pwc-archive/papers-with-abstracts huggingface dataset:
Accelerating Multi-Objective Collaborative Optimization of Doped Thermoelectric Materials via Artificial Intelligence
Authors: Yuxuan Zeng, Wenhao Xie, Wei Cao, Tan Peng, Yue Hou, Ziyu Wang, Jing Shi
Date: 2025-04-11 00:00:00

The thermoelectric performance of materials exhibits complex nonlinear dependencies on both elemental types and their proportions, rendering traditional trial-and-error approaches inefficient and time-consuming for material discovery. In this work, we present a deep learning model capable of accurately predicting thermoelectric properties of doped materials directly from their chemical formulas, achieving state-of-the-art performance. To enhance interpretability, we further incorporate sensitivity analysis techniques to elucidate how physical descriptors affect the thermoelectric figure of merit (zT). Moreover, we establish a coupled framework that integrates a surrogate model with a multi-objective genetic algorithm to efficiently explore the vast compositional space for high-performance candidates. Experimental validation confirms the discovery of a novel thermoelectric material with superior $zT$ values in the medium-temperature regime.
Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
Authors: Chaojian Li, Zhifan Ye, Massimiliano Lupo Pasini, Jong Youl Choi, Cheng Wan, Yingyan Celine Lin, Prasanna Balaprakash
Date: 2025-04-10 00:00:00
Tasks: Drug Discovery, scientific discovery

Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabyte-scale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling.
Fixation and extinction in time-fluctuating spatially structured metapopulations
Authors: Matthew Asker, Mohamed Swailem, Uwe C. Täuber, Mauro Mobilia
Date: 2025-04-11 00:00:00

Bacteria evolve in volatile environments and complex spatial structures. Migration, fluctuations, and environmental variability therefore have a significant impact on the evolution of microbial populations. We consider a class of spatially explicit metapopulation models arranged as regular (circulation) graphs where wild-type and mutant cells compete in a time-fluctuating environment where demes (subpopulations) are connected by slow cell migration. The carrying capacity is the same at each deme and endlessly switches between two values associated with harsh and mild environmental conditions. When the rate of switching is neither too slow nor too fast, the dynamics is characterised by bottlenecks and the population is prone to fluctuations or extinction. We analyse how slow migration, spatial structure, and fluctuations affect the phenomena of fixation and extinction on clique, cycle, and square lattice metapopulations. When the carrying capacity remains large, bottlenecks are weak, and deme extinction can be ignored. The dynamics is thus captured by a coarse-grained description within which the probability and mean time of fixation are obtained analytically. This allows us to show that, in contrast to what happens in static environments, the fixation probability depends on the migration rate. We also show that the fixation probability and mean fixation time can exhibit a non-monotonic dependence on the switching rate. When the carrying capacity is small under harsh conditions, bottlenecks are strong, and the metapopulation evolution is shaped by the coupling of deme extinction and strain competition. This yields rich dynamical scenarios, among which we identify the best conditions to eradicate mutants without dooming the metapopulation to extinction. We offer an interpretation of these findings in the context of an idealised treatment and discuss possible generalisations of our models.
Standardization of Weighted Ranking Correlation Coefficients
Authors: Pierangelo Lombardo
Date: 2025-04-11 00:00:00

A relevant problem in statistics is defining the correlation of two rankings of a list of items. Kendall's tau and Spearman's rho are two well established correlation coefficients, characterized by a symmetric form that ensures zero expected value between two pairs of rankings randomly chosen with uniform probability. However, in recent years, several weighted versions of the original Spearman and Kendall coefficients have emerged that take into account the greater importance of top ranks compared to low ranks, which is common in many contexts. The weighting schemes break the symmetry, causing a non-zero expected value between two random rankings. This issue is very relevant, as it undermines the concept of uncorrelation between rankings. In this paper, we address this problem by proposing a standardization function $g(x)$ that maps a correlation ranking coefficient $\Gamma$ in a standard form $g(\Gamma)$ that has zero expected value, while maintaining the relevant statistical properties of $\Gamma$.
Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chambers
Authors: Edgar E. Robles, Alejando Yankelevich, Wenjie Wu, Jianming Bian, Pierre Baldi
Date: 2025-04-11 00:00:00

Liquid argon time projection chambers are often used in neutrino physics and dark-matter searches because of their high spatial resolution. The images generated by these detectors are extremely sparse, as the energy values detected by most of the detector are equal to 0, meaning that despite their high resolution, most of the detector is unused in a particular interaction. Instead of representing all of the empty detections, the interaction is usually stored as a sparse matrix, a list of detection locations paired with their energy values. Traditional machine learning methods that have been applied to particle reconstruction such as convolutional neural networks (CNNs), however, cannot operate over data stored in this way and therefore must have the matrix fully instantiated as a dense matrix. Operating on dense matrices requires a lot of memory and computation time, in contrast to directly operating on the sparse matrix. We propose a machine learning model using a point set neural network that operates over a sparse matrix, greatly improving both processing speed and accuracy over methods that instantiate the dense matrix, as well as over other methods that operate over sparse matrices. Compared to competing state-of-the-art methods, our method improves classification performance by 14%, segmentation performance by more than 22%, while taking 80% less time and using 66% less memory. Compared to state-of-the-art CNN methods, our method improves classification performance by more than 86%, segmentation performance by more than 71%, while reducing runtime by 91% and reducing memory usage by 61%.
Bayesian Reasoning Enabled by Spin-Orbit Torque Magnetic Tunnel Junctions
Authors: Yingqian Xu, Xiaohan Li, Caihua Wan, Ran Zhang, Bin He, Shiqiang Liu, Jihao Xia, Dehao Kong, Shilong Xiong, Guoqiang Yu, Xiufeng Han
Date: 2025-04-11 00:00:00
Tasks: Diagnostic

Bayesian networks play an increasingly important role in data mining, inference, and reasoning with the rapid development of artificial intelligence. In this paper, we present proof-of-concept experiments demonstrating the use of spin-orbit torque magnetic tunnel junctions (SOT-MTJs) in Bayesian network reasoning. Not only can the target probability distribution function (PDF) of a Bayesian network be precisely formulated by a conditional probability table as usual but also quantitatively parameterized by a probabilistic forward propagating neuron network. Moreover, the parameters of the network can also approach the optimum through a simple point-by point training algorithm, by leveraging which we do not need to memorize all historical data nor statistically summarize conditional probabilities behind them, significantly improving storage efficiency and economizing data pretreatment. Furthermore, we developed a simple medical diagnostic system using the SOT-MTJ as a random number generator and sampler, showcasing the application of SOT-MTJ-based Bayesian reasoning. This SOT-MTJ-based Bayesian reasoning shows great promise in the field of artificial probabilistic neural network, broadening the scope of spintronic device applications and providing an efficient and low-storage solution for complex reasoning tasks.
Cellular Development Follows the Path of Minimum Action
Authors: Rohola Zandie, Farhan Khodaee, Yufan Xia, Elazer R. Edelman
Date: 2025-04-10 00:00:00

Cellular development follows a stochastic yet rule-governed trajectory, though the underlying principles remain elusive. Here, we propose that cellular development follows paths of least action, aligning with foundational physical laws that govern dynamic systems across nature. We introduce a computational framework that takes advantage of the deep connection between the principle of least action and maximum entropy to model developmental processes using Transformers architecture. This approach enables precise quantification of entropy production, information flow curvature, and local irreversibility for developmental asymmetry in single-cell RNA sequence data. Within this unified framework, we provide interpretable metrics: entropy to capture exploration-exploitation trade-offs, curvature to assess plasticity-elasticity dynamics, and entropy production to characterize dedifferentiation and transdifferentiation. We validate our method across both single-cell and embryonic development datasets, demonstrating its ability to reveal hidden thermodynamic and informational constraints shaping cellular fate decisions.
Efficient measurement of neutral-atom qubits with matched filters
Authors: Robert M. Kent, Linipun Phuttitarn, Chaithanya Naik Mude, Swamit Tannu, Mark Saffman, Gregory Lafyatis, Daniel J. Gauthier
Date: 2025-04-10 00:00:00

Quantum computers require high-fidelity measurement of many qubits to achieve a quantum advantage. Traditional approaches suffer from readout crosstalk for a neutral-atom quantum processor with a tightly spaced array. Although classical machine learning algorithms based on convolutional neural networks can improve fidelity, they are computationally expensive, making it difficult to scale them to large qubit counts. We present two simpler and scalable machine learning algorithms that realize matched filters for the readout problem. One is a local model that focuses on a single qubit, and the other uses information from neighboring qubits in the array to prevent crosstalk among the qubits. We demonstrate error reductions of up to 32% and 43% for the site and array models, respectively, compared to a conventional Gaussian threshold approach. Additionally, our array model uses two orders of magnitude fewer trainable parameters and four orders of magnitude fewer multiplications and nonlinear function evaluations than a recent convolutional neural network approach, with only a minor (3.5%) increase in error across different readout times. Another strength of our approach is its physical interpretability: the learned filter can be visualized to provide insights into experimental imperfections. We also show that a convolutional neural network model for improved can be pruned to have 70x and 4000x fewer parameters, respectively, while maintaining similar errors. Our work shows that simple machine learning approaches can achieve high-fidelity qubit measurements while remaining scalable to systems with larger qubit counts.
All Optical Echo State Network Reservoir Computing
Authors: Ishwar S Kaushik, Peter J Ehlers, Daniel Soh
Date: 2025-04-11 00:00:00
Tasks: All

We propose an innovative design for an all-optical Echo State Network (ESN), an advanced type of reservoir computer known for its universal computational capabilities. Our design enables fully optical implementation of arbitrary ESNs, featuring complete flexibility in optical matrix multiplication and nonlinear activation. Leveraging the nonlinear characteristics of stimulated Brillouin scattering (SBS), the architecture efficiently realizes measurement-free operations crucial for reservoir computing. The approach significantly reduces computational overhead and energy consumption compared to traditional software-based methods. Comprehensive simulations validate the system's memory capacity, nonlinear processing strength, and polynomial algebra capabilities, showcasing performance comparable to software ESNs across key benchmark tasks. Our design establishes a feasible, scalable, and universally applicable framework for optical reservoir computing, suitable for diverse machine learning applications.
Generalization Bounds in Hybrid Quantum-Classical Machine Learning Models
Authors: Tongyan Wu, Amine Bentellis, Alona Sakhnenko, Jeanette Miriam Lorenz
Date: 2025-04-11 00:00:00
Tasks: Generalization Bounds, Learning Theory

Hybrid classical-quantum models aim to harness the strengths of both quantum computing and classical machine learning, but their practical potential remains poorly understood. In this work, we develop a unified mathematical framework for analyzing generalization in hybrid models, offering insight into how these systems learn from data. We establish a novel generalization bound of the form $O\big( \sqrt{\frac{T\log{T}}{N}} + \frac{\alpha}{\sqrt{N}}\big)$ for $N$ training data points, $T$ trainable quantum gates, and bounded fully-connected layers $||F|| \leq \alpha$. This bound decomposes cleanly into quantum and classical contributions, extending prior work on both components and clarifying their interaction. We apply our results to the quantum-classical convolutional neural network (QCCNN), an architecture that integrates quantum convolutional layers with classical processing. Alongside the bound, we highlight conceptual limitations of applying classical statistical learning theory in the hybrid setting and suggest promising directions for future theoretical work.
DeQompile: quantum circuit decompilation using genetic programming for explainable quantum architecture search
Authors: Shubing Xie, Aritra Sarkar, Sebastian Feld
Date: 2025-04-11 00:00:00
Tasks: Program Synthesis, Quantum Machine Learning, Symbolic Regression

Demonstrating quantum advantage using conventional quantum algorithms remains challenging on current noisy gate-based quantum computers. Automated quantum circuit synthesis via quantum machine learning has emerged as a promising solution, employing trainable parametric quantum circuits to alleviate this. The circuit ansatz in these solutions is often designed through reinforcement learning-based quantum architecture search when the domain knowledge of the problem and hardware are not effective. However, the interpretability of these synthesized circuits remains a significant bottleneck, limiting their scalability and applicability across diverse problem domains. This work addresses the challenge of explainability in quantum architecture search (QAS) by introducing a novel genetic programming-based decompiler framework for reverse-engineering high-level quantum algorithms from low-level circuit representations. The proposed approach, implemented in the open-source tool DeQompile, employs program synthesis techniques, including symbolic regression and abstract syntax tree manipulation, to distill interpretable Qiskit algorithms from quantum assembly language. Validation of benchmark algorithms demonstrates the efficacy of our tool. By integrating the decompiler with online learning frameworks, this research potentiates explainable QAS by fostering the development of generalizable and provable quantum algorithms.
Robust Tests for Factor-Augmented Regressions with an Application to the novel EA-MD Dataset
Authors: Alessandro Morico, Ovidijus Stauskas
Date: 2025-04-11 00:00:00

We present four novel tests of equal predictive accuracy and encompassing for out-of-sample forecasts based on factor-augmented regression. We extend the work of Pitarakis (2023a,b) to develop the inferential theory of predictive regressions with generated regressors which are estimated by using Common Correlated Effects (henceforth CCE) - a technique that utilizes cross-sectional averages of grouped series. It is particularly useful since large datasets of such structure are becoming increasingly popular. Under our framework, CCE-based tests are asymptotically normal and robust to overspecification of the number of factors, which is in stark contrast to existing methodologies in the CCE context. Our tests are highly applicable in practice as they accommodate for different predictor types (e.g., stationary and highly persistent factors), and remain invariant to the location of structural breaks in loadings. Extensive Monte Carlo simulations indicate that our tests exhibit excellent local power properties. Finally, we apply our tests to a novel EA-MD-QD dataset by Barigozzi et al. (2024b), which covers Euro Area as a whole and primary member countries. We demonstrate that CCE factors offer a substantial predictive power even under varying data persistence and structural breaks.
An Introduction to Double/Debiased Machine Learning
Authors: Achim Ahrens, Victor Chernozhukov, Christian Hansen, Damian Kozbur, Mark Schaffer, Thomas Wiemann
Date: 2025-04-11 00:00:00
Tasks: parameter estimation

This paper provides a practical introduction to Double/Debiased Machine Learning (DML). DML provides a general approach to performing inference about a target parameter in the presence of nuisance parameters. The aim of DML is to reduce the impact of nuisance parameter estimation on estimators of the parameter of interest. We describe DML and its two essential components: Neyman orthogonality and cross-fitting. We highlight that DML reduces functional form dependence and accommodates the use of complex data types, such as text data. We illustrate its application through three empirical examples that demonstrate DML's applicability in cross-sectional and panel settings.
SME Gender-Related Innovation: A Non-Numerical Trend Analysis Using Positive, Zero, and Negative Quantities
Authors: Nina Bočková, Barbora Volná, Mirko Dohnal
Date: 2025-04-11 00:00:00

This paper addresses gender-related aspects of innovation processes in Small and Medium Enterprises (SMEs). Classical analytical and statistical approaches often struggle with the high complexity and insufficient data typical of gender-related innovation studies. We propose a trend-based modelling framework that requires minimal information and uses non-numerical quantifiers: increasing, constant, and decreasing. This approach enables the analysis of ten-dimensional models including variables such as Gender, Product Innovation, Process Innovation, and High-Risk Tolerance. Using trend-based artificial intelligence methods, we identify 13 distinct scenarios and all possible transitions between them. This allows for the evaluation of queries like: Can exports increase while gender parameters remain constant? Two versions of the GASI trend model are presented: the original and an expert-modified version addressing critiques related to scenario transitions. The final model confirms stability and supports the assumption that "no tree grows to heaven." Trend-based modelling offers a practical, interpretable alternative for complex, data-scarce systems.
The Influence of Culture on Migration Patterns
Authors: Tomáš Evan, Eva Fišerová, Aneta Elgnerová
Date: 2025-04-11 00:00:00

UN migration data and Hofstede's six cultural dimensions make it possible to find a connection between migration patterns and culture from a longterm perspective. Migrant patterns have been studied from the perspective of both immigrants and OECD host countries. This study tests two hypotheses: first, that the number of migrants leaving for OECD countries is influenced by cultural similarities to the host country; and second, that OECD host countries are more likely to accept culturally close migrants. Both hypotheses were tested using the Mann/Whitney U test for 93 countries between 1995 and 2015. The relationship between cultural and geodesic distance also analysed. The results indicate that cultural proximity significantly influences migration patterns, although the impact varies across countries. About two/thirds of OECD countries show a positive correlation between cultural similarity and geographic proximity, with notable exceptions, such as New Zealand and Australia, which exhibit a negative correlation. Countries such as Colombia, Denmark, and Japan maintain cultural distance, even from their neighbouring countries. Migrants from wealthier countries tend to select culturally similar destinations, whereas those from poorer countries often migrate to culturally distant destinations. Approximately half of OECD countries demonstrate a statistically significant bias towards accepting culturally close migrants. The results of this study highlight the importance of a critical debate that recognises and accepts the influence of culture on migration patterns.
Enhancing knowledge retention for continual learning with domain-specific adapters and features gating
Authors: Mohamed Abbas Hedjazi, Oussama Hadjerci, Adel Hafiane
Date: 2025-04-11 00:00:00
Tasks: Continual Learning, parameter-efficient fine-tuning

Continual learning empowers models to learn from a continuous stream of data while preserving previously acquired knowledge, effectively addressing the challenge of catastrophic forgetting. In this study, we propose a new approach that integrates adapters within the self-attention mechanisms of Vision Transformers to enhance knowledge retention when sequentially adding datasets from different domains. Unlike previous methods that continue learning with only one dataset, our approach introduces domain-specific output heads and feature gating, allowing the model to maintain high accuracy on previously learned tasks while incorporating only the essential information from multiple domains. The proposed method is compared to prominent parameter-efficient fine-tuning methods in the current state of the art. The results provide evidence that our method effectively alleviates the limitations of previous works. Furthermore, we conduct a comparative analysis using three datasets, CIFAR-100, Flowers102, and DTD, each representing a distinct domain, to investigate the impact of task order on model performance. Our findings underscore the critical role of dataset sequencing in shaping learning outcomes, demonstrating that strategic ordering can significantly improve the model's ability to adapt to evolving data distributions over time while preserving the integrity of previously learned knowledge.