pwc-archive/papers-with-abstracts huggingface dataset:
Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography
Authors: Navin Cooray, Zhenglin Li, Jinzhuo Wang, Christine Lo, Mahnaz Arvaneh, Mkael Symmonds, Michele Hu, Maarten De Vos, Lyudmila S Mihaylova
Date: 2023-02-15 00:00:00
Tasks: Decision Making, Specificity

Numerous sleep disorders are characterised by movement during sleep, these include rapid-eye movement sleep behaviour disorder (RBD) and periodic limb movement disorder. The process of diagnosing movement related sleep disorders requires laborious and time-consuming visual analysis of sleep recordings. This process involves sleep clinicians visually inspecting electromyogram (EMG) signals to identify abnormal movements. The distribution of characteristics that represent movement can be diverse and varied, ranging from brief moments of tensing to violent outbursts. This study proposes a framework for automated limb-movement detection by fusing data from two EMG sensors (from the left and right limb) through a Dirichlet process mixture model. Several features are extracted from 10 second mini-epochs, where each mini-epoch has been classified as 'leg-movement' or 'no leg-movement' based on annotations of movement from sleep clinicians. The distributions of the features from each category can be estimated accurately using Gaussian mixture models with the Dirichlet process as a prior. The available dataset includes 36 participants that have all been diagnosed with RBD. The performance of this framework was evaluated by a 10-fold cross validation scheme (participant independent). The study was compared to a random forest model and outperformed it with a mean accuracy, sensitivity, and specificity of 94\%, 48\%, and 95\%, respectively. These results demonstrate the ability of this framework to automate the detection of limb movement for the potential application of assisting clinical diagnosis and decision-making.
How to Extend 3D GBSM to RIS Cascade Channel with Non-ideal Phase Modulation?
Authors: Huiwen Gong, Jianhua Zhang, Yuxiang Zhang, Zhengfu Zhou, Guangyi Liu
Date: 2023-02-15 00:00:00
Tasks: 3D geometry

Reconfigurable intelligent surface (RIS) is envisioned as a promising technology for next-generation wireless communications. Its deployment introduces a RIS cascade link between the transmitter (Tx) and receiver (Rx), which makes its channel model significantly different from the Tx-Rx direct link. In this letter, a RIS cascade channel modeling method based on a 3D geometry-based stochastic model (GBSM) is proposed. The model follows a 3GPP standardized modeling framework and extends the traditional Tx-Rx channel to Tx-RIS-Rx cascade channel. In the modeling process, we consider the non-ideal phase modulation of the RIS element, so as to accurately characterize the dependence of its phase modulation on the incoming wave angle. The differences between the proposed cascade channel model and the channel model with ideal phase modulation are investigated. The simulation results show that the proposed model can better reflect the dependence of RIS on angle and polarization.
The Role of Physical Layer Security in Satellite-Based Networks
Authors: R. Singh, I. Ahmad, J. Huusko
Date: 2023-02-14 00:00:00

In the coming years, 6G will revolutionize the world with a large amount of bandwidth, high data rates, and extensive coverage in remote and rural areas. These goals can only be achieved by integrating terrestrial networks with non-terrestrial networks. On the other hand, these advancements are raising more concerns than other wireless links about malicious attacks on satellite-terrestrial links due to their openness. Over the years, physical layer security (PLS) has emerged as a good candidate to deal with security threats by exploring the randomness of wireless channels. In this direction, this paper reviews how PLS methods are implemented in satellite communications. Firstly, we discuss the ongoing research on satellite-based networks by highlighting the key points in the literature. Then, we revisit the research activities on PLS in satellite-based networks by categorizing the different system architectures. Finally, we highlight research directions and opportunities to leverage the PLS in future satellite-based networks.
The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach
Authors: Yongcun Song, Xiaoming Yuan, Hangrui Yue
Date: 2023-02-16 00:00:00

We study the combination of the alternating direction method of multipliers (ADMM) with physics-informed neural networks (PINNs) for a general class of nonsmooth partial differential equation (PDE)-constrained optimization problems, where additional regularization can be employed for constraints on the control or design variables. The resulting ADMM-PINNs algorithmic framework substantially enlarges the applicable range of PINNs to nonsmooth cases of PDE-constrained optimization problems. The application of the ADMM makes it possible to untie the PDE constraints and the nonsmooth regularization terms for iterations. Accordingly, at each iteration, one of the resulting subproblems is a smooth PDE-constrained optimization which can be efficiently solved by PINNs, and the other is a simple nonsmooth optimization problem which usually has a closed-form solution or can be efficiently solved by various standard optimization algorithms or pre-trained neural networks. The ADMM-PINNs algorithmic framework does not require to solve PDEs repeatedly, and it is mesh-free, easy to implement, and scalable to different PDE settings. We validate the efficiency of the ADMM-PINNs algorithmic framework by different prototype applications, including inverse potential problems, source identification in elliptic equations, control constrained optimal control of the Burgers equation, and sparse optimal control of parabolic equations.
On time-consistent equilibrium stopping under aggregation of diverse discount rates
Authors: Shuoqing Deng, Xiang Yu, Jiacheng Zhang
Date: 2023-02-15 00:00:00
Tasks: Decision Making, Diversity

This paper studies the central planner's decision making on behalf of a group of members with diverse discount rates. In the context of optimal stopping, we work with a smooth aggregation preference to incorporate all heterogeneous discount rates with an attitude function that reflects the aggregation rule in the same spirit of ambiguity aversion in the smooth ambiguity preference proposed in Klibanoff et al.(2005). The optimal stopping problem renders to be time inconsistent, for which we develop an iterative approach using consistent planning and characterize all time-consistent equilibria as fixed points of an operator in the setting of one-dimensional diffusion processes. We provide some sufficient conditions on both the underlying models and the attitude function such that the smallest equilibrium attains the optimal equilibrium in which the attitude function becomes equivalent to the linear aggregation rule as of diversity neutral. In addition, we show that the optimal equilibrium is a weak equilibrium in the existing literature. When the sufficient condition of the attitude function is violated, we can illustrate by various examples that the characterization of the optimal equilibrium may differ significantly from some existing results for an individual agent.
Policy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing
Authors: Mohamed Hamdouche, Pierre Henry-Labordere, Huyen Pham
Date: 2023-02-14 00:00:00
Tasks: Policy Gradient Methods

We develop policy gradients methods for stochastic control with exit time in a model-free setting. We propose two types of algorithms for learning either directly the optimal policy or by learning alternately the value function (critic) and the optimal control (actor). The use of randomized policies is crucial for overcoming notably the issue related to the exit time in the gradient computation. We demonstrate the effectiveness of our approach by implementing our numerical schemes in the application to the problem of share repurchase pricing. Our results show that the proposed policy gradient methods outperform PDE or other neural networks techniques in a model-based setting. Furthermore, our algorithms are flexible enough to incorporate realistic market conditions like e.g. price impact or transaction costs.
Forward Backward SDEs Systems for Utility Maximization in Jump Diffusion Models
Authors: Marina Santacroce, Paola Siri, Barbara Trivellato
Date: 2023-02-16 00:00:00

We consider the classical problem of maximizing the expected utility of terminal net wealth with a final random liability in a simple jump-diffusion model. In the spirit of Horst et al. (2014) and Santacroce-Trivellato (2014), under suitable conditions the optimal strategy is expressed in implicit form in terms of a forward backward system of equations. Some explicit results are presented for the pure jump model and for exponential utilities.
Equilibria of large random Lotka-Volterra systems with vanishing species: a mathematical approach
Authors: Imane Akjouj, Walid Hachem, Mylène Maïda, Jamal Najim
Date: 2023-02-15 00:00:00

Ecosystems with a large number of species are often modelled as Lotka-Volterra dynamical systems built around a large random interaction matrix. Under some known conditions, a global equilibrium exists and is unique. In this article, we rigorously study its statistical properties in the large dimensional regime. Such an equilibrium vector is known to be the solution of a so-called Linear Complementarity Problem (LCP). We describe its statistical properties by designing an Approximate Message Passing (AMP) algorithm, a technique that has recently aroused an intense research effort in the fields of statistical physics, Machine Learning, or communication theory. Interaction matrices taken from the Gaussian Orthogonal Ensemble, or following a Wishart distribution are considered. Beyond these models, the AMP approach developed in this article has the potential to describe the statistical properties of equilibria associated to more involved interaction matrix models.
Can molecular dynamics be used to simulate biomolecular recognition?
Authors: Malin Luking, David van der Spoel, Johan Elf, Gareth A. Tribello
Date: 2023-02-15 00:00:00

There are many problems in biochemistry that are difficult to study experimentally. Simulation methods are appealing due to direct availability of atomic coordinates as a function of time. However, direct molecular simulations are challenged by the size of systems and the time scales needed to describe relevant motions. In theory, enhanced sampling algorithms can help to overcome some of the limitations of molecular simulations. Here, we discuss a problem in biochemistry that offers a significant challenge for enhanced sampling methods and that could, therefore, serve as a benchmark for comparing approaches that use machine learning to find suitable collective variables. More in particular, we study the transitions LacI undergoes upon moving between being non-specifically and specifically bound to DNA. It is found that many degrees of freedom change during this transition and that the transition does not occur reversibly in simulations if only a subset of these degrees of freedom are biased. We also explain why this problem is so important to biologists and the transformative impact that a simulation of it would have on the understanding of DNA regulation.
Mathematical models for order of mutation problem in myeloproliferative neoplasm: non-additivity and non-commutativity
Authors: Yue Wang
Date: 2023-02-16 00:00:00

In some patients of myeloproliferative neoplasm, two genetic mutations can be found: JAK2 V617F and TET2. When one mutation is present or not, the other mutation has different effects on regulating gene expressions. Besides, when both mutations are present, the order of occurrence might make a difference. In this paper, we build nonlinear ordinary differential equation models and Markov chain models to explain such phenomena.
The most probable dynamics of receptor-ligand binding on cell membrane
Authors: Xi Chen, Hui Wang, Jinqiao Duan
Date: 2023-02-16 00:00:00

We devise a method for predicting certain receptor-ligand binding behaviors, based on stochastic dynamical modelling. We consider the dynamics of a receptor binding to a ligand on the cell membrane, where the receptor and ligand perform different motions and are thus modeled by stochastic differential equations with Gaussian noise or non-Gaussian noise. We use neural networks based on Onsager-Machlup function to compute the probability $P_1$ of the unbounded receptor diffusing to the cell membrane. Meanwhile, we compute the probability $P_2$ of extracellular ligand arriving at the cell membrane by solving the associated Fokker-Planck equation. Then, we could predict the most probable binding probability by combining $P_1$ and $P_2$. In this way, we conclude with some indication about where the ligand will most probably encounter the receptor, contributing to better understanding of cell's response to external stimuli and communication with other cells.
Theoretical and computational tools to model multistable gene regulatory networks
Authors: Federico Bocci, Dongya Jia, Qing Nie, Mohit Kumar Jolly, Jose Onuchic
Date: 2023-02-14 00:00:00

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.
Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD
Authors: Rohit Misra, Tapan K. Gandhi
Date: 2023-02-15 00:00:00
Tasks: Functional Connectivity, Time Series Analysis

Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in children and is characterised by inattention, impulsiveness and hyperactivity. While several studies have analysed the static functional connectivity in the resting-state functional MRI (rs-fMRI) of ADHD patients, detailed investigations are required to characterize the connectivity dynamics in the brain. In an attempt to establish a link between attention instability and the dynamic properties of Functional Connectivity (FC), we investigated the differences in temporal variability of FC between 40 children with ADHD and 40 Typically Developing (TD) children. Using a sliding-window method to segment the rs-fMRI scans in time, we employed seed-to-voxel correlation analysis for each window to obtain time-evolving seed connectivity maps for seeds placed in the posterior cingulate cortex (PCC) and the medial prefrontal cortex (mPFC). For each subject, the standard deviation of the voxel connectivity time series was used as a measure of the temporal variability of FC. Results showed that ADHD patients exhibited significantly higher variability in dFC than TD children in the cingulo-temporal, cingulo-parietal, fronto-temporal, and fronto-parietal networks ($p_{FWE} < 0.05$). Atypical temporal variability was observed in the left and right temporal gyri, the anterior cingulate cortex, and lateral regions of the right parietal cortex. The observations are consistent with visual attention issues, executive control deficit, and rightward parietal dysfunction reported in ADHD, respectively. These results help in understanding the disorder with a fresh perspective linking behavioural inattention with instability in FC in the brain.
A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging
Authors: Ali Fathi, Bernhard Hientzsch
Date: 2023-02-16 00:00:00

We consider two data-driven approaches to hedging, Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control, under a stepwise mean-variance objective. We compare their performance for a European call option in the presence of transaction costs under discrete trading schedules. We do this for a setting where stock prices follow Black-Scholes-Merton dynamics and the "book-keeping" price for the option is given by the Black-Scholes-Merton model with the same parameters. This simulated data setting provides a "sanitized" lab environment with simple enough features where we can conduct a detailed study of strengths, features, issues, and limitations of these two approaches. However, the formulation is model free and could allow any other setting with available book-keeping prices. We consider this study as a first step to develop, test, and validate autonomous hedging agents, and we provide blueprints for such efforts that address various concerns and requirements.
Information extraction and artwork pricing
Authors: Jaehyuk Choi, Lan Ju, Jian Li, Zhiyong Tu
Date: 2023-02-16 00:00:00

Traditional art pricing models often lack fine measurements of painting content. This paper proposes a new content measurement: the Shannon information quantity measured by the singular value decomposition (SVD) entropy of the painting image. Using a large sample of artworks' auction records and images, we show that the SVD entropy positively affects the sales price at 1% significance level. Compared to the other commonly adopted content variables, the SVD entropy has advantages in variable significance, sample robustness as well as model fit. Considering the convenient availability of digital painting images and the straightforward calculation algorithm of this measurement, we expect its wide application in future research.
Regime-switching affine term structures
Authors: Andreas Celary, Paul Eisenberg, Zehra Eksi
Date: 2023-02-15 00:00:00

We consider an HJM model setting for Markov-chain modulated forward rates. The underlying Markov chain is assumed to induce regime switches on the forward curve dynamics. Our primary focus is on the interest rate and energy futures markets. After deriving HJM-drift conditions for the two markets, we prove under the assumption of affine structure for the term structure that the forward curves are solutions to specific systems of ODEs that can be solved explicitly in many cases. This allows for a tractable model setting, and we present an algorithm for obtaining consistent forward curve models within our framework. We conclude by presenting some simple numerical examples.
A Look at Financial Dependencies by Means of Econophysics and Financial Economics
Authors: M. Raddant, T. Di Matteo
Date: 2023-02-16 00:00:00

This is a review about financial dependencies which merges efforts in econophysics and financial economics during the last few years. We focus on the most relevant contributions to the analysis of asset markets' dependencies, especially correlational studies, which in our opinion are beneficial for researchers in both fields. In econophysics, these dependencies can be modeled to describe financial markets as evolving complex networks. In particular we show that a useful way to describe dependencies is by means of information filtering networks that are able to retrieve relevant and meaningful information in complex financial data sets. In financial economics these dependencies can describe asset comovement and spill-overs. In particular, several models are presented that show how network and factor model approaches are related to modeling of multivariate volatility and asset returns respectively. Finally, we sketch out how these studies can inspire future research and how they contribute to support researchers in both fields to find a better and a stronger common language.
Pricing basket options with the first three moments of the basket: log-normal models and beyond
Authors: Dongdong Hu, Hasanjan Sayit, Frederi Viens
Date: 2023-02-16 00:00:00

Options on baskets (linear combinations) of assets are notoriously challenging to price using even the simplest log-normal continuous-time stochastic models for the individual assets. The paper [5] gives a closed form approximation formula for pricing basket options with potentially negative portfolio weights under log-normal models by moment matching. This approximation formula is conceptually simple, methodologically sound, and turns out to be highly accurate. However it involves solving a system of nonlinear equations which usually produces multiple solutions and which is sensitive to the selection of initial values in the numerical procedures, making the method computationally challenging. In the current paper, we take the moment-matching methodology in [5] a step further by obtaining a closed form solution for this non-linear system of equations, by identifying a unary cubic equation based solely on the basket's skewness, which parametrizes all model parameters, and we use it to express the approximation formula as an explicit function of the mean, variance, and skewness of the basket. Numerical comparisons with the baskets considered in [5] show a very high level of agreement, and thus of accuracy relative to the true basket option price.
Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing
Authors: Scott Howland, Lara Kassab, Keerti Kappagantula, Henry Kvinge, Tegan Emerson
Date: 2023-01-13 00:00:00

The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging manufacturing process, shear assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective we are able to evaluate our models' ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.