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Causal discovery identifies causal relationships in data, but the task is more complex for multivariate time series due to the computational demands of methods like VarLiNGAM, which combines a Vector Autoregressive Model with a Linear Non-Gaussian Acyclic Model. This study optimizes causal discovery specifically for time series data, which are common in practical applications. Time series causal discovery is particularly challenging because of temporal dependencies and potential time lag effects. By developing a specialized dataset generator and reducing the computational complexity of the VarLiNGAM model from \( O(m^3 \cdot n) \) to \( O(m^3 + m^2 \cdot n) \), this study enhances the feasibility of processing large datasets. The proposed methods were validated on advanced computational platforms and tested on simulated, real-world, and large-scale datasets, demonstrating improved efficiency and performance. The optimized algorithm achieved 7 to 13 times speedup compared to the original and about 4.5 times speedup compared to the GPU-accelerated version on large-scale datasets with feature sizes from 200 to 400. Our methods extend current causal discovery capabilities, making them more robust, scalable, and applicable to real-world scenarios, facilitating advancements in fields like healthcare and finance.

Due to their complex spatial structure and diverse geometric features, achieving high-precision and robust point cloud registration for complex Die Castings has been a significant challenge in the die-casting industry. Existing point cloud registration methods primarily optimize network models using well-established high-quality datasets, often neglecting practical application in real scenarios. To address this gap, this paper proposes a high-precision adaptive registration method called Multiscale Efficient Deep Closest Point (MEDPNet) and introduces a die-casting point cloud dataset, DieCastCloud, specifically designed to tackle the challenges of point cloud registration in the die-casting industry. The MEDPNet method performs coarse die-casting point cloud data registration using the Efficient-DCP method, followed by precision registration using the Multiscale feature fusion dual-channel registration (MDR) method. We enhance the modeling capability and computational efficiency of the model by replacing the attention mechanism of the Transformer in DCP with Efficient Attention and implementing a collaborative scale mechanism through the combination of serial and parallel blocks. Additionally, we propose the MDR method, which utilizes multilayer perceptrons (MLP), Normal Distributions Transform (NDT), and Iterative Closest Point (ICP) to achieve learnable adaptive fusion, enabling high-precision, scalable, and noise-resistant global point cloud registration. Our proposed method demonstrates excellent performance compared to state-of-the-art geometric and learning-based registration methods when applied to complex die-casting point cloud data.

We propose the Chiarella-Heston model, a new agent-based model for improving the effectiveness of deep hedging strategies. This model includes momentum traders, fundamental traders, and volatility traders. The volatility traders participate in the market by innovatively following a Heston-style volatility signal. The proposed model generalises both the extended Chiarella model and the Heston stochastic volatility model, and is calibrated to reproduce as many empirical stylized facts as possible. According to the stylised facts distance metric, the proposed model is able to reproduce more realistic financial time series than three baseline models: the extended Chiarella model, the Heston model, and the Geometric Brownian Motion. The proposed model is further validated by the Generalized Subtracted L-divergence metric. With the proposed Chiarella-Heston model, we generate a training dataset to train a deep hedging agent for optimal hedging strategies under various transaction cost levels. The deep hedging agent employs the Deep Deterministic Policy Gradient algorithm and is trained to maximize profits and minimize risks. Our testing results reveal that the deep hedging agent, trained with data generated by our proposed model, outperforms the baseline in most transaction cost levels. Furthermore, the testing process, which is conducted using empirical data, demonstrates the effective performance of the trained deep hedging agent in a realistic trading environment., Comment: Accepted in the 4th ACM International Conference on AI in Finance (ICAIF'23)

This paper introduces a novel optimization framework for deep neural network (DNN) hardware accelerators, enabling the rapid development of customized and automated design flows. More specifically, our approach aims to automate the selection and configuration of low-level optimization techniques, encompassing DNN and FPGA low-level optimizations. We introduce novel optimization and transformation tasks for building design-flow architectures, which are highly customizable and flexible, thereby enhancing the performance and efficiency of DNN accelerators. Our results demonstrate considerable reductions of up to 92\% in DSP usage and 89\% in LUT usage for two networks, while maintaining accuracy and eliminating the need for human effort or domain expertise. In comparison to state-of-the-art approaches, our design achieves higher accuracy and utilizes three times fewer DSP resources, underscoring the advantages of our proposed framework., Comment: 5 pages, Accepted at FPL'23

This article presents XGB-Chiarella, a powerful new approach for deploying agent-based models to generate realistic intra-day artificial financial price data. This approach is based on agent-based models, calibrated by XGBoost machine learning surrogate. Following the Extended Chiarella model, three types of trading agents are introduced in this agent-based model: fundamental traders, momentum traders, and noise traders. In particular, XGB-Chiarella focuses on configuring the simulation to accurately reflect real market behaviours. Instead of using the original Expectation-Maximisation algorithm for parameter estimation, the agent-based Extended Chiarella model is calibrated using XGBoost machine learning surrogate. It is shown that the machine learning surrogate learned in the proposed method is an accurate proxy of the true agent-based market simulation. The proposed calibration method is superior to the original Expectation-Maximisation parameter estimation in terms of the distance between historical and simulated stylised facts. With the same underlying model, the proposed methodology is capable of generating realistic price time series in various stocks listed at three different exchanges, which indicates the universality of intra-day price formation process. For the time scale (minutes) chosen in this paper, one agent per category is shown to be sufficient to capture the intra-day price formation process. The proposed XGB-Chiarella approach provides insights that the price formation process is comprised of the interactions between momentum traders, fundamental traders, and noise traders. It can also be used to enhance risk management by practitioners., Comment: Published in WILMOTT Magazine: May 2022 issue. arXiv admin note: text overlap with arXiv:2208.13654

This paper describes simulations and analysis of flash crash scenarios in an agent-based modelling framework. We design, implement, and assess a novel high-frequency agent-based financial market simulator that generates realistic millisecond-level financial price time series for the E-Mini S&P 500 futures market. Specifically, a microstructure model of a single security traded on a central limit order book is provided, where different types of traders follow different behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the model has excellent capability of reproducing realistic stylised facts in financial markets. By introducing an institutional trader that mimics the real-world Sell Algorithm on May 6th, 2010, the proposed high-frequency agent-based financial market simulator is used to simulate the Flash Crash that took place that day. We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. With the help of Monte Carlo simulations, we discover functional relationships between the amplitude of the simulated 2010 Flash Crash and three conditions: the percentage of volume of the Sell Algorithm, the market maker inventory limit, and the trading frequency of fundamental traders. Similar analyses are carried out for mini flash crash events. An innovative "Spiking Trader" is introduced to the model, aiming at precipitating mini flash crash events. We analyse the market dynamics during the course of a typical simulated mini flash crash event and study the conditions affecting its characteristics. The proposed model can be used for testing resiliency and robustness of trading algorithms and providing advice for policymakers.

In this work, we present a hardware compatible neural network training algorithm in which we used alternating direction method of multipliers (ADMM) and iterative least-square methods. The motive behind this approach was to conduct a method of training neural networks that is scalable and can be parallelised. These characteristics make this algorithm suitable for hardware implementation. We have achieved 6.9\% and 6.8\% better accuracy comparing to SGD and Adam respectively, with a four-layer neural network with hidden size of 28 on HIGGS dataset. Likewise, we could observe 21.0\% and 2.2\% accuracy improvement comparing to SGD and Adam respectively, on IRIS dataset with a three-layer neural network with hidden size of 8. This is while the use of matrix inversion, which is challenging for hardware implementation, is avoided in this method. We assessed the impact of avoiding matrix inversion on ADMM accuracy and we observed that we can safely replace matrix inversion with iterative least-square methods and maintain the desired performance. Also, the computational complexity of the implemented method is polynomial regarding dimensions of the input dataset and hidden size of the network.

In this project, we have successfully designed, implemented, deployed and tested a novel FPGA accelerated algorithm for neural network training. The algorithm itself was developed in an independent study option. This training method is based on Alternating Direction Method of Multipliers algorithm, which has strong parallel characteristics and avoids procedures such as matrix inversion that are problematic in hardware designs by employing LSMR. As an intermediate stage, we fully implemented the ADMM-LSMR method in C language for feed-forward neural networks with a flexible number of layers and hidden size. We demonstrated that the method can operate with fixed-point arithmetic without compromising the accuracy. Next, we devised an FPGA accelerated version of the algorithm using Intel FPGA SDK for OpenCL and performed extensive optimisation stages followed by successful deployment of the program on an Intel Arria 10 GX FPGA. The FPGA accelerated program showed up to 6 times speed up comparing to equivalent CPU implementation while achieving promising accuracy.