pwc-archive/papers-with-abstracts huggingface dataset:
A Physics-Constrained Neural Differential Equation Framework for Data-Driven Snowpack Simulation
Authors: Andrew Charbonneau, Katherine Deck, Tapio Schneider
Date: 2024-12-03 00:00:00

This paper presents a physics-constrained neural differential equation framework for parameterization, and employs it to model the time evolution of seasonal snow depth given hydrometeorological forcings. When trained on data from multiple SNOTEL sites, the parameterization predicts daily snow depth with under 9% median error and Nash Sutcliffe Efficiencies over 0.94 across a wide variety of snow climates. The parameterization also generalizes to new sites not seen during training, which is not often true for calibrated snow models. Requiring the parameterization to predict snow water equivalent in addition to snow depth only increases error to ~12%. The structure of the approach guarantees the satisfaction of physical constraints, enables these constraints during model training, and allows modeling at different temporal resolutions without additional retraining of the parameterization. These benefits hold potential in climate modeling, and could extend to other dynamical systems with physical constraints.
Design Multiband Monopole and Microstrip Patch Antennas using High Frequency Structure Simulator
Authors: Georgios Giannakopoulos, Khushbu Mehboob Shaikh
Date: 2024-12-09 00:00:00

This paper describes the design, construction and testing of a dual-band monopole microstrip patch antenna to operate in the GSM and DCS bands. For compactness the monopole antenna is mounted on top of a FR4 substrate and is designed as a planar structure which can be wrapped into a box like structure. The performance characteristics of the wrapped dual band monopole antenna are simulated on Ansoft HFSS before fabrication and testing on a Network Analyzer. The simulated HFSS return loss of the antenna shows broad agreement with experiment over the frequency range from 500MHz to 4GHz. Radiation patterns are generated in each band of operation of the antenna. The effect of feedline impedance matching on the performance of the monopole antenna is discussed.
Impacts of Climate Change-Induced Salinity Intrusion on Physiological Parameters of Aquatic Hydrophytes from Coastal Rivers of Bangladesh
Authors: Ulfat Jahan Farha, Zarin Subah, Md Helal Uddin, Harunur Rashid
Date: 2024-12-07 00:00:00

Changing temperature, precipitation regimes, and sea level rise, often associated with climate change, cause salinity intrusion into groundwater and surface water, affecting aquatic ecosystems. This study investigates the impacts of salinity on the physiological traits of freshwater hydrophytes, including Water Hyacinth (Eichhornia crassipes), Buffalo Spinach (Enhydra fluctuans), and Taro (Colocasia esculenta). The plants were exposed to salinity concentrations of 0, 10, 20, and 30 ppt for 48 hours. Parameters such as biomass, stomata density, transpiration rate, chlorophyll content, relative water content, and histo-architectural changes were analyzed. The results showed a decline in biomass, stomatal density, and relative water content with increasing salinity. Taro demonstrated higher salt tolerance compared to other species. Histological observations revealed deformities in root and tuber tissues under saline stress. These findings highlight the critical impacts of climate change-induced salinity on aquatic plant ecosystems.
Mapping The Layers of The Ocean Floor With a Convolutional Neural Network
Authors: Guilherme G. D. Fernandes, Vitor S. P. P. Oliveira, João P. I. Astolfo
Date: 2024-12-04 00:00:00

The mapping of ocean floor layers is a current challenge for the oil industry. Existing solution methods involve mapping through seismic methods and wave inversion, which are complex and computationally expensive. The introduction of artificial neural networks, specifically UNet, to predict velocity models based on seismic shots reflected from the ocean floor shows promise for optimising this process. In this study, two neural network architectures are validated for velocity model inversion and compared in terms of stability metrics such as loss function and similarity coefficient, as well as the differences between predicted and actual models. Indeed, neural networks prove promising as a solution to this challenge, achieving S{\o}rensen-Dice coefficient values above 70%.
Capacitive Touch Sensor Modeling With a Physics-informed Neural Network and Maxwell's Equations
Authors: Ganyong Mo, Krishna Kumar Narayanan, David Castells-Rufas, Jordi Carrabina
Date: 2024-11-23 00:00:00
Tasks: Sensor Modeling

Maxwell's equations are the fundamental equations for understanding electric and magnetic field interactions and play a crucial role in designing and optimizing sensor systems like capacitive touch sensors, which are widely prevalent in automotive switches and smartphones. Ensuring robust functionality and stability of the sensors in dynamic environments necessitates profound domain expertise and computationally intensive multi-physics simulations. This paper introduces a novel approach using a Physics-Informed Neural Network (PINN) based surrogate model to accelerate the design process. The PINN model solves the governing electrostatic equations describing the interaction between a finger and a capacitive sensor. Inputs include spatial coordinates from a 3D domain encompassing the finger, sensor, and PCB, along with finger distances. By incorporating the electrostatic equations directly into the neural network's loss function, the model captures the underlying physics. The learned model thus serves as a surrogate sensor model on which inference can be carried out in seconds for different experimental setups without the need to run simulations. Efficacy results evaluated on unseen test cases demonstrate the significant potential of PINNs in accelerating the development and design optimization of capacitive touch sensors.
GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction
Authors: Xiayin Lou, Peng Luo, Liqiu Meng
Date: 2024-12-05 00:00:00
Tasks: Conformal Prediction, Prediction, Spatial Interpolation

Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable predictions. Beyond achieving higher accuracy, it is equally important to obtain predictions with uncertainty measures to enhance model credibility and support responsible spatial prediction. Although geostatistic methods like Kriging offer some level of uncertainty assessment, such as Kriging variance, these measurements are not always accurate and lack general applicability to other spatial models. To address this issue, we propose a model-agnostic uncertainty assessment method called GeoConformal Prediction, which incorporates geographical weighting into conformal prediction. We applied it to two classic spatial prediction cases, spatial regression and spatial interpolation, to evaluate its reliability. First, in the spatial regression case, we used XGBoost to predict housing prices, followed by GeoConformal to calculate uncertainty. Our results show that GeoConformal achieved a coverage rate of 93.67%, while Bootstrap methods only reached a maximum coverage of 81.00% after 2000 runs. Next, we applied GeoConformal to spatial interpolation models. We found that the uncertainty obtained from GeoConformal aligned closely with the variance in Kriging. Finally, using GeoConformal, we analyzed the sources of uncertainty in spatial prediction. We found that explicitly including local features in AI models can significantly reduce prediction uncertainty, especially in areas with strong local dependence. Our findings suggest that GeoConformal holds potential not only for geographic knowledge discovery but also for guiding the design of future GeoAI models, paving the way for more reliable and interpretable spatial prediction frameworks.
A Physics-based Generative Model to Synthesize Training Datasets for MRI-based Fat Quantification
Authors: Juan P. Meneses, Yasmeen George, Christoph Hagemeyer, Zhaolin Chen, Sergio Uribe
Date: 2024-12-11 00:00:00
Tasks: Data Augmentation, Quantitative MRI

Deep learning-based techniques have potential to optimize scan and post-processing times required for MRI-based fat quantification, but they are constrained by the lack of large training datasets. Generative models are a promising tool to perform data augmentation by synthesizing realistic datasets. However no previous methods have been specifically designed to generate datasets for quantitative MRI (q-MRI) tasks, where reference quantitative maps and large variability in scanning protocols are usually required. We propose a Physics-Informed Latent Diffusion Model (PI-LDM) to synthesize quantitative parameter maps jointly with customizable MR images by incorporating the signal generation model. We assessed the quality of PI-LDM's synthesized data using metrics such as the Fr\'echet Inception Distance (FID), obtaining comparable scores to state-of-the-art generative methods (FID: 0.0459). We also trained a U-Net for the MRI-based fat quantification task incorporating synthetic datasets. When we used a few real (10 subjects, $~200$ slices) and numerous synthetic samples ($>3000$), fat fraction at specific liver ROIs showed a low bias on data obtained using the same protocol than training data ($0.10\%$ at $\hbox{ROI}_1$, $0.12\%$ at $\hbox{ROI}_2$) and on data acquired with an alternative protocol ($0.14\%$ at $\hbox{ROI}_1$, $0.62\%$ at $\hbox{ROI}_2$). Future work will be to extend PI-LDM to other q-MRI applications.
Continuous non-contact vital sign monitoring of neonates in intensive care units using RGB-D cameras
Authors: Silas Ruhrberg Estévez, Alex Grafton, Lynn Thomson, Joana Warnecke, Kathryn Beardsall, Joan Lasenby
Date: 2024-12-08 00:00:00

Neonates in intensive care require continuous monitoring. Current measurement devices are limited for long-term use due to the fragility of newborn skin and the interference of wires with medical care and parental interactions. Camera-based vital sign monitoring has the potential to address these limitations and has become of considerable interest in recent years due to the absence of physical contact between the recording equipment and the neonates, as well as the introduction of low-cost devices. We present a novel system to capture vital signs while offering clinical insights beyond current technologies using a single RGB-D camera. Heart rate and oxygen saturation were measured using colour and infrared signals with mean average errors (MAE) of 7.69 bpm and 3.37%, respectively. Using the depth signals, an MAE of 4.83 breaths per minute was achieved for respiratory rate. Tidal volume measurements were obtained with a MAE of 0.61 mL. Flow-volume loops can also be calculated from camera data, which have applications in respiratory disease diagnosis. Our system demonstrates promising capabilities for neonatal monitoring, augmenting current clinical recording techniques to potentially improve outcomes for neonates.
A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging
Authors: Han Yue, Jun Cheng, Yu-Xuan Ren, Chien-Chun Chen, Grant A. van Riessen, Philip Heng Wai Leong, Steve Feng Shu
Date: 2024-11-25 00:00:00
Tasks: Deep Learning, Retrieval, SSIM

Ptychographic imaging confronts inherent challenges in applying deep learning for phase retrieval from diffraction patterns. Conventional neural architectures, both convolutional neural networks and Transformer-based methods, are optimized for natural images with Euclidean spatial neighborhood-based inductive biases that exhibit geometric mismatch with the concentric coherent patterns characteristic of diffraction data in reciprocal space. In this paper, we present PPN, a physics-inspired deep learning network with Polar Coordinate Attention (PoCA) for ptychographic imaging, that aligns neural inductive biases with diffraction physics through a dual-branch architecture separating local feature extraction from non-local coherence modeling. It consists of a PoCA mechanism that replaces Euclidean spatial priors with physically consistent radial-angular correlations. PPN outperforms existing end-to-end models, with spectral and spatial analysis confirming its greater preservation of high-frequency details. Notably, PPN maintains robust performance compared to iterative methods even at low overlap ratios, making it well suited for high-throughput imaging in real-world acquisition scenarios for samples with consistent structural characteristics.
Self-similarity in pandemic spread and fractal containment policies
Authors: Alexander F. Siegenfeld, Asier Piñeiro Orioli, Robin Na, Blake Elias, Yaneer Bar-Yam
Date: 2024-12-12 00:00:00

Although pandemics are often studied as if populations are well-mixed, disease transmission networks exhibit a multi-scale structure stretching from the individual all the way up to the entire globe. The COVID-19 pandemic has led to an intense debate about whether interventions should prioritize public health or the economy, leading to a surge of studies analyzing the health and economic costs of various response strategies. Here we show that describing disease transmission in a self-similar (fractal) manner across multiple geographic scales allows for the design of multi-scale containment measures that substantially reduce both these costs. We characterize response strategies using multi-scale reproduction numbers -- a generalization of the basic reproduction number $R_0$ -- that describe pandemic spread at multiple levels of scale and provide robust upper bounds on disease transmission. Stable elimination is guaranteed if there exists a scale such that the reproduction number among regions of that scale is less than $1$, even if the basic reproduction number $R_0$ is greater than $1$. We support our theoretical results using simulations of a heterogeneous SIS model for disease spread in the United States constructed using county-level commuting, air travel, and population data.
High-dimensional covariance matrix estimators on simulated portfolios with complex structures
Authors: Andrés García-Medina
Date: 2024-12-11 00:00:00

We study the allocation of synthetic portfolios under hierarchical nested, one-factor, and diagonal structures of the population covariance matrix in a high-dimensional scenario. The noise reduction approaches for the sample realizations are based on random matrices, free probability, deterministic equivalents, and their combination with a data science hierarchical method known as two-step covariance estimators. The financial performance metrics from the simulations are compared with empirical data from companies comprising the S&P 500 index using a moving window and walk-forward analysis. The portfolio allocation strategies analyzed include the minimum variance portfolio (both with and without short-selling constraints) and the hierarchical risk parity approach. Our proposed hierarchical nested covariance model shows signatures of complex system interactions. The empirical financial data reproduces stylized portfolio facts observed in the complex and one-factor covariance models. The two-step estimators proposed here improve several financial metrics under the analyzed investment strategies. The results pave the way for new risk management and diversification approaches when the number of assets is of the same order as the number of transaction days in the investment portfolio.
Geometric Deep Learning for Realized Covariance Matrix Forecasting
Authors: Andrea Bucci, Michele Palma, Chao Zhang
Date: 2024-12-12 00:00:00
Tasks: Deep Learning

Traditional methods employed in matrix volatility forecasting often overlook the inherent Riemannian manifold structure of symmetric positive definite matrices, treating them as elements of Euclidean space, which can lead to suboptimal predictive performance. Moreover, they often struggle to handle high-dimensional matrices. In this paper, we propose a novel approach for forecasting realized covariance matrices of asset returns using a Riemannian-geometry-aware deep learning framework. In this way, we account for the geometric properties of the covariance matrices, including possible non-linear dynamics and efficient handling of high-dimensionality. Moreover, building upon a Fr\'echet sample mean of realized covariance matrices, we are able to extend the HAR model to the matrix-variate. We demonstrate the efficacy of our approach using daily realized covariance matrices for the 50 most capitalized companies in the S&P 500 index, showing that our method outperforms traditional approaches in terms of predictive accuracy.
A Kernel Score Perspective on Forecast Disagreement and the Linear Pool
Authors: Fabian Krüger
Date: 2024-12-12 00:00:00

The variance of a linearly combined forecast distribution (or linear pool) consists of two components: The average variance of the component distributions (`average uncertainty'), and the average squared difference between the components' means and the pool's mean (`disagreement'). This paper shows that similar decompositions hold for a class of uncertainty measures that can be constructed as entropy functions of kernel scores. The latter are a rich family of scoring rules that covers point and distribution forecasts for univariate and multivariate, discrete and continuous settings. We further show that the disagreement term is useful for understanding the ex-post performance of the linear pool (as compared to the component distributions), and motivates using the linear pool instead of other forecast combination techniques. From a practical perspective, the results in this paper suggest principled measures of forecast disagreement in a wide range of applied settings.
Panel Stochastic Frontier Models with Latent Group Structures
Authors: Kazuki Tomioka, Thomas T. Yang, Xibin Zhang
Date: 2024-12-12 00:00:00

Stochastic frontier models have attracted significant interest over the years due to their unique feature of including a distinct inefficiency term alongside the usual error term. To effectively separate these two components, strong distributional assumptions are often necessary. To overcome this limitation, numerous studies have sought to relax or generalize these models for more robust estimation. In line with these efforts, we introduce a latent group structure that accommodates heterogeneity across firms, addressing not only the stochastic frontiers but also the distribution of the inefficiency term. This framework accounts for the distinctive features of stochastic frontier models, and we propose a practical estimation procedure to implement it. Simulation studies demonstrate the strong performance of our proposed method, which is further illustrated through an application to study the cost efficiency of the U.S. commercial banking sector.
Machine Learning the Macroeconomic Effects of Financial Shocks
Authors: Niko Hauzenberger, Florian Huber, Karin Klieber, Massimiliano Marcellino
Date: 2024-12-10 00:00:00

We propose a method to learn the nonlinear impulse responses to structural shocks using neural networks, and apply it to uncover the effects of US financial shocks. The results reveal substantial asymmetries with respect to the sign of the shock. Adverse financial shocks have powerful effects on the US economy, while benign shocks trigger much smaller reactions. Instead, with respect to the size of the shocks, we find no discernible asymmetries.
High-frequency and heteroskedasticity identification in multicountry models: Revisiting spillovers of monetary shocks
Authors: Michael Pfarrhofer, Anna Stelzer
Date: 2019-12-06 00:00:00

We explore the international transmission of monetary policy and central bank information shocks originating from the United States and the euro area. Employing a panel vector autoregression, we use macroeconomic and financial variables across several major economies to address both static and dynamic spillovers. To identify structural shocks, we introduce a novel approach that combines external instruments with heteroskedasticity-based identification and sign restrictions. Our results suggest significant spillovers from European Central Bank and Federal Reserve policies to each other's economies, global aggregates, and other countries. These effects are more pronounced for central bank information shocks than for pure monetary policy shocks, and the dominance of the US in the global economy is reflected in our findings.
Inference after discretizing time-varying unobserved heterogeneity
Authors: Jad Beyhum, Martin Mugnier
Date: 2024-12-10 00:00:00
Tasks: valid

Approximating time-varying unobserved heterogeneity by discrete types has become increasingly popular in economics. Yet, provably valid post-clustering inference for target parameters in models that do not impose an exact group structure is still lacking. This paper fills this gap in the leading case of a linear panel data model with nonseparable two-way unobserved heterogeneity. Building on insights from the double machine learning literature, we propose a simple inference procedure based on a bias-reducing moment. Asymptotic theory and simulations suggest excellent performance. In the application on fiscal policy we revisit, the novel approach yields conclusions in line with economic theory.
Density forecast transformations
Authors: Matteo Mogliani, Florens Odendahl
Date: 2024-12-08 00:00:00

The popular choice of using a $direct$ forecasting scheme implies that the individual predictions do not contain information on cross-horizon dependence. However, this dependence is needed if the forecaster has to construct, based on $direct$ density forecasts, predictive objects that are functions of several horizons ($e.g.$ when constructing annual-average growth rates from quarter-on-quarter growth rates). To address this issue we propose to use copulas to combine the individual $h$-step-ahead predictive distributions into a joint predictive distribution. Our method is particularly appealing to practitioners for whom changing the $direct$ forecasting specification is too costly. In a Monte Carlo study, we demonstrate that our approach leads to a better approximation of the true density than an approach that ignores the potential dependence. We show the superior performance of our method in several empirical examples, where we construct (i) quarterly forecasts using month-on-month $direct$ forecasts, (ii) annual-average forecasts using monthly year-on-year $direct$ forecasts, and (iii) annual-average forecasts using quarter-on-quarter $direct$ forecasts.
Estimating Spillover Effects in the Presence of Isolated Nodes
Authors: Bora Kim
Date: 2024-12-08 00:00:00
Tasks: regression

In estimating spillover effects under network interference, practitioners often use linear regression with either the number or fraction of treated neighbors as regressors. An often overlooked fact is that the latter is undefined for units without neighbors (``isolated nodes"). The common practice is to impute this fraction as zero for isolated nodes. This paper shows that such practice introduces bias through theoretical derivations and simulations. Causal interpretations of the commonly used spillover regression coefficients are also provided.