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Fast computation of global solutions to the single-period unit commitment problem

JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第3期44卷 1511-1536页

作者： Lu, Cheng Deng, Zhibin Fang, Shu-Cherng Jin, Qingwei Xing, Wenxun North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Sch Econ & Management Beijing 100190 Peoples R China North Carolina State Univ Dept Ind & Syst Engn Raleigh NC 27695 USA Zhejiang Univ Dept Management Sci & Engn Hangzhou 310058 Zhejiang Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

The single-period unit commitment problem has significant applications in electricity markets. An efficient global algorithm not only provides the optimal schedule that achieves the lowest cost, but also plays an important role for deriving the market-clearing price. As of today, the problem is mainly solved by using a general-purpose mixed-integer quadratic programming solver such as CPLEX or Gurobi. This paper proposes an extremely efficient global optimization algorithm for solving the problem. We propose a conjugate function based convex relaxation and design a special dual algorithm to compute a tight lower bound of the problem in O(n log n) complexity. Then, a branch-and-bound algorithm is designed for finding a global solution to the problem. Computational experiments show that the proposed algorithm solves test instances with 500 integer variables in less than 0.01 s, whereas current state-of-the-art solvers fail to solve the same test instances in one hour. This superior performance of the proposed algorithm clearly indicates its potential in day-ahead and real-time electricity markets.

关键词： Mixed-integer pogramming Quadratic programming Branch-and-bound algorithm

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Real Estate with AI: An agent based on LangChain

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Procedia Computer Science 2024年 242卷 1082-1088页

作者： Kasper Raupach Haurum Ruiqi Ma Wen Long Business School Aalborg University Aalborg 9220 Denmark Sino-Danish College University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China

Recent developments in large language models (LLMs) have opened new avenues for the real estate industry. These models not only understand language but also function as intelligent agents, engaging with investors through open-ended conversations and influencing their decision-making. Utilizing unstructured data from a professional Danish real estate website, we developed a real estate AI agent in both English and Danish using LangChain and Pinecone. Through testing and evaluation, our agent has demonstrated superior professional and concise outputs compared to other LLMs like Doubao and ChatGPT 4 and shown excellent performance and effectiveness. Our work serves as a reference for AI in real estate investment-related research and proposes new solutions to the "unprofessional foundation " and " expensive consulting fee" problems encountered by ordinary investors in their investment decisions.

关键词： Real Estate Decision-making LLMs LangChain Text data mining

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OPF-Miner: Order-Preserving Pattern mining With Forgetting Mechanism for Time Series

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2024年第12期36卷 8981-8995页

作者： Li, Yan Ma, Chenyu Gao, Rong Wu, Youxi Li, Jinyan Wang, Wenjian Wu, Xindong Hebei Univ Technol Sch Econ & Management Tianjin 300400 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300400 Peoples R China Shenzhen Univ Adv Technol Sch Comp Sci & Control Engn Shenzhen 518055 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 237016 Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important;however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed.

关键词： Time series analysis data mining Market research Clustering algorithms Pattern matching Symbols Indexes Forgetting mechanism order-preserving pattern (OPP) pattern mining time series

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Anomaly classification based on self-supervised learning and its application

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JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2024年第3期17卷

作者： Han, Yongsheng Qi, Zhiquan Tian, Yingjie Univ Chinese Acad Sci Sch Econ & Management 80 Zhongguancun East Rd Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Background: Anomaly classification remains a challenging task in computer vision applications across diverse practical fields such as industrial detection and security check. The purpose of this study is to develop a new anomaly classification method based on self-supervised learning (Self-ACM), which is expected to enhance anomaly classification accuracy significantly. Methods: The feature maps of images were abstracted using VGG16 model pre-trained on ImageNet, which were then fed into the innovative model to capture the normality distribution within the dataset domain. Then, a selfsupervised adversarial anomaly classification learning framework was proposed to facilitate the acquisition of a higher-level semantic representations for improved anomaly detection. Thirdly, we collected and constructed a novel terahertz (THZ) dataset, which serves as a pioneering resource for benchmarking anomaly classification tasks in the field. Results: Through a series of rigorous experiments, our findings unequivocally demonstrate the following key insights: Firstly, harnessing feature maps as input data yielded a significant enhancement in anomaly detection performance, underscoring the effectiveness of this approach. Secondly, the integration of self-supervision enriched the dataset with invaluable information, empowering both the discriminator and generator to acquire superior feature representations. The culmination of these advancements is our novel method achieving unparalleled state-of-the-art performance across multiple benchmark datasets. This breakthrough underscores the transformative impact of our approach on anomaly detection methodologies, solidifying its position as a pioneering solution in the field. Conclusion: The Self-ACM strategy not only advances anomaly detection methodologies but also offers a remarkable contribution to dataset creation, setting a new standard for anomaly classification research.

关键词： Self -supervised learning Anomaly classification Feature map Machine learning

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Impact of the China Railway Express on Employment Structure - Evidence from Urban Economic data

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Procedia Computer Science 2024年 242卷 749-757页

作者： Bochao Yan Hailiang Jiang Junfeng Wu Xin Tian China Agricultural University Beijing 100083 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Shanghai WinJoin Information Technology Co. Ltd. Shanghai 200126 China

As the main international trade channel of China’s “westward development”, the opening of China Railway Express has significantly improved the trade facilitation of countries along the route. This paper investigates the impact of the opening of China Railway Express sites on the employment choices of individuals by using the difference in difference method based on the economic panel data of China’s prefecture-level cities and the data on the opening of China Railway Express during the period of 2007~2019, which are matched by the correlation. The results of the study show that the opening of the China Railway Express increases the probability of individuals within the radiation area to enter the private sector for employment. The empirical results provide a new explanatory mechanism for the opening of the China Railway Express on economic growth, which has certain theoretical and policy value.

关键词： China Railway Express The Belt Road Employment Structure Quasi-natural Experiments

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Attention Transfer Network for Nature Image Matting

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第6期31卷 2192-2205页

作者： Zhou, Fenfen Tian, Yingjie Qi, Zhiquan Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Natural image matting is an important problem that widely applied in computer vision and graphics. Recent deep learning matting approaches have made an impressive process in both accuracy and efficiency. However, there are still two fundamental problems remain largely unsolved: 1) accurately separating an object from the image with similar foreground and background color or lots of details;2) exactly extracting an object with fine structures from complex background. In this paper, we propose an attention transfer network (ATNet) to overcome these challenges. Specifically, we firstly design a feature attention block to effectively distinguish the foreground object from the color-similar regions by activating foreground-related features as well as suppressing others. Then, we introduce a scale transfer block to magnify the feature maps without adding extra information. By integrating the above blocks into an attention transfer module, we effectively reduce the artificial content in results and decrease the computational complexity. Besides, we use a perceptual loss to measure the difference between the feature representations of the predictions and the ground-truths. It can further capture the high-frequency details of the image, and consequently, optimize the fine structures of the object. Extensive experiments on two publicly common datasets (i.e., Composition-1k matting dataset, and *** dataset) show that the proposed ATNet obtains significant improvements over the previous methods. The source code and compiled models have been made publicly available at https://***/ailsaim/ATNet.

关键词： Image color analysis Semantics Loss measurement Task analysis Machine learning Feature extraction Computer vision Natural image matting feature attention scale transfer perceptual loss

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Incomplete-view oriented kernel learning method with generalization error bound

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Information Sciences 2021年 581卷 951-977页

作者： Tian, Yingjie Fu, Saiji Tang, Jingjing School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China

Multi-view learning (MVL) is a promising direction and most MVL methods work under the assumption that data are complete in all views. However, this assumption is often violated in practice due to the difficulties of high cost, equipment failure, and so on. Besides, the amount of data with missing views is particularly huge. Therefore, it is challenging yet valuable to fully leverage the large-scale incomplete-view data. Enlightened by the reduced support vector machine (RSVM) and multi-view privileged support vector machine (PSVM-2V), this paper proposes an efficient kernel method called reduced PSVM-2V (RPSVM-2V). It can not only provide a novel solution to process incomplete-view data, but also can be adjusted to address large-scale complete-view learning problem efficiently. In addition, the idea of replacing the full kernel with a smaller rectangular reduced kernel is extended to develop another two MVL methods, i.e., reduced SVM-2K (RSVM-2K) and reduced multiple kernel learning method (RMKL). Furthermore, we analyze the generalization error bounds of RPSVM-2V and two extensions by using Rademacher complexity. The comprehensive experiments demonstrate that our proposed models can achieve comparable performance with less time and memory cost. The spectral analysis has further verified the effectiveness of the reduced kernel used in our models. © 2021 Elsevier Inc.

关键词： Support vector machines

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A Comparative Study of the Effects of Two Monetary Policy Tools on Money Market Interest Rates

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Procedia Computer Science 2024年 242卷 482-491页

作者： Shengjie Pan Shuyang Peng Jiayu Wan Yi Qu Zhejiang Mercantile Exchange Co. Ltd. Zhoushan City 316000 China School of Economics and Management Southeast University Nanjing 211189 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Economics and Management Nanjing Forestry University Nanjing 210037 China

This paper focuses on the regulation effect of money market interest rates from monetary policy tools. By employing Time-Varying Parameter Stochastic Volatility Vector Autoregressive model, we comparatively analyze the effects of the Standing Lending Facility (SLF) and the 7-Day Reverse Repurchase Agreements in the Open Market (RR7D) on the 7-Day Repo Rate of Deposit-taking Financial Institutions (DR007). It is found that SLF can marginally improve DR007 in the short term but can effectively lower it in the medium to long term by acting as the ceiling of the interest rate corridor. RR7D can improve DR007 with a stronger impact, compared to the SLF. The effectiveness of these monetary policy tools remains relatively stable under different socio-economic backgrounds, achieving the intended objectives of the central bank. Finally, the study proposes suggestions, close integration of the interest rate corridor and open market operations in monetary policy regulation, and encourages the use of SLFs to support the high-quality development of the national economy.

关键词： Monetary Policy tools Market interest rates TVP-SV-VAR

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Weakly Supervised Text-based Actor-Action Video Segmentation by Clip-level Multi-instance Learning

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2023年第1期19卷 12-12页

作者： Chen, Weidong Li, Guorong Zhang, Xinfeng Wang, Shuhui Li, Liang Huang, Qingming UCAS Sch Comp Sci & Technol Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci ICT Key Lab Intelligent Informat Proc Beijing 100049 Peoples R China

In real-world scenarios, it is common that a video contains multiple actors and their activities. Selectively localizing one specific actor and its action spatially and temporally via a language query becomes a vital and challenging task. Existing fully supervised methods require extensive elaborately annotated data and are sensitive to the class labels, which cannot satisfy real-world applications' needs. Thus, we introduce the task of weakly supervised actor-action video segmentation from a sentence query (AAVSS) in this work, where only the video-sentence pairs are provided. To the best of our knowledge, our work is the first to perform AAVSS under weakly supervised situations. However, this task is extremely challenging not only because the task aims to learn the complex interactions between two heterogeneous modalities but also because the task needs to learn fine-grained analysis of video content without pixel-level annotations. To overcome the challenges, we propose a two-stage network. The network first follows the sentence guidance to localize the candidate region and then performs segmentation to achieve selective segmentation. Specifically, a novel tracker-based clip-level multiple instance learning paradigm is proposed in this article to learn the matches between regions and sentences, which makes our two-stage network robust to the region proposal network. Furthermore, two intrinsic characteristics of the video, temporal consistency and motion information, are utilized in companion with the weak supervision to facilitate the region-query matching. Through extensive experiments, the proposed method achieves comparable performance to state-of-the-art fully supervised approaches on two large-scale benchmarks, including A2D Sentences and J-HMDB Sentences.

关键词： Multiple instance learning weakly supervised learning video actor-action segmentation cross-modal learning

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Evaluation of China's Digital Economy: A Case Study using Entropy and TOPSIS 11

Evaluation of China's Digital Economy: A Case Study using En...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Liu, Junjie Wang, Boya Xue, Jiayu Qu, Yi Shi, Yong Sino-Danish College University of Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth;Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy;Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces. © 2024 The Authors.

关键词： Digital economy Evaluation Multi-source information TOPSIS

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