pwc-archive/papers-with-abstracts huggingface dataset:
From elephant to goldfish (and back): memory in stochastic Volterra processes
Authors: Ofelia Bonesini, Giorgia Callegaro, Martino Grasselli, Gilles Pagès
Date: 2023-06-05 00:00:00

We propose a new theoretical framework that exploits convolution kernels to transform a Volterra path-dependent (non-Markovian) stochastic process into a standard (Markovian) diffusion process. This transformation is achieved by embedding a Markovian "memory process" within the dynamics of the non-Markovian process. We discuss existence and path-wise regularity of solutions for the stochastic Volterra equations introduced and we provide a financial application to volatility modeling. We also propose a numerical scheme for simulating the processes. The numerical scheme exhibits a strong convergence rate of 1/2, which is independent of the roughness parameter of the volatility process. This is a significant improvement compared to Euler schemes used in similar models. We propose a new theoretical framework that exploits convolution kernels to transform a Volterra path-dependent (non-Markovian) stochastic process into a standard (Markovian) diffusion process. This transformation is achieved by embedding a Markovian "memory process" (the goldfish) within the dynamics of the non-Markovian process (the elephant). Most notably, it is also possible to go back, i.e., the transformation is reversible. We discuss existence and path-wise regularity of solutions for the stochastic Volterra equations introduced and we propose a numerical scheme for simulating the processes, which exhibits a remarkable convergence rate of $1/2$. In particular, in the fractional kernel case, the strong convergence rate is independent of the roughness parameter, which is a positive novelty in contrast with what happens in the available Euler schemes in the literature in rough volatility models.
Branching model with state dependent offspring distribution for Chlamydia spread
Authors: Péter Kevei, Máté Szalai
Date: 2023-06-05 00:00:00
Tasks: Form, Stochastic Optimization

Chlamydiae are bacteria with an interesting unusual developmental cycle. A single bacterium in its infectious form (elementary body, EB) enters the host cell, where it converts into its dividing form (reticulate body, RB), and divides by binary fission. Since only the EB form is infectious, before the host cell dies, RBs start to convert into EBs. After the host cell dies RBs do not survive. We model the population growth by a 2-type discrete-time branching process, where the probability of duplication depends on the state. Maximizing the EB production leads to a stochastic optimization problem. Simulation study shows that our novel model is able to reproduce the main features of the development of the population.
Intermittent migration can induce pulses of speciation in a two-island system
Authors: Débora Princepe, Simone Czarnobai, Rodrigo A. Caetano, Flavia M. D. Marquitti, Marcus A. M. de Aguiar, Sabrina B. L. Araujo
Date: 2023-06-05 00:00:00
Tasks: Diversity

Geographic barriers prevent migration between populations, thereby facilitating speciation through allopatry. However, these barriers can exhibit dynamic behavior in nature, promoting cycles of expansion and contraction of populations. Such oscillations cause temporal variations in migration that do not necessarily prevent speciation; on the contrary, they have been suggested as a driving force for diversification. Here we present a study on a two-island neutral speciation model in scenarios with intermittent migration driven by sea-level fluctuations. Mating is constrained to genetically compatible individuals inhabiting the same island, and offspring inherit nuclear genomes from both parents with recombination. We observe pulses of speciation that would not occur in strict isolation or continuous migration. According to the seabed height, which modulates the duration of the isolation and connection periods, the maximum richness occurs at different times and in an ephemeral fashion. The expansion-contraction dynamics can accelerate diversification, but a long time in isolation can reduce the richness to one species per island, resembling patterns described by the taxon pulse hypothesis of diversification. Together with other studies, our results support the relevance of research on the impact of variable migration on diversification, suggested to be related to regions of high diversity.
Microbiome abundance patterns as attractors and the implications for the inference of microbial interaction networks
Authors: Isabella-Hilda Mendler, Barbara Drossel, Marc-Thorsten Hütt
Date: 2023-06-03 00:00:00

Inferring microbial interaction networks from abundance patterns is an important approach to advance our understanding of microbial communities in general and the human microbiome in particular. Here we suggest discriminating two levels of information contained in microbial abundance data: (1) the quantitative abundance values and (2) the pattern of presences and absences of microbial organisms. The latter allows for a binary view on microbiome data and a novel interpretation of microbial data as attractors, or more precisely as fixed points, of a Boolean network. Starting from these attractors, our aim is to infer an interaction network between the species present in the microbiome samples. To accomplish this task, we introduce a novel inference method that combines the previously published ESABO (Entropy Shifts of Abundance vectors under Boolean Operations) method with an evolutionary algorithm. The key idea of our approach is that the inferred network should reproduce the original set of (observed) binary abundance patterns as attractors. We study the accuracy and runtime properties of this evolutionary method, as well as its behavior under incomplete knowledge of the attractor sets. Based on this theoretical understanding of the method we then show an application to empirical data.
Ebola transmission dynamics: will future Ebola outbreaks become cyclic?
Authors: David Niyukuri, Kelly Joelle Gatore Sinigirira, Jean De Dieu Kwizera, Salma Omar Abd-Almageid Adam
Date: 2023-06-03 00:00:00

The Ebola Virus Disease (EVD) can persist in some body fluids after clinical recovery. In Guinea and the Democratic Republic of the Congo, there are well-documented cases of EVD reemergence that were associated with previous outbreaks. In many cases, male EVD survivors were associated with the re-introduction of new outbreaks. This has shown that even after controlling an EVD outbreak, a close biomedical monitoring of survivors and contacts is critical to avoiding future outbreaks. Thus, in order to explore the main features of EVD transmission dynamics in the context of re-emergence, we used a compartmental model by considering vaccination around EVD contacts. Analytical and numerical analyses of the model were conducted. The model is mathematically and epidemiologically well-posed. We computed the reproductive number (R0) and the disease equilibrium points (disease-free equilibrium and endemic equilibrium) for the re-emerging outbreak. The stability analysis of the model around those equilibrium points was performed. The model undergoes a backward bifurcation at R0 close to 1, regardless R0 < 1, the disease will not be eradicated. This means that R0 cannot be considered as an intervention control measure in our model.
Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model
Authors: Rui Li, Gabriel della Maggiora, Vardan Andriasyan, Anthony Petkidis, Artsemi Yushkevich, Mikhail Kudryashev, Artur Yakimovich
Date: 2023-06-05 00:00:00
Tasks: Deep Learning, Denoising, Image Generation, Image Reconstruction

Light microscopy is a widespread and inexpensive imaging technique facilitating biomedical discovery and diagnostics. However, light diffraction barrier and imperfections in optics limit the level of detail of the acquired images. The details lost can be reconstructed among others by deep learning models. Yet, deep learning models are prone to introduce artefacts and hallucinations into the reconstruction. Recent state-of-the-art image synthesis models like the denoising diffusion probabilistic models (DDPMs) are no exception to this. We propose to address this by incorporating the physical problem of microscopy image formation into the model's loss function. To overcome the lack of microscopy data, we train this model with synthetic data. We simulate the effects of the microscope optics through the theoretical point spread function and varying the noise levels to obtain synthetic data. Furthermore, we incorporate the physical model of a light microscope into the reverse process of a conditioned DDPM proposing a physics-informed DDPM (PI-DDPM). We show consistent improvement and artefact reductions when compared to model-based methods, deep-learning regression methods and regular conditioned DDPMs.
From multiscale biophysics to digital twins of tissues and organs: future opportunities for in silico pharmacology
Authors: Michael Taynnan Barros, Michelangelo Paci, Aapo Tervonen, Elisa Passini, Jussi Koivumäki, Jari Hyttinen, Kerstin Lenk
Date: 2023-06-04 00:00:00

With many advancements in in silico biology in recent years, the paramount challenge is to translate the accumulated knowledge into exciting industry partnerships and clinical applications. Achieving models that characterize the link of molecular interactions to the activity and structure of a whole organ are termed multiscale biophysics. Historically, the pharmaceutical industry has worked well with in silico models by leveraging their prediction capabilities for drug testing. However, the needed higher fidelity and higher resolution of models for efficient prediction of pharmacological phenomenon dictates that in silico approaches must account for the verifiable multiscale biophysical phenomena, as a spatial and temporal dimension variation for different processes and models. The collection of different multiscale models for different tissues and organs can compose digital twin solutions towards becoming a service for researchers, clinicians, and drug developers. Our paper has two main goals: 1) To clarify to what extent detailed single- and multiscale modeling has been accomplished thus far, we provide a review on this topic focusing on the biophysics of epithelial, cardiac, and brain tissues; 2) To discuss the present and future role of multiscale biophysics in in silico pharmacology as a digital twin solution by defining a roadmap from simple biophysical models to powerful prediction tools. Digital twins have the potential to pave the way for extensive clinical and pharmaceutical usage of multiscale models and our paper shows the basic fundamentals and opportunities towards their accurate development enabling the quantum leaps of future precise and personalized medical software.
CellProfiler plugins -- an easy image analysis platform integration for containers and Python tools
Authors: Erin Weisbart, Callum Tromans-Coia, Barbara Diaz-Rohrer, David R Stirling, Fernanda Garcia-Fossa, Rebecca A Senft, Mark C Hiner, Marcelo B de Jesus, Kevin W Eliceiri, Beth A Cimini
Date: 2023-06-02 00:00:00

CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community.
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Authors: Yangtian Zhang, Zuobai Zhang, Bozitao Zhong, Sanchit Misra, Jian Tang
Date: 2023-06-01 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Denoising, Protein Structure Prediction

Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Accurately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from $\chi_1$ to $\chi_4$ and training diffusion models for each torsional angle. We evaluate the method on several benchmarks for protein side-chain packing and show that our method achieves improvements of $11.9\%$ and $13.5\%$ in angle accuracy on CASP13 and CASP14, respectively, with a significantly smaller model size ($60\times$ fewer parameters). Additionally, we show the effectiveness of our method in enhancing side-chain predictions in the AlphaFold2 model. Code is available at https://github.com/DeepGraphLearning/DiffPack.
Biomarker Discovery with Quantum Neural Networks: A Case-study in CTLA4-Activation Pathways
Authors: Nam Nguyen
Date: 2023-05-15 00:00:00

Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery tasks. We propose a Quantum Neural Networks (QNNs) architecture to discover biomarkers for input activation pathways. The Maximum Relevance, Minimum Redundancy (mRMR) criteria is used to score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. The model indicates new biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks.
Optimal Market Making in the Chinese Stock Market: A Stochastic Control and Scenario Analysis
Authors: Shiqi Gong, Shuaiqiang Liu, Danny D. Sun
Date: 2023-06-05 00:00:00

Market making plays a crucial role in providing liquidity and maintaining stability in financial markets, making it an essential component of well-functioning capital markets. Despite its importance, there is limited research on market making in the Chinese stock market, which is one of the largest and most rapidly growing markets globally. To address this gap, we employ an optimal market making framework with an exponential CARA-type (Constant Absolute Risk Aversion) utility function that accounts for various market conditions, such as price drift, volatility, and stamp duty, and is capable of describing 3 major risks (i.e., inventory, execution and adverse selection risks) in market making practice, and provide an in-depth quantitative and scenario analysis of market making in the Chinese stock market. Our numerical experiments explore the impact of volatility on the market maker's inventory. Furthermore, we find that the stamp duty rate is a critical factor in market making, with a negative impact on both the profit of the market maker and the liquidity of the market. Additionally, our analysis emphasizes the significance of accurately estimating stock drift for managing inventory and adverse selection risks effectively and enhancing profit for the market maker. These findings offer valuable insights for both market makers and policymakers in the Chinese stock market and provide directions for further research in designing effective market making strategies and policies.
Financial sentiment analysis using FinBERT with application in predicting stock movement
Authors: Tingsong Jiang, Qingyun Zeng
Date: 2023-06-03 00:00:00
Tasks: Sentiment Analysis

In this study, we integrate sentiment analysis within a financial framework by leveraging FinBERT, a fine-tuned BERT model specialized for financial text, to construct an advanced deep learning model based on Long Short-Term Memory (LSTM) networks. Our objective is to forecast financial market trends with greater accuracy. To evaluate our model's predictive capabilities, we apply it to a comprehensive dataset of stock market news and perform a comparative analysis against standard BERT, standalone LSTM, and the traditional ARIMA models. Our findings indicate that incorporating sentiment analysis significantly enhances the model's ability to anticipate market fluctuations. Furthermore, we propose a suite of optimization techniques aimed at refining the model's performance, paving the way for more robust and reliable market prediction tools in the field of AI-driven finance.
The Role of Twitter in Cryptocurrency Pump-and-Dumps
Authors: David Ardia, Keven Bluteau
Date: 2023-06-03 00:00:00

We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics.
Towards a Technology-Driven Adaptive Decision Support System for Integrated Pavement and Maintenance strategies (TDADSS-IPM): focus on risk assessment framework for climate change adaptation
Authors: Shahrzad Pour, Amir Masoumi, Niels Skov Dujardin
Date: 2023-05-28 00:00:00

Decision Support Systems for pavement and maintenance strategies have traditionally been designed as silos led to local optimum systems. Moreover, since big data usage didn't exist as result of Industry 4.0 as of today, DSSs were not initially designed adaptive to the sources of uncertainties led to rigid decisions. Motivated by the vulnerability of the road assets to the climate phenomena, this paper takes a visionary step towards introducing a Technology-Driven Adaptive Decision Support System for Integrated Pavement and Maintenance activities called TDADSS-IPM. As part of such DSS, a bottom-up risk assessment model is met via Bayesian Belief Networks (BBN) to realize the actual condition of the Danish roads due to weather condition. Such model fills the gaps in the knowledge domain and develops a platform that can be trained over time, and applied in real-time to the actual event.
Optimization for truss design using Bayesian optimization
Authors: Bhawani Sandeep, Surjeet Singh, Sumit Kumar
Date: 2023-05-27 00:00:00
Tasks: Bayesian Optimization

In this work, geometry optimization of mechanical truss using computer-aided finite element analysis is presented. The shape of the truss is a dominant factor in determining the capacity of load it can bear. At a given parameter space, our goal is to find the parameters of a hull that maximize the load-bearing capacity and also don't yield to the induced stress. We rely on finite element analysis, which is a computationally costly design analysis tool for design evaluation. For such expensive to-evaluate functions, we chose Bayesian optimization as our optimization framework which has empirically proven sample efficient than other simulation-based optimization methods. By utilizing Bayesian optimization algorithms, the truss design involves iteratively evaluating a set of candidate truss designs and updating a probabilistic model of the design space based on the results. The model is used to predict the performance of each candidate design, and the next candidate design is selected based on the prediction and an acquisition function that balances exploration and exploitation of the design space. Our result can be used as a baseline for future study on AI-based optimization in expensive engineering domains especially in finite element Analysis.
Nonparametric Identification and Estimation of Earnings Dynamics using a Hidden Markov Model: Evidence from the PSID
Authors: Tong Zhou
Date: 2023-05-25 00:00:00

This paper presents a hidden Markov model designed to investigate the complex nature of earnings persistence. The proposed model assumes that the residuals of log-earnings consist of a persistent component and a transitory component, both following general Markov processes. Nonparametric identification is achieved through spectral decomposition of linear operators, and a modified stochastic EM algorithm is introduced for model estimation. Applying the framework to the Panel Study of Income Dynamics (PSID) dataset, we find that the earnings process displays nonlinear persistence, conditional skewness, and conditional kurtosis. Additionally, the transitory component is found to possess non-Gaussian properties, resulting in a significantly asymmetric distributional impact when high-earning households face negative shocks or low-earning households encounter positive shocks. Our empirical findings also reveal the presence of ARCH effects in earnings at horizons ranging from 2 to 8 years, further highlighting the complex dynamics of earnings persistence.
Disproving XAI Myths with Formal Methods -- Initial Results
Authors: Joao Marques-Silva
Date: 2023-05-13 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Misconceptions

The advances in Machine Learning (ML) in recent years have been both impressive and far-reaching. However, the deployment of ML models is still impaired by a lack of trust in how the best-performing ML models make predictions. The issue of lack of trust is even more acute in the uses of ML models in high-risk or safety-critical domains. eXplainable artificial intelligence (XAI) is at the core of ongoing efforts for delivering trustworthy AI. Unfortunately, XAI is riddled with critical misconceptions, that foster distrust instead of building trust. This paper details some of the most visible misconceptions in XAI, and shows how formal methods have been used, both to disprove those misconceptions, but also to devise practically effective alternatives.