GADY: Unsupervised Anomaly Detection on Dynamic Graphs

Anomaly detection on dynamic graphs refers to detecting entities whose behaviors obviously deviate from the norms observed within graphs and their temporal information. This field has drawn increasing attention due to its application in finance, network security, social networks, and more. However, existing methods face two challenges: dynamic structure constructing challenge - difficulties in capturing graph structure with complex time information and negative sampling challenge - unable to construct excellent negative samples for unsupervised learning. To address these challenges, we propose Unsupervised Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first challenge, we propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. Specifically, we employ a message-passing framework combined with positional features to get edge embeddings, which are decoded to identify anomalies. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Moreover, we design a loss function to alter the training goal of the generator while ensuring the diversity and quality of generated samples. Extensive experiments demonstrate that our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets. Supplementary experiments further validate the effectiveness of our model design and the necessity of each module

Removal of point source leakage from time-order data filtering

Time-ordered data (TOD) from ground-based CMB experiments are generally filtered before map-making to remove or reduce the contamination from the ground and the atmospheric emissions. However, when the observation region contains strong point sources, the filtering process will result in considerable leakage around the point sources in a measured CMB map, and leave spurious polarization signals. Therefore, such signals need to be assessed and removed before CMB science exploitation. In this work, we present a new method that we call "template fitting" and can effectively remove these leakage signals in pixel domain, not only satisfying the requirement for measuring primordial gravitational waves from CMB-$B$ modes, but also avoiding time-consuming operations on TOD.Comment: 13pages, 6 figures, 5 table

DNA-directed nanofabrication of high-performance carbon nanotube field-effect transistors

生物自组装结构具有精细的三维形貌，其关键结构参数小于光刻等传统纳米加工手段的分辨率极限。利用自组装的生物分子为加工模板，已经实现了金属、碳基、氧化物等材料的形貌可控合成。然而，基于生物模板的电学器件，其性能往往远落后于通过蚀刻或薄膜方法制备的同类器件，并且缺乏长程取向规整性，制约了生物模板在高性能器件中的应用。针对上述挑战，我校化学化工学院朱志教授课题组与北京大学孙伟研究员课题组、清华大学唐建石研究员课题组、美国国家标准与技术研究院郑明博士合作，探索了生物-碳纳米管复合界面及大面积取向排列的调控新方法。北京大学孙伟研究员团队长期从事核酸引导的精准纳米组装研究，厦门大学朱志教授团队长期从事微纳加工及微流控研究，双方紧密合作，优势互补，联合清华大学和美国国家标准与技术研究院等多团队联合攻关完成该工作。共同第一作者厦门大学化学化工学院2011协同创新中心博士研究生陈雅鸿负责了碳纳米管组装及大面积阵列化工作，孙伟研究员和朱志教授为论文的共同通讯作者。Biofabricated semiconductor arrays exhibit smaller channel pitches than existing lithographic feasibility. However, the metal ions within biolattices and the submicrometer dimensions of typical biotemplates result in both poor transport performance and small array uniformity. Using DNA-templated parallel carbon nanotube (CNT) arrays as model systems, we developed a rinsing-after-fixing approach to improve the key transport performance metrics by more than a factor of 10 folds over previous biotemplated field-effect transistors. We also used spatially confined placement of assembled CNT arrays within polymethyl methacrylate cavities to demonstrate centimeter-scale alignment. At the interface of high-performance electronics and biomolecular self-assembly, current approaches may enable scalable biotemplated electronics sensitive to local biological environments.W.S.,M.Z., Y.C., K.W., and Z.Z. acknowledge the National Science Foundation of China (Grant No. 21875003, 21991134, and 61621061) and PKU for financial support. Y.C., C.Y., and Z.Z. acknowledge the National Science Foundation of China (Grant No. 21775128, 21435004, and 21974113) for financial support. J.K.S., J.A.F., and M.Z. acknowledge NIST internal fund. 该研究工作得到国家自然科学基金等资助

Development and validation of a nomogram to predict the five-year risk of revascularization for non-culprit lesion progression in STEMI patients after primary PCI

BackgroundAcute ST-segment elevation myocardial infarction (STEMI) patients after primary PCI were readmitted for revascularization due to non-culprit lesion (NCL) progression.ObjectiveTo develop and validate a nomogram that can accurately predict the likelihood of NCL progression revascularization in STEMI patients following primary PCI.MethodsThe study enrolled 1,612 STEMI patients after primary PCI in our hospital from June 2009 to June 2018. Patients were randomly divided into training and validation sets in a 7:3 ratio. The independent risk factors were determined by LASSO regression and multivariable logistic regression analysis. Multivariate logistic regression analysis was utilized to develop a nomogram, which was then evaluated for its performance using the concordance statistics, calibration plots, and decision curve analysis (DCA).ResultsThe nomogram was composed of five predictors, including age (OR: 1.007 95% CI: 1.005–1.009, P < 0.001), body mass index (OR: 1.476, 95% CI: 1.363–1.600, P < 0.001), triglyceride and glucose index (OR: 1.050, 95% CI: 1.022–1.079, P < 0.001), Killip classification (OR: 1.594, 95% CI: 1.140–2.229, P = 0.006), and serum creatinine (OR: 1.007, 95% CI: 1.005–1.009, P < 0.001). Both the training and validation groups accurately predicted the occurrence of NCL progression revascularization (The area under the receiver operating characteristic curve values, 0.901 and 0.857). The calibration plots indicated an excellent agreement between prediction and observation in both sets. Furthermore, the DCA demonstrated that the model exhibited clinical efficacy.ConclusionA convenient and accurate nomogram was developed and validated for predicting the occurrence of NCL progression revascularization in STEMI patients after primary PCI

Progress in the Research on Branched Polymers with Emphasis on the Chinese Petrochemical Industry

Polymer flooding, one of the main methods for improving crude oil recovery using chemical flooding technology in China, is widely used for actual oil displacement. Partially hydrolyzed polyacrylamide (HPAM) is a commonly used linear polymer in polymer flooding, but it exhibits poor temperature and salt resistance due to its molecular structure. Therefore, branched polymers have been studied. This article provides a review of the specific synthetic methods and current practical applications in the petrochemical field of dendritic polymers and hyperbranched macromolecules. The advantages and disadvantages of each synthetic method for branched polymers are also elaborated. Finally, the application prospects of branched polymers are discussed. The feasibility of branched polymers in large quantities should be further verified through additional field tests, which should address concerns such as synthesis costs and reaction efficiency

Type-1 Robotic Assembly Line Balancing Problem That Considers Energy Consumption and Cross-Station Design

Robotic assembly lines are widely applied to mass production because of their adaptability and versatility. As we know, using robots will lead to energy-consumption and pollution problems, which has been a hot-button topic in recent years. In this paper, we consider an assembly line balancing problem with minimizing the number of workstations as the primary objective and minimizing energy consumption as the secondary objective. Further, we propose a novel mixed integer linear programming (MILP) model considering a realistic production process design—cross-station task, which is an important contribution of our paper. The “cross-station task” design has already been applied to practice but rarely studied academically in type-1 RALBP. A simulated annealing algorithm is developed, which incorporates a restart mechanism and an improvement strategy. Computational tests demonstrate that the proposed algorithm is superior to two other classic algorithms, which are the particle swarm algorithm and late acceptance hill-climbing algorithm

How exports affect green technology innovation in small and medium-sized enterprises? Evidence from Chinese companies listed on the growth enterprise market

Using matched data from China Stock Market and Accounting Research (CSMAR) and Wind databases, this paper explores the impact of exports on the green technology innovation (GTI) of small and medium-sized enterprises (SMEs). The mechanisms are analyzed through a two-way fixed effects model. First, exports contribute significantly to GTI of SMEs. Second, exports mainly contribute to GTI of SMEs by attracting government subsidies and increasing firms' environmental awareness. Third, from the perspective of heterogeneity, exports significantly positively impact GTI of SMEs especially in medium-and low-technology industries and in eastern China. The impact of exports on GTI is also examined by replacing core variables, modifying the sample for robustness testing, and utilizing both urban river density and distance from the center of the city to the nearest port as instrumental variables for endogeneity test. With the continuous development of international import and export trade, enterprises increase GTI research and development by attracting government subsidies, improving the quality of disclosed information, and increasing environmental awareness