arXiv subjects

Mingyu Hao

Publications and source records attributed to Mingyu Hao.

4 recordsLinked to original sources

arXiv2024

Graph Learning for Planning: The Story Thus Far and Open Challenges

Graph learning is naturally well suited for use in planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrary number of objects. In this paper, we study the usage of graph learning for planning thus far by studying the theoretical and empirical effects on learning and planning performance of (1) graph representations of planning tasks, (2) graph learning architectures, and (3) optimisation formulations for learning. Our studies accumulate in the GOOSE framework which learns domain knowledge from small planning tasks in order to scale up to much larger planning tasks. In this paper, we also highlight and propose the 5 open challenges in the general Learning for Planning field that we believe need to be addressed for advancing the state-of-the-art.

cs.AI↗

arXiv2024

Learning to Predict Short-Term Volatility with Order Flow Image Representation

Introduction: The paper addresses the challenging problem of predicting the short-term realized volatility of the Bitcoin price using order flow information. The inherent stochastic nature and anti-persistence of price pose difficulties in accurate prediction. Methods: To address this, we propose a method that transforms order flow data over a fixed time interval (snapshots) into images. The order flow includes trade sizes, trade directions, and limit order book, and is mapped into image colour channels. These images are then used to train both a simple 3-layer Convolutional Neural Network (CNN) and more advanced ResNet-18 and ConvMixer, with additionally supplementing them with hand-crafted features. The models are evaluated against classical GARCH, Multilayer Perceptron trained on raw data, and a naive guess method that considers current volatility as a prediction. Results: The experiments are conducted using price data from January 2021 and evaluate model performance in terms of root mean square error (RMSPE). The results show that our order flow representation with a CNN as a predictive model achieves the best performance, with an RMSPE of 0.85+/-1.1 for the model with aggregated features and 1.0+/-1.4 for the model without feature supplementation. ConvMixer with feature supplementation follows closely. In comparison, the RMSPE for the naive guess method was 1.4+/-3.0.

q-fin.RM↗

arXiv2023

Blockchain-enabled Parametric Solar Energy Insurance via Remote Sensing

Despite its popularity, the nature of solar energy is highly uncertain and weather dependent, affecting the business viability and investment of solar energy generation, especially for household users. To stabilize the income from solar energy generation, there have been limited traditional options, such as using energy storage to pool excessive solar energy in off-peak periods or financial derivatives from future markets to hedge energy prices. In this paper, we explore a novel idea of "parametric solar energy insurance", by which solar panel owners can insure their solar energy generation based on a verifiable geographically specific index (surface solar irradiation). Parametric solar energy insurance offers opportunities of financial subsidies for insufficient solar energy generation and amortizes the fluctuations of renewable energy generation geographically. Furthermore, we propose to leverage blockchain and remote sensing (satellite imagery) to provide a publicly verifiable platform for solar energy insurance, which not only automates the underwriting and claims of a solar energy insurance policy, but also improves its accountability and transparency. We utilize the state-of-the-art succinct zero-knowledge proofs (zk-SNARK) to realize privacy-preserving blockchain-based solar energy insurance on real-world permissionless blockchain platform Ethereum.

cs.CR↗

arXiv2023

Privacy-preserving Blockchain-enabled Parametric Insurance via Remote Sensing and IoT

Traditional Insurance, a popular approach of financial risk management, has suffered from the issues of high operational costs, opaqueness, inefficiency and a lack of trust. Recently, blockchain-enabled "parametric insurance" through authorized data sources (e.g., remote sensing and IoT) aims to overcome these issues by automating the underwriting and claim processes of insurance policies on a blockchain. However, the openness of blockchain platforms raises a concern of user privacy, as the private user data in insurance claims on a blockchain may be exposed to outsiders. In this paper, we propose a privacy-preserving parametric insurance framework based on succinct zero-knowledge proofs (zk-SNARKs), whereby an insuree submits a zero-knowledge proof (without revealing any private data) for the validity of an insurance claim and the authenticity of its data sources to a blockchain for transparent verification. Moreover, we extend the recent zk-SNARKs to support robust privacy protection for multiple heterogeneous data sources and improve its efficiency to cut the incurred gas cost by 80%. As a proof-of-concept, we implemented a working prototype of bushfire parametric insurance on real-world blockchain platform Ethereum, and present extensive empirical evaluations.

cs.CR↗