pwc-archive/papers-with-abstracts huggingface dataset:
Balancing Forecast Accuracy and Switching Costs in Online Optimization of Energy Management Systems
Authors: Evgenii Genov, Julian Ruddick, Christoph Bergmeir, Majid Vafaeipour, Thierry Coosemans, Salvador Garcia, Maarten Messagie
Date: 2024-06-29 00:00:00
Tasks: Decision Making, energy management, Management, Scheduling, Stochastic Optimization

This study investigates the integration of forecasting and optimization in energy management systems, with a focus on the role of switching costs -- penalties incurred from frequent operational adjustments. We develop a theoretical and empirical framework to examine how forecast accuracy and stability interact with switching costs in online decision-making settings. Our analysis spans both deterministic and stochastic optimization approaches, using point and probabilistic forecasts. A novel metric for measuring temporal consistency in probabilistic forecasts is introduced, and the framework is validated in a real-world battery scheduling case based on the CityLearn 2022 challenge. Results show that switching costs significantly alter the trade-off between forecast accuracy and stability, and that more stable forecasts can reduce the performance loss due to switching. Contrary to common practice, the findings suggest that, under non-negligible switching costs, longer commitment periods may lead to better overall outcomes. These insights have practical implications for the design of intelligent, forecast-aware energy management systems.
On the importance of learning non-local dynamics for stable data-driven climate modeling: A 1D gravity wave-QBO testbed
Authors: Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander
Date: 2024-07-07 00:00:00

Machine learning (ML) techniques, especially neural networks (NNs), have shown promise in learning subgrid-scale parameterizations for climate models. However, a major problem with data-driven parameterizations, particularly those learned with supervised algorithms, is model instability. Current remedies are often ad-hoc and lack a theoretical foundation. Here, we combine ML theory and climate physics to address a source of instability in NN-based parameterization. We demonstrate the importance of learning spatially $\textit{non-local}$ dynamics using a 1D model of the quasi-biennial oscillation (QBO) with gravity wave (GW) parameterization as a testbed. While common offline metrics fail to identify shortcomings in learning non-local dynamics, we show that the concept of receptive field (RF) can identify instability a-priori. We find that NN-based parameterizations that seem to accurately predict GW forcings from wind profiles ($\mathbf{R^2 \approx 0.99}$) cause unstable simulations when RF is too small to capture the non-local dynamics, while NNs of the same size but large-enough RF are stable. We examine three broad classes of architectures, namely convolutional NNs, Fourier neural operators, and fully-connected NNs; the latter two have inherently large RFs. We also demonstrate that learning non-local dynamics is crucial for the stability and accuracy of a data-driven spatiotemporal emulator of the zonal wind field. Given the ubiquity of non-local dynamics in the climate system, we expect the use of effective RF, which can be computed for any NN architecture, to be important for many applications. This work highlights the necessity of integrating ML theory with physics to design and analyze data-driven algorithms for weather and climate modeling.
A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling
Authors: Jose González-Abad
Date: 2024-06-26 00:00:00

Deep learning has emerged as a promising tool for precipitation downscaling. However, current models rely on likelihood-based loss functions to properly model the precipitation distribution, leading to spatially inconsistent projections when sampling. This work explores a novel approach by fusing the strengths of likelihood-based and adversarial losses used in generative models. As a result, we propose a likelihood-based generative approach for precipitation downscaling, leveraging the benefits of both methods.
Src Kinase Slows Collective Rotation of Confined Epithelial Cell Monolayers
Authors: Nastassia Pricoupenko, Flavia Marsigliesi, Philippe Marcq, Carles Blanch-Mercader, Isabelle Bonnet
Date: 2024-07-09 00:00:00
Tasks: Friction

Collective cell migration is key during development, wound healing and metastasis and relies on coordinated cell behaviors at the group level. Src kinase is a key signalling protein for physiological functions of epithelia, as it regulates many cellular processes, including adhesion, motility, and mechanotransduction. Its over-activation is associated to cancer aggressiveness. Here, we take advantage of optogenetics to precisely control Src activation in time and show that its pathological-like activation slows collective rotation of epithelial cells confined into circular adhesive patches. We interpret velocity, force and stress data during period of non-activation and period of activation of Src thanks to an hydrodynamic description of the cell assembly as a polar active fluid. Src activation leads to a 2-fold decrease in the ratio of polar angle to friction, which could result from increased adhesiveness at the cell-substrate interface. Measuring internal stress allows us to show that active stresses are subdominant compared to traction forces. Our work reveals the importance of fine-tuning the level of Src activity for coordinated collective behaviors.
Novel second-order model for tumor evolution: description of cytostatic and cytotoxic effects
Authors: Carlos M. Nieto, Oscar M. Pimentel, Fabio D. Lora-Clavijo
Date: 2024-07-06 00:00:00

Cancer is a disease that takes millions of lives every year. Then, to propose treatments, avoid recurrence, and improve the patient's life quality, we need to analyze this disease from a biophysical perspective with a solid mathematical formulation. In this paper we introduce a novel deterministic model for the evolution of tumors under several conditions (untreated tumors and treated tumors using chemotherapy). Our model is characterized by a second-order differential equation, whose origin and interpretation are presented by exploiting our understanding of fluid mechanics (via continuity equations) and the theory of differential equations. Additionally, we show that our model can fit various experimental data sets. Thus, we prove that our nuanced and general model can describe accelerated growth, as well as cytostatic and cytotoxic effects. All in all, our model opens up a new window in the understanding of tumor evolution and represents a promising connection between the macroscopic and microscopic descriptions of cancer.
A Unified Intracellular pH Landscape with SITE-pHorin: a Quantum-Entanglement-Enhanced pH Probe
Authors: Shu-Ang Li, Xiao-Yan Meng, Su Zhang, Ying-Jie Zhang, Run-Zhou Yang, Dian-Dian Wang, Yang Yang, Pei-Pei Liu, Jian-Sheng Kang
Date: 2024-07-05 00:00:00

An accurate map of intracellular organelle pH is crucial for comprehending cellular metabolism and organellar functions. However, a unified intracellular pH spectrum using a single probe is still lack. Here, we developed a novel quantum entanglement-enhanced pH-sensitive probe called SITE-pHorin, which featured a wide pH-sensitive range and ratiometric quantitative measurement capabilities. Subsequently, we measured the pH of various organelles and their sub-compartments, including mitochondrial sub-spaces, Golgi stacks, endoplasmic reticulum, lysosomes, peroxisomes, and endosomes in COS-7 cells. For the long-standing debate on mitochondrial compartments pH, we measured the pH of mitochondrial cristae as 6.60 \pm 0.40, the pH of mitochondrial intermembrane space as 6.95 \pm 0.30, and two populations of mitochondrial matrix pH at approximately 7.20 \pm 0.27 and 7.50 \pm 0.16, respectively. Notably, the lysosome pH exhibited a single, narrow Gaussian distribution centered at 4.79 \pm 0.17. Furthermore, quantum chemistry computations revealed that both the deprotonation of the residue Y182 and the discrete curvature of deformed benzene ring in chromophore are both necessary for the quantum entanglement mechanism of SITE-pHorin. Intriguingly, our findings reveal an accurate pH gradient (0.6-0.9 pH unit) between mitochondrial cristae and matrix, suggesting prior knowledge about \Delta pH (0.4-0.6) and mitochondrial proton motive force (pmf) are underestimated.
Swin UNETR segmentation with automated geometry filtering for biomechanical modeling of knee joint cartilage
Authors: Reza Kakavand, Peyman Tahghighi, Reza Ahmadi, W. Brent Edwards, Amin Komeili
Date: 2024-07-08 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Simulation studies, such as finite element (FE) modeling, offer insights into knee joint biomechanics, which may not be achieved through experimental methods without direct involvement of patients. While generic FE models have been used to predict tissue biomechanics, they overlook variations in population-specific geometry, loading, and material properties. In contrast, subject-specific models account for these factors, delivering enhanced predictive precision but requiring significant effort and time for development. This study aimed to facilitate subject-specific knee joint FE modeling by integrating an automated cartilage segmentation algorithm using a 3D Swin UNETR. This algorithm provided initial segmentation of knee cartilage, followed by automated geometry filtering to refine surface roughness and continuity. In addition to the standard metrics of image segmentation performance, such as Dice similarity coefficient (DSC) and Hausdorff distance, the method's effectiveness was also assessed in FE simulation. Nine pairs of knee cartilage FE models, using manual and automated segmentation methods, were developed to compare the predicted stress and strain responses during gait. The automated segmentation achieved high Dice similarity coefficients of 89.4% for femoral and 85.1% for tibial cartilage, with a Hausdorff distance of 2.3 mm between the automated and manual segmentation. Mechanical results including maximum principal stress and strain, fluid pressure, fibril strain, and contact area showed no significant differences between the manual and automated FE models. These findings demonstrate the effectiveness of the proposed automated segmentation method in creating accurate knee joint FE models.
Laser driven miniature diamond implant for wireless retinal prostheses
Authors: Arman Ahnood, Ross Cheriton, Anne Bruneau, James A. Belcourt, Jean Pierre Ndabakuranye, William Lemaire, Rob Hilkes, Réjean Fontaine, John P. D. Cook, Karin Hinzer, Steven Prawer
Date: 2024-06-21 00:00:00

The design and benchtop operation of a wireless miniature epiretinal stimulator implant is reported. The implant is optically powered and controlled using safe illumination at near-infrared wavelengths. An application-specific integrated circuit (ASIC) hosting a digital control unit is used to control the implant's electrodes. The ASIC is powered using an advanced photovoltaic (PV) cell and programmed using a single photodiode. Diamond packaging technology is utilized to achieve high-density integration of the implant optoelectronic circuitry, as well as individual connections between a stimulator chip and 256 electrodes, within a 4.6 mm x 3.7 mm x 0.9 mm implant package. An ultrahigh efficiency PV cell with a monochromatic power conversion efficiency of 55% is used to power the implant. On-board photodetection circuity with a bandwidth of 3.7 MHz is used for forward data telemetry of stimulation parameters. In comparison to implants which utilize inductively coupled coils, laser power delivery enables a high degree of miniaturization and lower surgical complexity. The device presented combines the benefits of implant miniaturization and a flexible stimulation strategy provided by a dedicated stimulator chip. This development provides a route to fully wireless miniaturized minimally invasive implants with sophisticated functionalities.
Tumor likelihood estimation on MRI prostate data by utilizing k-Space information
Authors: M. Rempe, F. Hörst, C. Seibold, B. Hadaschik, M. Schlimbach, J. Egger, K. Kröninger, F. Breuer, M. Blaimer, J. Kleesiek
Date: 2024-06-04 00:00:00

We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Space. Using a publicly available MRI raw dataset with 312 subject and a total of 9508 slices, we show the advantage of utilizing the k-Space for better prostate cancer likelihood estimation in comparison to just using the magnitudinal information in the image domain, with an AUROC of $86.1\%\pm1.8\%$. Additionally, by using high undersampling rates and a simple principal component analysis (PCA) for coil compression, we reduce the time needed for reconstruction by avoiding the time intensive GRAPPA reconstruction algorithm. By using digital undersampling for our experiments, we show that scanning and reconstruction time could be reduced. Even with an undersampling factor of 16, our approach achieves meaningful results, with an AUROC of $71.4\%\pm2.9\%$, using the PCA coil combination and taking into account the k-Space information. With this study, we were able to show the feasibility of preserving phase and k-Space information, with consistent results. Besides preserving valuable information for further diagnostics, this approach can work without the time intensive ADC and reconstruction calculations, greatly reducing the post processing, as well as potential scanning time, increasing patient comfort and allowing a close to real-time prediction.
Dealing with idiosyncratic cross-correlation when constructing confidence regions for PC factors
Authors: Diego Fresoli, Pilar Poncela, Esther Ruiz
Date: 2024-07-09 00:00:00
Tasks: valid

In this paper, we propose a computationally simple estimator of the asymptotic covariance matrix of the Principal Components (PC) factors valid in the presence of cross-correlated idiosyncratic components. The proposed estimator of the asymptotic Mean Square Error (MSE) of PC factors is based on adaptive thresholding the sample covariances of the id iosyncratic residuals with the threshold based on their individual variances. We compare the nite sample performance of condence regions for the PC factors obtained using the proposed asymptotic MSE with those of available extant asymptotic and bootstrap regions and show that the former beats all alternative procedures for a wide variety of idiosyncratic cross-correlation structures.
Causes and Electoral Consequences of Political Assassinations: The Role of Organized Crime in Mexico
Authors: Roxana Gutiérrez-Romero, Nayely Iturbe
Date: 2024-07-09 00:00:00

Mexico has experienced a notable surge in assassinations of political candidates and mayors. This article argues that these killings are largely driven by organized crime, aiming to influence candidate selection, control local governments for rent-seeking, and retaliate against government crackdowns. Using a new dataset of political assassinations in Mexico from 2000 to 2021 and instrumental variables, we address endogeneity concerns in the location and timing of government crackdowns. Our instruments include historical Chinese immigration patterns linked to opium cultivation in Mexico, local corn prices, and U.S. illicit drug prices. The findings reveal that candidates in municipalities near oil pipelines face an increased risk of assassination due to drug trafficking organizations expanding into oil theft, particularly during elections and fuel price hikes. Government arrests or killings of organized crime members trigger retaliatory violence, further endangering incumbent mayors. This political violence has a negligible impact on voter turnout, as it targets politicians rather than voters. However, voter turnout increases in areas where authorities disrupt drug smuggling, raising the chances of the local party being re-elected. These results offer new insights into how criminal groups attempt to capture local governments and the implications for democracy under criminal governance.
Femicide Laws, Unilateral Divorce, and Abortion Decriminalization Fail to Stop Women's Killings in Mexico
Authors: Roxana Gutiérrez-Romero
Date: 2024-07-09 00:00:00

This paper evaluates the effectiveness of femicide laws in combating gender-based killings of women, a major cause of premature female mortality globally. Focusing on Mexico, a pioneer in adopting such legislation, the paper leverages variations in the enactment of femicide laws and associated prison sentences across states. Using the difference-in-difference estimator, the analysis reveals that these laws have not significantly affected the incidence of femicides, homicides of women, or reports of women who have disappeared. These findings remain robust even when accounting for differences in prison sentencing, whether states also implemented unilateral divorce laws, or decriminalized abortion alongside femicide legislation. The results suggest that legislative measures are insufficient to address violence against women in settings where impunity prevails.
Conditional Rank-Rank Regression
Authors: Victor Chernozhukov, Iván Fernández-Val, Jonas Meier, Aico van Vuuren, Francis Vella
Date: 2024-07-08 00:00:00
Tasks: regression

Rank-rank regression is commonly employed in economic research as a way of capturing the relationship between two economic variables. It frequently features in studies of intergenerational mobility as the resulting coefficient, capturing the rank correlation between the variables, is easy to interpret and measures overall persistence. However, in many applications it is common practice to include other covariates to account for differences in persistence levels between groups defined by the values of these covariates. In these instances the resulting coefficients can be difficult to interpret. We propose the conditional rank-rank regression, which uses conditional ranks instead of unconditional ranks, to measure average within-group persistence. The difference between conditional and unconditional rank-rank regression coefficients can then be interpreted as a measure of between-group persistence. We develop a flexible estimation approach using distribution regression and establish a theoretical framework for large sample inference. An empirical study on intergenerational income mobility in Switzerland demonstrates the advantages of this approach. The study reveals stronger intergenerational persistence between fathers and sons compared to fathers and daughters, with the within-group persistence explaining 62% of the overall income persistence for sons and 52% for daughters. Smaller families and those with highly educated fathers exhibit greater persistence in economic status.
Methodology for Calculating CO2 Absorption by Tree Planting for Greening Projects
Authors: Kento Ichii, Toshiki Muraoka, Nobumichi Shinohara, Shunsuke Managi, Shutaro Takeda
Date: 2024-07-08 00:00:00

In order to explore the possibility of carbon credits for greening projects, which play an important role in climate change mitigation, this paper examines a formula for estimating the amount of carbon fixation for greening activities in urban areas through tree planting. The usefulness of the formula studied was examined by conducting calculations based on actual data through measurements made by on-site surveys of a greening companie. A series of calculation results suggest that this formula may be useful. Recognizing carbon credits for green businesses for the carbon sequestration of their projects is an important incentive not only as part of environmental improvement and climate change action, but also to improve the health and well-being of local communities and to generate economic benefits. This study is a pioneering exploration of the methodology.
A Convexified Matching Approach to Imputation and Individualized Inference
Authors: YoonHaeng Hur, Tengyuan Liang
Date: 2024-07-07 00:00:00
Tasks: counterfactual, Imputation

We introduce a new convexified matching method for missing value imputation and individualized inference inspired by computational optimal transport. Our method integrates favorable features from mainstream imputation approaches: optimal matching, regression imputation, and synthetic control. We impute counterfactual outcomes based on convex combinations of observed outcomes, defined based on an optimal coupling between the treated and control data sets. The optimal coupling problem is considered a convex relaxation to the combinatorial optimal matching problem. We estimate granular-level individual treatment effects while maintaining a desirable aggregate-level summary by properly constraining the coupling. We construct transparent, individual confidence intervals for the estimated counterfactual outcomes. We devise fast iterative entropic-regularized algorithms to solve the optimal coupling problem that scales favorably when the number of units to match is large. Entropic regularization plays a crucial role in both inference and computation; it helps control the width of the individual confidence intervals and design fast optimization algorithms.
Learning control variables and instruments for causal analysis in observational data
Authors: Nicolas Apfel, Julia Hatamyar, Martin Huber, Jannis Kueck
Date: 2024-07-05 00:00:00

This study introduces a data-driven, machine learning-based method to detect suitable control variables and instruments for assessing the causal effect of a treatment on an outcome in observational data, if they exist. Our approach tests the joint existence of instruments, which are associated with the treatment but not directly with the outcome (at least conditional on observables), and suitable control variables, conditional on which the treatment is exogenous, and learns the partition of instruments and control variables from the observed data. The detection of sets of instruments and control variables relies on the condition that proper instruments are conditionally independent of the outcome given the treatment and suitable control variables. We establish the consistency of our method for detecting control variables and instruments under certain regularity conditions, investigate the finite sample performance through a simulation study, and provide an empirical application to labor market data from the Job Corps study.