Detecting Directed Interactions of Networks by Random Variable Resetting

We propose a novel method of detecting directed interactions of a general dynamic network from measured data. By repeating random state variable resetting of a target node and appropriately averaging over the measurable data, the pairwise coupling function between the target and the response nodes can be inferred. This method is applicable to a wide class of networks with nonlinear dynamics, hidden variables and strong noise. The numerical results have fully verified the validity of the theoretical derivation

Design and Production of 3D Animation Short Film “Relict”

Relict is a science fiction 3D animated short film that surrounds a fantasy of future Beijing, the capital city of the People\u27s Republic of China. The short film critically explores three important problems, which are heatedly discussed nowadays in Beijing, of large population, environmental pollution and declination of people’s living standard through a fictional point of view of several Landmark buildings of the metropolis. These three issues I hope to discuss in this short film are closely related to each other. Firstly, the large population problem is caused by the fact of its greater resources in politics, economy and education as the center and capital city of China. More and more young people seek to settle in Beijing for splendid development which unavoidably leads to the problems of cheaper labor, higher pressure and the rising of the housing prices and causes living standard going constantly down in Beijing. The great air pollution is mainly caused by large number of automobile exhaust and coal heating in winter which is also closely related with overpopulated problem. The author of the thesis made this short film to arouse his audience\u27s reflection on these issues through modeling and rendering by 3D technology, using realistic visual style and science fiction story setting. The duration of the short film is 2 minutes and 20 seconds. This thesis would faithfully record the creative thinking and film production process of this animated short film

Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control

Building agents using large language models (LLMs) to control computers is an emerging research field, where the agent perceives computer states and performs actions to accomplish complex tasks. Previous computer agents have demonstrated the benefits of in-context learning (ICL); however, their performance is hindered by several issues. First, the limited context length of LLMs and complex computer states restrict the number of exemplars, as a single webpage can consume the entire context. Second, the exemplars in current methods, such as high-level plans and multi-choice questions, cannot represent complete trajectories, leading to suboptimal performance in tasks that require many steps or repeated actions. Third, existing computer agents rely on task-specific exemplars and overlook the similarity among tasks, resulting in poor generalization to novel tasks. To address these challenges, we introduce Synapse, featuring three key components: i) state abstraction, which filters out task-irrelevant information from raw states, allowing more exemplars within the limited context, ii) trajectory-as-exemplar prompting, which prompts the LLM with complete trajectories of the abstracted states and actions for improved multi-step decision-making, and iii) exemplar memory, which stores the embeddings of exemplars and retrieves them via similarity search for generalization to novel tasks. We evaluate Synapse on MiniWoB++, a standard task suite, and Mind2Web, a real-world website benchmark. In MiniWoB++, Synapse achieves a 99.2% average success rate (a 10% relative improvement) across 64 tasks using demonstrations from only 48 tasks. Notably, Synapse is the first ICL method to solve the book-flight task in MiniWoB++. Synapse also exhibits a 53% relative improvement in average step success rate over the previous state-of-the-art prompting scheme in Mind2Web.Comment: 22 pages, 7 figure

Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach

Quantitative investment is a fundamental financial task that highly relies on accurate stock prediction and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, we observe that the performance of existing DL methods is sensitive to random seeds and network initialization. To design more profitable DL methods, we analyze this phenomenon and find two major limitations of existing works. First, there is a noticeable gap between accurate financial predictions and profitable investment strategies. Second, investment decisions are made based on only one individual predictor without consideration of model uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle these two limitations, we first reformulate quantitative investment as a multi-task learning problem. Later on, we propose AlphaMix, a novel two-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up trading strategy design workflow of successful trading firms. In Stage one, multiple independent trading experts are jointly optimized with an individual uncertainty-aware loss function. In Stage two, we train neural routers (corresponding to the role of a portfolio manager) to dynamically deploy these experts on an as-needed basis. AlphaMix is also a universal framework that is applicable to various backbone network architectures with consistent performance gains. Through extensive experiments on long-term real-world data spanning over five years on two of the most influential financial markets (US and China), we demonstrate that AlphaMix significantly outperforms many state-of-the-art baselines in terms of four financial criteria

Why Trick Me: The Honeypot Traps on Decentralized Exchanges

Decentralized Exchanges (DEXs) are one of the most important infrastructures in the world of Decentralized Finance (DeFi) and are generally considered more reliable than centralized exchanges (CEXs). However, some well-known decentralized exchanges (e.g., Uniswap) allow the deployment of any unaudited ERC20 tokens, resulting in the creation of numerous honeypot traps designed to steal traders' assets: traders can exchange valuable assets (e.g., ETH) for fraudulent tokens in liquidity pools but are unable to exchange them back for the original assets. In this paper, we introduce honeypot traps on decentralized exchanges and provide a taxonomy for these traps according to the attack effect. For different types of traps, we design a detection scheme based on historical data analysis and transaction simulation. We randomly select 10,000 pools from Uniswap V2 \& V3, and then utilize our method to check these pools.Finally, we discover 8,443 abnormal pools, which shows that honeypot traps may exist widely in exchanges like Uniswap. Furthermore, we discuss possible mitigation and defense strategies to protect traders' assets

A taxonomical study of agility strategies and supporting supply chain management practices

Since the turn of the century, manufacturing industry has witnessed significant structural changes. Agility, which aims to provide companies with competitive capabilities so that they can prosper from dynamic and continuous changes in the business environment, has become a prevailing manufacturing strategy. However, how to develop a manufacturing strategy based on agility, and how to design and manage global supply chain networks effectively to implement these strategy, are not fully understood. This thesis presents survey based research that was carried out on a number of U.K. manufacturing companies. The research revisited the taxonomy of agility strategies for manufacturing industry developed by Zhang and Sharifi (2007) and investigated the methods of supply chain management employed by different strategic groups. The findings show that whilst the three broad types of agility strategies discovered in previous work (Zhang and Sharifi, 2007) have remained two sub types of agility strategies have been identified. They are named Responsive players, Quick operators, Quick innovators, Proactive players 1 and Proactive players 2. Responsive players placed a high emphasis on supplier selection related practices; Quick operators placed a high emphasis on sourcing management related practices; Quick innovators placed a high emphasis on relationship management related practices; and Proactive players 1 and 2 placed high emphases on almost all practices. This research has made contributions to the theory development of agility strategy and has provides a managerial guide with companies to improve the implementation of agility strategies in supply chains