pwc-archive/papers-with-abstracts huggingface dataset:
Innovation-exnovation dynamics on trees and trusses
Authors: Edward D. Lee, Ernesto Ortega-Díaz
Date: 2025-02-28 00:00:00
Tasks: Diversity

Innovation and its complement exnovation describe the progression of realized possibilities from the past to the future, and the process depends on the structure of the underlying graph. For example, the phylogenetic tree represents the unique path of mutations to a single species. To a technology, paths are manifold, like a "truss." We solve for the phase diagram of a model, where a population innovates while outrunning exnovation. The dynamics progress on random graphs that capture the degree of historical contingency. Higher connectivity speeds innovation but also increases the risk of system collapse. We show how dynamics and structural connectivity conspire to unleash innovative diversity or to drive it extinct.
Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
Authors: Ira J. S. Shokar, Peter H. Haynes, Rich R. Kerswell
Date: 2025-02-28 00:00:00
Tasks: Deep Learning

We demonstrate that a deep learning emulator for chaotic systems can forecast phenomena absent from training data. Using the Kuramoto-Sivashinsky and beta-plane turbulence models, we evaluate the emulator through scenarios probing the fundamental phenomena of both systems: forecasting spontaneous relaminarisation, capturing initialisation of arbitrary chaotic states, zero-shot prediction of dynamics with parameter values outside of the training range, and characterisation of dynamical statistics from artificially restricted training datasets. Our results show that deep learning emulators can uncover emergent behaviours and rare events in complex systems by learning underlying mathematical rules, rather than merely mimicking observed patterns.
Microscopic Propagator Imaging (MPI) with Diffusion MRI
Authors: Tommaso Zajac, Gloria Menegaz, Marco Pizzolato
Date: 2025-02-28 00:00:00
Tasks: Diffusion  MRI

We propose Microscopic Propagator Imaging (MPI) as a novel method to retrieve the indices of the microscopic propagator which is the probability density function of water displacements due to diffusion within the nervous tissue microstructures. Unlike the Ensemble Average Propagator indices or the Diffusion Tensor Imaging metrics, MPI indices are independent from the mesoscopic organization of the tissue such as the presence of multiple axonal bundle directions and orientation dispersion. As a consequence, MPI indices are more specific to the volumes, sizes, and types of microstructures, like axons and cells, that are present in the tissue. Thus, changes in MPI indices can be more directly linked to alterations in the presence and integrity of microstructures themselves. The methodology behind MPI is rooted on zonal modeling of spherical harmonics, signal simulation, and machine learning regression, and is demonstrated on both synthetic and Human Diffusion MRI data.
Towards Ultimate NMR Resolution with Deep Learning
Authors: Amir Jahangiri, Tatiana Agback, Ulrika Brath, Vladislav Orekhov
Date: 2025-02-28 00:00:00
Tasks: Deep Learning

In multidimensional NMR spectroscopy, practical resolution is defined as the ability to distinguish and accurately determine signal positions against a background of overlapping peaks, thermal noise, and spectral artifacts. In the pursuit of ultimate resolution, we introduce Peak Probability Presentations ($P^3$)- a statistical spectral representation that assigns a probability to each spectral point, indicating the likelihood of a peak maximum occurring at that location. The mapping between the spectrum and $P^3$ is achieved using MR-Ai, a physics-inspired deep learning neural network architecture, designed to handle multidimensional NMR spectra. Furthermore, we demonstrate that MR-Ai enables coprocessing of multiple spectra, facilitating direct information exchange between datasets. This feature significantly enhances spectral quality, particularly in cases of highly sparse sampling. Performance of MR-Ai and high value of the $P^3$ are demonstrated on the synthetic data and spectra of Tau, MATL1, Calmodulin, and several other proteins.
Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking)
Authors: Yoonsoo Nam, Seok Hyeong Lee, Clementine C J Domine, Yeachan Park, Charles London, Wonyl Choi, Niclas Goring, Seungjai Lee
Date: 2025-02-28 00:00:00
Tasks: Position

In physics, complex systems are often simplified into minimal, solvable models that retain only the core principles. In machine learning, layerwise linear models (e.g., linear neural networks) act as simplified representations of neural network dynamics. These models follow the dynamical feedback principle, which describes how layers mutually govern and amplify each other's evolution. This principle extends beyond the simplified models, successfully explaining a wide range of dynamical phenomena in deep neural networks, including neural collapse, emergence, lazy and rich regimes, and grokking. In this position paper, we call for the use of layerwise linear models retaining the core principles of neural dynamical phenomena to accelerate the science of deep learning.
Einleitung [Introduction]
Authors: Vincent C. Müller
Date: 2025-02-28 00:00:00
Tasks: Philosophy

Hilary Putnam's biography and philosophical development reflect the history of Anglo-Saxon philosophy over the last 40 years. Putnam has influenced this history significantly for almost as long. In this introduction, the main aim is to present the context in which Putnam stands and from which his philosophical contributions can be understood. In the context of a sketch of Putnam's philosophical development, a preliminary historical classification of his work will also be attempted, even if this is not the place for a comprehensive critique or presentation: The introduction must remain at a fairly elementary level and of course cannot replace a reading of the texts. Since Putnam's work is certainly part of a rapprochement between 'analytic' and 'continental' philosophy, the introduction to the texts translated here should finally make clear what Putnam has to offer non-analytically oriented readers. Hilary Putnams Biographie und philosophische Entwicklung spiegeln die Geschichte der angels\"achsischen Philosophie in den letzten 40 Jahren. Beinahe ebenso lange hat Putnam diese Geschichte wesentlich beeinflu{\ss}t. In der vorliegenden Einleitung soll vor allem der Kontext dargestellt werden, in dem Putnam steht und aus dem heraus verst\"andlich wird, was er philosophisch zu sagen hat. Im Rahmen einer Skizze von Putnams philosophischer Entwicklung soll zudem eine vorl\"aufige philosophiehistorische Einordnung versucht werden, auch wenn hier nicht der Ort f\"ur eine umfassende Kritik oder Darstellung sein kann: Die Einleitung mu{\ss} auf recht elementarem Niveau bleiben und kann eine Lekt\"ure der Texte nat\"urlich nicht ersetzen. Da Putnams Werk sicherlich Teil einer Ann\"aherung von 'analytischer' und 'kontinentaler' Philosophie ist, soll bei der Einf\"uhrung in die hier \"ubersetzten Texte schlie{\ss}lich deutlich werden, was Putnam nicht analytisch orientierten Lesern zu bieten hat.
QFAL: Quantum Federated Adversarial Learning
Authors: Walid El Maouaki, Nouhaila Innan, Alberto Marchisio, Taoufik Said, Mohamed Bennai, Muhammad Shafique
Date: 2025-02-28 00:00:00
Tasks: Adversarial Attack, Federated Learning

Quantum federated learning (QFL) merges the privacy advantages of federated systems with the computational potential of quantum neural networks (QNNs), yet its vulnerability to adversarial attacks remains poorly understood. This work pioneers the integration of adversarial training into QFL, proposing a robust framework, quantum federated adversarial learning (QFAL), where clients collaboratively defend against perturbations by combining local adversarial example generation with federated averaging (FedAvg). We systematically evaluate the interplay between three critical factors: client count (5, 10, 15), adversarial training coverage (0-100%), and adversarial attack perturbation strength (epsilon = 0.01-0.5), using the MNIST dataset. Our experimental results show that while fewer clients often yield higher clean-data accuracy, larger federations can more effectively balance accuracy and robustness when partially adversarially trained. Notably, even limited adversarial coverage (e.g., 20%-50%) can significantly improve resilience to moderate perturbations, though at the cost of reduced baseline performance. Conversely, full adversarial training (100%) may regain high clean accuracy but is vulnerable under stronger attacks. These findings underscore an inherent trade-off between robust and standard objectives, which is further complicated by quantum-specific factors. We conclude that a carefully chosen combination of client count and adversarial coverage is critical for mitigating adversarial vulnerabilities in QFL. Moreover, we highlight opportunities for future research, including adaptive adversarial training schedules, more diverse quantum encoding schemes, and personalized defense strategies to further enhance the robustness-accuracy trade-off in real-world quantum federated environments.
Quantum-aware Transformer model for state classification
Authors: Przemysław Sekuła, Michał Romaszewski, Przemysław Głomb, Michał Cholewa, Łukasz Pawela
Date: 2025-02-28 00:00:00
Tasks: Classification, model

Entanglement is a fundamental feature of quantum mechanics, playing a crucial role in quantum information processing. However, classifying entangled states, particularly in the mixed-state regime, remains a challenging problem, especially as system dimensions increase. In this work, we focus on bipartite quantum states and present a data-driven approach to entanglement classification using transformer-based neural networks. Our dataset consists of a diverse set of bipartite states, including pure separable states, Werner entangled states, general entangled states, and maximally entangled states. We pretrain the transformer in an unsupervised fashion by masking elements of vectorized Hermitian matrix representations of quantum states, allowing the model to learn structural properties of quantum density matrices. This approach enables the model to generalize entanglement characteristics across different classes of states. Once trained, our method achieves near-perfect classification accuracy, effectively distinguishing between separable and entangled states. Compared to previous Machine Learning, our method successfully adapts transformers for quantum state analysis, demonstrating their ability to systematically identify entanglement in bipartite systems. These results highlight the potential of modern machine learning techniques in automating entanglement detection and classification, bridging the gap between quantum information theory and artificial intelligence.
AutoQML: A Framework for Automated Quantum Machine Learning
Authors: Marco Roth, David A. Kreplin, Daniel Basilewitsch, João F. Bravo, Dennis Klau, Milan Marinov, Daniel Pranjic, Horst Stuehler, Moritz Willmann, Marc-André Zöller
Date: 2025-02-28 00:00:00
Tasks: AutoML, Hyperparameter Optimization, Quantum Machine Learning

Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum computing. However, the complexity of QML presents substantial entry barriers. We introduce \emph{AutoQML}, a novel framework that adapts the AutoML approach to QML, providing a modular and unified programming interface to facilitate the development of QML pipelines. AutoQML leverages the QML library sQUlearn to support a variety of QML algorithms. The framework is capable of constructing end-to-end pipelines for supervised learning tasks, ensuring accessibility and efficacy. We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines that are competitive with both classical ML models and manually crafted quantum solutions.
Structural breaks detection and variable selection in dynamic linear regression via the Iterative Fused LASSO in high dimension
Authors: Angelo Milfont, Alvaro Veiga
Date: 2025-02-28 00:00:00
Tasks: Variable Selection

We aim to develop a time series modeling methodology tailored to high-dimensional environments, addressing two critical challenges: variable selection from a large pool of candidates, and the detection of structural break points, where the model's parameters shift. This effort centers on formulating a least squares estimation problem with regularization constraints, drawing on techniques such as Fused LASSO and AdaLASSO, which are well-established in machine learning. Our primary achievement is the creation of an efficient algorithm capable of handling high-dimensional cases within practical time limits. By addressing these pivotal challenges, our methodology holds the potential for widespread adoption. To validate its effectiveness, we detail the iterative algorithm and benchmark its performance against the widely recognized Path Algorithm for Generalized Lasso. Comprehensive simulations and performance analyses highlight the algorithm's strengths. Additionally, we demonstrate the methodology's applicability and robustness through simulated case studies and a real-world example involving a stock portfolio dataset. These examples underscore the methodology's practical utility and potential impact across diverse high-dimensional settings.
Solar prosumage under different pricing regimes: Interactions with the transmission grid
Authors: Dana Kirchem, Mario Kendziorski, Enno Wiebrow, Wolf-Peter Schill, Claudia Kemfert, Christian von Hirschhausen
Date: 2025-02-28 00:00:00

Solar prosumers, residential electricity consumers equipped with photovoltaic (PV) systems and battery storage, are transforming electricity markets. Their interactions with the transmission grid under varying tariff designs are not yet fully understood. We explore the influence of different pricing regimes on prosumer investment and dispatch decisions and their subsequent impact on the transmission grid. Using an integrated modeling approach that combines two open-source dispatch, investment and grid models, we simulate prosumage behavior in Germany's electricity market under real-time pricing or time-invariant pricing, as well as under zonal or nodal pricing. Our findings show that zonal pricing favors prosumer investments, while time-invariant pricing rather hinders it. In comparison, regional solar availability emerges as a larger driver for rooftop PV investments. The impact of prosumer strategies on grid congestion remains limited within the scope of our model-setup, in which home batteries cannot be used for energy arbitrage.
Tracks to Modernity: Railroads, Growth, and Social Movements in Denmark
Authors: Tom Görges, Magnus Ørberg Rove, Paul Sharp, Christian Vedel
Date: 2025-02-28 00:00:00

How do transport infrastructures shape economic transformation and social change? We examine the impact of railway expansion in nineteenth-century Denmark on local population growth, occupational shifts, and the diffusion of ideas. Using a historical panel dataset and a difference-in-differences approach, we document that railway access significantly increased population growth and accelerated structural change. Moreover, railway-connected areas were more likely to establish key institutions linked to civic engagement and the cooperative movement. These findings suggest that improved market access was not only a driver of economic modernization but also a catalyst for institutional and cultural transformation.
Retrieval Augmented Generation for Topic Modeling in Organizational Research: An Introduction with Empirical Demonstration
Authors: Gerion Spielberger, Florian M. Artinger, Jochen Reb, Rudolf Kerschreiter
Date: 2025-02-28 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Analyzing textual data is the cornerstone of qualitative research. While traditional methods such as grounded theory and content analysis are widely used, they are labor-intensive and time-consuming. Topic modeling offers an automated complement. Yet, existing approaches, including LLM-based topic modeling, still struggle with issues such as high data preprocessing requirements, interpretability, and reliability. This paper introduces Agentic Retrieval-Augmented Generation (Agentic RAG) as a method for topic modeling with LLMs. It integrates three key components: (1) retrieval, enabling automatized access to external data beyond an LLM's pre-trained knowledge; (2) generation, leveraging LLM capabilities for text synthesis; and (3) agent-driven learning, iteratively refining retrieval and query formulation processes. To empirically validate Agentic RAG for topic modeling, we reanalyze a Twitter/X dataset, previously examined by Mu et al. (2024a). Our findings demonstrate that the approach is more efficient, interpretable and at the same time achieves higher reliability and validity in comparison to the standard machine learning approach but also in comparison to LLM prompting for topic modeling. These results highlight Agentic RAG's ability to generate semantically relevant and reproducible topics, positioning it as a robust, scalable, and transparent alternative for AI-driven qualitative research in leadership, managerial, and organizational research.
Natural Asset Beta
Authors: Daniel Grainger
Date: 2025-02-28 00:00:00

Natural capital accounting is important to efforts that attempt to measure the value of nature to decide on how best to trade-off natural resource productive use and conservation. Much work on measuring reciprocal physical stocks and flows of natural resources between nature and the economy has occurred. However, the current open problem of translating natural resource physical stocks and flows into monetary ones presents a barrier to estimating opportunity costs to inform resource allocation decisions. The research reported here extends a theoretical model on economic sustainability and derives a novel asset beta notion for nature. Like traditional firm asset beta notions, this underpins corporate finance notions for nature. Supply and demand curves estimating the exchange value of the natural resource are possible. A real-world data set is utilised for a preliminary proof of concept test. The results show promise, but future research is suggested investigating the sensitivity of the estimates.
Network effects and incumbent response to entry threats: empirical evidence from the airline industry
Authors: Steve Lawford
Date: 2025-02-26 00:00:00

I investigate how incumbents in the U.S. airline industry respond to threatened and actual route entry by Southwest Airlines. I use a two-way fixed effects and event study approach, and the latest available data from 1999-2022, to identify a firm's price and quantity response. I find evidence that incumbents cut fares preemptively (post-entry) by 6-8% (16-18%) although the significance, pattern, and timing of the preemptive cuts are quite different to Goolsbee and Syverson's (2008) earlier results. Incumbents increase capacity preemptively by 10-40%, up to six quarters before the entry threat is established, and by 27-46% post-entry. My results suggest a clear shift in firms' strategic response from price to quantity. I also investigate the impact of an incumbent's network structure on its preemptive and post-entry behaviour. While the results on price are unclear, a firm's post-entry capacity reaction depends strongly on its global network structure as well as the local importance (centrality) of the route.
The Luce Model, Regularity, and Choice Overload
Authors: Daniele Caliari, Henrik Petri
Date: 2025-02-28 00:00:00
Tasks: model

We characterize regularity (Block & Marschak, 1960) within a novel stochastic model: the General Threshold Luce model [GTLM]. We apply our results to study choice overload, identified by regularity violations that impose a welfare cost on the decision-maker. Generalizing our characterization results, we identify necessary and sufficient conditions for choice overload within GTLMs and, in doing so, disentangle two well-known causes: low discriminatory power (Frick, 2016) and limited attention (Lleras et al., 2017).
TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction
Authors: Tatiana A. Bubba, Matteo Santacesaria, Andrea Sebastiani
Date: 2025-02-28 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction

Deep learning has emerged as a powerful tool for solving inverse problems in imaging, including computed tomography (CT). However, most approaches require paired training data with ground truth images, which can be difficult to obtain, e.g., in medical applications. We present TomoSelfDEQ, a self-supervised Deep Equilibrium (DEQ) framework for sparse-angle CT reconstruction that trains directly on undersampled measurements. We establish theoretical guarantees showing that, under suitable assumptions, our self-supervised updates match those of fully-supervised training with a loss including the (possibly non-unitary) forward operator like the CT forward map. Numerical experiments on sparse-angle CT data confirm this finding, also demonstrating that TomoSelfDEQ outperforms existing self-supervised methods, achieving state-of-the-art results with as few as 16 projection angles.
AutoComb: Automated Comb Sign Detector for 3D CTE Scans
Authors: Shashwat Gupta, Sarthak Gupta, Akshan Agrawal, Mahim Naaz, Rajanikanth Yadav, Priyanka Bagade
Date: 2025-02-28 00:00:00
Tasks: Diagnostic

Comb Sign is an important imaging biomarker to detect multiple gastrointestinal diseases. It shows up as increased blood flow along the intestinal wall indicating potential abnormality, which helps doctors diagnose inflammatory conditions. Despite its clinical significance, current detection methods are manual, time-intensive, and prone to subjective interpretation due to the need for multi-planar image-orientation. To the best of our knowledge, we are the first to propose a fully automated technique for the detection of Comb Sign from CTE scans. Our novel approach is based on developing a probabilistic map that shows areas of pathological hypervascularity by identifying fine vascular bifurcations and wall enhancement via processing through stepwise algorithmic modules. These modules include utilising deep learning segmentation model, a Gaussian Mixture Model (GMM), vessel extraction using vesselness filter, iterative probabilistic enhancement of vesselness via neighborhood maximization and a distance-based weighting scheme over the vessels. Experimental results demonstrate that our pipeline effectively identifies Comb Sign, offering an objective, accurate, and reliable tool to enhance diagnostic accuracy in Crohn's disease and related hypervascular conditions where Comb Sign is considered as one of the important biomarkers.