pwc-archive/papers-with-abstracts huggingface dataset:
Optimal dosing of anti-cancer treatment under drug-induced plasticity
Authors: Einar Bjarki Gunnarsson, Benedikt Vilji Magnússon, Jasmine Foo
Date: 2024-12-20 00:00:00

While cancer has traditionally been considered a genetic disease, mounting evidence indicates an important role for non-genetic (epigenetic) mechanisms. Common anti-cancer drugs have recently been observed to induce the adoption of reversible drug-tolerant cell states, thereby accelerating the evolution of drug resistance. Determining how to optimally balance the competing goals of killing the tumor bulk and delaying resistance evolution in this scenario is a nontrivial question of high clinical importance. In this work, we use a combined mathematical and computational approach to study optimal dosing of anti-cancer drug treatment under drug-induced cell plasticity. Our results show that the optimal treatment steers the tumor into a fixed equilibrium composition while balancing the trade-off between cell kill and tolerance induction in a precisely quantifiable way. Under linear induction of tolerance, a low-dose constant strategy is optimal in equilibrium, while under uniform induction of tolerance, alternating between a large dose and no dose is best. The directionality of drug induction, whether the drug elevates transitions from sensitivity to tolerance or inhibits transitions back, significantly affects optimal dosing. To demonstrate the applicability of our approach, we use it to identify an optimal low-dose strategy for colorectal cancer using publicly available in vitro data.
Stochastic Model of siRNA Endosomal Escape Mediated by Fusogenic Peptides in OVCAR-3
Authors: Nisha Yadav, Jessica Boulos, Keisha Cook, Angela Alexander-Bryant
Date: 2024-12-20 00:00:00
Tasks: Bayesian Inference, Image Segmentation, parameter estimation, Semantic Segmentation, Specificity, Synthetic Data Generation

Gene silencing via small interfering RNA (siRNA) represents a transformative tool in cancer therapy, offering specificity and reduced off-target effects compared to conventional treatments. A crucial step in siRNA-based therapies is endosomal escape, the release of siRNA from endosomes into the cytoplasm. Quantifying endosomal escape is challenging due to the dynamic nature of the process and limitations in imaging and analytical techniques. Traditional methods often rely on fluorescence intensity measurements or manual image processing, which are time-intensive and fail to capture continuous dynamics. This paper presents a novel computational framework that integrates automated image processing to analyze time-lapse fluorescent microscopy data of endosomal escape, hierarchical Bayesian inference, and stochastic simulations. Our method employs image segmentation techniques such as binary masks, Gaussian filters, and multichannel color quantification to extract precise spatial and temporal data from microscopy images. Using a hierarchical Bayesian approach, we estimate the parameters of a compartmental model that describes endosomal escape dynamics, accounting for variability over time. These parameters inform a Gillespie stochastic simulation algorithm, ensuring realistic simulations of siRNA release events over time. Our framework provides a scalable and reproducible method for quantifying endosomal escape. The model captures uncertainty and variability in parameter estimation, and endosomal escape dynamics. Additionally, synthetic data generation allows researchers to validate experimental findings and explore alternative conditions without extensive laboratory work. This integrated approach not only improves the accuracy of endosomal escape quantification but also provides predictive insights for optimizing siRNA delivery systems and advancing gene therapy research.
actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions
Authors: Julia K. Varga, Sergey Ovchinnikov, Ora Schueler-Furman
Date: 2024-12-20 00:00:00

One of the main advantages of deep learning models of protein structure, such as Alphafold2, is their ability to accurately estimate the confidence of a generated structural model, which allows us to focus on highly confident predictions.The ipTM score provides a confidence estimate of interchain contacts in protein-protein interactions. However, interactions, in particular motif-mediated interactions, often also contain regions that remain flexible upon binding. These non-interacting flanking regions are assigned low confidence values and will affect iPTM, as it considers all interchain residue pairs, and two models of the same motif-domain interaction, but differing in the length of their flanking regions, would be assigned very different values. Here we propose actifpTM (actual interface pTM), a modified ipTM measure, that focuses on the confident region of an interaction, resulting in a more robust measure of interaction confidence, even when not the full interaction is structured. actifpTM has been incorporated into ColabFold.
Homogenization of Ordinary Differential Equations for the Fast Prediction of Diabetes Progression
Authors: Lea Multerer, Pierluigi Francesco De Paola, Marta Lenatti, Alessia Paglialonga, Laura Azzimonti
Date: 2024-12-20 00:00:00

The impact of physical activity on a person's progression to type 2 diabetes is multifaceted. Systems of ordinary differential equations have been crucial in simulating this progression. However, such models often operate on multiple timescales, making them computationally expensive when simulating long-term effects. To overcome this, we propose a homogenized version of a two-timescale model that captures the short- and long-term effects of physical activity on blood glucose regulation. By invoking the homogenized contribution of a physical activity session into the long-term effects, we reduce the full model from 12 to 7 state variables, while preserving its key dynamics. The homogenized model offers a computational speedup of over 1000 times, since a numerical solver can take time steps at the scale of the long-term effects. We prove that the error introduced by the homogenization is bounded over time and validate the theoretical findings through a simulation study. The significant reduction in computational time opens the door to apply the homogenized model in medical decision support systems. It supports the development of personalized physical activity plans that can effectively reduce the risk of developing type 2 diabetes.
Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution
Authors: Jiaqi Xiong, Nan Yin, Shiyang Liang, Haoyang Li, Yingxu Wang, Duo Ai, Fang Pan, JingJie Wang
Date: 2024-12-18 00:00:00
Tasks: Graph Embedding

Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology, and several innovative computational methods have been introduced. However, most of these studies have not considered the skewed degree distribution of genes. Specifically, some genes may regulate multiple target genes while some genes may be regulated by multiple regulator genes. Such a skewed degree distribution issue significantly complicates the application of directed graph embedding methods. To tackle this issue, we propose the Cross-Attention Complex Dual Graph Embedding Model (XATGRN). Our XATGRN employs a cross-attention mechanism to effectively capture intricate gene interactions from gene expression profiles. Additionally, it uses a Dual Complex Graph Embedding approach to manage the skewed degree distribution, thereby ensuring precise prediction of regulatory relationships and their directionality. Our model consistently outperforms existing state-of-the-art methods across various datasets, underscoring its efficacy in elucidating complex gene regulatory mechanisms. Our codes used in this paper are publicly available at: https://github.com/kikixiong/XATGRN.
Correct implied volatility shapes and reliable pricing in the rough Heston model
Authors: Svetlana Boyarchenko, Sergei Levendorskiǐ
Date: 2024-12-20 00:00:00
Tasks: Math

We use modifications of the Adams method and very fast and accurate sinh-acceleration method of the Fourier inversion (iFT) (S.Boyarchenko and Levendorski\u{i}, IJTAF 2019, v.22) to evaluate prices of vanilla options; for options of moderate and long maturities and strikes not very far from the spot, thousands of prices can be calculated in several msec. with relative errors of the order of 0.5\% and smaller running Matlab on a Mac with moderate characteristics. We demonstrate that for the calibrated set of parameters in Euch and Rosenbaum, Math. Finance 2019, v. 29, the correct implied volatility surface is significantly flatter and fits the data very poorly, hence, the calibration results in op.cit. is an example of the {\em ghost calibration} (M.Boyarchenko and Levendorki\u{i}, Quantitative Finance 2015, v. 15): the errors of the model and numerical method almost cancel one another. We explain how calibration errors of this sort are generated by each of popular versions of numerical realizations of iFT (Carr-Madan, Lipton-Lewis and COS methods) with prefixed parameters of a numerical method, resulting in spurious volatility smiles and skews. We suggest a general {\em Conformal Bootstrap principle} which allows one to avoid ghost calibration errors. We outline schemes of application of Conformal Bootstrap principle and the method of the paper to the design of accurate and fast calibration procedures.
Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading
Authors: Akash Deep, Abootaleb Shirvani, Chris Monico, Svetlozar Rachev, Frank J. Fabozzi
Date: 2024-12-19 00:00:00
Proceeding: Journal of Risk and Financial Management 2025 3
Tasks: Algorithmic Trading, Feature Engineering, Feature Importance, feature selection, Navigate, Stock Price Prediction

Because of the theoretical challenges posed by the Efficient Market Hypothesis to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the minute-level frequency, where effects of the microstructure of the market dominate. This study evaluates the integration of traditional technical indicators with random forest regression models using minute-level SPY data, analyzing 13 distinct model configurations. Our empirical results reveal a stark contrast between in-sample and out-of-sample performance, with $R^2$ values deteriorating from 0.749--0.812 during training to negative values in testing. A feature importance analysis demonstrates that primary price-based features dominate the predictions made by the model, accounting for over 60% of the importance, while established technical indicators, such as RSI and Bollinger Bands, account for only 14%--15%. Although the indicator-enhanced models achieved superior risk-adjusted metrics, with Rachev ratios between 0.919 and 0.961, they consistently underperformed a simple buy-and-hold strategy, generating returns ranging from -2.4% to -3.9%. These findings challenge conventional assumptions about the usefulness of technical indicators in algorithmic trading, suggesting that in high-frequency contexts, they may be more relevant to risk management rather than to predicting returns. For practitioners and researchers, our findings indicate that successful high-frequency trading strategies should focus on adaptive feature selection and regime-specific modeling rather than relying on traditional technical indicators, as well as indicating the critical importance of robust out-of-sample testing in the development of a model.
Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision
Authors: Mohan Jiang, Yaxin Liang, Siyuan Han, Kunyuan Ma, Yuan Chen, Zhen Xu
Date: 2024-12-04 00:00:00
Tasks: Prediction

This study explores the application of generative adversarial networks in financial market supervision, especially for solving the problem of data imbalance to improve the accuracy of risk prediction. Since financial market data are often imbalanced, especially high-risk events such as market manipulation and systemic risk occur less frequently, traditional models have difficulty effectively identifying these minority events. This study proposes to generate synthetic data with similar characteristics to these minority events through GAN to balance the dataset, thereby improving the prediction performance of the model in financial supervision. Experimental results show that compared with traditional oversampling and undersampling methods, the data generated by GAN has significant advantages in dealing with imbalance problems and improving the prediction accuracy of the model. This method has broad application potential in financial regulatory agencies such as the U.S. Securities and Exchange Commission (SEC), the Financial Industry Regulatory Authority (FINRA), the Federal Deposit Insurance Corporation (FDIC), and the Federal Reserve.
Multimodal Deep Reinforcement Learning for Portfolio Optimization
Authors: Sumit Nawathe, Ravi Panguluri, James Zhang, Sashwat Venkatesh
Date: 2024-12-23 00:00:00
Tasks: Articles, Benchmarking, Deep Reinforcement Learning, Portfolio Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentiment Analysis

We propose a reinforcement learning (RL) framework that leverages multimodal data including historical stock prices, sentiment analysis, and topic embeddings from news articles, to optimize trading strategies for SP100 stocks. Building upon recent advancements in financial reinforcement learning, we aim to enhance the state space representation by integrating financial sentiment data from SEC filings and news headlines and refining the reward function to better align with portfolio performance metrics. Our methodology includes deep reinforcement learning with state tensors comprising price data, sentiment scores, and news embeddings, processed through advanced feature extraction models like CNNs and RNNs. By benchmarking against traditional portfolio optimization techniques and advanced strategies, we demonstrate the efficacy of our approach in delivering superior portfolio performance. Empirical results showcase the potential of our agent to outperform standard benchmarks, especially when utilizing combined data sources under profit-based reward functions.
Shifting the yield curve for fixed-income and derivatives portfolios
Authors: Michele Leonardo Bianchi, Dario Ruzzi, Anatoli Segura
Date: 2024-12-20 00:00:00

We use granular regulatory data on euro interest rate swap trades between January 2021 and June 2023 to assess whether derivative positions of Italian banks can offset losses on their debt securities holdings should interest rates rise unexpectedly. At the aggregate level of the banking system, we find that a 100-basis-point upward shift of the yield curve increases on average the value of swaps by 3.65% of Common Equity Tier 1 (CET1), compensating in part for the losses of 2.64% and 5.98% of CET1 recorded on debt securities valued at fair value and amortised cost. Variation exists across institutions, with some bank swap positions playing an offsetting role and some exacerbating bond market exposures to interest rate risk. Nevertheless, we conclude that, on aggregate, Italian banks use swaps as hedging instruments to reduce their interest rate exposures, which improves their ability to cope with the recent tightening of monetary policy. Finally, we draw on our swap pricing model to conduct an extensive data quality analysis of the transaction-level information available to authorities, and we show that the errors in fitting value changes over time are significantly lower compared to those in fitting the values themselves.
Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study
Authors: Yilie Huang, Yanwei Jia, Xun Yu Zhou
Date: 2024-12-08 00:00:00
Tasks: Reinforcement Learning (RL)

We study continuous-time mean--variance portfolio selection in markets where stock prices are diffusion processes driven by observable factors that are also diffusion processes yet the coefficients of these processes are unknown. Based on the recently developed reinforcement learning (RL) theory for diffusion processes, we present a general data-driven RL algorithm that learns the pre-committed investment strategy directly without attempting to learn or estimate the market coefficients. For multi-stock Black--Scholes markets without factors, we further devise a baseline algorithm and prove its performance guarantee by deriving a sublinear regret bound in terms of Sharpe ratio. For performance enhancement and practical implementation, we modify the baseline algorithm into four variants, and carry out an extensive empirical study to compare their performance, in terms of a host of common metrics, with a large number of widely used portfolio allocation strategies on S\&P 500 constituents. The results demonstrate that the continuous-time RL strategies are consistently among the best especially in a volatile bear market, and decisively outperform the model-based continuous-time counterparts by significant margins.
Risk spillovers between the BRICS and the U.S. staple grain futures markets
Authors: Ying-Hui Shao, Yan-Hong Yang, Wei-Xing Zhou
Date: 2024-12-20 00:00:00

This study examines contemporaneous and lagged spillover effects in BRICS staple grain futures markets and their linkages with U.S. markets. The results show that contemporaneous spillovers dominate, while net spillovers are driven by lagged connectedness. Systemic risk is lower in intra-BRICS markets compared to those including the U.S., highlighting the U.S. grain market's significant influence. Brazilian and U.S. grains are key net spillover contributors, excluding U.S. rice, while South African staple grains act as major net receivers. Particularly, the spillover between soybeans is the strongest. The study also reveals heterogeneous impacts of the Russia-Ukraine conflict and Black Sea Grain Initiative on grain futures.
Battery valuation on electricity intraday markets with liquidity costs
Authors: Enzo Cognéville, Thomas Deschatre, Xavier Warin
Date: 2024-12-20 00:00:00

In this paper, we propose a complete modelling framework to value several batteries in the electricity intraday market at the trading session scale. The model consists of a stochastic model for the 24 mid-prices (one price per delivery hour) combined with a deterministic model for the liquidity costs (representing the cost of going deeper in the order book). A stochastic optimisation framework based on dynamic programming is used to calculate the value of the batteries. We carry out a back test for the years 2021, 2022 and 2023 for the German market and for the French market. We show that it is essential to take liquidity into account, especially when the number of batteries is large: it allows much higher profits and avoids high losses using our liquidity model. The use of our stochastic model for the mid-price also significantly improves the results (compared to a deterministic framework where the mid-price forecast is the spot price).
Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks
Authors: Adamantios Ntakaris, Gbenga Ibikunle
Date: 2024-11-23 00:00:00
Tasks: Clustering, Feature Importance, feature selection

This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting that involves a dual competitive feature importance mechanism and clustering via shallow neural network topology for fast training. By incorporating the k-means algorithm into the radial basis function neural network (RBFNN), the proposed method addresses the challenges of manual clustering and the reliance on potentially uninformative features. More specifically, our approach involves a dual competitive mechanism for feature importance, combining the mean-decrease impurity (MDI) method and a gradient descent (GD) based feature importance mechanism. This approach, tested on HFT Level 1 order book data for 20 S&P 500 stocks, enhances the forecasting ability of the RBFNN regressor. Our findings suggest that an autonomous approach to feature selection and clustering is crucial, as each stock requires a different input feature space. Overall, by automating the feature selection and clustering processes, we remove the need for manual topological grid search and provide a more efficient way to predict LOB's mid-price.
Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education
Authors: Angxuan Chen, Yuang Wei, Huixiao Le, Yan Zhang
Date: 2024-12-05 00:00:00
Tasks: Active Learning

This study investigates the potential of using ChatGPT as a teachable agent to support students' learning by teaching process, specifically in programming education. While learning by teaching is an effective pedagogical strategy for promoting active learning, traditional teachable agents have limitations, particularly in facilitating natural language dialogue. Our research explored whether ChatGPT, with its ability to engage learners in natural conversations, can support this process. The findings reveal that interacting with ChatGPT improves students' knowledge gains and programming abilities, particularly in writing readable and logically sound code. However, it had limited impact on developing learners' error-correction skills, likely because ChatGPT tends to generate correct code, reducing opportunities for students to practice debugging. Additionally, students' self-regulated learning (SRL) abilities improved, suggesting that teaching ChatGPT fosters learners' higher self-efficacy and better implementation of SRL strategies. This study discussed the role of natural dialogue in fostering socialized learning by teaching, and explored ChatGPT's specific contributions in supporting students' SRL through the learning by teaching process. Overall, the study highlights ChatGPT's potential as a teachable agent, offering insights for future research on ChatGPT-supported education.
Multi-head attention debiasing and contrastive learning for mitigating Dataset Artifacts in Natural Language Inference
Authors: Karthik Sivakoti
Date: 2024-12-16 00:00:00
Tasks: Contrastive Learning, Natural Language Inference, Negation

While Natural Language Inference (NLI) models have achieved high performances on benchmark datasets, there are still concerns whether they truly capture the intended task, or largely exploit dataset artifacts. Through detailed analysis of the Stanford Natural Language Inference (SNLI) dataset, we have uncovered complex patterns of various types of artifacts and their interactions, leading to the development of our novel structural debiasing approach. Our fine-grained analysis of 9,782 validation examples reveals four major categories of artifacts: length-based patterns, lexical overlap, subset relationships, and negation patterns. Our multi-head debiasing architecture achieves substantial improvements across all bias categories: length bias accuracy improved from 86.03% to 90.06%, overlap bias from 91.88% to 93.13%, subset bias from 95.43% to 96.49%, and negation bias from 88.69% to 94.64%. Overall, our approach reduces the error rate from 14.19% to 10.42% while maintaining high performance on unbiased examples. Analysis of 1,026 error cases shows significant improvement in handling neutral relationships, traditionally one of the most challenging areas for NLI systems.