pwc-archive/papers-with-abstracts huggingface dataset:
Neutral phylogenetic models and their role in tree-based biodiversity measures
Authors: Mike Steel
Date: 2024-05-28 00:00:00
Tasks: Diversity

A wide variety of stochastic models of cladogenesis (based on speciation and extinction) lead to an identical distribution on phylogenetic tree shapes once the edge lengths are ignored. By contrast, the distribution of the tree's edge lengths is generally quite sensitive to the underlying model. In this paper, we review the impact of different model choices on tree shape and edge length distribution, and its impact for studying the properties of phylogenetic diversity (PD) as a measure of biodiversity, and the loss of PD as species become extinct at the present. We also compare PD with a stochastic model of feature diversity, and investigate some mathematical links and inequalities between these two measures plus their predictions concerning the loss of biodiversity under extinction at the present.
Systems-level health of patients living with end-stage kidney disease using standard lab values
Authors: Glen Pridham, Karthik K. Tennankore, Kenneth Rockwood, George Worthen, Andrew D. Rutenberg
Date: 2024-05-30 00:00:00

We present a systems-level analysis of end-stage kidney disease (ESKD) with a dynamical network analysis of 14 commonly measured blood-based biomarkers in patients undergoing regular haemodialysis. Utilizing a validated pipeline for declining homeostatic systems, our approach learns a dynamical model together with an invertible transformation that simplifies the behaviour of observed biomarkers into natural variables. Within the natural variables, we identified two distinct dynamical behaviours: (i) stochastic accumulation, the random accumulation of abnormal values, and (ii) mallostasis, a deterministic drift towards worse health. These behaviours are identified by persistent fluctuations indicating weak stability, or a gradual shift in homeostatic set point, respectively. Both lead to worsening natural variable values, making the natural variables salient survival predictors with preferred directions of increasing risk. When this worsening is transformed back into observable biomarkers, it generates a coherent spectrum of worsening medical signs characteristic of a medical syndrome. Specifically, we found that small modules of natural variables corresponded to two existing syndromes commonly afflicting ESKD patients: protein-energy wasting and sepsis. We also identified new prospective syndromes. Our findings suggest that natural variables are robust, systems-level biomarkers, capturing the complex, holistic changes in health associated with ESKD.
Medication Recommendation via Dual Molecular Modalities and Multi-Step Enhancement
Authors: Shi Mu, Chen Li, Xiang Li, Shunpan Liang
Date: 2024-05-30 00:00:00
Tasks: Contrastive Learning, Recommendation Systems

Existing works based on molecular knowledge neglect the 3D geometric structure of molecules and fail to learn the high-dimensional information of medications, leading to structural confusion. Additionally, it does not extract key substructures from a single patient visit, resulting in the failure to identify medication molecules suitable for the current patient visit. To address the above limitations, we propose a bimodal molecular recommendation framework named BiMoRec, which introduces 3D molecular structures to obtain atomic 3D coordinates and edge indices, overcoming the inherent lack of high-dimensional molecular information in 2D molecular structures. To retain the fast training and prediction efficiency of the recommendation system, we use bimodal graph contrastive pretraining to maximize the mutual information between the two molecular modalities, achieving the fusion of 2D and 3D molecular graphs. Additionally, we designed a molecular multi-step enhancement mechanism to re-calibrate the molecular weights. Specifically, we employ a pre-training method that captures both 2D and 3D molecular structure representations, along with substructure representations, and leverages contrastive learning to extract mutual information. We then use the pre-trained encoder to generate molecular representations, enhancing them through a three-step process: intra-visit, molecular per-visit, and latest-visit. Finally, we apply temporal information aggregation to generate the final medication combinations. Our implementation on the MIMIC-III and MIMIC-IV datasets demonstrates that our method achieves state-of-the-art performance.
Categorization of 33 computational methods to detect spatially variable genes from spatially resolved transcriptomics data
Authors: Guanao Yan, Shuo Harper Hua, Jingyi Jessica Li
Date: 2024-05-29 00:00:00
Tasks: Benchmarking, Specificity

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 33 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.
The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study
Authors: Fekede Asefa Kumsa, Jay H. Fowke, Soheil Hashtarkhani, Brianna M. White, Martha J. Shrubsole, Arash Shaban-Nejad
Date: 2024-05-28 00:00:00

Prostate cancer is one of the leading causes of cancer-related mortality among men in the U.S. We examined the role of neighborhood obesogenic attributes on prostate cancer risk and mortality in the Southern Community Cohort Study (SCCS). From 34,166 SCCS male participants, 28,356 were included in the analysis. We assessed relationship between neighborhood socioeconomic status (nSES) and neighborhood obesogenic environment indices including restaurant environment index, retail food environment index, parks, recreational facilities, and businesses and prostate cancer risk and mortality by controlling for individual-level factors using a multivariable Cox proportional hazards model. We further stratified prostate cancer risk analysis by race and body mass index (BMI). Median follow-up time was 133 months, and mean age was 51.62 years. There were 1,524 (5.37%) prostate cancer diagnoses and 98 (6.43%) prostate cancer deaths during follow-up. Compared to participants residing in wealthiest quintile, those residing in the poorest quintile had a higher risk of prostate cancer, particularly among non-obese men with a BMI less than 30. The restaurant environment index was associated with a higher prostate cancer risk in overweight (BMI equal or greater 25) White men. Obese Black individuals without any neighborhood recreational facilities had a 42% higher risk compared to those with any access. Compared to residents in wealthiest quintile and most walkable area, those residing within the poorest quintile or the least walkable area had a higher risk of prostate cancer death.
EPI-VALID : Validation of an algorithm for identifying patients with epilepsy in the SNDS using data from the CONSTANCES cohort
Authors: Adeline Degremont, Catherine Bisquay, Pierre-Alain Jachiet
Date: 2024-05-27 00:00:00
Tasks: Specificity, valid

Introduction: The HAS conducted a study in 2018 using the French National Health Data System (SNDS) on the care pathway of patients with epilepsy. This study used 2 algorithms to identify patients with epilepsy, based on hospitalization for epilepsy, insurance for chronic severe epilepsy, and antiepileptic drug (AE) dispensing, with exclusion of AEs not specific to epilepsy offering a low population estimate, or exclusion of dispensing in a context other than epilepsy (migraine, neuropathic pain, bipolar disorder, alcohol dependence, anxiety) offering a high population estimate. To study the characteristics of these 2 algorithms (low and high populations), we used data from CONSTANCES, a French generalist cohort and its control sample, which are matched to SNDS data. Method: This is a validation study of a method for identifying patients with epilepsy in the SNDS. The study population was patients identified as epileptic according to the algorithms defined in the introduction, applied over the year 2018 and the reference population was all patients for whom there is a mention of epilepsy (free text field) in the inclusion and/or follow-up questionnaires of the CONSTANCES cohort for the years prior to 2019. Results: Among 156,819 patients present in the CONSTANCES cohort in 2018, 689 patients mentioned epilepsy in one of the questionnaires (0.4%). This number is lower than expected: in the literature, the prevalence of epilepsy in the general population is estimated at 0.6-0.7% in adults. This is due to under-reporting and lack of representativeness of epilepsy in the cohort, which we were able to estimate from the control sample data. Aware of these two limitations, we compared the characteristics of the algorithms with those of a control algorithm using insurance for chronic severe epilepsy and/or hospitalization for epilepsy as inclusion criteria in the SNDS. The accuracy of our SNDS algorithms is not very high (17.8% and 35% respectively in high and low populations) compared with the control algorithm (58.1%). However, they do improve sensitivity (+33 and +14 points respectively), with little loss of specificity (-1.2 and -0.3 points respectively). Discussion: The analysis of false-positive patients has enabled us to identify ways of improving the algorithms. Indeed, some AE drugs could be excluded from the inclusion criteria, as they are widely used in another indication. For example, eliminating pregabalin and gabapentin from the high-population algorithm would significantly improve its accuracy (45.3% vs. 17.8%).
Exploration of methods for computing sensitivities in ODE models at dynamic and steady states
Authors: Polina Lakrisenko, Dilan Pathirana, Daniel Weindl, Jan Hasenauer
Date: 2024-05-26 00:00:00
Tasks: Numerical Integration

Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based optimization is used. The gradient depends on derivatives of the state variables with respect to parameters, also called state sensitivities, which are expensive to compute. In many cases, steady-state computation is a part of model simulation, either due to steady-state data or an assumption that the system is at steady state at the initial time point. Various methods are available for steady-state and gradient computation. Yet, the most efficient pair of methods (one for steady states, one for gradients) for a particular model is often not clear. Moreover, depending on the model and the available data, some methods may not be applicable or sufficiently robust. In order to facilitate the selection of methods, we explore six method pairs for computing the steady state and sensitivities at steady state using six real-world problems. The method pairs involve numerical integration or Newton's method to compute the steady-state, and -- for both forward and adjoint sensitivity analysis -- numerical integration or a tailored method to compute the sensitivities at steady-state. Our evaluation shows that the two method pairs that combine numerical integration for the steady-state with a tailored method for the sensitivities at steady-state were the most robust, and amongst the most computationally-efficient. We also observed that while Newton's method for steady-state computation yields a substantial speedup compared to numerical integration, it may lead to a large number of simulation failures. Overall, our study provides a concise overview across current methods for computing sensitivities at steady state, guiding modelers to choose the right methods.
Local nature of 0.1 Hz oscillations in microcirculation is confirmed by imaging photoplethysmography
Authors: Irina A. Mizeva, Natalia P. Podolyan, Oleg V. Mamontov, Anastasiia V. Sakovskaia, Alexei A. Kamshilin
Date: 2024-05-29 00:00:00
Tasks: Diagnostic, Heart Rate Variability

Low-frequency oscillations in the human circulatory system is important for basic physiology and practical applications in clinical medicine. Our objective was to study which mechanism (central or local) is responsible for changes in blood flow fluctuations at around 0.1 Hz. We used the method of imaging photoplethysmography synchronized with electrocardiography to measure blood-flow response to local forearm heating of 18 healthy male volunteers. The dynamics of peripheral perfusion was revealed by a correlation processing of photoplethysmography data, and the central hemodynamics was assessed from the electrocardiogram. Wavelet analysis was used to estimate the dynamics of spectral components. Our results show that skin heating leads to multiple increase in local perfusion accompanied by drop in blood flow oscillations at 0.1 Hz, whereas no changes in heart rate variability was observed. After switching off the heating, perfusion remains at the high level, regardless decrease in skin temperature. The 0.1 Hz oscillations are smoothly recovered to the base level. In conclusion, we confirm the local nature of fluctuations in peripheral blood flow in the frequency band of about 0.1 Hz. A significant, but time-delayed, recovery of fluctuation energy in this frequency range after cessation of the skin warming was discovered. This study reveals a novel factor involved in the regulation microcirculatory vascular tone. A comprehensive study of hemodynamics using the new technique of imaging photoplethysmography synchronized with electrocardiography is a prerequisite for development of a valuable diagnostic tool.
On in-silico estimation of left ventricular end-diastolic pressure from cardiac strains
Authors: Emilio A. Mendiola, Raza Rana Mehdi, Dipan J. Shah, Reza Avazmohammadi
Date: 2024-05-28 00:00:00

Left ventricular diastolic dysfunction (LVDD) is a group of diseases that adversely affect the passive phase of the cardiac cycle and can lead to heart failure. While left ventricular end-diastolic pressure (LVEDP) is a valuable prognostic measure in LVDD patients, traditional invasive methods of measuring LVEDP present risks and limitations, highlighting the need for alternative approaches. This paper investigates the possibility of measuring LVEDP non-invasively using inverse in-silico modeling. We propose the adoption of patient-specific cardiac modeling and simulation to estimate LVEDP and myocardial stiffness from cardiac strains. We have developed a high-fidelity patient-specific computational model of the left ventricle. Through an inverse modeling approach, myocardial stiffness and LVEDP were accurately estimated from cardiac strains that can be acquired from in vivo imaging, indicating the feasibility of computational modeling to augment current approaches in the measurement of ventricular pressure. Integration of such computational platforms into clinical practice holds promise for early detection and comprehensive assessment of LVDD with reduced risk for patients.
Gaussian Recombining Split Tree
Authors: Yury Lebedev, Arunava Banerjee
Date: 2024-05-25 00:00:00

Binomial trees are widely used in the financial sector for valuing securities with early exercise characteristics, such as American stock options. However, while effective in many scenarios, pricing options with CRR binomial trees are limited. Major limitations are volatility estimation, constant volatility assumption, subjectivity in parameter choices, and impracticality of instantaneous delta hedging. This paper presents a novel tree: Gaussian Recombining Split Tree (GRST), which is recombining and does not need log-normality or normality market assumption. GRST generates a discrete probability mass function of market data distribution, which approximates a Gaussian distribution with known parameters at any chosen time interval. GRST Mixture builds upon the GRST concept while being flexible to fit a large class of market distributions and when given a 1-D time series data and moments of distributions at each time interval, fits a Gaussian mixture with the same mixture component probabilities applied at each time interval. Gaussian Recombining Split Tre Mixture comprises several GRST tied using Gaussian mixture component probabilities at the first node. Our extensive empirical analysis shows that the option prices from the GRST align closely with the market.
DSPO: An End-to-End Framework for Direct Sorted Portfolio Construction
Authors: Jianyuan Zhong, Zhijian Xu, Saizhuo Wang, Xiangyu Wen, Jian Guo, Qiang Xu
Date: 2024-05-24 00:00:00
Tasks: Portfolio Optimization

In quantitative investment, constructing characteristic-sorted portfolios is a crucial strategy for asset allocation. Traditional methods transform raw stock data of varying frequencies into predictive characteristic factors for asset sorting, often requiring extensive manual design and misalignment between prediction and optimization goals. To address these challenges, we introduce Direct Sorted Portfolio Optimization (DSPO), an innovative end-to-end framework that efficiently processes raw stock data to construct sorted portfolios directly. DSPO's neural network architecture seamlessly transitions stock data from input to output while effectively modeling the intra-dependency of time-steps and inter-dependency among all tradable stocks. Additionally, we incorporate a novel Monotonical Logistic Regression loss, which directly maximizes the likelihood of constructing optimal sorted portfolios. To the best of our knowledge, DSPO is the first method capable of handling market cross-sections with thousands of tradable stocks fully end-to-end from raw multi-frequency data. Empirical results demonstrate DSPO's effectiveness, yielding a RankIC of 10.12\% and an accumulated return of 121.94\% on the New York Stock Exchange in 2023-2024, and a RankIC of 9.11\% with a return of 108.74\% in other markets during 2021-2022.
Visualization of Board of Director Connections for Analysis in Socially Responsible Investing
Authors: Alice Da Fonseca, Peter Lake, Ariana Barrenechea
Date: 2024-05-30 00:00:00
Tasks: Data Visualization

This project is a collaboration between industry and academia to delve into Finance Social Networks, specifically the Board of Directors of public companies. Knowing the connections between Directors and Executives in different companies can generate powerful stories and meaningful insights on investments. A proof of concept in the form of a Data Visualization tool reveals its strength in investigating corporate governance and sustainability, as well as in the partnership between industry and academic institutions.
A Tick-by-Tick Solution for Concentrated Liquidity Provisioning
Authors: Corinne Powers
Date: 2024-05-29 00:00:00

Automated market makers with concentrated liquidity capabilities are programmable at the tick level. The maximization of earned fees, plus depreciated reserves, is a convex optimization problem whose vector solution gives the best provision of liquidity at each tick under a given set of parameter estimates for swap volume and price volatility. Surprisingly, early results show that concentrating liquidity around the current price is usually not the best strategy.
Optimizing Broker Performance Evaluation through Intraday Modeling of Execution Cost
Authors: Zoltan Eisler, Johannes Muhle-Karbe
Date: 2024-05-29 00:00:00

Minimizing execution costs for large orders is a fundamental challenge in finance. Firms often depend on brokers to manage their trades due to limited internal resources for optimizing trading strategies. This paper presents a methodology for evaluating the effectiveness of broker execution algorithms using trading data. We focus on two primary cost components: a linear cost that quantifies short-term execution quality and a quadratic cost associated with the price impact of trades. Using a model with transient price impact, we derive analytical formulas for estimating these costs. Furthermore, we enhance estimation accuracy by introducing novel methods such as weighting price changes based on their expected impact content. Our results demonstrate substantial improvements in estimating both linear and impact costs, providing a robust and efficient framework for selecting the most cost-effective brokers.
Risk-Neutral Generative Networks
Authors: Zhonghao Xian, Xing Yan, Cheuk Hang Leung, Qi Wu
Date: 2024-05-28 00:00:00
Tasks: Benchmarking

We present a functional generative approach to extract risk-neutral densities from market prices of options. Specifically, we model the log-returns on the time-to-maturity continuum as a stochastic curve driven by standard normal. We then use neural nets to represent the term structures of the location, the scale, and the higher-order moments, and impose stringent conditions on the learning process to ensure the neural net-based curve representation is free of static arbitrage. This specification is structurally clear in that it separates the modeling of randomness from the modeling of the term structures of the parameters. It is data adaptive in that we use neural nets to represent the shape of the stochastic curve. It is also generative in that the functional form of the stochastic curve, although parameterized by neural nets, is an explicit and deterministic function of the standard normal. This explicitness allows for the efficient generation of samples to price options across strikes and maturities, without compromising data adaptability. We have validated the effectiveness of this approach by benchmarking it against a comprehensive set of baseline models. Experiments show that the extracted risk-neutral densities accommodate a diverse range of shapes. Its accuracy significantly outperforms the extensive set of baseline models--including three parametric models and nine stochastic process models--in terms of accuracy and stability. The success of this approach is attributed to its capacity to offer flexible term structures for risk-neutral skewness and kurtosis.
Analisis cuantitativo de riesgos utilizando "MCSimulRisk" como herramienta didactica
Authors: Fernando Acebes, David Curto, Juan de Anton, Felix Villafanez
Date: 2024-05-31 00:00:00
Tasks: Management

Risk management is a fundamental discipline in project management, which includes, among others, quantitative risk analysis. Throughout several years of teaching, we have observed difficulties in students performing Monte Carlo Simulation within the quantitative analysis of risks. This article aims to present MCSimulRisk as a teaching tool that allows students to perform Monte Carlo simulation and apply it to projects of any complexity simply and intuitively. This tool allows for incorporating any uncertainty identified in the project into the model.