pwc-archive/papers-with-abstracts huggingface dataset:
Micro-Ring Perceptron Sensor for High-Speed, Low-Power Radio-Frequency Signal
Authors: Bo-Han Wu, Shi-Yuan Ma, Sri Krishna Vadlamani, Hyeongrak Choi, Dirk Englund
Date: 2025-04-19 00:00:00

Radio-frequency (RF) sensing enables long-range, high-resolution detection for applications such as radar and wireless communication. RF photonic sensing mitigates the bandwidth limitations and high transmission losses of electronic systems by transducing the detected RF signals into broadband optical carriers. However, these sensing systems remain limited by detector noise and Nyquist rate sampling with analog-to-digital converters, particularly under low-power and high-data rate conditions. To overcome these limitations, we introduce the micro-ring perceptron (MiRP) sensor, a physics-inspired AI framework that integrates the micro-ring (MiR) dynamics-based analog processor with a machine-learning-driven digital backend. By embedding the nonlinear optical dynamics of MiRs into an end-to-end architecture, MiRP sensing maps the input signal into a learned feature space for the subsequent digital neural network. The trick is to encode the entire temporal structure of the incoming signal into each output sample in order to enable effectively sub-Nyquist sampling without loss of task-relevant information. Evaluations of three target classification datasets demonstrate the performance advantages of MiRP sensing. For example, on MNIST, MiRP detection achieves $94\pm0.1$\% accuracy at $1/49$ the Nyquist rate at the input RF signal of $1$~ pW, compared to $11\pm0.4$\% for the conventional RF detection method. Thus, our sensor framework provides a robust and efficient solution for the detection of low-power and high-speed signals in real-world sensing applications.
Plasma State Monitoring and Disruption Characterization using Multimodal VAEs
Authors: Yoeri Poels, Alessandro Pau, Christian Donner, Giulio Romanelli, Olivier Sauter, Cristina Venturini, Vlado Menkovski, The TCV Team, the WPTE team
Date: 2025-04-24 00:00:00
Tasks: counterfactual, Diagnostic

When a plasma disrupts in a tokamak, significant heat and electromagnetic loads are deposited onto the surrounding device components. These forces scale with plasma current and magnetic field strength, making disruptions one of the key challenges for future devices. Unfortunately, disruptions are not fully understood, with many different underlying causes that are difficult to anticipate. Data-driven models have shown success in predicting them, but they only provide limited interpretability. On the other hand, large-scale statistical analyses have been a great asset to understanding disruptive patterns. In this paper, we leverage data-driven methods to find an interpretable representation of the plasma state for disruption characterization. Specifically, we use a latent variable model to represent diagnostic measurements as a low-dimensional, latent representation. We build upon the Variational Autoencoder (VAE) framework, and extend it for (1) continuous projections of plasma trajectories; (2) a multimodal structure to separate operating regimes; and (3) separation with respect to disruptive regimes. Subsequently, we can identify continuous indicators for the disruption rate and the disruptivity based on statistical properties of measurement data. The proposed method is demonstrated using a dataset of approximately 1600 TCV discharges, selecting for flat-top disruptions or regular terminations. We evaluate the method with respect to (1) the identified disruption risk and its correlation with other plasma properties; (2) the ability to distinguish different types of disruptions; and (3) downstream analyses. For the latter, we conduct a demonstrative study on identifying parameters connected to disruptions using counterfactual-like analysis. Overall, the method can adequately identify distinct operating regimes characterized by varying proximity to disruptions in an interpretable manner.
DebtStreamness: An Ecological Approach to Credit Flows in Inter-Firm Networks
Authors: Anahí Rodríguez-Martínez, Silvia Bartolucci, Francesco Caravelli, Victoria Landaberry, Pierpaolo Vivo, Fabio Caccioli
Date: 2025-05-02 00:00:00

Understanding how credit flows through inter-firm networks is critical for assessing financial stability and systemic risk. In this study, we introduce DebtStreamness, a novel metric inspired by trophic levels in ecological food webs, to quantify the position of firms within credit chains. By viewing credit as the ``primary energy source'' of the economy, we measure how far credit travels through inter-firm relationships before reaching its final borrowers. Applying this framework to Uruguay's inter-firm credit network, using survey data from the Central Bank, we find that credit chains are generally short, with a tiered structure in which some firms act as intermediaries, lending to others further along the chain. We also find that local network motifs such as loops can substantially increase a firm's DebtStreamness, even when its direct borrowing from banks remains the same. Comparing our results with standard economic classifications based on input-output linkages, we find that DebtStreamness captures distinct financial structures not visible through production data. We further validate our approach using two maximum-entropy network reconstruction methods, demonstrating the robustness of DebtStreamness in capturing systemic credit structures. These results suggest that DebtStreamness offers a complementary ecological perspective on systemic credit risk and highlights the role of hidden financial intermediation in firm networks.
Metric Similarity and Manifold Learning of Circular Dichroism Spectra of Proteins
Authors: Gionni Marchetti
Date: 2025-04-27 00:00:00

We present a machine learning analysis of circular dichroism spectra of globular proteins from the SP175 database, using the optimal transport-based $1$-Wasserstein distance $\mathcal{W}_1$ (with order $p=1$) and the manifold learning algorithm $t$-SNE. Our results demonstrate that $\mathcal{W}_1$ is consistent with both Euclidean and Manhattan metrics while exhibiting robustness to noise. On the other hand, $t$-SNE uncovers meaningful structure in the high-dimensional data. The clustering in the $t$-SNE embedding is primarily determined by proteins with distinct secondary structure compositions: one cluster predominantly contains $\beta$-rich proteins, while the other consists mainly of proteins with mixed $\alpha/\beta$ and $\alpha$-helical content.
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions
Authors: Jonah Ekelund, Savvas Raptis, Vicki Toy-Edens, Wenli Mo, Drew L. Turner, Ian J. Cohen, Stefano Markidis
Date: 2025-04-22 00:00:00
Tasks: Event Detection, Outlier Detection, Time Series

Analyzing multi-featured time series data is critical for space missions making efficient event detection, potentially onboard, essential for automatic analysis. However, limited onboard computational resources and data downlink constraints necessitate robust methods for identifying regions of interest in real time. This work presents an adaptive outlier detection algorithm based on the reconstruction error of Principal Component Analysis (PCA) for feature reduction, designed explicitly for space mission applications. The algorithm adapts dynamically to evolving data distributions by using Incremental PCA, enabling deployment without a predefined model for all possible conditions. A pre-scaling process normalizes each feature's magnitude while preserving relative variance within feature types. We demonstrate the algorithm's effectiveness in detecting space plasma events, such as distinct space environments, dayside and nightside transients phenomena, and transition layers through NASA's MMS mission observations. Additionally, we apply the method to NASA's THEMIS data, successfully identifying a dayside transient using onboard-available measurements.
Quantum Measurement, Entanglement and the Warping Mechanism of Human Perception
Authors: Diederik Aerts, Jonito Aerts Arguëlles, Sandro Sozzo
Date: 2025-05-01 00:00:00

We prove that the quantum measurement process contains the same warping mechanism that occurs in categorical perception, a phenomenon ubiquitous in human perception. This warping causes stimuli belonging to the same category to be perceived as more similar, while stimuli belonging to different categories are perceived as more different. As a result of a detailed study of the quantum measurement using the Bloch representation, we identify the natural metric for pure states, namely the Fubini Study metric, and the natural metric for density states, namely the trace class metric. The warping mechanism of categorical perception is then manifested, when the distances between pure states, playing the role of stimuli for the quantum measurement, are warped into the distances between density states, playing the role of percepts for quantum measurement. We work out the example of a two-dimensional quantum model, a qubit, with 'light' and 'dark' as the two eigenstates, and show how the typical contraction and dilation warping of human perception manifest themselves in this example of the quantum measurement model of light and dark.
Learning to Learn with Quantum Optimization via Quantum Neural Networks
Authors: Kuan-Cheng Chen, Hiromichi Matsuyama, Wei-Hao Huang
Date: 2025-05-01 00:00:00
Tasks: Combinatorial Optimization, Meta-Learning

Quantum Approximate Optimization Algorithms (QAOA) promise efficient solutions to classically intractable combinatorial optimization problems by harnessing shallow-depth quantum circuits. Yet, their performance and scalability often hinge on effective parameter optimization, which remains nontrivial due to rugged energy landscapes and hardware noise. In this work, we introduce a quantum meta-learning framework that combines quantum neural networks, specifically Quantum Long Short-Term Memory (QLSTM) architectures, with QAOA. By training the QLSTM optimizer on smaller graph instances, our approach rapidly generalizes to larger, more complex problems, substantially reducing the number of iterations required for convergence. Through comprehensive benchmarks on Max-Cut and Sherrington-Kirkpatrick model instances, we demonstrate that QLSTM-based optimizers converge faster and achieve higher approximation ratios compared to classical baselines, thereby offering a robust pathway toward scalable quantum optimization in the NISQ era.
Efficient Quantum-Safe Homomorphic Encryption for Quantum Computer Programs
Authors: Ben Goertzel
Date: 2025-04-30 00:00:00

We present a lattice-based scheme for homomorphic evaluation of quantum programs and proofs that remains secure against quantum adversaries. Classical homomorphic encryption is lifted to the quantum setting by replacing composite-order groups with Module Learning-With-Errors (MLWE) lattices and by generalizing polynomial functors to bounded natural super functors (BNSFs). A secret depolarizing BNSF mask hides amplitudes, while each quantum state is stored as an MLWE ciphertext pair. We formalize security with the qIND-CPA game that allows coherent access to the encryption oracle and give a four-hybrid reduction to decisional MLWE. The design also covers practical issues usually left open. A typed QC-bridge keeps classical bits produced by measurements encrypted yet still usable as controls, with weak-measurement semantics for expectation-value workloads. Encrypted Pauli twirls add circuit privacy. If a fixed knowledge base is needed, its axioms are shipped as MLWE "capsules"; the evaluator can use them but cannot read them. A rho-calculus driver schedules encrypted tasks across several QPUs and records an auditable trace on an RChain-style ledger. Performance analysis shows that the extra lattice arithmetic fits inside today's QPU idle windows: a 100-qubit, depth-10^3 teleportation-based proof runs in about 10 ms, the public key (seed only) is 32 bytes, and even a CCA-level key stays below 300 kB. A photonic Dirac-3 prototype that executes homomorphic teleportation plus knowledge-base-relative amplitude checks appears feasible with current hardware. These results indicate that fully homomorphic, knowledge-base-aware quantum reasoning is compatible with near-term quantum clouds and standard post-quantum security assumptions.
QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks
Authors: Hanjing Xu, Xiaoyuan Liu, Alex Pothen, Ilya Safro
Date: 2025-04-29 00:00:00
Tasks: Combinatorial Optimization, Graph Attention

The quantum approximate optimization algorithm (QAOA) is one of the promising variational approaches of quantum computing to solve combinatorial optimization problems. In QAOA, variational parameters need to be optimized by solving a series of nonlinear, nonconvex optimization programs. In this work, we propose a QAOA parameter transfer scheme using Graph Attention Networks (GAT) to solve Maximum Independent Set (MIS) problems. We prepare optimized parameters for graphs of 12 and 14 vertices and use GATs to transfer their parameters to larger graphs. Additionally, we design a hybrid distributed resource-aware algorithm for MIS (HyDRA-MIS), which decomposes large problems into smaller ones that can fit onto noisy intermediate-scale quantum (NISQ) computers. We integrate our GAT-based parameter transfer approach to HyDRA-MIS and demonstrate competitive results compared to KaMIS, a state-of-the-art classical MIS solver, on graphs with several thousands vertices.
Asset Pricing in Pre-trained Transformer
Authors: Shanyan Lai
Date: 2025-05-02 00:00:00

This paper proposes an innovative Transformer model, Single-directional representative from Transformer (SERT), for US large capital stock pricing. It also innovatively applies the pre-trained Transformer models under the stock pricing and factor investment context. They are compared with standard Transformer models and encoder-only Transformer models in three periods covering the entire COVID-19 pandemic to examine the model adaptivity and suitability during the extreme market fluctuations. Namely, pre-COVID-19 period (mild up-trend), COVID-19 period (sharp up-trend with deep down shock) and 1-year post-COVID-19 (high fluctuation sideways movement). The best proposed SERT model achieves the highest out-of-sample R2, 11.2% and 10.91% respectively, when extreme market fluctuation takes place followed by pre-trained Transformer models (10.38% and 9.15%). Their Trend-following-based strategy wise performance also proves their excellent capability for hedging downside risks during market shocks. The proposed SERT model achieves a Sortino ratio 47% higher than the buy-and-hold benchmark in the equal-weighted portfolio and 28% higher in the value-weighted portfolio when the pandemic period is attended. It proves that Transformer models have a great capability to capture patterns of temporal sparsity data in the asset pricing factor model, especially with considerable volatilities. We also find the softmax signal filter as the common configuration of Transformer models in alternative contexts, which only eliminates differences between models, but does not improve strategy-wise performance, while increasing attention heads improve the model performance insignificantly and applying the 'layer norm first' method do not boost the model performance in our case.
Slope Consistency of Quasi-Maximum Likelihood Estimator for Binary Choice Models
Authors: Yoosoon Chang, Joon Y. Park, Guo Yan
Date: 2025-05-05 00:00:00

This paper revisits the slope consistency of QMLE for binary choice models. Ruud (1983, \emph{Econometrica}) introduced a set of conditions under which QMLE may yield a constant multiple of the slope coefficient of binary choice models asymptotically. However, he did not fully establish slope consistency of QMLE, which requires the existence of a positive multiple of slope coefficient identified as an interior maximizer of the population QMLE likelihood function over an appropriately restricted parameter space. We fill this gap by providing a formal proof for slope consistency under the same set of conditions for any binary choice model identified as in Horowitz (1992, \emph{Econometrica}). Our result implies that the logistic regression, which is used extensively in machine learning to analyze binary outcomes associated with a large number of covariates, yields a consistent estimate for the slope coefficient of binary choice models under suitable conditions.
Identification and estimation of dynamic random coefficient models
Authors: Wooyong Lee
Date: 2025-05-02 00:00:00

I study panel data linear models with predetermined regressors (such as lagged dependent variables) where coefficients are individual-specific, allowing for heterogeneity in the effects of the regressors on the dependent variable. I show that the model is not point-identified in a short panel context but rather partially identified, and I characterize the identified sets for the mean, variance, and CDF of the coefficient distribution. This characterization is general, accommodating discrete, continuous, and unbounded data, and it leads to computationally tractable estimation and inference procedures. I apply the method to study lifecycle earnings dynamics among U.S. households using the Panel Study of Income Dynamics (PSID) dataset. The results suggest substantial unobserved heterogeneity in earnings persistence, implying that households face varying levels of earnings risk which, in turn, contribute to heterogeneity in their consumption and savings behaviors.
Multiscale Causal Analysis of Market Efficiency via News Uncertainty Networks and the Financial Chaos Index
Authors: Masoud Ataei
Date: 2025-05-02 00:00:00

This study evaluates the scale-dependent informational efficiency of stock markets using the Financial Chaos Index, a tensor-eigenvalue-based measure of realized volatility. Incorporating Granger causality and network-theoretic analysis across a range of economic, policy, and news-based uncertainty indices, we assess whether public information is efficiently incorporated into asset price fluctuations. Based on a 34-year time period from 1990 to 2023, at the daily frequency, the semi-strong form of the Efficient Market Hypothesis is rejected at the 1\% level of significance, indicating that asset price changes respond predictably to lagged news-based uncertainty. In contrast, at the monthly frequency, such predictive structure largely vanishes, supporting informational efficiency at coarser temporal resolutions. A structural analysis of the Granger causality network reveals that fiscal and monetary policy uncertainties act as core initiators of systemic volatility, while peripheral indices, such as those related to healthcare and consumer prices, serve as latent bridges that become activated under crisis conditions. These findings underscore the role of time-scale decomposition and structural asymmetries in diagnosing market inefficiencies and mapping the propagation of macro-financial uncertainty.
Predicting the Price of Gold in the Financial Markets Using Hybrid Models
Authors: Mohammadhossein Rashidi, Mohammad Modarres
Date: 2025-05-02 00:00:00
Tasks: regression, Time Series, Time Series Prediction

Predicting the price that has the least error and can provide the best and highest accuracy has been one of the most challenging issues and one of the most critical concerns among capital market activists and researchers. Therefore, a model that can solve problems and provide results with high accuracy is one of the topics of interest among researchers. In this project, using time series prediction models such as ARIMA to estimate the price, variables, and indicators related to technical analysis show the behavior of traders involved in involving psychological factors for the model. By linking all of these variables to stepwise regression, we identify the best variables influencing the prediction of the variable. Finally, we enter the selected variables as inputs to the artificial neural network. In other words, we want to call this whole prediction process the "ARIMA_Stepwise Regression_Neural Network" model and try to predict the price of gold in international financial markets. This approach is expected to be able to be used to predict the types of stocks, commodities, currency pairs, financial market indicators, and other items used in local and international financial markets. Moreover, a comparison between the results of this method and time series methods is also expressed. Finally, based on the results, it can be seen that the resulting hybrid model has the highest accuracy compared to the time series method, regression, and stepwise regression.
Detecting multiple change points in linear models with heteroscedastic errors
Authors: Lajos Horvath, Gregory Rice, Yuqian Zhao
Date: 2025-05-02 00:00:00
Tasks: regression

The problem of detecting change points in the regression parameters of a linear regression model with errors and covariates exhibiting heteroscedasticity is considered. Asymptotic results for weighted functionals of the cumulative sum (CUSUM) processes of model residuals are established when the model errors are weakly dependent and non-stationary, allowing for either abrupt or smooth changes in their variance. These theoretical results illuminate how to adapt standard change point test statistics for linear models to this setting. We studied such adapted change-point tests in simulation experiments, along with a finite sample adjustment to the proposed testing procedures. The results suggest that these methods perform well in practice for detecting multiple change points in the linear model parameters and controlling the Type I error rate in the presence of heteroscedasticity. We illustrate the use of these approaches in applications to test for instability in predictive regression models and explanatory asset pricing models.
Model Checks in a Kernel Ridge Regression Framework
Authors: Yuhao Li
Date: 2025-05-02 00:00:00
Tasks: regression, valid

We propose new reproducing kernel-based tests for model checking in conditional moment restriction models. By regressing estimated residuals on kernel functions via kernel ridge regression (KRR), we obtain a coefficient function in a reproducing kernel Hilbert space (RKHS) that is zero if and only if the model is correctly specified. We introduce two classes of test statistics: (i) projection-based tests, using RKHS inner products to capture global deviations, and (ii) random location tests, evaluating the KRR estimator at randomly chosen covariate points to detect local departures. The tests are consistent against fixed alternatives and sensitive to local alternatives at the $n^{-1/2}$ rate. When nuisance parameters are estimated, Neyman orthogonality projections ensure valid inference without repeated estimation in bootstrap samples. The random location tests are interpretable and can visualize model misspecification. Simulations show strong power and size control, especially in higher dimensions, outperforming existing methods.