pwc-archive/papers-with-abstracts huggingface dataset:
Fast Two-photon Microscopy by Neuroimaging with Oblong Random Acquisition (NORA)
Authors: Esther Whang, Skyler Thomas, Ji Yi, Adam S. Charles
Date: 2025-03-19 00:00:00
Tasks: Anatomy, Matrix Completion

Advances in neural imaging have enabled neuroscientists to study how large neural populations conspire to produce perception, behavior and cognition. Despite many advances in optical methods, there exists a fundamental tradeoff between imaging speed, field of view, and resolution that limits the scope of neural imaging, especially for the raster-scanning multi-photon imaging needed to image deeper into the brain. One approach to overcoming this trade-off is computational imaging: the co-development of optics designed to encode the target images into fewer measurements that are faster to acquire, with algorithms that compensate by inverting the optical coding to recover a larger or higher resolution image. We present here one such approach for raster-scanning two-photon imaging: Neuroimaging with Oblong Random Acquisition (NORA). NORA quickly acquires each frame in a microscopy video by subsampling only a fraction of the fast scanning lines, ignoring large portions of each frame. NORA mitigates the loss of information by 1) extending the point-spread function in the slow-scan direction to effectively integrate the fluorescence of several lines into a single set of measurements and 2) imaging different, randomly selected, lines at each frame. Rather than reconstruct the video frame-by-frame, NORA recovers full video sequences via nuclear-norm minimization on the pixels-by-time matrix, for which we prove theoretical guarantees on recovery. We simulated NORA imaging using the Neural Anatomy and Optical Microscopy (NAOMi) biophysical simulator, and used the simulations to demonstrate that NORA can accurately recover 400 um X 400 um fields of view at subsampling rates up to 20X, despite realistic noise and motion conditions. As NORA requires minimal changes to current microscopy systems, our results indicate that NORA can provide a promising avenue towards fast imaging of neural circuits.
Beacon2Science: Enhancing STEREO/HI beacon data1 with machine learning for efficient CME tracking
Authors: Justin Le Louëdec, Maike Bauer, Tanja Amerstorfer, Jackie A. Davies
Date: 2025-03-19 00:00:00

Observing and forecasting coronal mass ejections (CME) in real-time is crucial due to the strong geomagnetic storms they can generate that can have a potentially damaging effect, for example, on satellites and electrical devices. With its near-real-time availability, STEREO/HI beacon data is the perfect candidate for early forecasting of CMEs. However, previous work concluded that CME arrival prediction based on beacon data could not achieve the same accuracy as with high-resolution science data due to data gaps and lower quality. We present our novel pipeline entitled ''Beacon2Science'', bridging the gap between beacon and science data to improve CME tracking. Through this pipeline, we first enhance the quality (signal-to-noise ratio and spatial resolution) of beacon data. We then increase the time resolution of enhanced beacon images through learned interpolation to match science data's 40-minute resolution. We maximize information coherence between consecutive frames with adapted model architecture and loss functions through the different steps. The improved beacon images are comparable to science data, showing better CME visibility than the original beacon data. Furthermore, we compare CMEs tracked in beacon, enhanced beacon, and science images. The tracks extracted from enhanced beacon data are closer to those from science images, with a mean average error of $\sim 0.5 ^\circ$ of elongation compared to $1^\circ$ with original beacon data. The work presented in this paper paves the way for its application to forthcoming missions such as Vigil and PUNCH.
Probabilistic Quantum SVM Training on Ising Machine
Authors: Haoqi He, Yan Xiao
Date: 2025-03-20 00:00:00
Tasks: Binary Classification, Multi-class Classification

Quantum computing holds significant potential to accelerate machine learning algorithms, especially in solving optimization problems like those encountered in Support Vector Machine (SVM) training. However, current QUBO-based Quantum SVM (QSVM) methods rely solely on binary optimal solutions, limiting their ability to identify fuzzy boundaries in data. Additionally, the limited qubit count in contemporary quantum devices constrains training on larger datasets. In this paper, we propose a probabilistic quantum SVM training framework suitable for Coherent Ising Machines (CIMs). By formulating the SVM training problem as a QUBO model, we leverage CIMs' energy minimization capabilities and introduce a Boltzmann distribution-based probabilistic approach to better approximate optimal SVM solutions, enhancing robustness. To address qubit limitations, we employ batch processing and multi-batch ensemble strategies, enabling small-scale quantum devices to train SVMs on larger datasets and support multi-class classification tasks via a one-vs-one approach. Our method is validated through simulations and real-machine experiments on binary and multi-class datasets. On the banknote binary classification dataset, our CIM-based QSVM, utilizing an energy-based probabilistic approach, achieved up to 20% higher accuracy compared to the original QSVM, while training up to $10^4$ times faster than simulated annealing methods. Compared with classical SVM, our approach either matched or reduced training time. On the IRIS three-class dataset, our improved QSVM outperformed existing QSVM models in all key metrics. As quantum technology advances, increased qubit counts are expected to further enhance QSVM performance relative to classical SVM.
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware
Authors: Rahul Bhowmick, Harsh Wadhwa, Avinash Singh, Tania Sidana, Quoc Hoan Tran, Krishna Kumar Sabapathy
Date: 2025-03-20 00:00:00
Tasks: Combinatorial Optimization, State Estimation

Quantum computers offer a promising route to tackling problems that are classically intractable such as in prime-factorization, solving large-scale linear algebra and simulating complex quantum systems, but require fault-tolerant quantum hardware. On the other hand, variational quantum algorithms (VQAs) have the potential to provide a near-term route to quantum utility or advantage, and is usually constructed by using parametrized quantum circuits (PQCs) in combination with a classical optimizer for training. Although VQAs have been proposed for a multitude of tasks such as ground-state estimation, combinatorial optimization and unitary compilation, there remain major challenges in its trainability and resource costs on quantum hardware. Here we address these challenges by adopting Hardware Efficient and dynamical LIe algebra Supported Ansatz (HELIA), and propose two training schemes that combine an existing g-sim method (that uses the underlying group structure of the operators) and the Parameter-Shift Rule (PSR). Our improvement comes from distributing the resources required for gradient estimation and training to both classical and quantum hardware. We numerically test our proposal for ground-state estimation using Variational Quantum Eigensolver (VQE) and classification of quantum phases using quantum neural networks. Our methods show better accuracy and success of trials, and also need fewer calls to the quantum hardware on an average than using only PSR (upto 60% reduction), that runs exclusively on quantum hardware. We also numerically demonstrate the capability of HELIA in mitigating barren plateaus, paving the way for training large-scale quantum models.
HiQ-Lip: The First Quantum-Classical Hierarchical Method for Global Lipschitz Constant Estimation of ReLU Networks
Authors: Haoqi He, Yan Xiao
Date: 2025-03-20 00:00:00

Estimating the global Lipschitz constant of neural networks is crucial for understanding and improving their robustness and generalization capabilities. However, precise calculations are NP-hard, and current semidefinite programming (SDP) methods face challenges such as high memory usage and slow processing speeds. In this paper, we propose \textbf{HiQ-Lip}, a hybrid quantum-classical hierarchical method that leverages Coherent Ising Machines (CIMs) to estimate the global Lipschitz constant. We tackle the estimation by converting it into a Quadratic Unconstrained Binary Optimization (QUBO) problem and implement a multilevel graph coarsening and refinement strategy to adapt to the constraints of contemporary quantum hardware. Our experimental evaluations on fully connected neural networks demonstrate that HiQ-Lip not only provides estimates comparable to state-of-the-art methods but also significantly accelerates the computation process. In specific tests involving two-layer neural networks with 256 hidden neurons, HiQ-Lip doubles the solving speed and offers more accurate upper bounds than the existing best method, LiPopt. These findings highlight the promising utility of small-scale quantum devices in advancing the estimation of neural network robustness.
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction
Authors: Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique, Rajeev Singh
Date: 2025-03-19 00:00:00
Tasks: Stock Market Prediction, Time Series, Time Series Analysis, Time Series Forecasting

Financial time-series forecasting remains a challenging task due to complex temporal dependencies and market fluctuations. This study explores the potential of hybrid quantum-classical approaches to assist in financial trend prediction by leveraging quantum resources for improved feature representation and learning. A custom Quantum Neural Network (QNN) regressor is introduced, designed with a novel ansatz tailored for financial applications. Two hybrid optimization strategies are proposed: (1) a sequential approach where classical recurrent models (RNN/LSTM) extract temporal dependencies before quantum processing, and (2) a joint learning framework that optimizes classical and quantum parameters simultaneously. Systematic evaluation using TimeSeriesSplit, k-fold cross-validation, and predictive error analysis highlights the ability of these hybrid models to integrate quantum computing into financial forecasting workflows. The findings demonstrate how quantum-assisted learning can contribute to financial modeling, offering insights into the practical role of quantum resources in time-series analysis.
Partial Identification in Moment Models with Incomplete Data via Optimal Transport
Authors: Yanqin Fan, Brendan Pass, Xuetao Shi
Date: 2025-03-20 00:00:00
Tasks: Fairness

In this paper, we develop a unified approach to study partial identification of a finite-dimensional parameter defined by a moment equality model with incomplete data. We establish a novel characterization of the identified set for the true parameter in terms of a continuum of inequalities defined by optimal transport costs. For a special class of moment functions, we show that the identified set is convex, and its support function can be easily computed by solving an optimal transport problem. We demonstrate the generality and effectiveness of our approach through several running examples, including the linear projection model and two algorithmic fairness measures.
Linear programming approach to partially identified econometric models
Authors: Andrei Voronin
Date: 2025-03-19 00:00:00

Sharp bounds on partially identified parameters are often given by the values of linear programs (LPs). This paper introduces a novel estimator of the LP value. Unlike existing procedures, our estimator is root-n-consistent, pointwise in the probability measure, whenever the population LP is feasible and finite. Our estimator is valid under point-identification, over-identifying constraints, and solution multiplicity. Turning to uniformity properties, we prove that the LP value cannot be uniformly consistently estimated without restricting the set of possible distributions. We then show that our estimator achieves uniform consistency under a condition that is minimal for the existence of any such estimator. We obtain computationally efficient, asymptotically normal inference procedure with exact asymptotic coverage at any fixed probability measure. To complement our estimation results, we derive LP sharp bounds in a general identification setting. We apply our findings to estimating returns to education. To that end, we propose the conditionally monotone IV assumption (cMIV) that tightens the classical monotone IV (MIV) bounds and is testable under a mild regularity condition. Under cMIV, university education in Colombia is shown to increase the average wage by at least $5.5\%$, whereas classical conditions fail to yield an informative bound.
Testing Conditional Stochastic Dominance at Target Points
Authors: Federico A. Bugni, Ivan A. Canay, Deborah Kim
Date: 2025-03-18 00:00:00

This paper introduces a novel test for conditional stochastic dominance (CSD) at specific values of the conditioning covariates, referred to as target points. The test is relevant for analyzing income inequality, evaluating treatment effects, and studying discrimination. We propose a Kolmogorov--Smirnov-type test statistic that utilizes induced order statistics from independent samples. Notably, the test features a data-independent critical value, eliminating the need for resampling techniques such as the bootstrap. Our approach avoids kernel smoothing and parametric assumptions, instead relying on a tuning parameter to select relevant observations. We establish the asymptotic properties of our test, showing that the induced order statistics converge to independent draws from the true conditional distributions and that the test is asymptotically of level $\alpha$ under weak regularity conditions. While our results apply to both continuous and discrete data, in the discrete case, the critical value only provides a valid upper bound. To address this, we propose a refined critical value that significantly enhances power, requiring only knowledge of the support size of the distributions. Additionally, we analyze the test's behavior in the limit experiment, demonstrating that it reduces to a problem analogous to testing unconditional stochastic dominance in finite samples. This framework allows us to prove the validity of permutation-based tests for stochastic dominance when the random variables are continuous. Monte Carlo simulations confirm the strong finite-sample performance of our method.
The Impact Of Industrial Production On Economic Growth: New Empirical Evidence For Turkiye In A Material Development Framework
Authors: Ali Doğdu, Murad Kayacan
Date: 2025-03-20 00:00:00

There are different views in the literature regarding economic growth and development. Growth and development hypotheses have been addressed from many different perspectives. It is aimed to examine the view of production with the Heavy Industry Initiative proposed by Prof. Dr. Necmettin Erbakan a crucial part of Material Development in T\"urkiye. In this context, an examination has been conducted on GDP, Mining, Manufacturing, Energy, and Chemical Industry Productions in T\"urkiye between 1995-2023, focusing on percentage changes. The stationarity of our data set has been checked, and a VAR estimation has been performed by determining the lag length. Long-term relationships have been identified through the Johansen Cointegration test, and after confirming that there are no issues of autocorrelation and changing variance, variance decomposition has been carried out. Thus, inferences have been made regarding the effects of the identified sectors on GDP. Based on the analysis results, it has been observed that there is a stronger relationship between GDP and the Manufacturing Sector compared to the Mining, Energy, and Chemical Sectors, and it has been concluded that sectoral shocks diminish over time. Additionally, VAR Impulse-Response Analysis has been conducted among the variables. In this context, it has been observed that the variables in the dataset have an impact on each other in both the short and long term. It has been concluded that the shocks were more pronounced in the initial periods, while the economy reached a balance over time. In this regard, it has been confirmed that the heavy industry initiative exhibited different responses sectorally, while also being significant in terms of economic growth and development.
Income Inequality, Food Aid, and 'Zero Hunger': Evaluating Effectiveness During Lula's Administration
Authors: Bo Wu
Date: 2025-03-20 00:00:00
Tasks: Descriptive

Income inequality has been an important social issue that has attracted widespread attention. Taking the Zero Hunger Program in Brazil as a case study, this study analyzes the impact of policy changes on the income distribution of the Brazilian population during the implementation of the program using a breakpoint regression approach. The data for the study come from a variety of sources, including the Brazilian Ministry of Development, Social Assistance and the Family, and are analyzed using detailed descriptive statistics from the CEIC Brazil In-Depth Database. The results of the study show that during the Lula administration, Brazil's Zero Hunger Program substantially reduced income inequality, provided more substantial income security for the poor, and reduced the income gap between the rich and the poor. In terms of gender differences, the program led to a larger increase in the income of the male labor force at the right age compared to the female labor force, further highlighting the positive impact of the policy on the male labor force. These results are further confirmed by sensitivity analysis and provide useful lessons for subsequent policy formulation. This study's deep dive into the effects of the Zero Hunger program provides a valuable contribution to academic social science research and policy development.
The Striking Impact of Natural Hazard Risk on Global Green Hydrogen Cost
Authors: Maximilian Stargardt, Justus Hugenberg, Christoph Winkler, Heidi Heinrichs, Jochen Linßen, Detlef Stolten
Date: 2025-03-20 00:00:00

Due to climate change, natural hazards that affect energy infrastructure will become more frequent in the future. However, to incorporate natural hazard risk into infrastructure investment decisions, we develop an approach to translate this risk into discount rates. Thus, our newly developed discount rate approach incorporates both economic risk and natural hazard risk. To illustrate the impact of including the risk of natural hazards, we apply country-specific discount rates for hydrogen production costs. The country-specific relative difference in hydrogen generation cost ranges from a 96% surplus in the Philippines to a -63% cost reduction in Kyrgyzstan compared to a discount rate that only consists of economic risks. The inclusion of natural hazard risk changes the cost ranking of technologies as outcome of energy system models and thus policy recommendations. The derived discount rates for 254 countries worldwide are published in this publication for further use.
Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers
Authors: Gaurab Aryal, Anirban Chattopadhyaya, Federico Ciliberto
Date: 2025-03-20 00:00:00

We propose an ensemble approach to evaluate mergers that combines retrospective and prospective modeling for a more reliable analysis. We begin with a retrospective analysis based on an event study of three major U.S. airline mergers and document the fragility of the findings. We then develop a structural model that nests the event study in its reduced form, clarifying the implicit assumptions in retrospective analyses that create this fragility while separating efficiency gains from changes in firms' conduct. Using only the pre-merger data, we develop a regression-based approach that leverages exogenous changes in market structure to forecast prices after the merger. Finally, we implement structural merger analysis and show how estimates from all approaches can be synthesized for comprehensive evaluation. This methodological integration uncovers a fundamental tension: merger-induced efficiency gains were limited or, if significant, offset by increased coordination among remaining firms.
A Study on the Impact of Environmental Liability Insurance on Industrial Carbon Emissions
Authors: Bo Wu
Date: 2025-03-19 00:00:00

In order to explore whether environmental liability insurance has an important impact on industrial emission reduction, this paper selects provincial (city) level panel data from 2010 to 2020 and constructs a two-way fixed effect model to analyze the impact of environmental liability insurance on carbon emissions from both direct and indirect levels. The empirical analysis results show that: at the direct level, the development of environmental liability insurance has the effect of reducing industrial carbon emissions, and its effect is heterogeneous. At the indirect level, the role of environmental liability insurance is weaker in areas with developed financial industry and underdeveloped financial industry. Further heterogeneity analysis shows that in the industrial developed areas, the effect of environmental liability insurance on carbon emissions is more obvious. Based on this, countermeasures and suggestions are put forward from the aspects of expanding the coverage of environmental liability insurance, innovating the development of environmental liability insurance and improving the level of industrialization.
Are Elites Meritocratic and Efficiency-Seeking? Evidence from MBA Students
Authors: Marcel Preuss, Germán Reyes, Jason Somerville, Joy Wu
Date: 2025-03-19 00:00:00
Tasks: Fairness

Elites disproportionately influence policymaking, yet little is known about their fairness and efficiency preferences -- key determinants of individuals' support for redistributive policies. We investigate these preferences using an incentivized lab experiment with a group of future elites -- Ivy League MBA students. We find that MBA students implement substantially more unequal earnings distributions than the average American, regardless of whether inequality stems from luck or merit. Their redistributive choices are also highly responsive to efficiency costs, with an elasticity an order of magnitude larger than that found in representative US samples. Analyzing fairness ideals, we find that MBA students are less likely to be strict meritocrats than the general public. These findings provide novel insights into how elites' redistributive preferences may shape high levels of inequality and limited redistributive policy in the United States.