pwc-archive/papers-with-abstracts huggingface dataset:
Attention Based Molecule Generation via Hierarchical Variational Autoencoder
Authors: Divahar Sivanesan
Date: 2024-01-18 00:00:00

Molecule generation is a task made very difficult by the complex ways in which we represent molecules computationally. A common technique used in molecular generative modeling is to use SMILES strings with recurrent neural networks built into variational autoencoders - but these suffer from a myriad of issues: vanishing gradients, long-range forgetting, and invalid molecules. In this work, we show that by combining recurrent neural networks with convolutional networks in a hierarchical manner, we are able to both extract autoregressive information from SMILES strings while maintaining signal and long-range dependencies. This allows for generations with very high validity rates on the order of 95% when reconstructing known molecules. We also observe an average Tanimoto similarity of .6 between test set and reconstructed molecules, which suggests our method is able to map between SMILES strings and their learned representations in a more effective way than prior works using similar methods.
Exploring gene content with pangene graphs
Authors: Heng Li, Maximillian Marin, Maha Reda Farhat
Date: 2024-02-25 00:00:00

Motivation: The gene content regulates the biology of an organism. It varies between species and between individuals of the same species. Although tools have been developed to identify gene content changes in bacterial genomes, none is applicable to collections of large eukaryotic genomes such as the human pangenome. Results: We developed pangene, a computational tool to identify gene orientation, gene order and gene copy-number changes in a collection of genomes. Pangene aligns a set of input protein sequences to the genomes, resolves redundancies between protein sequences and constructs a gene graph with each genome represented as a walk in the graph. It additionally finds subgraphs, which we call bibubbles, that capture gene content changes. Applied to the human pangenome, pangene identifies known gene-level variations and reveals complex haplotypes that are not well studied before. Pangene also works with high-quality bacterial pangenome and reports similar numbers of core and accessory genes in comparison to existing tools. Availability and implementation: Source code at https://github.com/lh3/pangene; pre-built pangene graphs can be downloaded from https://zenodo.org/records/8118576 and visualized at https://pangene.bioinweb.org
Methylation Operation Wizard (MeOW): Identification of differentially methylated regions in long-read sequencing data
Authors: Miranda PG Zalusky, Danny E Miller
Date: 2024-02-27 00:00:00

Long-read sequencing (LRS) is able to simultaneously capture information about both DNA sequence and modifications, such as CpG methylation in a single sequencing experiment. Here we present Methylation Operation Wizard (MeOW), a program to identify and prioritize differentially methylated regions (DMRs) genome-wide using LRS data. MeOW can be run using either a file containing counts of per-nucleotide methylated CpG sites or with a bam file containing modified base tags.
Modelling 1D Partially Absorbing Boundaries for Brownian Molecular Communication Channels
Authors: Caglar Koca, Ozgur B. Akan
Date: 2024-02-24 00:00:00

Molecular Communication (MC) architectures suffer from molecular build-up in the channel if they do not have appropriate reuptake mechanisms. The molecular build-up either leads to intersymbol interference (ISI) or reduces the transmission rate. To measure the molecular build-up, we derive analytic expressions for the incidence rate and absorption rate for one-dimensional MC channels where molecular dispersion obeys the Brownian Motion. We verify each of our key results with Monte Carlo simulations. Our results contribute to the development of more complicated models and analytic expressions to measure the molecular build-up and the impact of ISI in MC.
Comparison of the Effects of Interaction with Intentional Agent and Artificial Intelligence using fNIRS
Authors: Mohammad Ghalavand, Javad Hatami, Seyed Kamaledin Setarehdan, Fatimah Nosrati, Hananeh Ghalavand, Ali Nikhalat-Jahromi
Date: 2024-02-27 00:00:00
Tasks: Decision Making

As societal interactions increasingly involve both intentional and unintentional agents, understanding their effects on human cognition becomes paramount. This study investigates the neural correlates of interacting with intentional versus artificial agents in a simulated tennis game scenario. Employing functional near-infrared spectroscopy (fNIRS), brain activity in 50 male participants during gameplay against both types of opponents was analyzed. The used methodological approach ensures ecological validity by simulating real-world decision-making scenarios while participants undergo fNIRS scanning, avoiding the constraints of traditional neuroimaging methods. Six prefrontal cortex channels are focused on, leveraging the 10-20 system, to capture nuanced differences in brain activity. Wavelet analysis was utilized to dissect the data into frequency-specific differences, revealing subtle variations across different channels and frequency bands. Moreover, activity was quantified by comparing average data signals between rest and play modes across all points using Generalized Linear Model (GLM). The findings unveil significant differences in neural activation patterns, particularly in one specific channel and frequency range, suggesting distinct cognitive processing when interacting with intentional agents. These results align with previous neuroimaging studies and contribute to understanding the neural underpinnings of human-agent interactions in naturalistic settings. While acknowledging study limitations, including sample homogeneity and spatial accuracy constraints, the study's findings underscore the potential of fNIRS in exploring complex cognitive phenomena beyond laboratory confines.
Implications of self-identified race, ethnicity, and genetic ancestry on genetic association studies in biobanks within health systems
Authors: Ruth Johnson, Bogdan Pasaniuc
Date: 2024-02-24 00:00:00

Precision medicine aims to create biomedical solutions tailored to specific factors that affect disease risk and treatment responses within the population. The success of the genomics era and recent widespread availability of electronic health records (EHR) has ushered in a new wave of genomic biobanks connected to EHR databases (EHR-linked biobanks). This perspective aims to discuss how race, ethnicity, and genetic ancestry are currently utilized to study common disease variation through genetic association studies. Although genetic ancestry plays a significant role in shaping the genetic landscape underlying disease risk in humans, the overall risk of a disease is caused by a complex combination of environmental, sociocultural, and genetic factors. When using EHR-linked biobanks to interrogate underlying disease etiology, it is also important to be aware of how the biases associated with commonly used descent-associated concepts such as race and ethnicity can propagate to downstream analyses. We intend for this resource to support researchers who perform or analyze genetic association studies in the EHR-linked biobank setting such as those involved in consortium-wide biobanking efforts. We provide background on how race, ethnicity, and genetic ancestry play a role in current association studies, highlight considerations where there is no consensus about best practices, and provide transparency about the current shortcomings.
The Evolution of Raw Data Archiving and the Growth of Its Importance in Crystallography
Authors: John R. Helliwell, James R. Hester, Loes Kroon-Batenburg, Brian McMahon, Selina L. S. Storm
Date: 2024-02-23 00:00:00

The hardware for data archiving has expanded capacities for digital storage enormously in the past decade or more. This article charts the efforts of IUCr to facilitate discussions and plans relating to raw data archiving and reuse within the various communities of crystallography, diffraction, and scattering.
Exact and efficient phylodynamic simulation from arbitrarily large populations
Authors: Michael Celentano, William S. DeWitt, Sebastian Prillo, Yun S. Song
Date: 2024-02-27 00:00:00

Many biological studies involve inferring the evolutionary history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the underlying population dynamics and the ascertainment process. A crucial component of this phylodynamic modeling involves tree simulation, which is used to benchmark probabilistic inference methods. To simulate an ascertained tree, one must first simulate the full population tree and then prune unobserved lineages. Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded. In most biological scenarios, simulations of the entire population are prohibitively expensive due to computational demands placed on lineages without sampled descendants. Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling model, there exists an equivalent process with complete sampling and no death, a property which we leverage to develop a highly efficient algorithm for simulating trees. Our algorithm scales linearly with the size of the final simulated tree and is independent of the population size, enabling simulations from extremely large populations beyond the reach of current methods but essential for various biological applications. We anticipate that this unprecedented speedup will significantly advance the development of novel inference methods that require extensive training data.
The structure is the message: preserving experimental context through tensor decomposition
Authors: Zhixin Cyrillus Tan, Aaron S. Meyer
Date: 2024-02-26 00:00:00
Tasks: Tensor Decomposition

Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, "The medium is the message." In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We introduce tensor-structured analyses and decompositions to preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit.
Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data
Authors: Aokun Chen, Qian Li, Yu Huang, Yongqiu Li, Yu-Neng Chuang, Xia Hu, Serena Guo, Yonghui Wu, Yi Guo, Jiang Bian
Date: 2024-02-03 00:00:00
Tasks: Articles, Literature Mining

A comprehensive view of factors associated with AD/ADRD will significantly aid in studies to develop new treatments for AD/ADRD and identify high-risk populations and patients for prevention efforts. In our study, we summarized the risk factors for AD/ADRD by reviewing existing meta-analyses and review articles on risk and preventive factors for AD/ADRD. In total, we extracted 477 risk factors in 10 categories from 537 studies. We constructed an interactive knowledge map to disseminate our study results. Most of the risk factors are accessible from structured Electronic Health Records (EHRs), and clinical narratives show promise as information sources. However, evaluating genomic risk factors using RWD remains a challenge, as genetic testing for AD/ADRD is still not a common practice and is poorly documented in both structured and unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk factors, literature mining via NLP methods offers a solution to automatically update our knowledge map.
Stochastic Expansion for the Pricing of Asian and Basket Options
Authors: Fabien Le Floc'h
Date: 2024-02-27 00:00:00

We present closed analytical approximations for the pricing of basket options, also applicable to Asian options with discrete averaging under the Black-Scholes model with time-dependent parameters. The formulae are obtained by using a stochastic Taylor expansion around a log-normal proxy model and are found to be highly accurate for Asian options in practice as well as for vanilla options with discrete dividends.
Limit Order Book Simulations: A Review
Authors: Konark Jain, Nick Firoozye, Jonathan Kochems, Philip Treleaven
Date: 2024-02-27 00:00:00
Tasks: Algorithmic Trading

Limit Order Books (LOBs) serve as a mechanism for buyers and sellers to interact with each other in the financial markets. Modelling and simulating LOBs is quite often necessary for calibrating and fine-tuning the automated trading strategies developed in algorithmic trading research. The recent AI revolution and availability of faster and cheaper compute power has enabled the modelling and simulations to grow richer and even use modern AI techniques. In this review we examine the various kinds of LOB simulation models present in the current state of the art. We provide a classification of the models on the basis of their methodology and provide an aggregate view of the popular stylized facts used in the literature to test the models. We additionally provide a focused study of price impact's presence in the models since it is one of the more crucial phenomena to model in algorithmic trading. Finally, we conduct a comparative analysis of various qualities of fits of these models and how they perform when tested against empirical data.
Alternative models for FX: pricing double barrier options in regime-switching Lévy models with memory
Authors: Svetlana Boyarchenko, Sergei Levendorskiĭ
Date: 2024-02-26 00:00:00

This paper is a supplement to our recent paper ``Alternative models for FX, arbitrage opportunities and efficient pricing of double barrier options in L\'evy models". We introduce the class of regime-switching L\'evy models with memory, which take into account the evolution of the stochastic parameters in the past. This generalization of the class of L\'evy models modulated by Markov chains is similar in spirit to rough volatility models. It is flexible and suitable for application of the machine-learning tools. We formulate the modification of the numerical method in ``Alternative models for FX, arbitrage opportunities and efficient pricing of double barrier options in L\'evy models", which has the same number of the main time-consuming blocks as the method for Markovian regime-switching models.
Finding Near-Optimal Portfolios With Quality-Diversity
Authors: Bruno Gašperov, Marko Đurasević, Domagoj Jakobovic
Date: 2024-02-25 00:00:00
Tasks: Diversity, Portfolio Optimization

The majority of standard approaches to financial portfolio optimization (PO) are based on the mean-variance (MV) framework. Given a risk aversion coefficient, the MV procedure yields a single portfolio that represents the optimal trade-off between risk and return. However, the resulting optimal portfolio is known to be highly sensitive to the input parameters, i.e., the estimates of the return covariance matrix and the mean return vector. It has been shown that a more robust and flexible alternative lies in determining the entire region of near-optimal portfolios. In this paper, we present a novel approach for finding a diverse set of such portfolios based on quality-diversity (QD) optimization. More specifically, we employ the CVT-MAP-Elites algorithm, which is scalable to high-dimensional settings with potentially hundreds of behavioral descriptors and/or assets. The results highlight the promising features of QD as a novel tool in PO.
Optimizing Neural Networks for Bermudan Option Pricing: Convergence Acceleration, Future Exposure Evaluation and Interpolation in Counterparty Credit Risk
Authors: Vikranth Lokeshwar Dhandapani, Shashi Jain
Date: 2024-02-24 00:00:00

This paper presents a Monte-Carlo-based artificial neural network framework for pricing Bermudan options, offering several notable advantages. These advantages encompass the efficient static hedging of the target Bermudan option and the effective generation of exposure profiles for risk management. We also introduce a novel optimisation algorithm designed to expedite the convergence of the neural network framework proposed by Lokeshwar et al. (2022) supported by a comprehensive error convergence analysis. We conduct an extensive comparative analysis of the Present Value (PV) distribution under Markovian and no-arbitrage assumptions. We compare the proposed neural network model in conjunction with the approach initially introduced by Longstaff and Schwartz (2001) and benchmark it against the COS model, the pricing model pioneered by Fang and Oosterlee (2009), across all Bermudan exercise time points. Additionally, we evaluate exposure profiles, including Expected Exposure and Potential Future Exposure, generated by our proposed model and the Longstaff-Schwartz model, comparing them against the COS model. We also derive exposure profiles at finer non-standard grid points or risk horizons using the proposed approach, juxtaposed with the Longstaff Schwartz method with linear interpolation and benchmark against the COS method. In addition, we explore the effectiveness of various interpolation schemes within the context of the Longstaff-Schwartz method for generating exposures at finer grid horizons.
Sizing the bets in a focused portfolio
Authors: Vuko Vukcevic, Robert Keser
Date: 2024-02-23 00:00:00

The paper provides a mathematical model and a tool for the focused investing strategy as advocated by Buffett, Munger, and others from this investment community. The approach presented here assumes that the investor's role is to think about probabilities of different outcomes for a set of businesses. Based on these assumptions, the tool calculates the optimal allocation of capital for each of the investment candidates. The model is based on a generalized Kelly Criterion with options to provide constraints that ensure: no shorting, limited use of leverage, providing a maximum limit to the risk of permanent loss of capital, and maximum individual allocation. The software is applied to an example portfolio from which certain observations about excessive diversification are obtained. In addition, the software is made available for public use.
Withdrawal Success Optimization in a Pooled Annuity Fund
Authors: Hayden Brown
Date: 2024-02-27 00:00:00

Consider a closed pooled annuity fund investing in n assets with discrete-time rebalancing. At time 0, each annuitant makes an initial contribution to the fund, committing to a predetermined schedule of withdrawals. Require annuitants to be homogeneous in the sense that their initial contributions and predetermined withdrawal schedules are identical, and their mortality distributions are identical and independent. Under the forementioned setup, the probability for a particular annuitant to complete the prescribed withdrawals until death is maximized over progressively measurable portfolio weight functions. Applications consider fund portfolios that mix two assets: the S&P Composite Index and an inflation-protected bond. The maximum probability is computed for annually rebalanced schedules consisting of an initial investment and then equal annual withdrawals until death. A considerable increase in the maximum probability is achieved by increasing the number of annuitants initially in the pool. For example, when the per-annuitant initial contribution and annual withdrawal amount are held constant, starting with 20 annuitants instead of just 1 can increase the maximum probability (measured on a scale from 0 to 1) by as much as .15.
Navigating Complexity: Constrained Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints
Authors: Mehmet Caner, Qingliang Fan, YingYing Li
Date: 2024-02-27 00:00:00

This paper analyzes the statistical properties of constrained portfolio formation in a high dimensional portfolio with a large number of assets. Namely, we consider portfolios with tracking error constraints, portfolios with tracking error jointly with weight (equality or inequality) restrictions, and portfolios with only weight restrictions. Tracking error is the portfolio's performance measured against a benchmark (an index usually), {\color{black}{and weight constraints refers to specific allocation of assets within the portfolio, which often come in the form of regulatory requirement or fund prospectus.}} We show how these portfolios can be estimated consistently in large dimensions, even when the number of assets is larger than the time span of the portfolio. We also provide rate of convergence results for weights of the constrained portfolio, risk of the constrained portfolio and the Sharpe Ratio of the constrained portfolio. To achieve those results we use a new machine learning technique that merges factor models with nodewise regression in statistics. Simulation results and empirics show very good performance of our method.
Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization
Authors: Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su
Date: 2024-02-23 00:00:00
Tasks: Deep Reinforcement Learning, Portfolio Optimization

As a model-free algorithm, deep reinforcement learning (DRL) agent learns and makes decisions by interacting with the environment in an unsupervised way. In recent years, DRL algorithms have been widely applied by scholars for portfolio optimization in consecutive trading periods, since the DRL agent can dynamically adapt to market changes and does not rely on the specification of the joint dynamics across the assets. However, typical DRL agents for portfolio optimization cannot learn a policy that is aware of the dynamic correlation between portfolio asset returns. Since the dynamic correlations among portfolio assets are crucial in optimizing the portfolio, the lack of such knowledge makes it difficult for the DRL agent to maximize the return per unit of risk, especially when the target market permits short selling (i.e., the US stock market). In this research, we propose a hybrid portfolio optimization model combining the DRL agent and the Black-Litterman (BL) model to enable the DRL agent to learn the dynamic correlation between the portfolio asset returns and implement an efficacious long/short strategy based on the correlation. Essentially, the DRL agent is trained to learn the policy to apply the BL model to determine the target portfolio weights. To test our DRL agent, we construct the portfolio based on all the Dow Jones Industrial Average constitute stocks. Empirical results of the experiments conducted on real-world United States stock market data demonstrate that our DRL agent significantly outperforms various comparison portfolio choice strategies and alternative DRL frameworks by at least 42% in terms of accumulated return. In terms of the return per unit of risk, our DRL agent significantly outperforms various comparative portfolio choice strategies and alternative strategies based on other machine learning frameworks.