Research and Practice on the Training Mode of Master of Finance Under the Background of Fin Tech

In the trend of Fin Tech, the training mode of Master of Finance(MF) is in urgent need of reform and innovation. In this paper, the author proposes to build a three in one postgraduate training model of "science and technology, application and internationalization" to strengthen the training quality of MF, through the establishment of a curriculum system that integrates Fin Tech and finance; organic embedding of cross courses such as artificial intelligence; building a multi faculty, implementing the dual tutor system; building an international training platform, cultivating the international vision of teachers and students; exploring the training mechanism of professional qualification embedding, encouraging students to obtain high-end professional qualification certificates such as CFA;developing practice bases for in-depth cooperation

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Energy internet provides an open framework for integrating every piece of equipment involved in energy generation, transmission, transformation, distribution, and consumption with novel information and communication technologies. In this chapter, the authors adopt a combination of game theory and big data to address the coordinated management of renewable and traditional energy, which is a typical issue on energy interconnections. The authors formulate the energy management problem as a three‐stage Stackelberg game and employ the backward induction method to derive the closed‐form expressions of the optimal strategies. Next, we study the big data‐based power generation forecasting techniques and introduce a scheme of the wind power forecasting, which can assist the microgrid to make strategies. Simulation results show that more accurate prediction results of wind power are conducive to better energy management

UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction

Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the development and operation of the smart city. As an emerging building block, multi-sourced urban data are usually integrated as urban knowledge graphs (UrbanKGs) to provide critical knowledge for urban spatiotemporal prediction models. However, existing UrbanKGs are often tailored for specific downstream prediction tasks and are not publicly available, which limits the potential advancement. This paper presents UUKG, the unified urban knowledge graph dataset for knowledge-enhanced urban spatiotemporal predictions. Specifically, we first construct UrbanKGs consisting of millions of triplets for two metropolises by connecting heterogeneous urban entities such as administrative boroughs, POIs, and road segments. Moreover, we conduct qualitative and quantitative analysis on constructed UrbanKGs and uncover diverse high-order structural patterns, such as hierarchies and cycles, that can be leveraged to benefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs, we implement and evaluate 15 KG embedding methods on the KG completion task and integrate the learned KG embeddings into 9 spatiotemporal models for five different USTP tasks. The extensive experimental results not only provide benchmarks of knowledge-enhanced USTP models under different task settings but also highlight the potential of state-of-the-art high-order structure-aware UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications. The dataset and source code are available at https://github.com/usail-hkust/UUKG/.Comment: NeurIPS 2023 Track on Datasets and Benchmark

Online rumors during the COVID-19 pandemic: co-evolution of themes and emotions

IntroductionDuring public health emergencies, online rumors spread widely on social media, causing public information anxiety and emotional fluctuations. Analyzing the co-evolution patterns of online rumor themes and emotions is essential for implementing proactive and precise governance of online rumors during such events.MethodsRumor texts from mainstream fact-checking platforms during the COVID-19 pandemic were collected and analyzed in phases based on the crisis lifecycle theory. The LDA topic model was applied to analyze the distribution of rumor themes at different stages. The Baidu AI Sentiment Analysis API was used to study the emotional tendencies of rumors at different stages. Line graphs were utilized to analyze the co-evolution characteristics of rumor themes and emotions.ResultsDuring the COVID-19 pandemic, the themes of online rumors can be categorized into five types: epidemic prevention and control, panic-inducing, production and livelihood, virus dissemination, and social figures. These themes exhibited repetition and fluctuation at different stages of the pandemic. The emotions embedded in pandemic-related online rumors evolved with the progression of the pandemic. Panic-inducing rumors co-evolved with negative emotions, while epidemic prevention and control rumors co-evolved with positive emotions.ConclusionThe study results help to understand the public’s focus and emotional tendencies at different stages of the COVID-19 pandemic, thereby enabling targeted public opinion guidance and crisis management

Genome-wide analysis of alternative splicing of pre-mRNA under salt stress in Arabidopsis

BACKGROUND: Alternative splicing (AS) of precursor mRNA (pre-mRNA) is an important gene regulation process that potentially regulates many physiological processes in plants, including the response to abiotic stresses such as salt stress. RESULTS: To analyze global changes in AS under salt stress, we obtained high-coverage (~200 times) RNA sequencing data from Arabidopsis thaliana seedlings that were treated with different concentrations of NaCl. We detected that ~49% of all intron-containing genes were alternatively spliced under salt stress, 10% of which experienced significant differential alternative splicing (DAS). Furthermore, AS increased significantly under salt stress compared with under unstressed conditions. We demonstrated that most DAS genes were not differentially regulated by salt stress, suggesting that AS may represent an independent layer of gene regulation in response to stress. Our analysis of functional categories suggested that DAS genes were associated with specific functional pathways, such as the pathways for the responses to stresses and RNA splicing. We revealed that serine/arginine-rich (SR) splicing factors were frequently and specifically regulated in AS under salt stresses, suggesting a complex loop in AS regulation for stress adaptation. We also showed that alternative splicing site selection (SS) occurred most frequently at 4 nucleotides upstream or downstream of the dominant sites and that exon skipping tended to link with alternative SS. CONCLUSIONS: Our study provided a comprehensive view of AS under salt stress and revealed novel insights into the potential roles of AS in plant response to salt stress. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-431) contains supplementary material, which is available to authorized users

Deca : a garbage collection optimizer for in-memory data processing

In-memory caching of intermediate data and active combining of data in shuffle buffers have been shown to be very effective in minimizing the recomputation and I/O cost in big data processing systems such as Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap. These generated objects may quickly saturate the garbage collector, especially when handling a large dataset, and hence, limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user-defined functions and data types, obtains the expected lifetime of the data objects and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca,1 a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar lifetimes into byte arrays and releases their space altogether when their lifetimes come to an end. When systems are processing very large data, Deca also provides field-oriented memory pages to ensure high compression efficiency. Extensive experimental studies using both synthetic and real datasets show that, in comparing to Spark, Deca is able to (1) reduce the garbage collection time by up to 99.9%, (2) reduce the memory consumption by up to 46.6% and the storage space by 23.4%, (3) achieve 1.2× to 22.7× speedup in terms of execution time in cases without data spilling and 16× to 41.6× speedup in cases with data spilling, and (4) provide similar performance compared to domain-specific systems

Absence of integrin-mediated TGFβ1 activation in vivo recapitulates the phenotype of TGFβ1-null mice

The multifunctional cytokine transforming growth factor (TGF) β1 is secreted in a latent complex with its processed propeptide (latency-associated peptide [LAP]). TGFβ1 must be functionally released from this complex before it can engage TGFβ receptors. One mechanism of latent TGFβ1 activation involves interaction of the integrins αvβ6 and αvβ8 with an RGD sequence in LAP; other putative latent TGFβ1 activators include thrombospondin-1, oxidants, and various proteases. To assess the contribution of RGD-binding integrins to TGFβ1 activation in vivo, we created a mutation in Tgfb1 encoding a nonfunctional variant of the RGD sequence (RGE). Mice with this mutation (Tgfb1RGE/RGE) display the major features of Tgfb1−/− mice (vasculogenesis defects, multiorgan inflammation, and lack of Langerhans cells) despite production of normal levels of latent TGFβ1. These findings indicate that RGD-binding integrins are requisite latent TGFβ1 activators during development and in the immune system

A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation

As an important step of synthetic aperture radar image interpretation, synthetic aperture radar image segmentation aims at segmenting an image into different regions in terms of homogeneity. Because of the deficiency of the labeled samples and the existence of speckling noise, synthetic aperture radar image segmentation is a challenging task. We present a new method for synthetic aperture radar image segmentation in this article. Due to the large size of the original synthetic aperture radar image, we first divide the input image into small slices. Then the image slices are input to the attention-based fully convolutional network for obtaining the segmentation results. Finally, the fully connected conditional random field is adopted for improving the segmentation performance of the network. The innovations of our method are as follows: 1) The attention-based fully convolutional network is embedded with the multiscale attention network which is capable of enhancing the extraction of the image features through three strategies, namely, multiscale feature extraction, channel attention extraction, and spatial attention extraction. 2) We design a new loss function for the attention fully convolutional network by combining Lovasz-Softmax and cross-entropy losses. The new loss allows us to simultaneously optimize the intersection over union and the pixel classification accuracy of the segmentation results. The experiments are performed on two airborne synthetic aperture radar image databases. It has been proved that our method is superior to other state-of- the-art image segmentation approaches

Hsp47 Promotes Cancer Metastasis by Enhancing Collagen-Dependent Cancer Cell-Platelet Interaction

Increased expression of extracellular matrix (ECM) proteins in circulating tumor cells (CTCs) suggests potential function of cancer cell-produced ECM in initiation of cancer cell colonization. Here, we showed that collagen and heat shock protein 47 (Hsp47), a chaperone facilitating collagen secretion and deposition, were highly expressed during the epithelial-mesenchymal transition (EMT) and in CTCs. Hsp47 expression induced mesenchymal phenotypes in mammary epithelial cells (MECs), enhanced platelet recruitment, and promoted lung retention and colonization of cancer cells. Platelet depletion in vivo abolished Hsp47-induced cancer cell retention in the lung, suggesting that Hsp47 promotes cancer cell colonization by enhancing cancer cell–platelet interaction. Using rescue experiments and functional blocking antibodies, we identified type I collagen as the key mediator of Hsp47-induced cancer cell–platelet interaction. We also found that Hsp47-dependent collagen deposition and platelet recruitment facilitated cancer cell clustering and extravasation in vitro. By analyzing DNA/RNA sequencing data generated from human breast cancer tissues, we showed that gene amplification and increased expression of Hsp47 were associated with cancer metastasis. These results suggest that targeting the Hsp47/collagen axis is a promising strategy to block cancer cell–platelet interaction and cancer colonization in secondary organs