus unable to generate high-quality virtual humans and animate them. In this work, we propose AvatarGen, the first method that enables not only geometry-aware clothed human synthesis with high-fidelity appearances but also disentangled human animation controllability, while only requiring 2D images for training. Specifically, we decompose the generative 3D human synthesis into pose-guided mapping and canonical representation with predefined human pose and shape, such that the canonical representation can be explicitly driven to different poses and shapes with the guidance of a 3D parametric human model SMPL. AvatarGen further introduces a deformation network to learn non-rigid deformations for modeling fine-grained geometric details and pose-dependent dynamics. To improve the geometry quality of the generated human avatars, it leverages the signed distance field as geometric proxy, which allows more direct regularization from the 3D geometric priors of SMPL. Benefiting from these designs, our method can generate animatable 3D human avatars with high-quality appearance and geometry modeling, significantly outperforming previous 3D GANs. Furthermore, it is competent for many applications, e.g., single-view reconstruction, re-animation, and text-guided synthesis/editing. Code and pre-trained model will be available at http://jeff95.me/projects/avatargen.html., Comment: First two authors contributed equally. Our code and models will be available at http://jeff95.me/projects/avatargen.html. arXiv admin note: substantial text overlap with arXiv:2208.00561

Additional file 3: Figure S1. The α-diversity of the (A, B) bacterial and (C, D) fungal communities in the rhizosphere of wild rice populations of the in situ natural reserves (ZTI, Zhangtang; AJSI, Anjiashan; STSI, Shuitaoshu) and ex situ (ZT, Zhangtang; AJS, Anjiashan; STS, Shuitaoshu).

Additional file 2: Table S2. Details of the sampling sites.

The recent discovery of superconductivity up to 32 K in the pressurized MoB2 reignites the interests in exploring high-Tc superconductors in transition-metal diborides. Inspired by that work, we turn our attention to the 5d transition-metal diborides. Here we systematically investigate the responses of both structural and physical properties of WB2 and ReB2 to external pressure, which possess different types of boron layers. Similar to MoB2, the pressure-induced superconductivity was also observed in WB2 above 60 GPa with a maximum Tc of 15 K at 100 GPa, while no superconductivity was detected in ReB2 in this pressure range. Interestingly, the structures at ambient pressure for both WB2 and ReB2 persist to high pressure without structural phase transitions. Theoretical calculations suggest that the ratio of flat boron layers in this class of transition-metal diborides may be crucial for the appearance of high Tc. The combined theoretical and experimental results highlight the effect of geometry of boron layers on superconductivity and shed light on the exploration of novel high-Tc superconductors in borides., 17 pages,5 figures

Unsupervised domain adaptation (UDA) aims at exploiting related but different data sources to tackle a common task in a target domain. UDA remains a central yet challenging problem in machine learning. In this paper, we present an approach tailored to moderate-dimensional tabular problems which are hugely important in industrial applications and less well-served by the plethora of methods designed for image and language data. Knothe-Rosenblatt Domain Adaptation (KRDA) is based on the Knothe-Rosenblatt transport: we exploit autoregressive density estimation algorithms to accurately model the different sources by an autoregressive model using a mixture of Gaussians. KRDA then takes advantage of the triangularity of the autoregressive models to build an explicit mapping of the source samples into the target domain. We show that the transfer map built by KRDA preserves each component quantiles of the observations, hence aligning the representations of the different data sets in the same target domain. Finally, we show that KRDA has state-of-the-art performance on both synthetic and real world UDA problems., 16 pages, 3 figures

For DC circuit breaker has not been applied in UHVDC project, the fast-isolating switches are used to isolate faults of the converter station or transmission line in the multi-terminal UHVDC transmission system. New problems which are not existed in two-terminal HVDC system and DC power grid but faced by the multi-terminal UHVDC transmission system are analyzed. Especially, the fault clear problems of pole protection action, valve protection action, DC line fault restart, metallic line fault restart and pole balance. On the basis of those analysis, fault clear strategies to solve these problems are put forward exploringly.

Because of the lack of single test methods for DC protection system in converter station, this paper firstly analyzes the difference between DC protection system and relay protection device in substation, and explains that DC protection system has its own characteristics and cannot copy the whole way of relay protection device. On the base of the detailed analysis of the development history and standard evolution of the control and protection system of substation and converter station, a general test architecture scheme is proposed, which can be used for the single test of DC protection system on the premise of making full use of the existing relay protection tester, that is, the way of adding the specification conversion device in the middle. At the same time, based on analyzing the characteristics of various standard protocols, IEC 60044-8 is selected as the communication protocol between the tester and the protocol conversion device. Based on this architecture and protocol, the single test of the DC protection system is realized.

In this paper we study mean field games with possibly multiple mean field equilibria. Instead of focusing on the individual equilibria, we propose to study the set of values over all possible equilibria, which we call the set value of the mean field game. When the mean field equilibrium is unique, typically under certain monotonicity conditions, our set value reduces to the singleton of the standard value function which solves the master equation. The set value is by nature unique, and we shall establish two crucial properties: (i) the dynamic programming principle, also called time consistency; and (ii) the convergence of the set values of the corresponding $N$-player games, which can be viewed as a type of stability result. To our best knowledge, this is the first work in the literature which studies the dynamic value of mean field games without requiring the uniqueness of mean field equilibria. We emphasize that the set value is very sensitive to the choice of the admissible controls. In particular, for the convergence one has to restrict to corresponding types of equilibria for the N-player game and for the mean field game. We shall illustrate this point by investigating three cases, two in finite state space models and the other in a diffusion model.

Background:Blattella germanica is a widespread urban invader insect that can spread numerous types of human pathogens, including bacteria, fungi, and protozoa. Despite the medical significance of B. germanica, the genetic diversity of this species has not been investigated across its wide geographical distribution in China.Objective:In this study, the genetic variation of B. germanica was evaluated in central China.Methods:Fragments of the mitochondrial cytochrome c oxidase subunit I (COI) gene and the 16S rRNA gene were amplified in 36 B. germanica isolates from 7 regions. The sequence data for COI and 16S rRNA genes were analyzed using bioinformatics methods.Results:In total, 13 haplotypes were found among the concatenated sequences. Each sampled population, and the total population, had high haplotype diversity (Hd) that was accompanied by low nucleotide diversity (Pi). Molecular genetic variation analysis indicated that 84.33% of the genetic variation derived from intra-region sequences. Phylogenetic analysis indicated that the B. germanica isolates from central China should be classified as a single population. Demographic analysis rejected the hypothesis of sudden population expansion of the B. germanica population.Conclusion:The 36 isolates of B. germanica sampled in this study had high genetic variation and belonged to the same species. They should be classified as a single population. The mismatch distribution analysis and BSP analysis did not support a demographic population expansion of the B. germanica population, which provided useful knowledge for monitoring changes in parasite populations for future control strategies.

Background: Bone loss induced by microgravity is a serious problem in space flight. However, the effects of acupuncture stimulation on osteoporosis induced by microgravity have not been studied. With the goal of developing an effective countermeasure, our aim was to evaluate the effects of electroacupuncture (EA) stimulation at BL20, BL23, and SP6 on osteoporosis induced by simulated microgravity in rats. Methods: Thirty male Wistar rats (aged 10 weeks) were randomly divided into three groups: healthy control group (CON, n = 10), hind limb unloading by tail-suspension group (T-S, n = 10), and EA treatment group (TRE, n = 10). Rats in the T-S and TRE groups were subjected to tail-suspension at −30° for 30 days, while the CON group experienced freedom of activity. In this period, the TRE group received EA treatment at BL20, BL23, and SP6 for 30 min every other day, which continued for 30 days. The microarchitecture of the proximal tibia and the biomechanical features of the femur in the rats were analyzed. In addition, the levels of serum biomarkers bone alkaline phosphatase (BALP) and osteocalcin (BGP) were measured. Results: Compared with the CON group, the value of bone volume/total volume (BV/TV) and trabecular number (Tb.N) of the tibias in the TRE group remarkably decreased ( p Conclusions: These findings indicate that EA stimulation at BL20, BL23, and SP6 retards osteoporosis induced by hind limb unloading in rats.

Our study aims to summarize the clinical characteristics of patients with severe or critically ill coronavirus disease 2019 (COVID-19). Five databases were electronically searched to collect studies describing clinical characteristics of severe or critically ill COVID-19 patients and published between January 1, 2020 and April 12, 2020. Three reviewers independently collected the literature, extracted the required data, and assessed the risk of publication bias of the included studies before including the studies in the meta-analysis. A total of 40 studies involving 2459 patients with severe or critically ill COVID-19 patients were included. Meta-analysis showed that a greater proportion of severe or critically COVID-19 patients were male (62.3%), and the 2 main clinical symptoms were fever (87.4%) and cough (66.3%). Other common clinical symptoms included dyspnea (45.3%), chest tightness (37.4%), fatigue (36.6%), and expectoration (31.9%). Minor symptoms included myalgia (19.5%), dizziness (11.5%), headache (11.4%), diarrhea (11.2%), pharyngalgia (11.0%), nausea, and vomiting (5.9%). Most patients showed elevated levels of C-reactive protein (83.5%) and D-dimer (73.3%), lymphopenia (70.3%), and normal leukocyte counts (56.9%). Other findings included abnormal levels of liver function (39.8%), elevated procalcitonin (36.6%), leukocytosis (21.7%), thrombocytopenia (19.0%), and leucopenia (18.2%). Most patients showed acute respiratory distress syndrome (60.8%). Other complications included acute cardiac injury (37.1%), shock (32.0%), and acute kidney injury (22.0%). The most common symptoms of severe or critically ill COVID-19 patients were fever and cough. Most patients showed lymphopenia, elevated levels of C-reactive protein and D-dimer. A large percentage of patients progress to ARDS, acute cardiac injury, acute kidney injury and shock were also common.