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Option Pricing Based on GA-BP neural network

Procedia Computer Science 2022年 199卷 1340-1354页

作者： Long Qian Jianbin Zhao Yue Ma International College of Zhengzhou University Zhengzhou University Zhengzhou 450052 China Zhengzhou Key Laboratory of Big Data Analysis and Application Henan Academy of Big Data Zhengzhou University Zhengzhou 450052 China

Machine Learning has received widespread attention in the financial field, especially in the issue of option pricing. Compared with the original BSM (Black-Scholes-Merton) option pricing method, this paper introduces the genetic algorithm into the option pricing process based on the characteristics of financial data, and constructs a GA-BP neural network (Neural Networks) option pricing Model and empirical analysis of European call options based on the Shanghai and Shenzhen 300 Index. The research results show that the option price calculation result of the GA-BP neural network algorithm has better accuracy than the BP neural network model or the classic BSM method, while taking into account efficiency, which is helpful to predicting option prices in financial practice to a certain extent.

关键词： BP Neural Network Genetic Algorithm Option Pricing BSM Model

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A Cognitive Solver with Autonomously Knowledge Learning for Reasoning Mathematical Answers

A Cognitive Solver with Autonomously Knowledge Learning for ...

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IEEE International Conference on data Mining (ICDM)

作者： Jiayu Liu Zhenya Huang Xin Lin Qi Liu Jianhui Ma Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science & School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence

Reasoning answers to mathematical problems requires machines to think and operate like a human to learn knowledge from mathematical data, which is one of the fundamental tasks for exploring general artificial intelligence. Most solutions focus on mimicking how humans understand problems, which generate the necessary expressions for answers. However, they are still far from enough since they ignore the core ability of humans to acquire knowledge from experience. In this paper, we propose a Cognitive Solver (CogSolver) that is capable of autonomously learning knowledge from scratch to solve mathematical problems, inspired by two cognitive science theories. Specifically, we draw one insight from the dual process theory to establish an intelligent BRAIN-ARM framework, and refer to another information processing theory to summarize the knowledge learning process into Store-Apply-Update steps. In CogSolver, the BRAIN system stores three types of mathematical knowledge, including semantics knowledge, relation knowledge, and mathematic rule knowledge. Then, the ARM system applies the knowledge in BRAIN to answer the problems. Specifically, we design a knowledge-aware module and a commutative module in ARM to improve its reasoning ability, where the knowledge is organically integrated into answer reasoning process. After solving the problems, BRAIN updates the stored knowledge according to the feedback of ARM, where we develop knowledge filters to eliminate the redundant ones and further form a more reasonable knowledge base. Our CogSolver carries out the above three steps iteratively, which behaves more like a human. We conduct extensive experiments on real-world math word problem datasets. The experimental results demonstrate the improvement in answer reasoning and clearly show how CogSolver gains knowledge from the problems, leading to superior interpretability. Our codes are available at https://***/bigdata-ustc/CogSolver.

关键词： Commutation Semantics Knowledge based systems Information processing Cognition Mathematics Filtering theory

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Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

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Engineering applications of Artificial Intelligence 2025年 156卷

作者： Lu, Runuo Dong, Shoubin Jia, Jianxin Wang, Xusheng Liu, Kai Chen, Jinsong Guo, Shanxin Zheng, Xiaorou Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute EspooFI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS. ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) Geographic location variation. (II) Geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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The Influence of Enterprise Financing Structure in China on Business Performance—Simulation analysis Based on the System Dynamics 12th

The Influence of Enterprise Financing Structure in China on ...

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12th EAI International Conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Li, Chenggang Wan, Jun College of Big Data Application and Economics Guizhou University of Finance and Economics Guiyang550025 China Guizhou Key Laboratory of Big Data Statistics Analysis Guizhou University of Finance and Economics Guiyang Guizhou China

ISBN: (纸本)9783030727949

Enterprise financing structure has an impact on the profitability and solvency of the enterprise to some extent, which is also called the influence on business performance. Enterprise managers and scholars have always focused on the influence of enterprise financing structure on business performance. This paper selects the data of the listed companies from 2010 to 2018, uses the method of the system dynamics, constructs the system dynamics model of enterprise financing structure, and simulates and analyzes the influence of equity financing and debt financing on enterprise business performance. The simulation shows: (1) Equity financing has an active impact on the rate of total assets and scale, while a negative impact on asset liability ratio. (2) Debt financing has an active impact on the rate of total assets, liability assets and scale. (3) Between the two external financing, the influence of equity financing on the business performance is better than the debt financing’s. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021.

关键词： Commerce

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Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing

arXiv

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arXiv 2024年

作者： He, Liyang Chen, Enhong Huang, Zhenya Wang, Fei Wang, Shijin Liu, Jiayu Sha, Jing School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China iFLYTEK AI Research State Key Laboratory of Cognitive Intelligence Hefei China

Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult to overlook. Knowledge distillation provides a means for practical model compression to alleviate this delay. Nevertheless, the prevailing knowledge distillation approaches are not explicitly designed for semantic hashing. They ignore the unique search paradigm of semantic hashing, the inherent necessities of the distillation process, and the property of hash codes. In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. To ensure the effectiveness of two kinds of search paradigms in the context of semantic hashing, BRCD first aligns the semantic spaces between the teacher and student models through a contrastive knowledge distillation objective. Additionally, to eliminate noisy augmentations and ensure robust optimization, a cluster-based method within the knowledge distillation process is introduced. Furthermore, through a bit-level analysis, we uncover the presence of redundancy bits resulting from the bit independence property. To mitigate these effects, we introduce a bit mask mechanism in our knowledge distillation objective. Finally, extensive experiments not only showcase the noteworthy performance of our BRCD method in comparison to other knowledge distillation methods but also substantiate the generality of our methods across diverse semantic hashing models and backbones. The code for BRCD is available at https://***/hly1998/BRCD. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Spatiotemporal distribution and inter-media transfer of polycyclic aromatic hydrocarbons in Shanghai, China: Historical patterns and future trends

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Science of the Total Environment 2024年 945卷 173938页

作者： Li, Ting Nie, Ning Miao, Yiyi Zhao, Dengzhong Liu, Min Key Laboratory of Geographic Information Science of the Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China Changjiang River Scientific Research Institute Wuhan430010 China

Polycyclic Aromatic Hydrocarbons (PAHs) represent pervasive pollutants, posing health risks in urban environments. It is essential to comprehend the spatiotemporal distributions, composition profiles, and inter-media transfer processes of PAHs in various environmental compartments, influenced by both natural changes and anthropogenic activities. This study integrates historical and future spatiotemporally changing environmental parameters, including climate data, GDP, population data, land-use types, and hydrological variables, into the Multimedia Urban Model (MUM). This integration enables the simulation of spatiotemporal distributions and inter-media transfer fluxes of PAHs among six different media from the 2010s to the 2100s under two distinct Shared Socio-economic Pathways (SSP) scenarios in the megacity of Shanghai, China. The MUM model, featuring diverse gridded parameters, effectively captures PAH concentrations and movement across environmental compartments. Results indicate a decreasing trend in PAHs concentrations in the 2100s compared to the 2010s, with PAH concentrations in water, sediment, vegetation, and organic film covering impermeable surfaces under the SSP3–7.0 scenario higher than those of the SSP1–2.6 scenario. Low molecular weight PAHs dominate in the sediment, water, and air, whereas high molecular weight PAHs prevail in the organic film, vegetation, and soil. Sediment and soil serve as the predominant sinks for PAHs. The primary transport processes for PAH movement include air-film, air-soil, film-water, soil-air, and water-air. Almost all transfer fluxes exhibit a declining trend in future periods except for the air-film transport pathway. The principal input and removal routes for PAHs in Shanghai involve the advection of air and water. The study provides essential insights into the environmental behavior of PAHs and informs targeted pollution control in Shanghai. Additionally, it serves as a technical reference for similar pollution predic

关键词： Soils

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FEDGT: FEDERATED NODE CLASSIFICATION WITH SCALABLE GRAPH TRANSFORMER

arXiv

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arXiv 2024年

作者： Zhang, Zaixi Hu, Qingyong Yu, Yang Gao, Weibo Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Hong Kong University of Science and Technology Hong Kong

Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local systems. For example, recently proposed subgraph federated learning methods train Graph Neural Networks (GNNs) distributively on local subgraphs and aggregate GNN parameters with a central server. However, existing methods have the following limitations: (1) The links between local subgraphs are missing in subgraph federated learning. This could severely damage the performance of GNNs that follow message-passing paradigms to update node/edge features. (2) Most existing methods overlook the subgraph heterogeneity issue, brought by subgraphs being from different parts of the whole graph. To address the aforementioned challenges, we propose a scalable Federated Graph Transformer (FedGT) in the paper. Firstly, we design a hybrid attention scheme to reduce the complexity of the Graph Transformer to linear while ensuring a global receptive field with theoretical bounds. Specifically, each node attends to the sampled local neighbors and a set of curated global nodes to learn both local and global information and be robust to missing links. The global nodes are dynamically updated during training with an online clustering algorithm to capture the data distribution of the corresponding local subgraph. Secondly, FedGT computes clients' similarity based on the aligned global nodes with optimal transport. The similarity is then used to perform weighted averaging for personalized aggregation, which well addresses the data heterogeneity problem. Finally, extensive experimental results on 6 datasets and 2 subgraph settings demonstrate the superiority of FedGT. © 2024, CC BY.

关键词： Graph neural networks

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Adaptive normalization for non-stationary time series forecasting: a temporal slice perspective 23

Adaptive normalization for non-stationary time series foreca...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhiding Liu Mingyue Cheng Zhi Li Zhenya Huang Qi Liu Yanhu Xie Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Shenzhen International Graduate School Tsinghua University The First Affiliated Hospital of University of Science and Technology of China

Deep learning models have progressively advanced time series forecasting due to their powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to make accurate predictions due to the existence of non-stationarity in real-world data, denoting the data distribution rapidly changes over time. To mitigate such a dilemma, several efforts have been conducted by reducing the non-stationarity with normalization operation. However, these methods typically overlook the distribution discrepancy between the input series and the horizon series, and assume that all time points within the same instance share the same statistical properties, which is too ideal and may lead to suboptimal relative improvements. To this end, we propose a novel slice-level adaptive normalization, referred to SAN, which is a novel scheme for empowering time series forecasting with more flexible normalization and denormalization. SAN includes two crucial designs. First, SAN tries to eliminate the non-stationarity of time series in units of a local temporal slice (i.e., sub-series) rather than a global instance. Second, SAN employs a slight network module to independently model the evolving trends of statistical properties of raw time series. Consequently, SAN could serve as a general model-agnostic plugin and better alleviate the impact of the non-stationary nature of time series data. We instantiate the proposed SAN on four widely used forecasting models and test their prediction results on benchmark datasets to evaluate its effectiveness. Also, we report some insightful findings to deeply analyze and understand our proposed SAN. We make our codes publicly available https://***/icantnamemyself/SAN.

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Using Multidisciplinary analysis to Develop Adaptation Options against Extreme Coastal Floods

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International Journal of Disaster Risk Science 2022年第4期13卷 577-591页

作者： Xinmeng Shan Jun Wang Jiahong Wen Hengzhi Hu Lei Wang Jie Yin Mengya Li Key Laboratory of Geographic Information Science Ministry of EducationEast China Normal UniversityShanghai 200241China School of Geographic Sciences East China Normal UniversityShanghai 200241China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural ResourcesShanghai 200241China School of Environmental and Geographical Sciences Shanghai Normal UniversityShanghai 200234China Department of Hospitality Management Shanghai Business SchoolShanghai 200235China Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70810USA

Long-term flood risk adaptation and decision making are complex because the future is full of deep *** and robustness can be used to deal with future *** study developed an integrated modeling framework that extends previous studies to the spatial domain to assess the future flood risks and the cost and benefit of three adaptation measures for four types of buildings in *** options analysis(ROA)and dynamic adaptive policy pathways(DAPP)were integrated to develop a dynamic adaptation pathway and identify robust adaptation *** results show that:(1)Sea level rise and land subsidence will significantly exacerbate the flood risks in Shanghai;(2)Among the three flood control measures,wet-floodproofing has the best economic performance in terms of both the net present value and the benefit/cost ratio,followed by dry-floodproofing,and elevation;(3)Dryfloodproofing can be used at the beginning of the future period(2030–2100),and it can be replaced by wet-floodproofing in 2035–2042;the elevation measure also shows good performance at the beginning of implementation,but its performance will decline after 2041–2045;(4)The combined strategy of dry-and wet-floodproofing in 2044–2046and a hybrid strategy combining the three measures should be the optimal solution for reducing the flood risks in 2047–*** methodology developed in this study can provide insights for coastal cities to formulate cost-effective and feasible adaptation strategies in a deeply uncertain future.

关键词： Adaptation options Building-level measures Climate change Coastal floods Cost-benefit analysis Shanghai

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Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation

arXiv

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arXiv 2024年

作者： Wang, Hao Han, Yongqiang Wang, Kefan Cheng, Kai Wang, Zhen Guo, Wei Liu, Yong Lian, Defu Chen, Enhong The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230026 China SUN YAT-SEN University China Huawei's Noah's Ark Laboratory Hong Kong

In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit semantic relationships between these behaviors to enhance recommendation performance. However, these methods often entail high computational complexity. To address concerns regarding efficiency, pre-training presents a viable solution. Its objective is to extract knowledge from extensive pre-training data and fine-tune the model for downstream tasks. Nevertheless, previous pre-training methods have primarily focused on single-behavior data, while multi-behavior data contains significant noise. Additionally, the fully fine-tuning strategy adopted by these methods still imposes a considerable computational burden. In response to this challenge, we propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. Specifically, in the pre-training stage, we commence by proposing a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales, thereby facilitating the comprehension of the contextual semantics of multi-behavior sequences. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module, which generates personalized, progressive, and diverse prompts to fully exploit the potential of the pre-trained model effectively. Extensive experiments on three real-world datasets have unequivocally demonstrated that DPCPL not only exhibits high efficiency and effectiveness, requiring minimal parameter adjustments but also surpasses the state-of-the-art performance across a diverse range of downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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