pwc-archive/papers-with-abstracts huggingface dataset:
Genome hybridization: A universal way for the origin and diversification of organelles as well as the origin and speciation of eukaryotes
Authors: Qing-lin Dong, Xiang-ying Xing
Date: 2023-10-15 00:00:00

The origin of organelles (mitochondrion, chloroplast and nucleus) remains enigmatic. The endosymbiotic hypothesis that chloroplasts, mitochondria and nuclei descend from the endosymbiotic cyanobacterium, bacterium and archaebacterium respectively is dominant yet uncompelling, while our discovery of de novo organelle biogenesis in the cyanobacterium TDX16 that had acquired the genome of its green algal host Haematococcus pluvialis disproves this hypothesis. In light of organelle biogenesis in the cyanobacterium TDX16 in combination with the relevant cellular and molecular evidence, we propose genome hybridization hypothesis (GHH) that the origin of organelles and origin of eukaryotes as well as the diversification of organelles and speciation of eukaryotes are unified and achieved by genome hybridization: the endosymbiotic cyanobacteria/bacteria obtain the genomes of their archaebacterial or eukaryotic hosts and hybridize with their own ones resulting in expanded genomes containing a mixture of hybrid prokaryotic genes and eukaryotic genes, and thus have to compartmentalize to accommodate different genes for specialized function of photosynthesis (chloroplast), respiration (mitochondrion) and DNA preservation (nucleus), and consequently turn into photosynthetic/heterotrophic eukaryotes. Accordingly, eukaryotes and their organelles are of multiple origins, while the formation of cancer cells is the speciation of eukaryotes as cancer cells are new species of unicellular eukaryotes arising from bacteria. Therefore, GHH provides a theoretical framework unifying evolutionary biology, cancer biology and cell biology and directing the integrated multidisciplinary research.
Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph
Authors: Will Dumm, Mary Barker, William Howard-Snyder, William S. DeWitt, Frederick A. Matsen IV
Date: 2023-10-11 00:00:00
Tasks: Uncertainty Quantification

In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees. This goal is typically addressed using Bayesian techniques, however, current Bayesian methods do not scale to large data sets. Furthermore, for large data sets with relatively low signal one cannot even store every good tree individually, especially when the trees are required to be bifurcating. In this paper, we develop a novel object called the "history subpartition directed acyclic graph" (or "history sDAG" for short) that compactly represents an ensemble of trees with labels (e.g. ancestral sequences) mapped onto the internal nodes. The history sDAG can be built efficiently and can also be efficiently trimmed to only represent maximally parsimonious trees. We show that the history sDAG allows us to find many additional equally parsimonious trees, extending combinatorially beyond the ensemble used to construct it. We argue that this object could be useful as the "skeleton" of a more complete uncertainty quantification.
Influencing factors on false positive rates when classifying tumor cell line response to drug treatment
Authors: Priyanka Vasanthakumari, Thomas Brettin, Yitan Zhu, Hyunseung Yoo, Maulik Shukla, Alexander Partin, Fangfang Xia, Oleksandr Narykov, Rick L. Stevens
Date: 2023-10-17 00:00:00
Tasks: Drug Response Prediction

Informed selection of drug candidates for laboratory experimentation provides an efficient means of identifying suitable anti-cancer treatments. The advancement of artificial intelligence has led to the development of computational models to predict cancer cell line response to drug treatment. It is important to analyze the false positive rate (FPR) of the models, to increase the number of effective treatments identified and to minimize unnecessary laboratory experimentation. Such analysis will also aid in identifying drugs or cancer types that require more data collection to improve model predictions. This work uses an attention based neural network classification model to identify responsive/non-responsive drug treatments across multiple types of cancer cell lines. Two data filtering techniques have been applied to generate 10 data subsets, including removing samples for which dose response curves are poorly fitted and removing samples whose area under the dose response curve (AUC) values are marginal around 0.5 from the training set. One hundred trials of 10-fold cross-validation analysis is performed to test the model prediction performance on all the data subsets and the subset with the best model prediction performance is selected for further analysis. Several error analysis metrics such as the false positive rate (FPR), and the prediction uncertainty are evaluated, and the results are summarized by cancer type and drug mechanism of action (MoA) category. The FPR of cancer type spans between 0.262 and 0.5189, while that of drug MoA category spans almost the full range of [0, 1]. This study identifies cancer types and drug MoAs with high FPRs. Additional drug screening data of these cancer and drug categories may improve response modeling. Our results also demonstrate that the two data filtering approaches help improve the drug response prediction performance.
Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration
Authors: Jie Tang, Bin He, Junkai Xu, Tian Tan, Zhipeng Wang, Yanmin Zhou, Shuo Jiang
Date: 2023-10-16 00:00:00

Falls represent a significant cause of injury among the elderly population. Extensive research has been devoted to the utilization of wearable IMU sensors in conjunction with machine learning techniques for fall detection. To address the challenge of acquiring costly training data, this paper presents a novel method that generates a substantial volume of synthetic IMU data with minimal real fall experiments. First, unmarked 3D motion capture technology is employed to reconstruct human movements. Subsequently, utilizing the biomechanical simulation platform Opensim and forward kinematic methods, an ample amount of training data from various body segments can be custom generated. An LSTM model is trained, achieving testing accuracies of 91.99% and 86.62% on two distinct datasets of actual fall-related IMU data, demonstrated the comparable performance of models trained using genuine IMU data. Building upon the simulation framework, this paper further optimized the single IMU attachment position and multiple IMU combinations on fall detection. The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
Authors: Yufei Huang, Siyuan Li, Jin Su, Lirong Wu, Odin Zhang, Haitao Lin, Jingqi Qi, Zihan Liu, Zhangyang Gao, Yuyang Liu, Jiangbin Zheng, Stan. ZQ. Li
Date: 2023-10-14 00:00:00
Tasks: Graph structure learning, Prediction, Property Prediction, Protein Function Prediction, Representation Learning

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community.
Docking Peptides into HIV/FIV Protease with Deep Learning and Focused Peptide Docking Methods
Authors: Katherine Ge, Dayna Olson, Michel F. Sanner
Date: 2023-10-14 00:00:00
Tasks: Drug Design, Molecular Docking

Molecular docking is a structure-based computational drug design technique for predicting the interaction between a small molecule (ligand) and a macromolecule (receptor). Over the past three decades various docking software programs have been developed, mostly for drug-like molecules. With the recent interest in peptides as therapeutic molecules, several peptide docking methods have also been developed. AutoDock CrankPep (ADCP), is a state-of-the-art peptide docking tool that predicts the interaction of peptide with up to 20 amino acids in a user defined region of a macromolecule, i.e.focused docking. Recent advances in deep learning (DL) approaches have shown remarkable success in docking linear peptides composed of natural amino acids only. Unlike ADCP, these methods provide a confidence level in their predictions. Here we explore whether ADCP and various DL methods (AlphaFold2 Monomer, AlphaFold2 Multimer, and OmegaFold) and their prediction confidence metric can be used to discriminate native and non-native substrates for HIV and FIV proteases. We found that ADCP successfully predicts the interactions of native peptides but fails to discriminate non-native ones. This was expected as conventional docking methods report solutions maximizing ligand receptor interactions for any ligand. Surprisingly, DL methods underperform when docking native peptides into these particular docking targets but achieve high success rates with non-native peptides. While AlphaFold managed to successfully dock a few of the native peptides, OmegaFold failed to successfully dock any of them. Overall, none of these methods is currently able to distinguish between native and non-native peptides, warranting further exploration of specialized methodologies.
Evaluation of the mitotic score of invasive breast carcinomas on digital slide: development and contribution of a mitosis detection algorithm
Authors: Loris Guichard, Clara Simmat, Margot Dupeux, Stéphane Sockeel, Nicolas Pozin, Magali Lacroix-Triki, Catherine Miquel, Marie Sockeel, Sophie Prévot
Date: 2023-10-16 00:00:00
Tasks: Mitosis Detection, whole slide images

Introduction: Nottingham grading system is a major prognostic factor for invasive breast carcinoma (IBC). Its determination requires the evaluation of the mitotic score (MS) which is subject to low intra- and inter-observer reproducibility. The MS shall be performed in the most proliferative area of the tumor, which determination is hard but critical. Artificial intelligence based tools could help pathologists to detect mitosis on whole slide images (WSI). Objective: The aim of this study was to evaluate the contribution of a mitosis detection algorithms pecifically developed to assist the pathologist during the evaluation of the MS on WSI. Methods: Algorithmic mitosis detection is a two-step process: first the algorithm detects candidate objects resembling mitosis, then the selection is refined by a classifier. The densest mitoticregions are shown to the pathologist, then he can establish the MS with algorithm results. For this study, three expert pathologists have determined a consensual ground truth for MS on fifty WSI of IBC. Those slides were also submitted to two readers pathologists who evaluated the MS of each slide twice, with and without the assistance of the algorithm, with a four week wash-out period. Interobserver reproducibility was measured by evaluating the scores obtained with, and without assistance between two readers pathologists and was also measured between each reader pathologist and the expert ground truth to determine the accuracy of the established score. Results:Baseline linearly weighted Cohen's Kappa for interobserver agreement of MS between two readers pathologists was 0.482. Using the algorithm generated mitotic detection in WSI, the agreement score increased to 0.672. Baseline linearly weighted Cohen's Kappa for interobserver agreement of MS between each reader pathologist and expert consensus was 0.378 and 0.457 for pathologist 1 and 2 respectively. Using the algorithm generated mitoticdetection in WSI, the agreement score increased respectively to 0.629 and 0.726. Conclusion:The use of the developed algorithm constitutes a viable approach to assist the pathologist for the evaluation of the MS of IBC on WSI. Its use makes it possible to improve interobserver reproducibility between pathologists and the accuracy of the score established by expert consensus. The use of such a tool constitutes a new approach in the evaluation of the mitoticscore which could lead to an evolution of practices.
Quantifying the relative importance of the spatial and temporal resolution in energy systems optimisation model
Authors: Nandi Moksnes, William Usher
Date: 2023-10-16 00:00:00

An increasing number of studies using energy system optimisation models are conducted with higher spatial and temporal resolution. This comes with a computational cost which places a limit on the size, complexity, and detail of the model. In this paper, we explore the relative importance of structural aspects of energy system models, spatial and temporal resolution, compared to uncertainties in input parameters such as final energy demand, discount rate and capital costs. We use global sensitivity analysis to uncover these interactions for two developing countries, Kenya, and Benin, which still lack universal access to electricity. We find that temporal resolution has a high influence on all assessed results parameters, and spatial resolution has a significant influence on the expansion of distribution lines to the unelectrified population. The larger overall influence of temporal resolution indicates that this should be prioritised compared to spatial resolution.
A generalization of the rational rough Heston approximation
Authors: Jim Gatheral, Radoš Radoičić
Date: 2023-10-13 00:00:00

Previously, in [GR19], we derived a rational approximation of the solution of the rough Heston fractional ODE in the special case \lambda = 0, which corresponds to a pure power-law kernel. In this paper we extend this solution to the general case of the Mittag-Leffler kernel with \lambda \geq 0. We provide numerical evidence of the convergence of the solution.
Unveiling Early Warning Signals of Systemic Risks in Banks: A Recurrence Network-Based Approach
Authors: Shijia Song, Handong Li
Date: 2023-10-16 00:00:00
Tasks: Time Series, Time Series Analysis

Bank crisis is challenging to define but can be manifested through bank contagion. This study presents a comprehensive framework grounded in nonlinear time series analysis to identify potential early warning signals (EWS) for impending phase transitions in bank systems, with the goal of anticipating severe bank crisis. In contrast to traditional analyses of exposure networks using low-frequency data, we argue that studying the dynamic relationships among bank stocks using high-frequency data offers a more insightful perspective on changes in the banking system. We construct multiple recurrence networks (MRNs) based on multidimensional returns of listed banks' stocks in China, aiming to monitor the nonlinear dynamics of the system through the corresponding indicators and topological structures. Empirical findings indicate that key indicators of MRNs, specifically the average mutual information, provide valuable insights into periods of extreme volatility of bank system. This paper contributes to the ongoing discourse on early warning signals for bank instability, highlighting the applicability of predicting systemic risks in the context of banking networks.
Perpetual Futures Pricing
Authors: Damien Ackerer, Julien Hugonnier, Urban Jermann
Date: 2023-10-18 00:00:00

Perpetual futures are contracts without expiration date in which the anchoring of the futures price to the spot price is ensured by periodic funding payments from long to short. We derive explicit expressions for the no-arbitrage price of various perpetual contracts, including linear, inverse, and quantos futures in both discrete and continuous-time. In particular, we show that the futures price is given by the risk-neutral expectation of the spot sampled at a random time that reflects the intensity of the price anchoring. Furthermore, we identify funding specifications that guarantee the coincidence of futures and spot prices, and show that for such specifications perpetual futures contracts can be replicated by dynamic trading in primitive securities.
All AMMs are CFMMs. All DeFi markets have invariants. A DeFi market is arbitrage-free if and only if it has an increasing invariant
Authors: Roger Lee
Date: 2023-10-15 00:00:00
Tasks: All

In a universal framework that expresses any market system in terms of state transition rules, we prove that every DeFi market system has an invariant function and is thus by definition a CFMM; indeed, all automated market makers (AMMs) are CFMMs. Invariants connect directly to arbitrage and to completeness, according to two fundamental equivalences. First, a DeFi market system is, we prove, arbitrage-free if and only if it has a strictly increasing invariant, where arbitrage-free means that no state can be transformed into a dominated state by any sequence of transactions. Second, the invariant is, we prove, unique if and only if the market system is complete, meaning that it allows transitions between all pairs of states in the state space, in at least one direction. Thus a necessary and sufficient condition for no-arbitrage (respectively, for completeness) is the existence of the increasing (respectively, the uniqueness of the) invariant, which, therefore, fulfills in nonlinear DeFi theory the foundational role parallel to the existence (respectively, uniqueness) of the pricing measure in the Fundamental Theorem of Asset Pricing for linear markets. Moreover, a market system is recoverable by its invariant if and only if it is complete; and in all cases, complete or incomplete, every market system has, and is recoverable by, a multi-invariant. A market system is arbitrage-free if and only if its multi-invariant is increasing. Our examples illustrate (non)existence of various specific types of arbitrage in the context of various specific types of market systems -- with or without fees, with or without liquidity operations, and with or without coordination among multiple pools -- but the fundamental theorems have full generality, applicable to any DeFi market system and to any notion of arbitrage expressible as a strict partial order.
Uncovering Market Disorder and Liquidity Trends Detection
Authors: Etienne Chevalier, Yadh Hafsi, Vathana Ly Vath
Date: 2023-10-13 00:00:00

The primary objective of this paper is to conceive and develop a new methodology to detect notable changes in liquidity within an order-driven market. We study a market liquidity model which allows us to dynamically quantify the level of liquidity of a traded asset using its limit order book data. The proposed metric holds potential for enhancing the aggressiveness of optimal execution algorithms, minimizing market impact and transaction costs, and serving as a reliable indicator of market liquidity for market makers. As part of our approach, we employ Marked Hawkes processes to model trades-through which constitute our liquidity proxy. Subsequently, our focus lies in accurately identifying the moment when a significant increase or decrease in its intensity takes place. We consider the minimax quickest detection problem of unobservable changes in the intensity of a doubly-stochastic Poisson process. The goal is to develop a stopping rule that minimizes the robust Lorden criterion, measured in terms of the number of events until detection, for both worst-case delay and false alarm constraint. We prove our procedure's optimality in the case of a Cox process with simultaneous jumps, while considering a finite time horizon. Finally, this novel approach is empirically validated by means of real market data analyses.
A Framework for Treating Model Uncertainty in the Asset Liability Management Problem
Authors: Georgios I. Papayiannis
Date: 2023-10-18 00:00:00
Tasks: Decision Making, Management

The problem of asset liability management (ALM) is a classic problem of the financial mathematics and of great interest for the banking institutions and insurance companies. Several formulations of this problem under various model settings have been studied under the Mean-Variance (MV) principle perspective. In this paper, the ALM problem is revisited under the context of model uncertainty in the one-stage framework. In practice, uncertainty issues appear to several aspects of the problem, e.g. liability process characteristics, market conditions, inflation rates, inside information effects, etc. A framework relying on the notion of the Wasserstein barycenter is presented which is able to treat robustly this type of ambiguities by appropriate handling the various information sources (models) and appropriately reformulating the relevant decision making problem. The proposed framework can be applied to a number of different model settings leading to the selection of investment portfolios that remain robust to the various uncertainties appearing in the market. The paper is concluded with a numerical experiment for a static version of the ALM problem, employing standard modelling approaches, illustrating the capabilities of the proposed method with very satisfactory results in retrieving the true optimal strategy even in high noise cases.
Statistical arbitrage portfolio construction based on preference relations
Authors: Fredi Šarić, Stjepan Begušić, Andro Merćep, Zvonko Kostanjčar
Date: 2023-10-12 00:00:00

Statistical arbitrage methods identify mispricings in securities with the goal of building portfolios which are weakly correlated with the market. In pairs trading, an arbitrage opportunity is identified by observing relative price movements between a pair of two securities. By simultaneously observing multiple pairs, one can exploit different arbitrage opportunities and increase the performance of such methods. However, the use of a large number of pairs is difficult due to the increased probability of contradictory trade signals among different pairs. In this paper, we propose a novel portfolio construction method based on preference relation graphs, which can reconcile contradictory pairs trading signals across multiple security pairs. The proposed approach enables joint exploitation of arbitrage opportunities among a large number of securities. Experimental results using three decades of historical returns of roughly 500 stocks from the S\&P 500 index show that the portfolios based on preference relations exhibit robust returns even with high transaction costs, and that their performance improves with the number of securities considered.