pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of complex excitation patterns using Feynman-like diagrams
Authors: Louise Arno, Desmond Kabus, Hans Dierckx
Date: 2023-07-04 00:00:00

Many extended chemical and biological systems self-organise into complex patterns that drive the medium behaviour in a non-linear fashion. An important class of such systems are excitable media, including neural and cardiac tissues. In extended excitable media, wave breaks can form rotating patterns and turbulence. However, the onset, sustaining and elimination of such complex patterns is currently incompletely understood. The classical theory of phase singularities in excitable media was recently challenged, as extended lines of conduction block were identified as phase discontinuities. Here, we provide a theoretical framework that captures the rich dynamics in excitable systems in terms of three quasiparticles: heads, tails, and pivots. We propose to call these quasiparticles `cardions'. In simulations and experiments, we show that these basic building blocks combine into at least four different bound states. By representing their interactions similarly to Feynman diagrams in physics, the creation and annihilation of vortex pairs are shown to be sequences of dynamical creation, annihilation, and recombination of the identified quasiparticles. We draw such diagrams for numerical simulations, as well as optical voltage mapping experiments performed on cultured human atrial myocytes (hiAMs). Our results provide a new, unified language for a more detailed theory, analysis, and mechanistic insights of dynamical transitions in excitation patterns.
Unification of species, gene, and cell trees for single-cell expression analyses
Authors: Samuel H. Church, Jasmine L. Mah, Casey W. Dunn
Date: 2023-07-05 00:00:00

Comparisons of single-cell RNA sequencing (scRNA-seq) data across species can reveal links between cellular gene expression and the evolution of cell functions, features, and phenotypes. These comparisons invoke evolutionary histories, as depicted with phylogenetic trees, that define relationships between species, genes, and cells. Here we illustrate a tree-based framework for comparing scRNA-seq data, and contrast this framework with existing methods. We describe how we can use trees to identify homologous and comparable groups of genes and cells, based on their predicted relationship to genes and cells present in the common ancestor. We advocate for mapping data to branches of phylogenetic trees to test hypotheses about the evolution of cellular gene expression. We describe the kinds of data that can be compared, and the types of questions that each comparison has the potential to address. Finally, we reconcile species phylogenies, gene phylogenies, cell phylogenies, and cell lineages as different representations of the same concept: the tree of cellular life. By integrating phylogenetic approaches into scRNA-seq analyses, we can overcome challenges for building informed comparisons across species, and robustly test hypotheses about gene and cell evolution.
On the connections between the spatial Lambda-Fleming-Viot model and other processes for analysing geo-referenced genetic data
Authors: Johannes Wirtz, Stéphane Guindon
Date: 2023-07-05 00:00:00

The introduction of the spatial Lambda-Fleming-Viot model (LV) in population genetics was mainly driven by the pioneering work of Alison Etheridge, in collaboration with Nick Barton and Amandine V\'eber about ten years ago (1,2). The LV model provides a sound mathematical framework for describing the evolution of a population of related individuals along a spatial continuum. It alleviates the "pain in the torus" issue with Wright and Mal\'ecot's isolation by distance model and is sampling consistent, making it a tool of choice for statistical inference. Yet, little is known about the potential connections between the LV and other stochastic processes generating trees and the spatial coordinates along the corresponding lineages. This work focuses on a version of the LV whereby lineages move infinitely rapidly over infinitely small distances. Using simulations, we show that the induced LV tree-generating process is well approximated by a birth-death model. Our results also indicate that Brownian motions modelling the movements of lineages along birth-death trees do not generally provide a good approximation of the LV due to habitat boundaries effects that play an increasingly important role in the long run. Finally, we describe efficient algorithms for fast simulation of the backward and forward in time versions of the LV model.
Coping with seasons: evolutionary dynamics of gene networks in a changing environment
Authors: Csenge Petak, Lapo Frati, Melissa H. Pespeni, Nick Cheney
Date: 2023-07-04 00:00:00

In environments that vary frequently and unpredictably, bet-hedgers can overtake the population. Diversifying bet-hedgers have a diverse set of offspring so that, no matter the conditions they find themselves in, at least some offspring will have high fitness. In contrast, conservative bet-hedgers have a set of offspring that all have an in-between phenotype compared to the specialists. Here, we use an evolutionary algorithm of gene regulatory networks to de novo evolve the two strategies and investigate their relative success in different parameter settings. We found that diversifying bet-hedgers almost always evolved first, but then eventually got outcompeted by conservative bet-hedgers. We argue that even though similar selection pressures apply to the two bet-hedger strategies, conservative bet-hedgers could win due to the robustness of their evolved networks, in contrast to the sensitive networks of the diversifying bet-hedgers. These results reveal an unexplored aspect of the evolution of bet-hedging that could shed more light on the principles of biological adaptation in variable environmental conditions.
Colimitation of light and nitrogen on algal growth revealed by an array microhabitat platform
Authors: Fangchen Liu, Larissa Gaul, Andrea Giometto, Mingming Wu
Date: 2023-07-05 00:00:00

Microalgae are key players in the global carbon cycle and emerging producers of biofuels. Algal growth is critically regulated by its complex microenvironment, including nitrogen and phosphorous levels, light intensity, and temperature. Mechanistic understanding of algal growth is important for maintaining a balanced ecosystem at a time of climate change and population expansion, as well as providing essential formulations for optimizing biofuel production. Current mathematical models for algal growth in complex environmental conditions are still in their infancy, due in part to the lack of experimental tools necessary to generate data amenable to theoretical modeling. Here, we present a high throughput microfluidic platform that allows for algal growth with precise control over light intensity and nutrient gradients, while also performing real-time microscopic imaging. We propose a general mathematical model that describes algal growth under multiple physical and chemical environments, which we have validated experimentally. We showed that light and nitrogen colimited the growth of the model alga Chlamydomonas reinhardtii following a multiplicative Monod kinetic model. The microfluidic platform presented here can be easily adapted to studies of other photosynthetic micro-organisms, and the algal growth model will be essential for future bioreactor designs and ecological predictions.
Inferring microbial interactions with their environment from genomic and metagenomic data
Authors: James D. Brunner, Laverne A. Gallegos-Graves, Marie E. Kroeger
Date: 2023-07-05 00:00:00

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as \emph{metabolically contextualized}, and so name our tool MetConSIN: \underline{Met}abolically \underline{Con}textualized \underline{S}pecies \underline{I}nteraction \underline{N}etworks.
Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis, and prediction in the life sciences
Authors: Ryan J. Murphy, Oliver J. Maclaren, Matthew J. Simpson
Date: 2023-07-04 00:00:00
Tasks: Caricature, Uncertainty Quantification

Throughout the life sciences we routinely seek to interpret measurements and observations using parameterised mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data. This is often achieved by assuming that the data are noisy measurements of the solution of a deterministic mathematical model, and that measurement errors are additive and normally distributed. While this assumption of additive Gaussian noise is extremely common and simple to implement and interpret, it is often unjustified and can lead to poor parameter estimates and non-physical predictions. One way to overcome this challenge is to implement a different measurement error model. In this review, we demonstrate how to implement a range of measurement error models in a likelihood-based framework for estimation, identifiability analysis, and prediction, called Profile-Wise Analysis. This frequentist approach to uncertainty quantification for mechanistic models leverages the profile likelihood for targeting parameters and understanding their influence on predictions. Case studies, motivated by simple caricature models routinely used in systems biology and mathematical biology literature, illustrate how the same ideas apply to different types of mathematical models. Open-source Julia code to reproduce results is available on GitHub.
Ramadan Fasting Model Exerts Hepatoprotective, Anti-obesity, and Anti-Hyperlipidemic Effects in an Experimentally-induced Nonalcoholic Fatty Liver in Rats
Authors: Abeer Alasmari, Abdulrahman Al-Khalifah, Ahmed BaHammam, Hesham Alodah, Ahmad Almnaizel, Noura Alshiban, Maha Alhussain
Date: 2023-07-04 00:00:00

Background: The epidemic of nonalcoholic fatty liver disease (NAFLD) and its metabolic effects present a serious public health concern. We hypothesized that the Ramadan fasting model (RFM), which involves fasting from dawn to dusk for a month, could provide potential therapeutic benefits and mitigate NAFLD. Accordingly, we aimed to validate this hypothesis using obese male rats. Methods: Rats were split into two groups (n = 24 per group), and they were given either a standard (S) or high-fat diet (HFD) for 12 weeks. During the last four weeks of the study period, both S- and HFD-fed rats were subdivided into eight groups to assess the effect of RFM with/without training (T) or glucose administration (G) on the lipid profile, liver enzymes, and liver structure (n=6/group). Results: The HFD+RFM groups exhibited a significantly lower final body weight than that the HFDC group. Serum cholesterol, low-density lipoprotein, and triglyceride levels were significantly lower in the HFD+RFM, HFD+RFM+T, and HFD+RFM+G groups than those in the HFDC group. Compared with the HFD-fed group, all groups had improved serum high-density lipoprotein levels. Furthermore, HFD groups subjected to RFM had reduced serum levels of aspartate transaminase and alanine transaminase compared with those of the HFD-fed group. Moreover, the liver histology has improved in rats subjected to RFM compared with that of HFD-fed rats, which exhibited macro and micro fat droplet accumulation. Conclusion: RFM can induce positive metabolic changes and improve alterations associated with NAFLD, including weight gain, lipid profile, liver enzymes, and hepatic steatosis.
Robust Hedging GANs
Authors: Yannick Limmer, Blanka Horvath
Date: 2023-07-05 00:00:00
Tasks: Prognosis

The availability of deep hedging has opened new horizons for solving hedging problems under a large variety of realistic market conditions. At the same time, any model - be it a traditional stochastic model or a market generator - is at best an approximation of market reality, prone to model-misspecification and estimation errors. This raises the question, how to furnish a modelling setup with tools that can address the risk of discrepancy between anticipated distribution and market reality, in an automated way. Automated robustification is currently attracting increased attention in numerous investment problems, but it is a delicate task due to its imminent implications on risk management. Hence, it is beyond doubt that more activity can be anticipated on this topic to converge towards a consensus on best practices. This paper presents a natural extension of the original deep hedging framework to address uncertainty in the data generating process via an adversarial approach inspired by GANs to automate robustification in our hedging objective. This is achieved through an interplay of three modular components: (i) a (deep) hedging engine, (ii) a data-generating process (that is model agnostic permitting a large variety of classical models as well as machine learning-based market generators), and (iii) a notion of distance on model space to measure deviations between our market prognosis and reality. We do not restrict the ambiguity set to a region around a reference model, but instead penalize deviations from the anticipated distribution. Our suggested choice for each component is motivated by model agnosticism, allowing a seamless transition between settings. Since all individual components are already used in practice, we believe that our framework is easily adaptable to existing functional settings.
Non-Concave Utility Maximization with Transaction Costs
Authors: Shuaijie Qian, Chen Yang
Date: 2023-07-05 00:00:00
Tasks: Position, valid

This paper studies a finite-horizon portfolio selection problem with non-concave terminal utility and proportional transaction costs, in which the commonly used concavification principle for terminal value is no longer applicable. We establish a proper theoretical characterization of this problem via a two-step procedure. First, we examine the asymptotic terminal behavior of the value function, which implies that any transaction close to maturity only provides a marginal contribution to the utility. Second, we establish the theoretical foundation in terms of the discontinuous viscosity solution, incorporating the proper characterization of the terminal condition. Via extensive numerical analyses involving several types of utility functions, we find that the introduction of transaction costs into non-concave utility maximization problems can make it optimal for investors to hold on to a larger long position in the risky asset compared to the frictionless case, or hold on to a large short position in the risky asset despite a positive risk premium.
A cohort-based Partial Internal Model for demographic risk
Authors: Francesco Della Corte, Gian Paolo Clemente, Nino Savelli
Date: 2023-07-06 00:00:00

We investigate the quantification of demographic risk in a framework consistent with the market-consistent valuation imposed by Solvency II. We provide compact formulas for evaluating inflows and outflows of a portfolio of insurance policies based on a cohort approach. In this context, we maintain the highest level of generality in order to consider both traditional policies and equity-linked policies: therefore, we propose a market-consistent valuation of the liabilities. In the second step we evaluate the Solvency Capital Requirement of the idiosyncratic risk, linked to accidental mortality, and the systematic risk one, also known as trend risk, proposing a formal closed formula for the former and an algorithm for the latter. We show that accidental volatility depends on the intrinsic characteristics of the policies of the cohort (Sums-at-Risk), on the age of the policyholders and on the variability of the sums insured; trend risk depends both on accidental volatility and on the longevity forecasting model used.
Over-the-Counter Market Making via Reinforcement Learning
Authors: Zhou Fang, Haiqing Xu
Date: 2023-07-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

The over-the-counter (OTC) market is characterized by a unique feature that allows market makers to adjust bid-ask spreads based on order size. However, this flexibility introduces complexity, transforming the market-making problem into a high-dimensional stochastic control problem that presents significant challenges. To address this, this paper proposes an innovative solution utilizing reinforcement learning techniques to tackle the OTC market-making problem. By assuming a linear inverse relationship between market order arrival intensity and bid-ask spreads, we demonstrate the optimal policy for bid-ask spreads follows a Gaussian distribution. We apply two reinforcement learning algorithms to conduct a numerical analysis, revealing the resulting return distribution and bid-ask spreads under different time and inventory levels.
Option Market Making via Reinforcement Learning
Authors: Zhou Fang, Haiqing Xu
Date: 2023-07-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Market making of options with different maturities and strikes is a challenging problem due to its highly dimensional nature. In this paper, we propose a novel approach that combines a stochastic policy and reinforcement learning-inspired techniques to determine the optimal policy for posting bid-ask spreads for an options market maker who trades options with different maturities and strikes.
Analyzing the Performance of ChatGPT in Cardiology and Vascular Pathologies
Authors: Walid Hariri
Date: 2023-04-15 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multiple-choice

The article aims to analyze the performance of ChatGPT, a large language model developed by OpenAI, in the context of cardiology and vascular pathologies. The study evaluated the accuracy of ChatGPT in answering challenging multiple-choice questions (QCM) using a dataset of 190 questions from the Siamois-QCM platform. The goal was to assess ChatGPT potential as a valuable tool in medical education compared to two well-ranked students of medicine. The results showed that ChatGPT outperformed the students, scoring 175 out of 190 correct answers with a percentage of 92.10\%, while the two students achieved scores of 163 and 159 with percentages of 85.78\% and 82.63\%, respectively. These results showcase how ChatGPT has the potential to be highly effective in the fields of cardiology and vascular pathologies by providing accurate answers to relevant questions.
Visual Question Answering (VQA) on Images with Superimposed Text
Authors: Venkat Kodali, Daniel Berleant
Date: 2023-06-13 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Superimposed text annotations have been under-investigated, yet are ubiquitous, useful and important, especially in medical images. Medical images also highlight the challenges posed by low resolution, noise and superimposed textual meta-information. Therefor we probed the impact of superimposing text onto medical images on VQA. Our results revealed that this textual meta-information can be added without severely degrading key measures of VQA performance. Our findings are significant because they validate the practice of superimposing text on images, even for medical images subjected to the VQA task using AI techniques. The work helps advance understanding of VQA in general and, in particular, in the domain of healthcare and medicine.
Illumination-Aware Multi-Task GANs for Foreground Segmentation
Authors: Dimitrios Sakkos, Edmond S. L. Ho, Hubert P. H.
Date: 2019-01-10 00:00:00
Proceeding: IEEE Access 2019 1
Tasks: Foreground Segmentation, Generative Adversarial Network, Segmentation, Video Background Subtraction

Foreground-background segmentation has been an active research area over the years. However, conventional models fail to produce accurate results when challenged with the videos of challenging illumination conditions. In this paper, we present a robust model that allows accurately extracting the foreground even in exceptionally dark or bright scenes and in continuously varying illumination in a video sequence. This is accomplished by a triple multi-task generative adversarial network (TMT-GAN) that effectively models the semantic relationship between the dark and bright images and performs binary segmentation end-to-end. Our contribution is twofold: first, we show that by jointly optimizing the GAN loss and the segmentation loss, our network simultaneously learns both tasks that mutually benefit each other. Second, fusing features of images with varying illumination into the segmentation branch vastly improve the performance of the network. Comparative evaluations on highly challenging real and synthetic benchmark datasets (ESI and SABS) demonstrate the robustness of TMT-GAN and its superiority over state-of-the-art approaches.
Communication-efficient Distributed Learning for Large Batch Optimization
Authors: Rui Liu, Barzan Mozafari
Date: 2022-07-17 00:00:00
Proceeding: Proceedings of the 39th International Conference on Machine Learning 2022 7

Many communication-efficient methods have been proposed for distributed learning, whereby gradient compression is used to reduce the communication cost. However, given recent advances in large batch optimization (e.g., large batch SGD and its variant LARS with layerwise adaptive learning rates), the compute power of each machine is being fully utilized. This means, in modern distributed learning, the per-machine computation cost is no longer negligible compared to the communication cost. In this paper, we propose new gradient compression methods for large batch optimization, JointSpar and its variant JointSpar-LARS with layerwise adaptive learning rates, that jointly reduce both the computation and the communication cost. To achieve this, we take advantage of the redundancy in the gradient computation, unlike the existing methods compute all coordinates of the gradient vector, even if some coordinates are later dropped for communication efficiency. JointSpar and its variant further reduce the training time by avoiding the wasted computation on dropped coordinates. While computationally more efficient, we prove that JointSpar and its variant also maintain the same convergence rates as their respective baseline methods. Extensive experiments show that, by reducing the time per iteration, our methods converge faster than state-of-the-art compression methods in terms of wall-clock time.