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Wildfires exert strong influences on the environment, ecology, economy and public security. However, the existing hourly Himawari-8 fire product produced by the Japan Meteorological Agency (JMA) presents large uncertainties and is not suitable for reliable real-time fire monitoring in China. To fill this gap, the National Satellite Meteorological Center (NSMC) proposed an adaptive hourly Himawari-8 fire product for China based on the original Himawari-8 source by employing a dynamical threshold for fire extraction and a database of ground thermal sources. According to the visually extracted reference and consistency check, we found that the NSMC-Himawari-8 fire product effectively removed a majority of false fire alarms included in the original Himawari-8 fire product. Based on a rare field-collected ground reference dataset, we evaluated the reliability of original Himawari-8 and NSMC-Himawari-8 fire products across China. The overall accuracy of the raw Himawari-8 fire product was 54 % and 59 % (not considering the omission errors), respectively. As a comparison, by identifying more real fire pixels and avoiding a majority of false fire alarms, the overall accuracy of the NSMC-Himawari-8 fire product was 80 % and 84 % (not considering the omission errors), respectively, making it an ideal source for improved real-time fire monitoring across China. This research also provides a useful reference for employing a local dataset of underlying surfaces and thermal sources to enhance the accuracy of global fire products in specific regions. The NSMC-Himawari-8 fire product can be downloaded at http://figshare.com (last access: 17 April 2023) with the following DOI: https://doi.org/10.6084/m9.figshare.21550248 (Chen et al., 2022a).

This paper considers distributed stochastic optimization, in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a network. Stochastic optimization problems are usually tackled by variants of projected stochastic gradient descent. However, projecting a point onto a feasible set is often expensive. The Frank-Wolfe (FW) method has well-documented merits in handling convex constraints, but existing stochastic FW algorithms are basically developed for centralized settings. In this context, the present work puts forth a distributed stochastic Frank-Wolfe solver, by judiciously combining Nesterov's momentum and gradient tracking techniques for stochastic convex and nonconvex optimization over networks. It is shown that the convergence rate of the proposed algorithm is $\mathcal{O}(k^{-\frac{1}{2}})$ for convex optimization, and $\mathcal{O}(1/\mathrm{log}_2(k))$ for nonconvex optimization. The efficacy of the algorithm is demonstrated by numerical simulations against a number of competing alternatives., Comment: 15 pages, 11 figures, 2 tables

The rapid recombination of carriers on plasmon metal nanoparticles leads to relatively low efficiency of traditional photocatalysts. The combination of a metal and a semiconductor allows to the separation of hot electrons and holes to improve photocatalytic efficiency. In this study, Au nanoparticles were integrated with semiconductor TiO2 nanoparticles of different sizes to improve the photocatalytic activity. Various techniques have been developed to study the mechanism of catalytic activity, the significance of band bending in the space-charge region within metal–semiconductor nanocomposites, and the built-in electric field. The results provide theoretical and experimental evidence for the design of a high-performance surface plasmon resonance (SPR) photocatalyst. To reveal the interface band structure, surface-enhanced Raman spectroscopy (SERS) was employed to analyze the band structure of the TiO2–metal composites. This approach was based on the electrochemical Stark effect and a molecular probe strategy, combined with X-ray photoelectron spectroscopy (XPS), Electrochemical impedance spectroscopy (EIS), and other techniques at the molecular level. The results demonstrated that charge transfer occurred spontaneously between the Au nanoparticles and TiO2, and that the TiO2–metal interface constitutes a Schottky barrier. Moreover, the size of the TiO2 nanoparticles affects the degree of band bending. Optimal state matching was achieved with TiO2 (60 nm)–Au, improving the photocatalytic activity of the nanocomposite. The photocatalytic coupling reaction of p-aminothiophenol (PATP) acted as a probe to study the catalytic performance of TiO2–metal nanocomposites. The results revealed that the introduction of TiO2 improves the SPR catalytic activity of Au, mainly through the efficient separation of electrons and holes at the TiO2–metal interface.

Trousseau's syndrome is a relatively rare reported event in immunotherapy-related clinical trials, mostly occurring in the early period of immune checkpoint inhibitor (ICI) therapy. Here, we report an unusual case of late and lethal Trousseau's syndrome during pembrolizumab maintenance therapy in a lung adenocarcinoma harboring tumor protein p53 (TP53) mutation. The patient has experienced severe coagulation abnormalities manifesting as cerebral infarction, partial infarction of both kidneys and spleen after 23 cycles of pembrolizumab use and was resistant to anticoagulants. The late occurrence of coagulation abnormalities in this case reveals a possible correlation between TP53 mutations and Trousseau's syndrome when patients are treated with ICIs.

Biliary tract cancers (BTCs) comprise a heterogeneous group of aggressive malignancies with unfavorable prognoses. The benefit of chemotherapy seems to have reached a bottleneck and, therefore, new effective therapeutic strategies for advanced BTCs are needed. Molecularly targeted therapies in selected patients are rapidly changing the situation. However, the low frequency of specific driver alterations in BTCs limits their wide application. Recently, immunotherapeutic approaches are also under active investigation in BTCs, but the role of immunotherapy in BTCs remains controversial.PubMed, Web of Science, and meeting resources were searched for relevant articles published from January 2017 to May 2022. The search aimed to identify current and emerging immunotherapeutic approaches for BTCs. Information on clinical trials was obtained from https://clinicaltrials.gov/ and http://www.chictr.org.cn/.Immunotherapy in BTC patients is currently under investigation, and most of the investigations focused on the application of immune checkpoint inhibitors (ICIs). However, only a subgroup of BTCs with microsatellite-instability high (MSI-H)/DNA mismatch repair-deficient (dMMR) or tumor mutational burden-high (TMB-H) benefit from monotherapy of ICIs, and limited activity was observed in the second or subsequent settings. Nevertheless, promising results come from studies of ICIs in combination with other therapeutic approaches, including chemotherapy, in advanced BTCs, with a moderate toxicity profile. Recent studies demonstrated that compared to GEMCIS alone, durvalumab plus GEMCIS significantly improved patient survival (TOPAZ-1 trial) and that ICIs-combined chemoimmunotherapy is poised to become a new frontline therapy option, regardless of TMB and MMR/MSI status. Adoptive cell therapy and peptide- or dendritic-based cancer vaccines are other immunotherapeutic options that are being studied in BTCs. Numerous biomarkers have been investigated to define their predictive role in response to ICIs, but no predictive biomarker has been validated, except MSI-H/dMMR.The role of immunotherapy in BTCs is currently under investigation and the results of ongoing studies are eagerly anticipated. Several studies have demonstrated the safety and efficacy of ICIs in combination with chemotherapy in treatment-naive patients, such as the phase III TOPAZ-1 trial, which will change the standard care of first-line chemotherapy for advanced BTCs. However, further research is needed to understand the best combination with immunotherapy and to discover more predictive biomarkers to guide clinical practice.

Hanbi Wang,1 Zhiyuan Zhang,2 Jie Chen,1 Han Dong,3 Ying Zou,4 Wei Wang,5 Qingmei Zheng,6 Ying Feng,7 Zhangyun Tan,8 Xiaoqin Zeng,9 Yinqing Zhao,8 Yanfang Wang,1 Aijun Sun1 1Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, People’s Republic of China; 2Department of International Medical Service, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China; 3Department of Obstetrics and Gynecology, Women and Children’s Hospital of Jinzhou, Jinzhou, Liaoning, People’s Republic of China; 4Department of Obstetrics and Gynecology, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, People’s Republic of China; 5Department of Reproductive Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China; 6Department of Gynecology, the Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 7Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China; 8Department of Obstetrics and Gynecology, Xinhui Maternity and Children’s Hospital, Nanning, People’s Republic of China; 9Department of Gynecology, Guangzhou Women and Children’s Medical Center, Guangzhou, People’s Republic of ChinaCorrespondence: Aijun Sun, Email saj@pumch.cnObjective: To explore the needs of obstetricians and gynecologists with different working years for the contents and forms of CME.Methods: The online questionnaire was distributed on the largest academic training platform for obstetrics and gynecology in China from April 2020 to May 2020. The survey contents mainly included the training forms and training contents of CME, as well as the mastery of diseases by doctors with different working years.Results: 4458 questionnaires were returned, of which 3954 questionnaires were included in the study. There was a significant (p < 0.001) positive correlation between the mastery of 19 diseases by obstetricians and gynecologists with different working years. After adjusting for specialist departments, hospital grades, nature of units and ages, it was found that there was no statistically significant difference between the groups in gynecological endocrine-related diseases, such as precocious puberty/delayed puberty, and there was also no statistically significant difference between the groups of shorter working years in infertility, menopausal syndrome, hyperprolactinemia and premature ovarian failure.Conclusion: The most popular training content for obstetricians and gynecologists with different working years is common gynecological endocrine diseases, of which abnormal uterine bleeding, menopausal syndrome, polycystic ovarian syndrome and cervical lesions are the most selected diseases, and the most popular form of training is online.Keywords: continuing medical education, working years, internet, obstetrics and gynecology

This paper proposes a novel approach to addressing the output synchronization problem in unknown heterogeneous multi-agent systems (MASs) using noisy data. Unlike existing studies that focus on noiseless data, we introduce a distributed data-driven controller that enables all heterogeneous followers to synchronize with a leader's trajectory. To handle the noise in the state-input-output data, we develop a data-based polytopic representation for the MAS. We tackle the issue of infeasibility in the set of output regulator equations caused by the noise by seeking approximate solutions via constrained fitting error minimization. This method utilizes measured data and a noise-matrix polytope to ensure near-optimal output synchronization. Stability conditions in the form of data-dependent semidefinite programs are derived, providing stabilizing controller gains for each follower. The proposed distributed data-driven control protocol achieves near-optimal output synchronization by ensuring the convergence of the tracking error to a bounded polytope, with the polytope size positively correlated with the noise bound. Numerical tests validate the practical merits of the proposed data-driven design and theory.

There has been an increasing use of master protocols in oncology clinical trials because of its efficiency and flexibility to accelerate cancer drug development. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple drugs and/or multiple subpopulations are studied in parallel under a single protocol. External data and evidence (EDE) can be used in the design and analysis of master protocols such as external controls for treatment effect estimation, which can further improve efficiency of the master protocol trial. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands and assessment of fit-for-use real-world data. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing a sensitivity analysis. Two illustrative examples are provided for master protocols using external controls.

This study investigates the impact of vegetation-climate feedback on the global monsoon (GM) during two key periods, the Last Interglacial (LIG, 127 000 years BP) and the mid-Holocene (MH, 6 000 years BP), using the earth system model EC-Earth3. The simulations reveal that changes in vegetation lead to an expansion of the GM area by 0.2% and 1.1% during the LIG and MH, respectively. Notably, the North African region experiences the most significant increase in vegetation, resulting in the largest expansion in the North African monsoon area, expanding by 7.6% and 6.0% during the LIG and MH, respectively. The enhanced vegetation feedbacks lead to surface warming in the Sahara, deepening of the Saharan Heat Low, strengthening of the monsoonal flow across North Africa, and increased rainfall in the region. However, the change in vegetation type resulted in opposite effects in the Asian and Australian monsoon regions during the LIG and MH periods. The Asian monsoon area reduces by 1.8% during the LIG and expands by 0.2% during the MH, while the Australian monsoon area reduces by 1.7% during the LIG and expands by 1.0% during the MH. These findings highlight that vegetation feedback has different impacts on the global monsoon during the LIG and MH periods. Overall, this study sheds light on the importance of vegetation feedback in shaping the global monsoon during critical periods in Earth's history., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Hyperspectral images (HSI) have a large amount of spectral information reflecting the characteristics of matter, while their spatial resolution is low due to the limitations of imaging technology. Complementary to this are multispectral images (MSI), e.g., RGB images, with high spatial resolution but insufficient spectral bands. Hyperspectral and multispectral image fusion is a technique for acquiring ideal images that have both high spatial and high spectral resolution cost-effectively. Many existing HSI and MSI fusion algorithms rely on known imaging degradation models, which are often not available in practice. In this paper, we propose a deep fusion method based on the conditional denoising diffusion probabilistic model, called DDPM-Fus. Specifically, the DDPM-Fus contains the forward diffusion process which gradually adds Gaussian noise to the high spatial resolution HSI (HrHSI) and another reverse denoising process which learns to predict the desired HrHSI from its noisy version conditioning on the corresponding high spatial resolution MSI (HrMSI) and low spatial resolution HSI (LrHSI). Once the training is completes, the proposed DDPM-Fus implements the reverse process on the test HrMSI and LrHSI to generate the fused HrHSI. Experiments conducted on one indoor and two remote sensing datasets show the superiority of the proposed model when compared with other advanced deep learningbased fusion methods. The codes of this work will be opensourced at this address: https://github.com/shuaikaishi/DDPMFus for reproducibility.

Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis. Discovering workload patterns from a business logic perspective is conducive to better understanding the trends and characteristics of the database system. However, existing workload pattern discovery systems are not suitable for large-scale cloud databases which are commonly employed by the industry. This is because the workload patterns of large-scale cloud databases are generally far more complicated than those of ordinary databases. In this paper, we propose Alibaba Workload Miner (AWM), a real-time system for discovering workload patterns in complicated large-scale workloads. AWM encodes and discovers the SQL query patterns logged from user requests and optimizes the querying processing based on the discovered patterns. First, Data Collection & Preprocessing Module collects streaming query logs and encodes them into high-dimensional feature embeddings with rich semantic contexts and execution features. Next, Online Workload Mining Module separates encoded queries by business groups and discovers the workload patterns for each group. Meanwhile, Offline Training Module collects labels and trains the classification model using the labels. Finally, Pattern-based Optimizing Module optimizes query processing in cloud databases by exploiting discovered patterns. Extensive experimental results on one synthetic dataset and two real-life datasets (extracted from Alibaba Cloud databases) show that AWM enhances the accuracy of pattern discovery by 66% and reduce the latency of online inference by 22%, compared with the state-of-the-arts., Proceedings of the VLDB Volume 16 (VLDB 2023)

Spinning neutron stars (NSs) will emit continuous gravitational waves (GWs) that carry a wealth of information about the compact object. If such a signal is detected, it will provide us with new insight into the physical properties of the matter under extreme conditions. According to binary population synthesis simulations, future space-based GW detectors, such as LISA and TianQin, can potentially detect some double NSs in tight binaries with orbital periods shorter than 10 minutes. Targeted searches for continuous GWs from the spinning NS in such a binary system identified by LISA/TianQin will be possible with the proposed next-generation ground-based GW observatories, such as Cosmic Explorer and Einstein Telescope. Searching for continuous GWs from such a tight binary system requires highly accurate waveform templates that account for the interaction of the NS with its companion. In this spirit, we derive analytic approximate GWs emitted by a triaxial non-aligned NS in a binary system in which the effects of spin-orbit coupling have been incorporated. The difference with the widely used waveform for the isolated NS is estimated and the parameter estimation accuracy of the signals using Cosmic Explorer is calculated. For a typical tight double NS system with a 6~min orbital period, the angular frequency correction of the spinning NS in this binary due to spin precession is $\sim 10^{-6}~{\rm Hz}$, which is in the same order of magnitude as the angular frequency of orbital precession. The fitting factor between the waveforms with and without spin precession will drop to less than 0.97 after a few days ($\sim 10^5~{\rm s}$). We find that spin-orbit coupling has the potential to improve the accuracy of parameter estimation, especially for the binary inclination angle and spin precession cone opening angle, by up to 3 orders of magnitude., 15 pages, 9 figures

Spectral unmixing has been extensively studied with a variety of methods and used in many applications. Recently, data-driven techniques with deep learning methods have obtained great attention to spectral unmixing for its superior learning ability to automatically learn the structure information. In particular, autoencoder based architectures are elaborately designed to solve blind unmixing and model complex nonlinear mixtures. Nevertheless, these methods perform unmixing task as blackboxes and lack of interpretability. On the other hand, conventional unmixing methods carefully design the regularizer to add explicit information, in which algorithms such as plug-and-play (PnP) strategies utilize off-the-shelf denoisers to plug powerful priors. In this paper, we propose a generic unmixing framework to integrate the autoencoder network with regularization by denoising (RED), named AE-RED. More specially, we decompose the unmixing optimized problem into two subproblems. The first one is solved using deep autoencoders to implicitly regularize the estimates and model the mixture mechanism. The second one leverages the denoiser to bring in the explicit information. In this way, both the characteristics of the deep autoencoder based unmixing methods and priors provided by denoisers are merged into our well-designed framework to enhance the unmixing performance. Experiment results on both synthetic and real data sets show the superiority of our proposed framework compared with state-of-the-art unmixing approaches.

Peer reviewed: True, Acknowledgements: We want to thank the Lee Lab, the FinnGen study, the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC), and the Genetic Epidemiology Research on Aging (GERA) for sharing data. XL: the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001). XYW: National Natural Science Foundation of China (81970494) and Key Project of Research and Development Plan of Hunan Province(2019SK2041); SCL: the Swedish Heart Lung Foundation (Hjärt-Lungfonden, 20210351), the Swedish Research Council (Vetenskapsrådet, 2019–00977), and the Swedish Cancer Society (Cancerfonden). Funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication., Funder: Cancerfonden; FundRef: http://dx.doi.org/10.13039/501100002794, BACKGROUND: Whether the positive associations of smoking and alcohol consumption with gastrointestinal diseases are causal is uncertain. We conducted this Mendelian randomization (MR) to comprehensively examine associations of smoking and alcohol consumption with common gastrointestinal diseases. METHODS: Genetic variants associated with smoking initiation and alcohol consumption at the genome-wide significance level were selected as instrumental variables. Genetic associations with 24 gastrointestinal diseases were obtained from the UK Biobank, FinnGen study, and other large consortia. Univariable and multivariable MR analyses were conducted to estimate the overall and independent MR associations after mutual adjustment for genetic liability to smoking and alcohol consumption. RESULTS: Genetic predisposition to smoking initiation was associated with increased risk of 20 of 24 gastrointestinal diseases, including 7 upper gastrointestinal diseases (gastroesophageal reflux, esophageal cancer, gastric ulcer, duodenal ulcer, acute gastritis, chronic gastritis, and gastric cancer), 4 lower gastrointestinal diseases (irritable bowel syndrome, diverticular disease, Crohn's disease, and ulcerative colitis), 8 hepatobiliary and pancreatic diseases (non-alcoholic fatty liver disease, alcoholic liver disease, cirrhosis, liver cancer, cholecystitis, cholelithiasis, and acute and chronic pancreatitis), and acute appendicitis. Fifteen out of 20 associations persisted after adjusting for genetically predicted alcohol consumption. Genetically predicted higher alcohol consumption was associated with increased risk of duodenal ulcer, alcoholic liver disease, cirrhosis, and chronic pancreatitis; however, the association for duodenal ulcer did not remain statistically significant after adjustment for genetic predisposition to smoking initiation. CONCLUSIONS: This study provides MR evidence supporting causal associations of smoking with a broad range of gastrointestinal diseases, whereas alcohol consumption was associated with only a few gastrointestinal diseases. FUNDING: The Natural Science Fund for Distinguished Young Scholars of Zhejiang Province; National Natural Science Foundation of China; Key Project of Research and Development Plan of Hunan Province; the Swedish Heart Lung Foundation; the Swedish Research Council; the Swedish Cancer Society.

Under the background of the era of big data, the physical education teaching evaluation system of colleges and universities has more characteristics and manifestations of the new era. At the same time, big data also brings more requirements for the improvement of the physical education teaching evaluation system of colleges and universities. Under such an environment and trend, colleges and universities need to make certain reform measures to meet the challenges calmly according to the requirements of the new era. This paper makes a systematic study on the construction of the evaluation system of physical education teaching in colleges and universities, expounds the theoretical basis of building an efficient evaluation system of physical education teaching, analyzes the components of the evaluation system, and gives the construction path of the evaluation system of physical education teaching in colleges and universities. The purpose of this paper is to provide some reference information for physical education in higher education and the cultivation of talents' comprehensive quality.

To identify bioactive metabolites from the fruiting body of Morchella sextelata, fourteen metabolites (1–14) including one undescribed morchesexten A (1) were isolated. Their structures including absolute configurations were assigned on the basis of spectroscopic data and quantum chemical computational methods. Furthermore, the anti-inflammatory and antioxidant activities of the isolated compounds were evaluated. Compounds 10–12 showed inhibitory effects on nitric oxide (NO) production with IC50 values of 15.2 ± 2.7, 10.2 ± 1.9 and 35.3 ± 10.5 μM, respectively. Compounds 7 and 9 exhibited strong antioxidant effect with IC50 values of 6.7 ± 0.4 and 7.3 ± 0.8 μM compared with Vit C (IC50 15.4 ± 0.2 μM).

Wireless sensor networks are widely used in the field of monitoring. For data collection of sensor nodes in large-scale monitoring scenarios, UAV-assisted wireless sensor networks have emerged. For the security and validity of data collection, a blockchain-enhanced data collection framework for UAV-assisted wireless sensor networks is presented in this work. To reduce data redundancy in wireless sensor networks, a sparsity-optimized and compressed sensing-based spatiotemporal data aggregation model is built. A UAV identity authentication mechanism based on a Merkle tree is also designed to ensure the security of data transmission. By combining blockchain building and data aggregation, a disaster semantic blockchain based on a data reconstruction-directed consensus mechanism is presented. Disaster semantics are extracted by analyzing the semantic association relationship of disaster, background, event, and sensor data. The experimental results show that the blockchain-enhanced spatiotemporal data aggregation effectively increases the network life cycle and data reconstruction accuracy. The disaster situation can be described accurately through the disaster semantic blockchain.

In clinical trials it is often desirable to test for superiority and non-inferiority simultaneously. The hypotheses are formulated to test whether a new treatment is superior to a control on at least one endpoint, and non-inferior on all endpoints. The simulation studies of \citet{logan2008superiority} shows that most of the current testing methods are very conservative, especially when the non-inferiority margins are close to zero. In this paper we propose a method for the superiority and non-inferiority problem based on the lower one-side confidence intervals. Theoretically, we prove that our testing method can control the type I error at a pre-specified level $\alpha$, such as 0.05, 0.025 or 0.01, which is also demonstrated in our simulation study. Meanwhile, the simulation study show that our method has a higher power than several alternative methods when the non-inferiority margins are close to zero or when both endpoints have a positive value, hence, the proposed method successfully avoid the deficiency of being conservative and has a higher power. A real example about the efficacy and toxicity of an inhaled drug for asthma compared to placebo is used to illustrate the proposed method.

Resistivity measurements are widely exploited to uncover electronic excitations and phase transitions in metallic solids. While single crystals are preferably studied to explore crystalline anisotropies, these usually cancel out in polycrystalline materials. Here we show that in polycrystalline Mn3Zn0.5Ge0.5N with non-collinear antiferromagnetic order, changes in the diagonal and, rather unexpected, off-diagonal components of the resistivity tensor occur at low temperatures indicating subtle transitions between magnetic phases of different symmetry. This is supported by neutron scattering and explained within a phenomenological model which suggests that the phase transitions in magnetic field are associated with field induced topological orbital momenta. The fact that we observe transitions between spin phases in a polycrystal, where effects of crystalline anisotropy are cancelled suggests that they are only controlled by exchange interactions. The observation of an off-diagonal resistivity extends the possibilities for realising antiferromagnetic spintronics with polycrystalline materials., 4 figures, 1 table

Xiaohong Fang,1 Kai Zhang,1 Jie Chen,1 Meitao Chen,1 Yanyan Wang,1 Jianping Zhong2 1School of Education, Huaibei Normal University, Huaibei, People’s Republic of China; 2School of Humanities and Education, Foshan University, Foshan, People’s Republic of ChinaCorrespondence: Kai Zhang, School of Education, Huaibei Normal University, Huaibei, Anhui, 235000, People’s Republic of China, Tel +86 15856116221, Email zhangkaitop@163.comPurpose: The prevalence of cyberbullying has increased along with the growth of social media, which has brought about many adverse effects on individual development. The current study aimed to explore the connection between covert narcissism and cyberbullying and to test the roles of hostile attribution bias and self-control in the relationship between covert narcissism and cyberbullying.Materials and Methods: A total of 672 Chinese college students filled up questionnaires measuring covert narcissism, cyberbullying, hostile attribution bias, and self-control.Results: The results indicated that covert narcissism positively and significantly predicted cyberbullying. Hostile attribution bias partially mediated the relationship between covert narcissism and cyberbullying. Additionally, self-control moderated the relationship between covert narcissism and cyberbullying. Specifically, the positive predictive effect of covert narcissism on cyberbullying gradually weakened as self-control improved.Conclusion: This study explored the underlying mechanism of cyberbullying and found that covert narcissism could affect cyberbullying through hostile attribution bias. Self-control moderated the relationship between covert narcissism and cyberbullying. The results have significant implications for the intervention and prevention of cyberbullying and additional evidence for the relationship between covert narcissism and cyberbullying.Keywords: covert narcissism, cyberbullying, hostile attribution bias, self-control, college students

In order to compare and investigate the sulfuric acid corrosion resistance of concrete for PHC pipe piles and two kinds of concrete commonly used in engineering, acid accelerated corrosion tests were conducted on specimens with three different strength grades of C30, C50, and C80 in a sulfuric acid solution with pH ≈ 0.85. The appearance of the specimens was observed, and the changes in mass loss percentage, corrosion depth, and stress–stain curves under uniaxial compressive loading were calculated and obtained with the corrosion time. From the comparison of corrosion depth and mass loss percentage of the concrete specimens with three different strength grades of C30, C50, and C80, it was found that the higher the strength grade of the concrete, the more severe the corrosion degree. The shapes of the stress–strain curves of three different strength grades of concrete specimens were basically the same. As the corrosion time was prolonged, the peak stress and the elastic modulus of concrete decreased. From the perspective of long-term corrosion, C80 specimens had a relatively smaller percentage of peak stress loss and a stronger resistance to peak stress loss. The research results provide references for the durability design of concrete structures and the prediction of concrete’s service life in a sulfuric acid environment.

The top-down and bottom-up methods are two mainstreams of referring segmentation, while both methods have their own intrinsic weaknesses. Top-down methods are chiefly disturbed by Polar Negative (PN) errors owing to the lack of fine-grained cross-modal alignment. Bottom-up methods are mainly perturbed by Inferior Positive (IP) errors due to the lack of prior object information. Nevertheless, we discover that two types of methods are highly complementary for restraining respective weaknesses but the direct average combination leads to harmful interference. In this context, we build Win-win Cooperation (WiCo) to exploit complementary nature of two types of methods on both interaction and integration aspects for achieving a win-win improvement. For the interaction aspect, Complementary Feature Interaction (CFI) provides fine-grained information to top-down branch and introduces prior object information to bottom-up branch for complementary feature enhancement. For the integration aspect, Gaussian Scoring Integration (GSI) models the gaussian performance distributions of two branches and weightedly integrates results by sampling confident scores from the distributions. With our WiCo, several prominent top-down and bottom-up combinations achieve remarkable improvements on three common datasets with reasonable extra costs, which justifies effectiveness and generality of our method., Accepted to IJCAI2023

Graph coarsening is a technique for solving large-scale graph problems by working on a smaller version of the original graph, and possibly interpolating the results back to the original graph. It has a long history in scientific computing and has recently gained popularity in machine learning, particularly in methods that preserve the graph spectrum. This work studies graph coarsening from a different perspective, developing a theory for preserving graph distances and proposing a method to achieve this. The geometric approach is useful when working with a collection of graphs, such as in graph classification and regression. In this study, we consider a graph as an element on a metric space equipped with the Gromov--Wasserstein (GW) distance, and bound the difference between the distance of two graphs and their coarsened versions. Minimizing this difference can be done using the popular weighted kernel $K$-means method, which improves existing spectrum-preserving methods with the proper choice of the kernel. The study includes a set of experiments to support the theory and method, including approximating the GW distance, preserving the graph spectrum, classifying graphs using spectral information, and performing regression using graph convolutional networks. Code is available at https://github.com/ychen-stat-ml/GW-Graph-Coarsening ., To appear at ICML 2023. Code is available at https://github.com/ychen-stat-ml/GW-Graph-Coarsening

To address the issues of low efficiency and lengthy running time associated with trajectory planning for 6-degree-of-freedom manipulators, this paper introduces a novel solution that generates a time-optimal path for a manipulator while adhering to its kinematic limitations. The proposed method comprises several stages. Firstly, the kinematics of the manipulator are analyzed. Secondly, the manipulator’s joint-space path points are interpolated via the quintic B-spline curve. Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) is improved by applying reinforcement learning to optimize its crossover and mutation probabilities, and the variable neighborhood search (VNS) algorithm is integrated to enhance its local search capability. Finally, the joint increments and running time of the manipulator are optimized using the improved NSGA-II, and the time-optimal trajectory is then determined by simulation on MATLAB. Furthermore, compared with other conventional optimization methods, the proposed approach has reduced the total running time by 19.26%, effectively improving the working efficiency of the manipulator.

Learning an effective global model on private and decentralized datasets has become an increasingly important challenge of machine learning when applied in practice. Existing distributed learning paradigms, such as Federated Learning, enable this via model aggregation which enforces a strong form of modeling homogeneity and synchronicity across clients. This is however not suitable to many practical scenarios. For example, in distributed sensing, heterogeneous sensors reading data from different views of the same phenomenon would need to use different models for different data modalities. Local learning therefore happens in isolation but inference requires merging the local models to achieve consensus. To enable consensus among local models, we propose a feature fusion approach that extracts local representations from local models and incorporates them into a global representation that improves the prediction performance. Achieving this requires addressing two non-trivial problems. First, we need to learn an alignment between similar feature components which are arbitrarily arranged across clients to enable representation aggregation. Second, we need to learn a consensus graph that captures the high-order interactions between local feature spaces and how to combine them to achieve a better prediction. This paper presents solutions to these problems and demonstrates them in real-world applications on time series data such as power grids and traffic networks.

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://github.com/rain305f/OSP., Accpected by CVPR 2023

In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best predictive performance. To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations. Recently, with the recognition that interpretability is just as important, researchers are compromising on small increases in predictive performance to develop algorithms that are inherently interpretable. While doing so, the ML community has rediscovered the use of low-order functional ANOVA (fANOVA) models that have been known in the statistical literature for some time. This paper starts with a description of challenges with post hoc explainability and reviews the fANOVA framework with a focus on main effects and second-order interactions. This is followed by an overview of two recently developed techniques: Explainable Boosting Machines or EBM (Lou et al., 2013) and GAMI-Net (Yang et al., 2021b). The paper proposes a new algorithm, called GAMI-Lin-T, that also uses trees like EBM, but it does linear fits instead of piecewise constants within the partitions. There are many other differences, including the development of a new interaction filtering algorithm. Finally, the paper uses simulated and real datasets to compare selected ML algorithms. The results show that GAMI-Lin-T and GAMI-Net have comparable performances, and both are generally better than EBM., 24 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:2207.06950

Heatmap-based anatomical landmark detection is still facing two unresolved challenges: 1) inability to accurately evaluate the distribution of heatmap; 2) inability to effectively exploit global spatial structure information. To address the computational inability challenge, we propose a novel position-aware and sample-aware central loss. Specifically, our central loss can absorb position information, enabling accurate evaluation of the heatmap distribution. More advanced is that our central loss is sample-aware, which can adaptively distinguish easy and hard samples and make the model more focused on hard samples while solving the challenge of extreme imbalance between landmarks and non-landmarks. To address the challenge of ignoring structure information, a Coordinated Transformer, called CoorTransformer, is proposed, which establishes long-range dependencies under the guidance of landmark coordination information, making the attention more focused on the sparse landmarks while taking advantage of global spatial structure. Furthermore, CoorTransformer can speed up convergence, effectively avoiding the defect that Transformers have difficulty converging in sparse representation learning. Using the advanced CoorTransformer and central loss, we propose a generalized detection model that can handle various scenarios, inherently exploiting the underlying relationship between landmarks and incorporating rich structural knowledge around the target landmarks. We analyzed and evaluated CoorTransformer and central loss on three challenging landmark detection tasks. The experimental results show that our CoorTransformer outperforms state-of-the-art methods, and the central loss significantly improves the performance of the model with p-values< 0.05.

Video question answering aims at answering a question about the video content by reasoning the alignment semantics within them. However, since relying heavily on human instructions, i.e., annotations or priors, current contrastive learning-based VideoQA methods remains challenging to perform fine-grained visual-linguistic alignments. In this work, we innovatively resort to game theory, which can simulate complicated relationships among multiple players with specific interaction strategies, e.g., video, question, and answer as ternary players, to achieve fine-grained alignment for VideoQA task. Specifically, we carefully design a VideoQA-specific interaction strategy to tailor the characteristics of VideoQA, which can mathematically generate the fine-grained visual-linguistic alignment label without label-intensive efforts. Our TG-VQA outperforms existing state-of-the-art by a large margin (more than 5%) on long-term and short-term VideoQA datasets, verifying its effectiveness and generalization ability. Thanks to the guidance of game-theoretic interaction, our model impressively convergences well on limited data (${10}^4 ~videos$), surpassing most of those pre-trained on large-scale data ($10^7~videos$)., IJCAI 2023

Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants. To measure the atmosphere's ability to dilute air pollutants, Air Stagnation Index (ASI) has been introduced as an important meteorological index. Therefore, making long-lead ASI forecasts is vital to make plans in advance for air quality management. In this study, we found that autumn Ni\~no indices derived from sea surface temperature (SST) anomalies show a negative correlation with wintertime ASI in southern China, offering prospects for a prewinter forecast. We developed an LSTM-based model to predict the future wintertime ASI. Results demonstrated that multivariate inputs (past ASI and Ni\~no indices) achieve better forecast performance than univariate input (only past ASI). The model achieves a correlation coefficient of 0.778 between the actual and predicted ASI, exhibiting a high degree of consistency., Comment: ICLR 2023 Tackling Climate Change with Machine Learning Workshop

Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in the self-representation tensor. Current tensor decompositions for MSC suffer from highly unbalanced unfolding matrices or rotation sensitivity, failing to fully explore inter/intra-view information. Using the advanced tensor network, namely, multi-scale entanglement renormalization ansatz (MERA), we propose a low-rank MERA based MSC (MERA-MSC) algorithm, where MERA factorizes a tensor into contractions of one top core factor and the rest orthogonal/semi-orthogonal factors. Benefiting from multiple interactions among orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong representation power to capture the complex inter/intra-view information in the self-representation tensor. The alternating direction method of multipliers is adopted to solve the optimization model. Experimental results on five multi-view datasets demonstrate MERA-MSC has superiority against the compared algorithms on six evaluation metrics. Furthermore, we extend MERA-MSC by incorporating anchor learning to develop a scalable low-rank MERA based multi-view clustering method (sMREA-MVC). The effectiveness and efficiency of sMERA-MVC have been validated on three large-scale multi-view datasets. To our knowledge, this is the first work to introduce MERA to the multi-view clustering topic. The codes of MERA-MSC and sMERA-MVC are publicly available at https://github.com/longzhen520/MERA-MSC.

Denoising diffusion probabilistic models (DDPMs) have shown promising performance for speech synthesis. However, a large number of iterative steps are required to achieve high sample quality, which restricts the inference speed. Maintaining sample quality while increasing sampling speed has become a challenging task. In this paper, we propose a "Co"nsistency "Mo"del-based "Speech" synthesis method, CoMoSpeech, which achieve speech synthesis through a single diffusion sampling step while achieving high audio quality. The consistency constraint is applied to distill a consistency model from a well-designed diffusion-based teacher model, which ultimately yields superior performances in the distilled CoMoSpeech. Our experiments show that by generating audio recordings by a single sampling step, the CoMoSpeech achieves an inference speed more than 150 times faster than real-time on a single NVIDIA A100 GPU, which is comparable to FastSpeech2, making diffusion-sampling based speech synthesis truly practical. Meanwhile, objective and subjective evaluations on text-to-speech and singing voice synthesis show that the proposed teacher models yield the best audio quality, and the one-step sampling based CoMoSpeech achieves the best inference speed with better or comparable audio quality to other conventional multi-step diffusion model baselines. Audio samples are available at https://comospeech.github.io/.

BackgroundRecent studies had provided evidence that the gut microbiota is associated with sepsis. However, the potential causal relationship remained unclear.MethodsThe present study aimed to explore the causal effects between gut microbiota and sepsis by performing Mendelian randomization (MR) analysis utilizing publicly accessible genome-wide association study (GWAS) summary-level data. Gut microbiota GWAS (N = 18,340) were obtained from the MiBioGen study and GWAS-summary-level data for sepsis were gained from the UK Biobank (sepsis, 10,154 cases; 452,764 controls). Two strategies were used to select genetic variants, i.e., single nucleotide polymorphisms (SNPs) below the locus-wide significance level (1 × 10−5) and the genome-wide statistical significance threshold (5 × 10−8) were chosen as instrumental variables (IVs). The inverse variance weighted (IVW) was used as the primary method for MR study, supplemented by a series of other methods. Additionally, a set of sensitivity analysis methods, including the MR-Egger intercept test, Mendelian randomized polymorphism residual and outlier (MR-PRESSO) test, Cochran’s Q test, and leave-one-out test, were carried out to assess the robustness of our findings.ResultsOur study suggested that increased abundance of Deltaproteobacteria, Desulfovibrionales, Catenibacterium, and Hungatella were negatively associated with sepsis risk, while Clostridiaceae1, Alloprevotella, LachnospiraceaeND3007group, and Terrisporobacter were positively correlated with the risk of sepsis. Sensitivity analysis revealed no evidence of heterogeneity and pleiotropy.ConclusionThis study firstly found suggestive evidence of beneficial or detrimental causal associations of gut microbiota on sepsis risk by applying MR approach, which may provide valuable insights into the pathogenesis of microbiota-mediated sepsis and strategies for sepsis prevention and treatment.

Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the quality of solutions still has room for improvement relative to that by the exact methods such as OR-Tools. To address this issue, this paper presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making. The complex relationships between operations and machines are represented precisely and concisely, for which a dual-attention network (DAN) comprising several interconnected operation message attention blocks and machine message attention blocks is proposed. The DAN exploits the complicated relationships to construct production-adaptive operation and machine features to support high-quality decisionmaking. Experimental results using synthetic data as well as public benchmarks corroborate that the proposed approach outperforms both traditional PDRs and the state-of-the-art DRL method. Moreover, it achieves results comparable to exact methods in certain cases and demonstrates favorable generalization ability to large-scale and real-world unseen FJSP tasks.

Agile quadrotor flight relies on rapidly planning and accurately tracking time-optimal trajectories, a technology critical to their application in the wild. However, the computational burden of computing time-optimal trajectories based on the full quadrotor dynamics (typically on the order of minutes or even hours) can hinder its ability to respond quickly to changing scenarios. Additionally, modeling errors and external disturbances can lead to deviations from the desired trajectory during tracking in real time. This letter proposes a novel approach to computing time-optimal trajectories, by fixing the nodes with waypoint constraints and adopting separate sampling intervals for trajectories between waypoints, which significantly accelerates trajectory planning. Furthermore, the planned paths are tracked via a time-adaptive model predictive control scheme whose allocated tracking time can be adaptively adjusted on-the-fly, therefore enhancing the tracking accuracy and robustness. We evaluate our approach through simulations and experimentally validate its performance in dynamic waypoint scenarios for time-optimal trajectory replanning and trajectory tracking., submitted to IEEE Robotics and Automation Letters, for the associated video, see https://youtu.be/E6QVHWcvB6E

Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs, owing to the widespread use and success of GNNs in applications such as recommendation systems and financial forensics. This paper is concerned with minibatch training and inference with GNNs that employ node-wise sampling in distributed settings, where the necessary partitioning of vertex features across distributed storage causes feature communication to become a major bottleneck that hampers scalability. To significantly reduce the communication volume without compromising prediction accuracy, we propose a policy for caching data associated with frequently accessed vertices in remote partitions. The proposed policy is based on an analysis of vertex-wise inclusion probabilities (VIP) during multi-hop neighborhood sampling, which may expand the neighborhood far beyond the partition boundaries of the graph. VIP analysis not only enables the elimination of the communication bottleneck, but it also offers a means to organize in-memory data by prioritizing GPU storage for the most frequently accessed vertex features. We present SALIENT++, which extends the prior state-of-the-art SALIENT system to work with partitioned feature data and leverages the VIP-driven caching policy. SALIENT++ retains the local training efficiency and scalability of SALIENT by using a deep pipeline and drastically reducing communication volume while consuming only a fraction of the storage required by SALIENT. We provide experimental results with the Open Graph Benchmark data sets and demonstrate that training a 3-layer GraphSAGE model with SALIENT++ on 8 single-GPU machines is 7.1 faster than with SALIENT on 1 single-GPU machine, and 12.7 faster than with DistDGL on 8 single-GPU machines., MLSys 2023. Code is available at https://github.com/MITIBMxGraph/SALIENT_plusplus

In this paper, we study the global well-posedness of the stochastic S-KdV system in $H^1(\mathbb{R})\times H^1(\mathbb{R})$, which are driven by additive noises. It is difficult to show the global well-posedness of a related perturbation system even for smooth datum and stochastic forces. To overcome it, we introduce a new sequence of approximation equations, which is the key of this paper. We establish priori estimates, global well-posedness and convergences of these approximation equations, which help us to get a pathwise priori estimate of the initial system., Comment: 24 pages

Video frame interpolation(VFI) has witnessed great progress in recent years. While existing VFI models still struggle to achieve a good trade-off between accuracy and efficiency: fast models often have inferior accuracy; accurate models typically run slowly. However, easy samples with small motion or clear texture can achieve competitive results with simple models and do not require heavy computation. In this paper, we present an integrated pipeline which combines difficulty assessment with video frame interpolation. Specifically, it firstly leverages a pre-assessment model to measure the interpolation difficulty level of input frames, and then dynamically selects an appropriate VFI model to generate interpolation results. Furthermore, a large-scale VFI difficulty assessment dataset is collected and annotated to train our pre-assessment model. Extensive experiments show that easy samples pass through fast models while difficult samples inference with heavy models, and our proposed pipeline can improve the accuracy-efficiency trade-off for VFI.

Studying contemporary Chinese art is not purely an aesthetic choice, but rather a response to the context of the times. From the initial development in the 1870s to the capital intervention in the 1990s, Chinese contemporary art began to internationalize and globalize. The artists who matured during this period now form the backbone of Chinese contemporary art. Having experienced the turmoil of Chinese society, they explore politics and ideology through their artwork with great depth and nuance. Song Dong, himself an artist of this generation, has been exploring ideas related to everyday reality, memory, and social issues through his artwork. This thesis examines three of Song Dong's works, namely Touching my father (1997-2011), Father and Son in the Ancestral Temple (1998), and Waste Not (2005), all of which revolve around the theme of family. In addition to considering the artist himself and his familial relationships, this study contextualizes Song Dong's works within the social and political landscape of contemporary China. Through this analysis, the thesis aims to explore the theme of family in greater depth. It also involves an exploration of censorship in Chinese contemporary art and how artists such as Song Dong use subtle artistic language to express their personal opinions.