pwc-archive/papers-with-abstracts huggingface dataset:
Accounting for the Effects of Probabilistic Uncertainty During Fast Charging of Lithium-ion Batteries
Authors: Minsu Kim, Joachim Schaeffer, Marc D. Berliner, Berta Pedret Sagnier, Rolf Findeisen, Richard D. Braatz
Date: 2024-05-02 00:00:00
Tasks: Uncertainty Quantification

Batteries are nonlinear dynamical systems that can be modeled by Porous Electrode Theory models. The aim of optimal fast charging is to reduce the charging time while keeping battery degradation low. Most past studies assume that model parameters and ambient temperature are a fixed known value and that all PET model parameters are perfectly known. In real battery operation, however, the ambient temperature and the model parameters are uncertain. To ensure that operational constraints are satisfied at all times in the context of model-based optimal control, uncertainty quantification is required. Here, we analyze optimal fast charging for modest uncertainty in the ambient temperature and 23 model parameters. Uncertainty quantification of the battery model is carried out using non-intrusive polynomial chaos expansion and the results are verified with Monte Carlo simulations. The method is investigated for a constant current--constant voltage charging strategy for a battery for which the strategy is known to be standard for fast charging subject to operating below maximum current and charging constraints. Our results demonstrate that uncertainty in ambient temperature results in violations of constraints on the voltage and temperature. Our results identify a subset of key parameters that contribute to fast charging among the overall uncertain parameters. Additionally, it is shown that the constraints represented by voltage, temperature, and lithium-plating overpotential are violated due to uncertainties in the ambient temperature and parameters. The C-rate and charge constraints are then adjusted so that the probability of violating the degradation acceleration condition is below a pre-specified value. This approach demonstrates a computationally efficient approach for determining fast-charging protocols that take probabilistic uncertainties into account.
A Hybrid Probabilistic Battery Health Management Approach for Robust Inspection Drone Operations
Authors: Jokin Alcibar, Jose I. Aizpurua, Ekhi Zugastia, Oier Penagarikano
Date: 2024-04-24 00:00:00
Tasks: Management

Health monitoring of remote critical infrastructure is a complex and expensive activity due to the limited infrastructure accessibility. Inspection drones are ubiquitous assets that enhance the reliability of critical infrastructures through improved accessibility. However, due to the harsh operation environment, it is crucial to monitor their health to ensure successful inspection operations. The battery is a key component that determines the overall reliability of the inspection drones and, with an appropriate health management approach, contributes to reliable and robust inspections. In this context, this paper presents a novel hybrid probabilistic approach for battery end-of-discharge (EOD) voltage prediction of Li-Po batteries. The hybridization is achieved in an error-correction configuration, which combines physics-based discharge and probabilistic error-correction models to quantify the aleatoric and epistemic uncertainty. The performance of the hybrid probabilistic methodology was empirically evaluated on a dataset comprising EOD voltage under varying load conditions. The dataset was obtained from real inspection drones operated on different flights, focused on offshore wind turbine inspections. The proposed approach has been tested with different probabilistic methods and demonstrates 14.8% improved performance in probabilistic accuracy compared to the best probabilistic method. In addition, aleatoric and epistemic uncertainties provide robust estimations to enhance the diagnosis of battery health-states.
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies
Authors: Jonathan Serrano-Pérez, Raquel Díaz Hernández, L. Enrique Sucar
Date: 2024-05-03 00:00:00
Tasks: Image Augmentation

This work is focused on the morphological classification of galaxies following the Hubble sequence in which the different classes are arranged in a hierarchy. The proposed method, BCNN, is composed of two main modules. First, a convolutional neural network (CNN) is trained with images of the different classes of galaxies (image augmentation is carried out to balance some classes); the CNN outputs the probability for each class of the hierarchy, and its outputs/predictions feed the second module. The second module consists of a Bayesian network that represents the hierarchy and helps to improve the prediction accuracy by combining the predictions of the first phase while maintaining the hierarchical constraint (in a hierarchy, an instance associated with a node must be associated to all its ancestors), through probabilistic inference over the Bayesian network so that a consistent prediction is obtained. Different images from the Hubble telescope have been collected and labeled by experts, which are used to perform the experiments. The results show that BCNN performed better than several CNNs in multiple evaluation measures, reaching the next scores: 67% in exact match, 78% in accuracy, and 83% in hierarchical F-measure.
Bayesian optimization for stable properties amid processing fluctuations in sputter deposition
Authors: Ankit Shrivastava, Matias Kalaswad, Joyce O. Custer, David P. Adams, Habib N. Najm
Date: 2024-05-06 00:00:00
Tasks: Bayesian Optimization

We introduce a Bayesian optimization approach to guide the sputter deposition of molybdenum thin films, aiming to achieve desired residual stress and sheet resistance while minimizing susceptibility to stochastic fluctuations during deposition. Thin films are pivotal in numerous technologies, including semiconductors and optical devices, where their properties are critical. Sputter deposition parameters, such as deposition power, vacuum chamber pressure, and working distance, influence physical properties like residual stress and resistance. Excessive stress and high resistance can impair device performance, necessitating the selection of optimal process parameters. Furthermore, these parameters should ensure the consistency and reliability of thin film properties, assisting in the reproducibility of the devices. However, exploring the multidimensional design space for process optimization is expensive. Bayesian optimization is ideal for optimizing inputs/parameters of general black-box functions without reliance on gradient information. We utilize Bayesian optimization to optimize deposition power and pressure using a custom-built objective function incorporating observed stress and resistance data. Additionally, we integrate prior knowledge of stress variation with pressure into the objective function to prioritize films least affected by stochastic variations. Our findings demonstrate that Bayesian optimization effectively explores the design space and identifies optimal parameter combinations meeting desired stress and resistance specifications.
Coat stiffening can explain invagination of clathrin-coated membranes
Authors: Felix Frey, Ulrich S. Schwarz
Date: 2024-05-05 00:00:00

Clathrin-mediated endocytosis is the main pathway used by eukaryotic cells to take up extracellular material, but the dominant physical mechanisms driving this process are still elusive. Recently several high-resolution imaging techniques have been used on different cell lines to measure the geometrical properties of clathrin-coated pits over their whole lifetime. Here we first show that the combination of all datasets with the recently introduced cooperative curvature model defines a consensus pathway, which is characterized by a flat to-curved transition at finite area, followed by linear growth and subsequent saturation of curvature. We then apply an energetic model for the composite of plasma membrane and clathrin coat to this consensus pathway to show that the dominant mechanism for invagination could be coat stiffening, which might originate from cooperative interactions between the different clathrin molecules and progressively drives the system towards its intrinsic curvature. Our theory predicts that two length scales determine the invagination pathway, namely the patch size at which the flat-to-curved transition occurs and the final pit radius.
Loss Jump During Loss Switch in Solving PDEs with Neural Networks
Authors: Zhiwei Wang, Lulu Zhang, Zhongwang Zhang, Zhi-Qin John Xu
Date: 2024-05-06 00:00:00

Using neural networks to solve partial differential equations (PDEs) is gaining popularity as an alternative approach in the scientific computing community. Neural networks can integrate different types of information into the loss function. These include observation data, governing equations, and variational forms, etc. These loss functions can be broadly categorized into two types: observation data loss directly constrains and measures the model output, while other loss functions indirectly model the performance of the network, which can be classified as model loss. However, this alternative approach lacks a thorough understanding of its underlying mechanisms, including theoretical foundations and rigorous characterization of various phenomena. This work focuses on investigating how different loss functions impact the training of neural networks for solving PDEs. We discover a stable loss-jump phenomenon: when switching the loss function from the data loss to the model loss, which includes different orders of derivative information, the neural network solution significantly deviates from the exact solution immediately. Further experiments reveal that this phenomenon arises from the different frequency preferences of neural networks under different loss functions. We theoretically analyze the frequency preference of neural networks under model loss. This loss-jump phenomenon provides a valuable perspective for examining the underlying mechanisms of neural networks in solving PDEs.
Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanisms
Authors: Arthur Fyon, Alessio Franci, Pierre Sacré, Guillaume Drion
Date: 2024-05-03 00:00:00
Tasks: Dimensionality Reduction

Neuronal systems maintain stable functions despite large variability in their physiological components. Ion channel expression, in particular, is highly variable in neurons exhibiting similar electrophysiological phenotypes, which poses questions regarding how specific ion channel subsets reliably shape neuron intrinsic properties. Here, we use detailed conductance-based modeling to explore the origin of stable neuronal function from variable channel composition. Using dimensionality reduction, we uncover two principal dimensions in the channel conductance space that capture most of the variance of the observed variability. Those two dimensions correspond to two physiologically relevant sources of variability that can be explained by feedback mechanisms underlying regulation of neuronal activity, providing quantitative insights into how channel composition links to neuronal electrophysiological activity. These insights allowed us to understand and design a model-independent, reliable neuromodulation rule for variable neuronal populations.
Ambush strategy enhances organisms' performance in rock-paper-scissors games
Authors: R. Barbalho, S. Rodrigues, M. Tenorio, J. Menezes
Date: 2024-05-04 00:00:00
Tasks: Decision Making

We study a five-species cyclic system wherein individuals of one species strategically adapt their movements to enhance their performance in the spatial rock-paper-scissors game. Environmental cues enable the awareness of the presence of organisms targeted for elimination in the cyclic game. If the local density of target organisms is sufficiently high, individuals move towards concentrated areas for direct attack; otherwise, they employ an ambush tactic, maximising the chances of success by targeting regions likely to be dominated by opponents. Running stochastic simulations, we discover that the ambush strategy enhances the likelihood of individual success compared to direct attacks alone, leading to uneven spatial patterns characterised by spiral waves. We compute the autocorrelation function and measure how the ambush tactic unbalances the organisms' spatial organisation by calculating the characteristic length scale of typical spatial domains of each species. We demonstrate that the threshold for local species density influences the ambush strategy's effectiveness, while the neighbourhood perception range significantly impacts decision-making accuracy. The outcomes show that long-range perception improves performance by over 60\%, although there is potential interference in decision-making under high attack triggers. Understanding how organisms' adaptation to their environment enhances their performance may be helpful not only for ecologists but also for data scientists aiming to improve artificial intelligence systems.
One nose but two nostrils: Learn to align with sparse connections between two olfactory cortices
Authors: Bo Liu, Shanshan Qin, Venkatesh Murthy, Yuhai Tu
Date: 2024-05-06 00:00:00

The integration of neural representations in the two hemispheres is an important problem in neuroscience. Recent experiments revealed that odor responses in cortical neurons driven by separate stimulation of the two nostrils are highly correlated. This bilateral alignment points to structured inter-hemispheric connections, but detailed mechanism remains unclear. Here, we hypothesized that continuous exposure to environmental odors shapes these projections and modeled it as online learning with local Hebbian rule. We found that Hebbian learning with sparse connections achieves bilateral alignment, exhibiting a linear trade-off between speed and accuracy. We identified an inverse scaling relationship between the number of cortical neurons and the inter-hemispheric projection density required for desired alignment accuracy, i.e., more cortical neurons allow sparser inter-hemispheric projections. We next compared the alignment performance of local Hebbian rule and the global stochastic-gradient-descent (SGD) learning for artificial neural networks. We found that although SGD leads to the same alignment accuracy with modestly sparser connectivity, the same inverse scaling relation holds. We showed that their similar performance originates from the fact that the update vectors of the two learning rules align significantly throughout the learning process. This insight may inspire efficient sparse local learning algorithms for more complex problems.
Multi-grid reaction-diffusion master equation: applications to morphogen gradient modelling
Authors: Radek Erban, Stefanie Winkelmann
Date: 2024-05-03 00:00:00

The multi-grid reaction-diffusion master equation (mgRDME) provides a generalization of stochastic compartment-based reaction-diffusion modelling described by the standard reaction-diffusion master equation (RDME). By enabling different resolutions on lattices for biochemical species with different diffusion constants, the mgRDME approach improves both accuracy and efficiency of compartment-based reaction-diffusion simulations. The mgRDME framework is examined through its application to morphogen gradient formation in stochastic reaction-diffusion scenarios, using both an analytically tractable first-order reaction network and a model with a second-order reaction. The results obtained by the mgRDME modelling are compared with the standard RDME model and with the (more detailed) particle-based Brownian dynamics simulations. The dependence of error and numerical cost on the compartment sizes is defined and investigated through a multi-objective optimization problem.
Virtual Extended-Range Tomography (VERT): Contact-free realistic ultrasonic bone imaging
Authors: Aaron Chung-Jukko, Peter Huthwaite
Date: 2024-05-05 00:00:00

Ultrasound tomography generally struggles to reconstruct high-contrast and/or extended-range problems. A prime example is site-specific in-vivo bone imaging, crucial for accurately assessing the risk of life-threatening fractures, which are preventable given accurate diagnosis and treatment. In this type of problem, two main obstacles arise: (a) an external region prohibits access to the region of interest (ROI), and (b) high contrast exists between the two regions. These challenges impede existing algorithms -- including bent-ray tomography (BRT), known for its robustness, speed, and reasonable short-range resolution. We propose Virtual Extended-Range Tomography (VERT), which tackles these challenges through (a) placement of virtual transducers directly on the ROI, facilitating (b) rapid initialisation before BRT inversion. In-silico validation against BRT with and without a-priori information shows superior resolution and robustness -- while maintaining or even improving speed. These improvements are drastic where the external region is much larger than the ROI. Additional validation against the practically impossible -- BRT directly on the ROI -- demonstrates that VERT is approaching the resolution limit. The capability to solve high-contrast extended-range tomography problems without prior knowledge about the ROI's interior has many implications. VERT has the potential to unlock site-specific in-vivo bone imaging for assessing fracture risk, potentially saving millions of lives globally. In other applications, VERT may replace classical BRT to yield improvements in resolution, robustness and speed -- especially where the ROI does not cover the entire imaging array. For even higher resolution, VERT offers a reliable starting background to complement algorithms with less robustness and high computational costs.
Fluid-structure interaction simulations for the prediction of fractional flow reserve in pediatric patients with anomalous aortic origin of a coronary artery
Authors: Charles Puelz, Craig G. Rusin, Dan Lior, Shagun Sachdeva, Tam T. Doan, Lindsay F. Eilers, Dana Reaves-O'Neal, Silvana Molossi
Date: 2024-05-02 00:00:00

Computer simulations of blood flow in patients with anomalous aortic origin of a coronary artery (AAOCA) have the promise to provide insight into this complex disease. They provide an in-silico experimental platform to explore possible mechanisms of myocardial ischemia, a potentially deadly complication for patients with this defect. This paper focuses on the question of model calibration for fluid-structure interaction models of pediatric AAOCA patients. Imaging and cardiac catheterization data provide partial information for model construction and calibration. However, parameters for downstream boundary conditions needed for these models are difficult to estimate. Further, important model predictions, like fractional flow reserve (FFR), are sensitive to these parameters. We describe an approach to calibrate downstream boundary condition parameters to clinical measurements of resting FFR. The calibrated models are then used to predict FFR at stress, an invasively measured quantity that can be used in the clinical evaluation of these patients. We find reasonable agreement between the model predicted and clinically measured FFR at stress, indicating the credibility of this modeling framework for predicting hemodynamics of pediatric AAOCA patients. This approach could lead to important clinical applications since it may serve as a tool for risk stratifying children with AAOCA.
Tuning parameter selection in econometrics
Authors: Denis Chetverikov
Date: 2024-05-05 00:00:00
Tasks: Econometrics

I review some of the main methods for selecting tuning parameters in nonparametric and $\ell_1$-penalized estimation. For the nonparametric estimation, I consider the methods of Mallows, Stein, Lepski, cross-validation, penalization, and aggregation in the context of series estimation. For the $\ell_1$-penalized estimation, I consider the methods based on the theory of self-normalized moderate deviations, bootstrap, Stein's unbiased risk estimation, and cross-validation in the context of Lasso estimation. I explain the intuition behind each of the methods and discuss their comparative advantages. I also give some extensions.
Identifying and exploiting alpha in linear asset pricing models with strong, semi-strong, and latent factors
Authors: M. Hashem Pesaran, Ron P. Smith
Date: 2024-05-03 00:00:00

The risk premia of traded factors are the sum of factor means and a parameter vector we denote by {\phi} which is identified from the cross section regression of alpha of individual securities on the vector of factor loadings. If phi is non-zero one can construct "phi-portfolios" which exploit the systematic components of non-zero alpha. We show that for known values of betas and when phi is non-zero there exist phi-portfolios that dominate mean-variance portfolios. The paper then proposes a two-step bias corrected estimator of phi and derives its asymptotic distribution allowing for idiosyncratic pricing errors, weak missing factors, and weak error cross-sectional dependence. Small sample results from extensive Monte Carlo experiments show that the proposed estimator has the correct size with good power properties. The paper also provides an empirical application to a large number of U.S. securities with risk factors selected from a large number of potential risk factors according to their strength and constructs phi-portfolios and compares their Sharpe ratios to mean variance and S&P 500 portfolio.
Testing for an Explosive Bubble using High-Frequency Volatility
Authors: H. Peter Boswijk, Jun Yu, Yang Zu
Date: 2024-05-03 00:00:00

Based on a continuous-time stochastic volatility model with a linear drift, we develop a test for explosive behavior in financial asset prices at a low frequency when prices are sampled at a higher frequency. The test exploits the volatility information in the high-frequency data. The method consists of devolatizing log-asset price increments with realized volatility measures and performing a supremum-type recursive Dickey-Fuller test on the devolatized sample. The proposed test has a nuisance-parameter-free asymptotic distribution and is easy to implement. We study the size and power properties of the test in Monte Carlo simulations. A real-time date-stamping strategy based on the devolatized sample is proposed for the origination and conclusion dates of the explosive regime. Conditions under which the real-time date-stamping strategy is consistent are established. The test and the date-stamping strategy are applied to study explosive behavior in cryptocurrency and stock markets.
Unleashing the Power of AI: Transforming Marketing Decision-Making in Heavy Machinery with Machine Learning, Radar Chart Simulation, and Markov Chain Analysis
Authors: Tian Tian, Jiahao Deng
Date: 2024-05-03 00:00:00
Tasks: Decision Making, Management, Marketing, regression

This pioneering research introduces a novel approach for decision-makers in the heavy machinery industry, specifically focusing on production management. The study integrates machine learning techniques like Ridge Regression, Markov chain analysis, and radar charts to optimize North American Crawler Cranes market production processes. Ridge Regression enables growth pattern identification and performance assessment, facilitating comparisons and addressing industry challenges. Markov chain analysis evaluates risk factors, aiding in informed decision-making and risk management. Radar charts simulate benchmark product designs, enabling data-driven decisions for production optimization. This interdisciplinary approach equips decision-makers with transformative insights, enhancing competitiveness in the heavy machinery industry and beyond. By leveraging these techniques, companies can revolutionize their production management strategies, driving success in diverse markets.