pwc-archive/papers-with-abstracts huggingface dataset:
A universal niche geometry governs the response of ecosystems to environmental perturbations
Authors: Akshit Goyal, Jason W. Rocks, Pankaj Mehta
Date: 2024-03-02 00:00:00

How ecosystems respond to environmental perturbations is a fundamental question in ecology, made especially challenging due to the strong coupling between species and their environment. Here, we introduce a theoretical framework for calculating the steady-state response of ecosystems to environmental perturbations in generalized consumer-resource. Our construction is applicable to a wide class of systems, including models with non-reciprocal interactions, cross-feeding, and non-linear growth/consumption rates. Within our framework, all ecological variables are embedded into four distinct vector spaces and ecological interactions are represented by geometric transformations between these spaces. We show that near a steady state, such geometric transformations directly map environmental perturbations - in resource availability and mortality rates - to shifts in niche structure. We illustrate these ideas in a variety of settings including a minimal model for pH-induced toxicity in bacterial denitrification. We end by discussing the biological implications of our framework. In particular, we show that it is extremely difficult to distinguish cooperative and competitive interactions by measuring species' responses to external perturbations.
Piezoelectricity and flexoelectricity in biological cells: The role of cell structure and organelles
Authors: Akepogu Venkateshwarlu, Akshayveer, Sundeep Singh, Roderick Melnik
Date: 2024-03-04 00:00:00

Living tissues experience various external forces on cells, influencing their behaviour, physiology, shape, gene expression, and destiny through interactions with their environment. Despite much research done in this area, challenges remain in our better understanding of the behaviour of the cell in response to external stimuli, including the arrangement, quantity, and shape of organelles within the cell. This study explores the electromechanical behaviour of biological cells, including organelles like microtubules, mitochondria, nuclei, and cell membranes. Two distinct cell structures have been developed to explore the cell responses to mechanical displacement, resembling actual cell shapes. The finite element method has been utilized to integrate the linear piezoelectric and non-local flexoelectric effects accurately. It is found that the longitudinal stress is absent and only the transverse stress plays a crucial role when the mechanical load is imposed on the top side of the cell through compressive displacement. The impact of flexoelectricity is elucidated by introducing a new parameter called the maximum electric potential ratio ($V_{\text{R,max}}$). It has been found that $V_{\text{R,max}}$ depends upon the orientation angle and shape of the microtubules. Further, the study reveals that the number of microtubules significantly impacts effective elastic and piezoelectric coefficients, affecting cell behaviour based on structure, microtubule orientation, and mechanical stress direction. The insight obtained from the current study can assist in advancements in medical therapies such as tissue engineering and regenerative medicine.
Fluid ejections in nature
Authors: Elio J. Challita, Pankaj Rohilla, M. Saad Bhamla
Date: 2024-03-04 00:00:00

From microscopic fungi to colossal whales, fluidic ejections are a universal and intricate phenomenon in biology, serving vital functions such as animal excretion, venom spraying, prey hunting, spore dispersal, and plant guttation. This review delves into the complex fluid physics of ejections across various scales, exploring both muscle-powered active systems and passive mechanisms driven by gravity or osmosis. We introduce a framework using dimensionless numbers to delineate transitions from dripping to jetting and elucidate the governing forces. Highlighting the understudied area of complex fluid ejections, this work not only rationalizes the biophysics involved but also uncovers potential engineering applications in soft robotics, additive manufacturing, and drug delivery. By bridging biomechanics, the physics of living systems, and fluid dynamics, this review offers valuable insights into the diverse world of fluid ejections and paves the way for future bioinspired research across the spectrum of life.
Waves, patterns and bifurcations: a tutorial review on the vertebrate segmentation clock
Authors: Paul François, Victoria Mochulska
Date: 2024-03-01 00:00:00

Proper vertebrae formation relies on a tissue-wide oscillator called the segmentation clock. Individual cellular oscillators in the presomitic mesoderm are modulated by intercellular coupling and external signals, leading to the propagation of oscillatory waves of genetic expression eventually stabilizing into a static pattern. Here, we review 4 decades of biophysical models of this process, starting from the pioneering Clock and Wavefront model by Cooke and Zeeman, and the reaction-diffusion model by Meinhardt. We discuss how modern descriptions followed advances in molecular description and visualization of the process, reviewing phase models, delayed models, systems-level, and finally geometric models. We connect models to high-level aspects of embryonic development from embryonic scaling to wave propagation, up to reconstructed stem cell systems. We provide new analytical calculations and insights into classical and recent models, leading us to propose a geometric description of somitogenesis organized along two primary waves of differentiation.
Single-channel and multi-channel electrospinning for the fabrication of PLA/PCL tissue engineering scaffolds: comparative study of the materials physicochemical and biological properties
Authors: Semen Goreninskii, Ulyana Chernova, Elisaveta Prosetskaya, Alina Laushkina, Alexander Mishanin, Alexey Golovkin, Evgeny Bolbasov
Date: 2024-02-09 00:00:00

Fabrication of tissue engineering scaffolds with tailored physicochemical and biological characteristics is a relevant task in biomedical engineering. The present work was focused at the evaluation of the effect of fabrication approach (single-channel or multi-channel electrospinning) on the properties of the fabricated poly(lactic acid)(PLA)/poly(epsilon-caprolactone)(PCL) scaffolds with various polymer mass ratios (1/0, 2/1, 1/1, 1/2, and 0/1). The scaffolds with same morphology (regardless of electrospinning variant) were fabricated and characterized using SEM, water contact angle measurement, FTIR, XRD, tensile testing and in vitro experiment with multipotent mesenchymal stem cells. It was demonstrated, that multi-channel electrospinning prevents intermolecular interactions between the polymer components of the scaffold, preserving their crystal structure, what affects the mechanical characteristics of the scaffold (particularly, leads to 2-fold difference in elongation). Better adhesion of multipotent mesenchymal stem cells on the surface of the scaffolds fabricated using multichannel electrospinning was demonstrated.
Exact Enforcement of Temporal Continuity in Sequential Physics-Informed Neural Networks
Authors: Pratanu Roy, Stephen Castonguay
Date: 2024-02-15 00:00:00

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen-Cahn, and Korteweg-de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. The numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.
XProspeCT: CT Volume Generation from Paired X-Rays
Authors: Benjamin Paulson, Joshua Goldshteyn, Sydney Balboni, John Cisler, Andrew Crisler, Natalia Bukowski, Julia Kalish, Theodore Colwell
Date: 2024-02-11 00:00:00
Tasks: Computed Tomography (CT), Diagnostic

Computed tomography (CT) is a beneficial imaging tool for diagnostic purposes. CT scans provide detailed information concerning the internal anatomic structures of a patient, but present higher radiation dose and costs compared to X-ray imaging. In this paper, we build on previous research to convert orthogonal X-ray images into simulated CT volumes by exploring larger datasets and various model structures. Significant model variations include UNet architectures, custom connections, activation functions, loss functions, optimizers, and a novel back projection approach.
Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes
Authors: Matthias Plock, Felix Binkowski, Lin Zschiedrich, Philipp-Immanuel Schneider, Sven Burger
Date: 2024-01-19 00:00:00
Tasks: Gaussian Processes, Robust Design

We present a fabrication uncertainty aware and robust design optimization approach that can be used to obtain robust design estimates for nonlinear, nonconvex, and expensive model functions. It is founded on Gaussian processes and a Monte Carlo sampling procedure, and assumes knowledge about the uncertainties associated with a manufacturing process. The approach itself is iterative. First, a large parameter domain is sampled in a coarse fashion. This coarse sampling is used primarily to determine smaller candidate regions to investigate in a second, more refined sampling pass. This finer step is used to obtain an estimate of the expected performance of the found design parameter under the assumed manufacturing uncertainties. We apply the presented approach to the robust optimization of the Purcell enhancement of a photonic crystal nanobeam cavity. We obtain a predicted robust Purcell enhancement of $\overline{F}_{\mathrm{P}} \approx 3.3$. For comparison we also perform an optimization without robustness. We find that an unrobust optimum of $F_{\mathrm{P}} \approx 256.5$ dwindles to only $\overline{F}_{\mathrm{P}} \approx 0.7$ when fabrication uncertainties are taken into account. We thus demonstrate that the presented approach is able to find designs of significantly higher performance than those obtained with conventional optimization.
Phase retrieval beyond the homogeneous object assumption for X-ray in-line holographic imaging
Authors: Jens Lucht, Leon M. Lohse, Thorsten Hohage, Tim Salditt
Date: 2024-03-01 00:00:00
Tasks: Retrieval

X-ray near field holography has proven to be a powerful 2D and 3D imaging technique with applications ranging from biomedical research to material sciences. To reconstruct meaningful and quantitative images from the measurement intensities, however, it relies on computational phase retrieval which in many cases assumes the phase-shift and attenuation coefficient of the sample to be proportional. Here, we demonstrate an efficient phase retrieval algorithm that does not rely on this homogeneous-object assumption and is a generalization of the well-established contrast-transfer-function (CTF) approach. We then investigate its stability and present an experimental study comparing the proposed algorithm with established methods. The algorithm shows superior reconstruction quality compared to the established CTF-based method at similar computational cost. Our analysis provides a deeper fundamental understanding of the homogeneous object assumption and the proposed algorithm will help improve the image quality for near-field holography in biomedical applications
Counting the uncounted : estimating the unaccounted COVID-19 infections in India
Authors: Debashis Saikia, Kalpana Bora, Madhurjya P. Bora
Date: 2024-03-01 00:00:00

Undetected infectious populations have played a major role in the COVID-19 outbreak across the globe and estimation of this undetected class is a major concern in understanding the actual size of the COVID-19 infections. Due to the asymptomatic nature of some infections, many cases have gone undetected. Also, despite carrying COVID-19 symptoms, most of the infected population kept the infections hidden and stayed unreported, especially in a country like India. Based on these factors, we have added an undetected compartment to the already developed SEIR model [48] to estimate these uncounted infections. In this article, we have applied Physics Informed Neural Network (PINN) to estimate the undetected infectious populations in the 20 worst-affected Indian states as well as India as a whole. The analysis has been carried out for the first as well as second surge of COVID-19 infections in India. A ratio of the active undetected infectious to the active detected infectious population is calculated through the PINN analysis which gives a picture of the real size of the pandemic in India. The rate at which symptomatic infectious population goes undetected and are never reported is also estimated using the PINN method. Toward the end, an artificial neural network (ANN) based forecasting scenario of the pandemic in India is presented. The prediction is found to be reliable as the training of the neural network has been carried out using the unique features, obtained from the state-wide analysis of the newly proposed model as well as from the PINN analysis.
Entanglement: Balancing Punishment and Compensation, Repeated Dilemma Game-Theoretic Analysis of Maximum Compensation Problem for Bypass and Least Cost Paths in Fact-Checking, Case of Fake News with Weak Wallace's Law
Authors: Yasuko Kawahata
Date: 2024-02-18 00:00:00
Tasks: Fact Checking, Management

This research note is organized with respect to a novel approach to solving problems related to the spread of fake news and effective fact-checking. Focusing on the least-cost routing problem, the discussion is organized with respect to the use of Metzler functions and Metzler matrices to model the dynamics of information propagation among news providers. With this approach, we designed a strategy to minimize the spread of fake news, which is detrimental to informational health, while at the same time maximizing the spread of credible information. In particular, through the punitive dominance problem and the maximum compensation problem, we developed and examined a path to reassess the incentives of news providers to act and to analyze their impact on the equilibrium of the information market. By applying the concept of entanglement to the context of information propagation, we shed light on the complexity of interactions among news providers and contribute to the formulation of more effective information management strategies. This study provides new theoretical and practical insights into issues related to fake news and fact-checking, and will be examined against improving informational health and public digital health.This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.
Early quantum computing applications on the path towards precision medicine
Authors: Frederik F. Flöther
Date: 2024-03-05 00:00:00

The last few years have seen rapid progress in transitioning quantum computing from lab to industry. In healthcare and life sciences, more than 40 proof-of-concept experiments and studies have been conducted; an increasing number of these are even run on real quantum hardware. Major investments have been made with hundreds of millions of dollars already allocated towards quantum applications and hardware in medicine. In addition to pharmaceutical and life sciences uses, clinical and medical applications are now increasingly coming into the picture. This chapter focuses on three key use case areas associated with (precision) medicine, including genomics and clinical research, diagnostics, and treatments and interventions. Examples of organizations and the use cases they have been researching are given; ideas how the development of practical quantum computing applications can be further accelerated are described.
Matrix-based Prediction Approach for Intraday Instantaneous Volatility Vector
Authors: Sung Hoon Choi, Donggyu Kim
Date: 2024-03-05 00:00:00
Tasks: Prediction, Time Series

In this paper, we introduce a novel method for predicting intraday instantaneous volatility based on Ito semimartingale models using high-frequency financial data. Several studies have highlighted stylized volatility time series features, such as interday auto-regressive dynamics and the intraday U-shaped pattern. To accommodate these volatility features, we propose an interday-by-intraday instantaneous volatility matrix process that can be decomposed into low-rank conditional expected instantaneous volatility and noise matrices. To predict the low-rank conditional expected instantaneous volatility matrix, we propose the Two-sIde Projected-PCA (TIP-PCA) procedure. We establish asymptotic properties of the proposed estimators and conduct a simulation study to assess the finite sample performance of the proposed prediction method. Finally, we apply the TIP-PCA method to an out-of-sample instantaneous volatility vector prediction study using high-frequency data from the S&P 500 index and 11 sector index funds.
Improved Tests for Mediation
Authors: Grant Hillier, Kees Jan van Garderen, Noud van Giersbergen
Date: 2024-03-04 00:00:00

Testing for a mediation effect is important in many disciplines, but is made difficult - even asymptotically - by the influence of nuisance parameters. Classical tests such as likelihood ratio (LR) and Wald (Sobel) tests have very poor power properties in parts of the parameter space, and many attempts have been made to produce improved tests, with limited success. In this paper we show that augmenting the critical region of the LR test can produce a test with much improved behavior everywhere. In fact, we first show that there exists a test of this type that is (asymptotically) exact for certain test levels $\alpha $, including the common choices $\alpha =.01,.05,.10.$ The critical region of this exact test has some undesirable properties. We go on to show that there is a very simple class of augmented LR critical regions which provides tests that are nearly exact, and avoid the issues inherent in the exact test. We suggest an optimal and coherent member of this class, provide the table needed to implement the test and to report p-values if desired. Simulation confirms validity with non-Gaussian disturbances, under heteroskedasticity, and in a nonlinear (logit) model. A short application of the method to an entrepreneurial attitudes study is included for illustration.
Prices and preferences in the electric vehicle market
Authors: Chung Yi See, Vasco Rato Santos, Lucas Woodley, Megan Yeo, Daniel Palmer, Shuheng Zhang, and Ashley Nunes
Date: 2024-03-01 00:00:00

Although electric vehicles are less polluting than gasoline powered vehicles, adoption is challenged by higher procurement prices. Existing discourse emphasizes EV battery costs as being principally responsible for this price differential and widespread adoption is routinely conditioned upon battery costs declining. We scrutinize such reasoning by sourcing data on EV attributes and market conditions between 2011 and 2023. Our findings are fourfold. First, EV prices are influenced principally by the number of amenities, additional features, and dealer-installed accessories sold as standard on an EV, and to a lesser extent, by EV horsepower. Second, EV range is negatively correlated with EV price implying that range anxiety concerns may be less consequential than existing discourse suggests. Third, battery capacity is positively correlated with EV price, due to more capacity being synonymous with the delivery of more horsepower. Collectively, this suggests that higher procurement prices for EVs reflects consumer preference for vehicles that are feature dense and more powerful. Fourth and finally, accommodating these preferences have produced vehicles with lower fuel economy, a shift that reduces envisioned lifecycle emissions benefits by at least 3.26 percent, subject to the battery pack chemistry leveraged and the carbon intensity of the electrical grid. These findings warrant attention as decarbonization efforts increasingly emphasize electrification as a pathway for complying with domestic and international climate agreements.
Inference for Interval-Identified Parameters Selected from an Estimated Set
Authors: Sukjin Han, Adam McCloskey
Date: 2024-03-01 00:00:00

Interval identification of parameters such as average treatment effects, average partial effects and welfare is particularly common when using observational data and experimental data with imperfect compliance due to the endogeneity of individuals' treatment uptake. In this setting, the researcher is typically interested in a treatment or policy that is either selected from the estimated set of best-performers or arises from a data-dependent selection rule. In this paper, we develop new inference tools for interval-identified parameters chosen via these forms of selection. We develop three types of confidence intervals for data-dependent and interval-identified parameters, discuss how they apply to several examples of interest and prove their uniform asymptotic validity under weak assumptions.
Set-Valued Control Functions
Authors: Sukjin Han, Hiroaki Kaido
Date: 2024-03-01 00:00:00

The control function approach allows the researcher to identify various causal effects of interest. While powerful, it requires a strong invertibility assumption in the selection process, which limits its applicability. This paper expands the scope of the nonparametric control function approach by allowing the control function to be set-valued and derive sharp bounds on structural parameters. The proposed generalization accommodates a wide range of selection processes involving discrete endogenous variables, random coefficients, treatment selections with interference, and dynamic treatment selections. The framework also applies to partially observed or identified controls that are directly motivated from economic models.
The Cost of Coming Out
Authors: Enzo Brox, Riccardo Di Francesco
Date: 2024-03-06 00:00:00

The fear of social stigma leads many individuals worldwide to hesitate in disclosing their sexual orientation. Since concealing identity is costly, it is crucial to understand the extent of anti-LGB sentiments and reactions to coming out. This paper uses an innovative data source from a popular online game together with a natural experiment to overcome existing data and endogeneity issues. We exploit exogenous variation in the identity of a character to identify the effects of coming out on players' revealed preferences for that character across diverse regions globally. Our findings reveal a substantial and persistent negative impact of coming out.