pwc-archive/papers-with-abstracts huggingface dataset:
Variability of morphology in beat-to-beat photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment
Authors: Yu-Chieh Ho, Te-Sheng Lin, She-Chih Wang, Chen-Shi Chang, Yu-Ting Lin
Date: 2023-12-30 00:00:00
Tasks: Photoplethysmography (PPG)

We investigated the beat-to-beat fluctuation of the photoplethysmography (PPG) waveform. The motivation is that morphology variability extracted from the arterial blood pressure (ABP) has been found to correlate with baseline condition and short-term surgical outcome of the patients undergoing liver transplant surgery. Numerous interactions of physiological mechanisms regulating the cardiovascular system could underlie the variability of morphology. We used the unsupervised manifold learning algorithm, Dynamic Diffusion Map, to quantify the multivariate waveform morphological variation. Due to the physical principle of light absorption, PPG waveform signals are more susceptible to artifact and are nominally used only for visual inspection of data quality in clinical medical environment. But on the other hand, the noninvasive, easy-to-use nature of PPG grants a wider range of biomedical application, which inspired us to investigate the variability of morphology information from PPG waveform signal. We developed data analysis techniques to improve the performance and validated with the real-life clinical database.
$\textit{greylock}$: A Python Package for Measuring The Composition of Complex Datasets
Authors: Phuc Nguyen, Rohit Arora, Elliot D. Hill, Jasper Braun, Alexandra Morgan, Liza M. Quintana, Gabrielle Mazzoni, Ghee Rye Lee, Rima Arnaout, Ramy Arnaout
Date: 2023-12-29 00:00:00
Tasks: Diversity

Machine-learning datasets are typically characterized by measuring their size and class balance. However, there exists a richer and potentially more useful set of measures, termed diversity measures, that incorporate elements' frequencies and between-element similarities. Although these have been available in the R and Julia programming languages for other applications, they have not been as readily available in Python, which is widely used for machine learning, and are not easily applied to machine-learning-sized datasets without special coding considerations. To address these issues, we developed $\textit{greylock}$, a Python package that calculates diversity measures and is tailored to large datasets. $\textit{greylock}$ can calculate any of the frequency-sensitive measures of Hill's D-number framework, and going beyond Hill, their similarity-sensitive counterparts (Greylock is a mountain). $\textit{greylock}$ also outputs measures that compare datasets (beta diversities). We first briefly review the D-number framework, illustrating how it incorporates elements' frequencies and between-element similarities. We then describe $\textit{greylock}$'s key features and usage. We end with several examples - immunomics, metagenomics, computational pathology, and medical imaging - illustrating $\textit{greylock}$'s applicability across a range of dataset types and fields.
Mathematical Modeling of the Synergetic Effect between Radiotherapy and Immunotherapy
Authors: Yixun Xing, Casey Moore, Debabrata Saha, Dan Nguyen, MaryLena Bleile, Xun Jia, Robert Timmerman, Hao Peng, Steve Jiang
Date: 2023-12-28 00:00:00

Achieving effective synergy between radiotherapy and immunotherapy is critical for optimizing tumor control and treatment outcomes. To explore the underlying mechanisms of this synergy, we have investigated a novel treatment approach known as personalized ultra-fractionated stereotactic adaptive radiation therapy (PULSAR), which emphasizes the impact of radiation timing on treatment efficacy. However, the precise mechanism remains unclear. Building on insights from small animal PULSAR studies, we developed a mathematical framework consisting of multiple ordinary differential equations to elucidate the temporal dynamics of tumor control resulting from radiation and the adaptive immune response. The model accounts for the migration and infiltration of T-cells within the tumor microenvironment. This proposed model establishes a causal and quantitative link between radiation therapy and immunotherapy, providing a valuable in-silico analysis tool for designing future PULSAR trials.
Guidelines in Wastewater-based Epidemiology of SARS-CoV-2 with Diagnosis
Authors: Madiha Fatima, Zhihua Cao, Aichun Huang, Shengyuan Wu, Xinxian Fan, Yi Wang, Liu Jiren, Ziyun Zhu, Qiongrou Ye, Yuan Ma, Joseph K. F Chow, Peng Jia, Yangshou Liu, Yubin Lin, Manjun Ye, Tong Wu, ZHIXUN LI, Cong Cai, Wenhai Zhang, Cheris H. Q. Ding, Yuanzhe Cai, Feijuan Huang
Date: 2023-12-26 00:00:00
Tasks: Diagnostic, Epidemiology

With the global spread and increasing transmission rate of SARS-CoV-2, more and more laboratories and researchers are turning their attention to wastewater-based epidemiology (WBE), hoping it can become an effective tool for large-scale testing and provide more ac-curate predictions of the number of infected individuals. Based on the cases of sewage sampling and testing in some regions such as Hong Kong, Brazil, and the United States, the feasibility of detecting the novel coronavirus in sewage is extremely high. This study re-views domestic and international achievements in detecting SARS-CoV-2 through WBE and summarizes four aspects of COVID-19, including sampling methods, virus decay rate cal-culation, standardized population coverage of the watershed, algorithm prediction, and provides ideas for combining field modeling with epidemic prevention and control. Moreover, we highlighted some diagnostic techniques for detection of the virus from sew-age sample. Our review is a new approach in identification of the research gaps in waste water-based epidemiology and diagnosis and we also predict the future prospect of our analysis.
A mathematical study of the interaction between oxygen and lactate in an in-vivo and in-vitro tumor
Authors: Gopinath Sadhu, D C Dalal
Date: 2024-01-03 00:00:00

Micro-environmental acidity is a common feature of the tumor. One of the causes behind tumor acidity is lactate production by hypoxic cells of the tumor. Hypoxia is a direct result of the establishment of oxygen gradients. It is commonly observed in the tumor in an in-vitro experimental setup and also in-vivo situation. Here, we propose a mathematical model to analyses the production of lactate by hypoxic cells, and it is used as an alternative fuel by normoxic cells in tumor tissue in-vitro and in-vivo conditions. In this article, we study the effects of unequal oxygen concentration at the tumor boundaries on lactate status in the tumor. The effects of presence of the necrotic core in the tumor on the lactate concentration profile is examined. The results have good agreement with experimental data and align with the theoretical findings of previous studies. The analytical results show that lactate levels are elevated in an in-vivo tumor compared to that in an in-vitro tumor. Also, during the onset of necrotic core formation, the effects of necrotic core on lactate levels are noticed. Knowledge of the lactate status in a patient's tumor may be helpful in choosing the rightful and precious medicines for cancer treatment.
Reproductive outcome in female wistar rats treated with nhexane, dichloromethane and aqueous ethanol extracts of Cucurbita pepo seed
Authors: Anyanwu, C. F, Georgewill, O. A., Obinna, Victoria C
Date: 2023-12-31 00:00:00

In developing countries, healthcare challenges and expensive infertility treatments has resulted in resurgent interest in medicinal plants. This study was designed to determine if Curcubita pepo seed can enhance female fertility, by assessing the reproductive outcome in female wistar rats treated with n-hexane (nHE), dichloromethane (DCM) and aqueous ethanol (Aq. Eth) extracts of Curcubita pepo seed. Total of 48 rats randomly grouped into 12 (n=4), were treated for 21 days by oral gavage as follows: A (control) = 0.5ml 20% tween 80 (vehicle); B (positive control) = 10mg/kg clomiphene citrate, C, D & E = 142.86, 285.71 and 428.57 mg/kg nHE; F, G & H = 142.86, 285.71 and 428.57 mg/kg DCM ; and I, J & K =142.86, 285.71 and 428.57 mg/kg Aq.Eth extracts. Group L (positive control 2) = 10mg/kg clomiphene citrate for 8 days. Following treatment, the rats were paired with males for mating, designating the confirmation day as gestational day 0 (GD 0). On GD 20, the animals were laparatomised and reproductive outcome was determined by assessing foetal weight, foetal crown-rump length, litter size, number of implantation and resorption sites. Results showed all extracts had no significant (p >0.05) effect on the reproductive outcome indices. Clomiphene citrate significantly decreased reproductive outcome indices. In conclusion, Cucurbita pepo seed did not enhance the reproductive outcome of treated female rats at the doses and duration used in this study. This finding may serve as a springboard for future studies exploring the effect of C.pepo at different doses or durations.
Nash Equilibria in Greenhouse Gas Offset Credit Markets
Authors: Liam Welsh, Sebastian Jaimungal
Date: 2024-01-02 00:00:00

One approach to reducing greenhouse gas (GHG) emissions is to incentivize carbon capturing and carbon reducing projects while simultaneously penalising excess GHG output. In this work, we present a novel market framework and characterise the optimal behaviour of GHG offset credit (OC) market participants in both single-player and two-player settings. The single player setting is posed as an optimal stopping and control problem, while the two-player setting is posed as optimal stopping and mixed-Nash equilibria problem. We demonstrate the importance of acting optimally using numerical solutions and Monte Carlo simulations and explore the differences between the homogeneous and heterogeneous players. In both settings, we find that market participants benefit from optimal OC trading and OC generation.
Intraday Trading Algorithm for Predicting Cryptocurrency Price Movements Using Twitter Big Data Analysis
Authors: Vahidin Jeleskovic, Stephen Mackay
Date: 2023-12-31 00:00:00

Cryptocurrencies have emerged as a novel financial asset garnering significant attention in recent years. A defining characteristic of these digital currencies is their pronounced short-term market volatility, primarily influenced by widespread sentiment polarization, particularly on social media platforms such as Twitter. Recent research has underscored the correlation between sentiment expressed in various networks and the price dynamics of cryptocurrencies. This study delves into the 15-minute impact of informative tweets disseminated through foundation channels on trader behavior, with a focus on potential outcomes related to sentiment polarization. The primary objective is to identify factors that can predict positive price movements and potentially be leveraged through a trading algorithm. To accomplish this objective, we conduct a conditional examination of return and excess return rates within the 15 minutes following tweet publication. The empirical findings reveal statistically significant increases in return rates, particularly within the initial three minutes following tweet publication. Notably, adverse effects resulting from the messages were not observed. Surprisingly, sentiments were found to have no discerni-ble impact on cryptocurrency price movements. Our analysis further identifies that inves-tors are primarily influenced by the quality of tweet content, as reflected in the choice of words and tweet volume. While the basic trading algorithm presented in this study does yield some benefits within the 15-minute timeframe, these benefits are not statistically significant. Nevertheless, it serves as a foundational framework for potential enhance-ments and further investigations.
An arbitrage driven price dynamics of Automated Market Makers in the presence of fees
Authors: Joseph Najnudel, Shen-Ning Tung, Kazutoshi Yamazaki, Ju-Yi Yen
Date: 2024-01-03 00:00:00

We present a model for price dynamics in the Automated Market Makers (AMM) setting. Within this framework, we propose a reference market price following a geometric Brownian motion. The AMM price is constrained by upper and lower bounds, determined by constant multiplications of the reference price. Through the utilization of local times and excursion-theoretic approaches, we derive several analytical results, including its time-changed representation and limiting behavior.
A Portfolio's Common Causal Conditional Risk-neutral PDE
Authors: Alejandro Rodriguez Dominguez
Date: 2024-01-01 00:00:00
Tasks: Management

Portfolio's optimal drivers for diversification are common causes of the constituents' correlations. A closed-form formula for the conditional probability of the portfolio given its optimal common drivers is presented, with each pair constituent-common driver joint distribution modelled by Gaussian copulas. A conditional risk-neutral PDE is obtained for this conditional probability as a system of copulas' PDEs, allowing for dynamical risk management of a portfolio as shown in the experiments. Implied conditional portfolio volatilities and implied weights are new risk metrics that can be dynamically monitored from the PDEs or obtained from their solution.
On the implied volatility of Inverse options under stochastic volatility models
Authors: Elisa Alòs, Eulalia Nualart, Makar Pravosud
Date: 2023-12-31 00:00:00

In this paper we study short-time behavior of the at-the-money implied volatility for Inverse European options with fixed strike price. The asset price is assumed to follow a general stochastic volatility process. Using techniques of the Malliavin calculus such as the anticipating It^o's formula we first compute the level of the implied volatility of the option when the maturity converges to zero. Then, we find a short maturity asymptotic formula for the skew of the implied volatility that depends on the roughness of the volatility model. We also show that our results extend easily to Quanto-Inverse options. We apply our general results to the SABR and fractional Bergomi models, and provide some numerical simulations that confirm the accurateness of the asymptotic formula for the skew. Finally, we provide an empirical application using Bitcoin options traded on Debirit to show how our theoretical formulas can be used to model real market data of such options.
Optimization of portfolios with cryptocurrencies: Markowitz and GARCH-Copula model approach
Authors: Vahidin Jeleskovic, Claudio Latini, Zahid I. Younas, Mamdouh A. S. Al-Faryan
Date: 2023-12-31 00:00:00
Tasks: Portfolio Optimization

The growing interest in cryptocurrencies has drawn the attention of the financial world to this innovative medium of exchange. This study aims to explore the impact of cryptocurrencies on portfolio performance. We conduct our analysis retrospectively, assessing the performance achieved within a specific time frame by three distinct portfolios: one consisting solely of equities, bonds, and commodities; another composed exclusively of cryptocurrencies; and a third, which combines both 'traditional' assets and the best-performing cryptocurrency from the second portfolio.To achieve this, we employ the classic variance-covariance approach, utilizing the GARCH-Copula and GARCH-Vine Copula methods to calculate the risk structure. The optimal asset weights within the optimized portfolios are determined through the Markowitz optimization problem. Our analysis predominantly reveals that the portfolio comprising both cryptocurrency and traditional assets exhibits a higher Sharpe ratio from a retrospective viewpoint and demonstrates more stable performances from a prospective perspective. We also provide an explanation for our choice of portfolio optimization based on the Markowitz approach rather than CVaR and ES.
Enhancing CVaR portfolio optimisation performance with GAM factor models
Authors: Davide Lauria, W. Brent Lindquist, Svetlozar T. Rachev
Date: 2023-12-30 00:00:00
Tasks: Additive models

We propose a discrete-time econometric model that combines autoregressive filters with factor regressions to predict stock returns for portfolio optimisation purposes. In particular, we test both robust linear regressions and general additive models on two different investment universes composed of the Dow Jones Industrial Average and the Standard & Poor's 500 indexes, and we compare the out-of-sample performances of mean-CVaR optimal portfolios over a horizon of six years. The results show a substantial improvement in portfolio performances when the factor model is estimated with general additive models.
Sector Rotation by Factor Model and Fundamental Analysis
Authors: Runjia Yang, Beining Shi
Date: 2023-11-18 00:00:00
Tasks: Asset Management, Management

This study presents an analytical approach to sector rotation, leveraging both factor models and fundamental metrics. We initiate with a systematic classification of sectors, followed by an empirical investigation into their returns. Through factor analysis, the paper underscores the significance of momentum and short-term reversion in dictating sectoral shifts. A subsequent in-depth fundamental analysis evaluates metrics such as PE, PB, EV-to-EBITDA, Dividend Yield, among others. Our primary contribution lies in developing a predictive framework based on these fundamental indicators. The constructed models, post rigorous training, exhibit noteworthy predictive capabilities. The findings furnish a nuanced understanding of sector rotation strategies, with implications for asset management and portfolio construction in the financial domain.
A framework for the valuation of insurance liabilities by production cost
Authors: Christoph Moehr
Date: 2023-12-30 00:00:00

This paper sets out a framework for the valuation of insurance liabilities that is intended to be economically realistic, elementary, reasonably practically applicable, and as a special case to provide a basis for the valuation in regulatory solvency systems such as Solvency II and the SST. The valuation framework is based on the cost of producing the liabilities to an insurance company that is subject to solvency regulation (regulatory solvency capital requirements) and insolvency laws (consequences of failure) in finite discrete time. Starting from the replication approach of classical no-arbitrage theory, the framework additionally considers the nature and cost of capital (expressed by a ``financiability condition"), that the liabilities may be required to be fulfilled only ``in sufficiently many cases" (expressed by a ``fulfillment condition"), production using ``fully illiquid" assets in addition to tradables, and the asymmetry between assets and liabilities. We identify necessary and sufficient conditions on the capital investment under which the framework recovers the market prices of tradables, investigate extending production to take account of insolvency, implications of using illiquid assets in the production, and show how Solvency II and SST valuation can be derived with specific assumptions.
Causal Discovery in Financial Markets: A Framework for Nonstationary Time-Series Data
Authors: Agathe Sadeghi, Achintya Gopal, Mohammad Fesanghary
Date: 2023-12-28 00:00:00
Tasks: Causal Discovery, Time Series

This paper introduces a new causal structure learning method for nonstationary time series data, a common data type found in fields such as finance, economics, healthcare, and environmental science. Our work builds upon the constraint-based causal discovery from nonstationary data algorithm (CD-NOD). We introduce a refined version (CD-NOTS) which is designed specifically to account for lagged dependencies in time series data. We compare the performance of different algorithmic choices, such as the type of conditional independence test and the significance level, to help select the best hyperparameters given various scenarios of sample size, problem dimensionality, and availability of computational resources. Using the results from the simulated data, we apply CD-NOTS to a broad range of real-world financial applications in order to identify causal connections among nonstationary time series data, thereby illustrating applications in factor-based investing, portfolio diversification, and comprehension of market dynamics.
Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches
Authors: Cristina Chinazzo, Vahidin Jeleskovic
Date: 2024-01-04 00:00:00

This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's crucial to emphasize the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme moneyness or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets.
Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning
Authors: Sikiru Adewale, Tosin Ige, Bolanle Hafiz Matti
Date: 2023-10-02 00:00:00
Tasks: Decoder, Sentence, Video Captioning

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness.