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Book ; Online: Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series

Wang, Yuanrong / Aste, Tomaso

2022

Abstract: We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module. This module generates filtered (inverse) correlation graphs from multivariate time ... ...

Abstract	We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module. This module generates filtered (inverse) correlation graphs from multivariate time series before inputting them into a GNN. In contrast with existing sparsification methods adopted in graph neural network, our model explicitly leverage time-series filtering to overcome the low signal-to-noise ratio typical of complex systems data. We present a set of experiments, where we predict future sales from a synthetic time-series sales dataset. The proposed spatial-temporal graph neural network displays superior performances with respect to baseline approaches, with no graphical information, and with fully connected, disconnected graphs and unfiltered graphs. Comment: 7 pages, 1 figure, 3tables
Keywords	Computer Science - Machine Learning ; Quantitative Finance - Computational Finance
Subject code	006
Publishing date	2022-03-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Homological Convolutional Neural Networks

Briola, Antonio / Wang, Yuanrong / Bartolucci, Silvia / Aste, Tomaso

2023

Abstract: Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e.g., image, audio, and text data). However, tabular data still pose a challenge, with classic machine learning approaches ... ...

Abstract	Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e.g., image, audio, and text data). However, tabular data still pose a challenge, with classic machine learning approaches being often computationally cheaper and equally effective than increasingly complex deep learning architectures. The challenge arises from the fact that, in tabular data, the correlation among features is weaker than the one from spatial or semantic relationships in images or natural language, and the dependency structures need to be modeled without any prior information. In this work, we propose a novel deep learning architecture that exploits the data structural organization through topologically constrained network representations to gain relational information from sparse tabular inputs. The resulting model leverages the power of convolution and is centered on a limited number of concepts from network topology to guarantee: (i) a data-centric and deterministic building pipeline; (ii) a high level of interpretability over the inference process; and (iii) an adequate room for scalability. We test our model on 18 benchmark datasets against 5 classic machine learning and 3 deep learning models, demonstrating that our approach reaches state-of-the-art performances on these challenging datasets. The code to reproduce all our experiments is provided at https://github.com/FinancialComputingUCL/HomologicalCNN. Comment: 26 pages, 5 figures, 11 tables, 1 equation, 1 algorithm
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computational Complexity
Subject code	006
Publishing date	2023-08-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Homological Neural Networks

Wang, Yuanrong / Briola, Antonio / Aste, Tomaso

A Sparse Architecture for Multivariate Complexity

2023

Abstract: The rapid progress of Artificial Intelligence research came with the development of increasingly complex deep learning models, leading to growing challenges in terms of computational complexity, energy efficiency and interpretability. In this study, we ... ...

Abstract	The rapid progress of Artificial Intelligence research came with the development of increasingly complex deep learning models, leading to growing challenges in terms of computational complexity, energy efficiency and interpretability. In this study, we apply advanced network-based information filtering techniques to design a novel deep neural network unit characterized by a sparse higher-order graphical architecture built over the homological structure of underlying data. We demonstrate its effectiveness in two application domains which are traditionally challenging for deep learning: tabular data and time series regression problems. Results demonstrate the advantages of this novel design which can tie or overcome the results of state-of-the-art machine learning and deep learning models using only a fraction of parameters.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Publishing date	2023-06-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing

Saef, Danial / Wang, Yuanrong / Aste, Tomaso

2022

Abstract: The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address ... ...

Abstract	The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM). Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we applied this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options at the popular trading platform Deribit. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion.
Keywords	Quantitative Finance - Computational Finance ; Computer Science - Machine Learning ; G.3
Subject code	330
Publishing date	2022-08-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Dynamic DNA Methylation Regulates Season-Dependent Secondary Metabolism in the New Shoots of Tea Plants.

Han, Mengxue / Lin, Shijia / Zhu, Biying / Tong, Wei / Xia, Enhua / Wang, Yuanrong / Yang, Tianyuan / Zhang, Shupei / Wan, Xiaochun / Liu, Jianjun / Niu, Qingfeng / Zhu, Jianhua / Bao, Shilai / Zhang, Zhaoliang

Journal of agricultural and food chemistry

2024 Volume 72, Issue 8, Page(s) 3984–3997

Abstract: Plant secondary metabolites are critical quality-conferring compositions of plant-derived beverages, medicines, and industrial materials. The accumulations of secondary metabolites are highly variable among seasons; however, the underlying regulatory ... ...

Abstract	Plant secondary metabolites are critical quality-conferring compositions of plant-derived beverages, medicines, and industrial materials. The accumulations of secondary metabolites are highly variable among seasons; however, the underlying regulatory mechanism remains unclear, especially in epigenetic regulation. Here, we used tea plants to explore an important epigenetic mark DNA methylation (5mC)-mediated regulation of plant secondary metabolism in different seasons. Multiple omics analyses were performed on spring and summer new shoots. The results showed that flavonoids and theanine metabolism dominated in the metabolic response to seasons in the new shoots. In summer new shoots, the genes encoding DNA methyltransferases and demethylases were up-regulated, and the global CG and CHG methylation reduced and CHH methylation increased. 5mC methylation in promoter and gene body regions influenced the seasonal response of gene expression; the amplitude of 5mC methylation was highly correlated with that of gene transcriptions. These differentially methylated genes included those encoding enzymes and transcription factors which play important roles in flavonoid and theanine metabolic pathways. The regulatory role of 5mC methylation was further verified by applying a DNA methylation inhibitor. These findings highlight that dynamic DNA methylation plays an important role in seasonal-dependent secondary metabolism and provide new insights for improving tea quality.
MeSH term(s)	Secondary Metabolism ; Seasons ; DNA Methylation ; Epigenesis, Genetic ; Plant Leaves/genetics ; Plant Leaves/metabolism ; Camellia sinensis/genetics ; Camellia sinensis/metabolism ; Flavonoids/metabolism ; Tea/metabolism ; Gene Expression Regulation, Plant ; Plant Proteins/genetics ; Plant Proteins/metabolism
Chemical Substances	Flavonoids ; Tea ; Plant Proteins
Language	English
Publishing date	2024-02-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	241619-0
ISSN	1520-5118 ; 0021-8561
ISSN (online)	1520-5118
ISSN	0021-8561
DOI	10.1021/acs.jafc.3c08568
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Book ; Online: Domain-adapted Learning and Imitation

Wang, Yuanrong / Swaminathan, Vignesh Raja / Granger, Nikita P. / Perez, Carlos Ros / Michler, Christian

DRL for Power Arbitrage

2023

Abstract: In this paper, we discuss the Dutch power market, which is comprised of a day-ahead market and an intraday balancing market that operates like an auction. Due to fluctuations in power supply and demand, there is often an imbalance that leads to different ...

Abstract	In this paper, we discuss the Dutch power market, which is comprised of a day-ahead market and an intraday balancing market that operates like an auction. Due to fluctuations in power supply and demand, there is often an imbalance that leads to different prices in the two markets, providing an opportunity for arbitrage. To address this issue, we restructure the problem and propose a collaborative dual-agent reinforcement learning approach for this bi-level simulation and optimization of European power arbitrage trading. We also introduce two new implementations designed to incorporate domain-specific knowledge by imitating the trading behaviours of power traders. By utilizing reward engineering to imitate domain expertise, we are able to reform the reward system for the RL agent, which improves convergence during training and enhances overall performance. Additionally, the tranching of orders increases bidding success rates and significantly boosts profit and loss (P&L). Our study demonstrates that by leveraging domain expertise in a general learning problem, the performance can be improved substantially, and the final integrated approach leads to a three-fold improvement in cumulative P&L compared to the original agent. Furthermore, our methodology outperforms the highest benchmark policy by around 50% while maintaining efficient computational performance.
Keywords	Quantitative Finance - Trading and Market Microstructure ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-01-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Anatomy of a Stablecoin's failure

Briola, Antonio / Vidal-Tomás, David / Wang, Yuanrong / Aste, Tomaso

the Terra-Luna case

2022

Abstract: We quantitatively describe the main events that led to the Terra project's failure in May 2022. We first review, in a systematic way, news from heterogeneous social media sources; we discuss the fragility of the Terra project and its vicious dependence ... ...

Abstract	We quantitatively describe the main events that led to the Terra project's failure in May 2022. We first review, in a systematic way, news from heterogeneous social media sources; we discuss the fragility of the Terra project and its vicious dependence on the Anchor protocol. We hence identify the crash's trigger events, analysing hourly and transaction data for Bitcoin, Luna, and TerraUSD. Finally, using state-of-the-art techniques from network science, we study the evolution of dependency structures for 61 highly capitalised cryptocurrencies during the down-market and highlight the absence of herding behaviour. Comment: 17 pages, 7 figures, 6 tables, 1 appendix
Keywords	Quantitative Finance - General Finance ; Computer Science - Social and Information Networks ; Quantitative Finance - Statistical Finance
Publishing date	2022-07-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Report 30: The COVID-19 Epidemic Trends and Control Measures in Mainland China

Fu, Han Xi Xiaoyue Wang Haowei Boonyasiri Adhiratha Wang Yuanrong Imperial College London https www imperial ac uk

Abstract: From the Introduction: The Imperial College London COVID-19 [coronavirus disease 2019] Response Team initiated activities of data collation in mid-January, to understand the COVID-19 epidemic in China and its potential impact on other countries The ... ...

Abstract	From the Introduction: The Imperial College London COVID-19 [coronavirus disease 2019] Response Team initiated activities of data collation in mid-January, to understand the COVID-19 epidemic in China and its potential impact on other countries The Imperial Team, together with volunteers, made considerable efforts to collate aggregated data as well as individual patient information from publicly available, national and local situation reports published by health authorities in China Part of these collated data have been used to inform transmission dynamics and epidemiology of COIVD-19 in several studies of the Team, including disease severity and fatality, phylodynamics in Shandong, and the association between inner-city movement and transmission We additionally reviewed control measures, school reopening, and work resumption that may relate to the trends across provinces in China [ ] In this report, we publish the collated data and conduct a descriptive analysis of the subnational epidemic trends and interventions Drawing on epidemic progression and response measures in Chinese provinces affected by COVID-19 early on may provide insights for policy planning in other countries COVID-19 (Disease);Epidemiology;Public health
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #740234
Database	COVID19

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Article: Estimating the number of undetected COVID-19 cases among travellers from mainland China.

Bhatia, Sangeeta / Imai, Natsuko / Cuomo-Dannenburg, Gina / Baguelin, Marc / Boonyasiri, Adhiratha / Cori, Anne / Cucunubá, Zulma / Dorigatti, Ilaria / FitzJohn, Rich / Fu, Han / Gaythorpe, Katy / Ghani, Azra / Hamlet, Arran / Hinsley, Wes / Laydon, Daniel / Nedjati-Gilani, Gemma / Okell, Lucy / Riley, Steven / Thompson, Hayley /

van Elsland, Sabine / Volz, Erik / Wang, Haowei / Wang, Yuanrong / Whittaker, Charles / Xi, Xiaoyue / Donnelly, Christl A / Ferguson, Neil M

Wellcome open research

2021 Volume 5, Page(s) 143

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2021-12-06
Publishing country	England
Document type	Journal Article
ISSN	2398-502X
ISSN	2398-502X
DOI	10.12688/wellcomeopenres.15805.3
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Article ; Online: Estimates of the severity of coronavirus disease 2019: a model-based analysis.

Verity, Robert / Okell, Lucy C / Dorigatti, Ilaria / Winskill, Peter / Whittaker, Charles / Imai, Natsuko / Cuomo-Dannenburg, Gina / Thompson, Hayley / Walker, Patrick G T / Fu, Han / Dighe, Amy / Griffin, Jamie T / Baguelin, Marc / Bhatia, Sangeeta / Boonyasiri, Adhiratha / Cori, Anne / Cucunubá, Zulma / FitzJohn, Rich / Gaythorpe, Katy /

Green, Will / Hamlet, Arran / Hinsley, Wes / Laydon, Daniel / Nedjati-Gilani, Gemma / Riley, Steven / van Elsland, Sabine / Volz, Erik / Wang, Haowei / Wang, Yuanrong / Xi, Xiaoyue / Donnelly, Christl A / Ghani, Azra C / Ferguson, Neil M

The Lancet. Infectious diseases

2020 Volume 20, Issue 6, Page(s) 669–677

Abstract: Background: In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring ... ...

Abstract	Background: In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. Methods: We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. Findings: Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9-19·2) and to hospital discharge to be 24·7 days (22·9-28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56-3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23-1·53), with substantially higher ratios in older age groups (0·32% [0·27-0·38] in those aged <60 years vs 6·4% [5·7-7·2] in those aged ≥60 years), up to 13·4% (11·2-15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4-3·5] in those aged <60 years [n=360] and 4·5% [1·8-11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39-1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0-37·6) in those aged 80 years or older. Interpretation: These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. Funding: UK Medical Research Council.
MeSH term(s)	Adolescent ; Adult ; Aged ; Aged, 80 and over ; Betacoronavirus ; COVID-19 ; Child ; Child, Preschool ; China/epidemiology ; Coronavirus Infections/mortality ; Hospitalization/statistics & numerical data ; Humans ; Incidence ; Infant ; Infant, Newborn ; Middle Aged ; Models, Statistical ; Pandemics/statistics & numerical data ; Pneumonia, Viral/mortality ; SARS-CoV-2 ; Young Adult
Keywords	covid19
Language	English
Publishing date	2020-03-30
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2061641-7
ISSN	1474-4457 ; 1473-3099
ISSN (online)	1474-4457
ISSN	1473-3099
DOI	10.1016/S1473-3099(20)30243-7
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