pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Spatio-Temporal Resolution of Process-Based Life Cycle Analysis with Model-Based Systems Engineering \& Hetero-functional Graph Theory
Authors: Niraj Gohil, Nawshad Haque, Amgad Elgowainy, Amro M. Farid
Date: 2025-05-30 00:00:00

Life cycle analysis (LCA) has emerged as a vital tool for assessing the environmental impacts of products, processes, and systems throughout their entire lifecycle. It provides a systematic approach to quantifying resource consumption, emissions, and waste, enabling industries, researchers, and policymakers to identify hotspots for sustainability improvements. By providing a comprehensive assessment of systems, from raw material extraction to end-of-life disposal, LCA facilitates the development of environmentally sound strategies, thereby contributing significantly to sustainable engineering and informed decision-making. Despite its strengths and ubiquitous use, life cycle analysis has not been reconciled with the broader literature in model-based systems engineering and analysis, thus hindering its integration into the design of complex systems more generally. This lack of reconciliation poses a significant problem, as it hinders the seamless integration of environmental sustainability into the design and optimization of complex systems. Without alignment between life cycle analysis (LCA) and model-based systems engineering (MBSE), sustainability remains an isolated consideration rather than an inherent part of the system's architecture and design. The original contribution of this paper is twofold. First, the paper reconciles process-based life cycle analysis with the broader literature and vocabulary of model-based systems engineering and hetero-functional graph theory. It ultimately proves that model-based systems engineering and hetero-functional graph theory are a formal generalization of process-based life cycle analysis. Secondly, the paper demonstrates how model-based systems engineering and hetero-functional graph theory may be used to enhance the spatio-temporal resolution of process-based life cycle analysis in a manner that aligns with system design objectives.
MRDust: Wireless Implant Data Uplink & Localization via Magnetic Resonance Image Modulation
Authors: Biqi Rebekah Zhao, Alexander Chou, Robert Peltekov, Elad Alon, Chunlei Liu, Rikky Muller, Michael Lustig
Date: 2025-05-30 00:00:00
Tasks: Image Registration

Magnetic resonance imaging (MRI) exhibits rich and clinically useful endogenous contrast mechanisms, which can differentiate soft tissues and are sensitive to flow, diffusion, magnetic susceptibility, blood oxygenation level, and more. However, MRI sensitivity is ultimately constrained by Nuclear Magnetic Resonance (NMR) physics, and its spatiotemporal resolution is limited by SNR and spatial encoding. On the other hand, miniaturized implantable sensors offer highly localized physiological information, yet communication and localization can be challenging when multiple implants are present. This paper introduces the MRDust, an active ``contrast agent" that integrates active sensor implants with MRI, enabling the direct encoding of highly localized physiological data into MR images to augment the anatomical images. MRDust employs a micrometer-scale on-chip coil to actively modulate the local magnetic field, enabling MR signal amplitude and phase modulation for digital data transmission. Since MRI inherently captures the anatomical tissue structure, this method has the potential to enable simultaneous data communication, localization, and image registration with multiple implants. This paper presents the underlying physical principles, design tradeoffs, and design methodology for this approach. To validate the concept, a 900 $\times$ 990 $\mu$m$^2$ chip was designed using TSMC 28 nm technology, with an on-chip coil measuring 630 $\mu$m in diameter. The chip was tested with custom hardware in an MR750W GE3T MRI scanner. Successful voxel amplitude modulation is demonstrated with Spin-Echo Echo-Planar-Imaging (SE-EPI) sequence, achieving a contrast-to-noise ratio (CNR) of 25.58 with a power consumption of 130 $\mu$W.
Input-Power-to-State Stability of Time-Varying Systems
Authors: Hernan Haimovich, Shenyu Liu, Antonio Russo, Jose L. Mancilla-Aguilar
Date: 2025-05-30 00:00:00
Tasks: Form

When the state of a system may remain bounded even if both the input amplitude and energy are unbounded, then the state bounds given by the standard input-to-state stability (ISS) and integral-ISS (iISS) properties may provide no useful information. This paper considers an ISS-related concept suitable in such a case: input-power-to-state stability (IPSS). Necessary and sufficient conditions for IPSS are developed for time-varying systems under very mild assumptions on the dynamics. More precisely, it is shown that (a) the existence of a dissipation-form ISS-Lyapunov function implies IPSS, but not necessarily that of an implication-form one, (b) iISS with exponential class-$\KL$ function implies IPSS, and (c) ISS and stronger assumptions on the dynamics imply the existence of a dissipation-form ISS-Lyapunov function and hence IPSS. The latter result is based on a converse Lyapunov theorem for time-varying systems whose dynamics (i.e. state derivative) is not necessarily continuous with respect to time.
Robust Distribution Network Reconfiguration Using Mapping-based Column-and-Constraint Generation
Authors: Runjie Zhang, Kaiping Qu, Changhong Zhao, Wanjun Huang
Date: 2025-05-30 00:00:00

The integration of intermittent renewable energy sources into distribution networks introduces significant uncertainties and fluctuations, challenging their operational security, stability, and efficiency. This paper considers robust distribution network reconfiguration (RDNR) with renewable generator resizing, modeled as a two-stage robust optimization (RO) problem with decision-dependent uncertainty (DDU). Our model optimizes resizing decisions as the upper bounds of renewable generator outputs, while also optimizing the network topology. We design a mapping-based column-and-constraint generation (C&CG) algorithm to address the computational challenges raised by DDU. Sensitivity analyses further explore the impact of uncertainty set parameters on optimal solutions. Case studies demonstrate the effectiveness of the proposed algorithm in reducing computational complexity while ensuring solution optimality.
2D PZT MEMS Resonant Scanner Using a Three-Mask Process
Authors: Mehrdad Khodapanahandeh, Parviz Zolfaghari, Hakan Urey
Date: 2025-05-30 00:00:00

This work presents the design, simulation, fabrication, and characterization of a novel architectural compact two-dimensional (2D) resonant MEMS scanning mirror actuated by thin-film lead zirconate titanate (PZT). The device employs an innovative mechanically coupled dual-axis architecture fabricated using a three-mask process on an SOI-PZT deposited wafer, significantly reducing system complexity while achieving high performance. The scanner integrates a 1 $\times$ 1.4 mm oval mirror within a 7 $\times$ 4.7 mm die, actuated by PZT thin-film elements optimized for resonant operation at 3.6 kHz (vertical) and 54.2 kHz (horizontal) under 12 V$_{\mathrm{p-p}}$ periodic pulse driving. The system achieves optical scan angles of 4.8$^\circ$ and 11.5$^\circ$ in vertical and horizontal directions, respectively, with quality factors of 750 (vertical) and 1050 (horizontal). These values contribute to high scanning bandwidth-efficiency products of 24.2 deg$\cdot$mm$\cdot$kHz (vertical) and 623 deg$\cdot$mm$\cdot$kHz (horizontal), among the higher values reported for 2D PZT-MEMS scanners. Finite element analysis confirmed minimal stress and mirror deformation, and experimental validation demonstrated excellent agreement with simulation results. This architecture demonstrates the feasibility of high-resolution laser scanning, as required in applications such as OCT, LiDAR, and displays, by achieving performance levels in line with those used in such systems.
Damping LFOs: Grid Following with Power Oscillation Damping vs. Grid Forming vs. PSS
Authors: Tamojit Chakraborty, Anamitra Pal, Sam Maleki
Date: 2025-05-30 00:00:00

Low-frequency oscillations (LFOs) present a significant challenge to the stability and reliability of power systems, especially in grids with a high penetration of renewable energy sources. Traditional grid-following (GFL) inverters have proven less effective in damping such oscillations. This paper presents a GFL-power plant controller with an auxiliary power oscillation damping control for damping LFOs. This approach is compared with a traditional power system stabilizer (PSS) for a two-area power system. Next, the research is extended by deploying grid forming (GFM) controls, which by actively controlling the voltage and frequency dynamics emulate the behavior of traditional synchronous generators. The paper analyzes two GFM control strategies: virtual synchronous machine (VSM) and droop control, and demonstrates their effectiveness in damping LFOs in the test system. The simulation results reveal that the performance of the proposed GFM-VSM rivals that of the PSS and is better than the GFL-power oscillation damper.
Deception in Oligopoly Games via Adaptive Nash Seeking Systems
Authors: Michael Tang, Miroslav Krstic, Jorge Poveda
Date: 2025-05-30 00:00:00

In the theory of multi-agent systems, deception refers to the strategic manipulation of information to influence the behavior of other agents, ultimately altering the long-term dynamics of the entire system. Recently, this concept has been examined in the context of model-free Nash equilibrium seeking (NES) algorithms for noncooperative games. Specifically, it was demonstrated that players can exploit knowledge of other players' exploration signals to drive the system toward a ``deceptive" Nash equilibrium, while maintaining the stability of the closed-loop system. To extend this insight beyond the duopoly case, in this paper we conduct a comprehensive study of deception mechanisms in N-player oligopoly markets. By leveraging the structure of these games and employing stability techniques for nonlinear dynamical systems, we provide game-theoretic insights into deception and derive specialized results, including stability conditions. These results allow players to systematically adjust their NES dynamics by tuning gains and signal amplitudes, all while ensuring closed-loop stability. Additionally, we introduce novel sufficient conditions to demonstrate that the (practically) stable equilibrium point of the deceptive dynamics corresponds to a true Nash equilibrium of a different game, which we term the ``deceptive game." Our results show that, under the proposed adaptive dynamics with deception, a victim firm may develop a distorted perception of its competitors' product appeal, which could lead to setting suboptimal prices.
Controller Design for Bilinear Neural Feedback Loops
Authors: Dhruv Shah, Jorge Cortés
Date: 2025-05-30 00:00:00

This paper considers a class of bilinear systems with a neural network in the loop. These arise naturally when employing machine learning techniques to approximate general, non-affine in the input, control systems. We propose a controller design framework that combines linear fractional representations and tools from linear parameter varying control to guarantee local exponential stability of a desired equilibrium. The controller is obtained from the solution of linear matrix inequalities, which can be solved offline, making the approach suitable for online applications. The proposed methodology offers tools for stability and robustness analysis of deep neural networks interconnected with dynamical systems.
A Hetero-functional Graph Theory Perspective of Engineering Management of Mega-Projects
Authors: Amirreza Hosseini, Amro M. Farid
Date: 2025-05-29 00:00:00
Tasks: Management

Megaprojects are large-scale, complex, and one-off engineering endeavors that require significant investments from a public or private sector. Such projects generally cost more than a billion dollars, take many years to develop and construct, involve stakeholders both in the public and private sectors, and impact millions of people. Most of the extant megaproject research is concerned with understanding why the engineering management of megaprojects fails so frequently and which dimensions make them so difficult to manage, including size, uncertainty, complexity, urgency, and institutional structure \cite{denicol:2020:00}. Recently, the literature on mega-projects has advocated for a convergence of the engineering management and production system management literature. To that end, this paper proposes the use of Model-Based System Engineering (MBSE) and Hetero-Functional Graph Theory (HFGT), where the latter, quite interestingly, finds its origins in the mass-customized production system literature. More specifically, HFGT was developed so that the physical and informatic parts of production system planning, operations, and decision-making are readily reconfigured to support production customization at scale. As the literature on megaprojects is rapidly evolving with a significant amount of divergence between authors, this report builds upon the recent and extensive megaproject literature review provided by Denicol et. al. \cite{denicol:2020:00}. The paper concludes that MBSE and HFGT provide a means for addressing many of the concluding recommendations provided by Denicol et. al. MBSE and HFGT not only align with current research on megaprojects but also push the boundaries of how the engineering management of megaprojects can gain a unified theoretical foundation.
Savage-Dickey density ratio estimation with normalizing flows for Bayesian model comparison
Authors: Kiyam Lin, Alicja Polanska, Davide Piras, Alessio Spurio Mancini, Jason D. McEwen
Date: 2025-06-04 00:00:00
Tasks: Density Ratio Estimation

A core motivation of science is to evaluate which scientific model best explains observed data. Bayesian model comparison provides a principled statistical approach to comparing scientific models and has found widespread application within cosmology and astrophysics. Calculating the Bayesian evidence is computationally challenging, especially as we continue to explore increasingly more complex models. The Savage-Dickey density ratio (SDDR) provides a method to calculate the Bayes factor (evidence ratio) between two nested models using only posterior samples from the super model. The SDDR requires the calculation of a normalised marginal distribution over the extra parameters of the super model, which has typically been performed using classical density estimators, such as histograms. Classical density estimators, however, can struggle to scale to high-dimensional settings. We introduce a neural SDDR approach using normalizing flows that can scale to settings where the super model contains a large number of extra parameters. We demonstrate the effectiveness of this neural SDDR methodology applied to both toy and realistic cosmological examples. For a field-level inference setting, we show that Bayes factors computed for a Bayesian hierarchical model (BHM) and simulation-based inference (SBI) approach are consistent, providing further validation that SBI extracts as much cosmological information from the field as the BHM approach. The SDDR estimator with normalizing flows is implemented in the open-source harmonic Python package.
Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations
Authors: Christoph Schirninger, Robert Jarolim, Astrid M. Veronig, Christoph Kuckein
Date: 2025-06-05 00:00:00
Tasks: Image-to-Image Translation

Large aperture ground based solar telescopes allow the solar atmosphere to be resolved in unprecedented detail. However, observations are limited by Earths turbulent atmosphere, requiring post image corrections. Current reconstruction methods using short exposure bursts face challenges with strong turbulence and high computational costs. We introduce a deep learning approach that reconstructs 100 short exposure images into one high quality image in real time. Using unpaired image to image translation, our model is trained on degraded bursts with speckle reconstructions as references, improving robustness and generalization. Our method shows an improved robustness in terms of perceptual quality, especially when speckle reconstructions show artifacts. An evaluation with a varying number of images per burst demonstrates that our method makes efficient use of the combined image information and achieves the best reconstructions when provided with the full image burst.
An Exploratory Framework for Future SETI Applications: Detecting Generative Reactivity via Language Models
Authors: Po-Chieh Yu
Date: 2025-06-03 00:00:00

We present an exploratory framework to test whether noise-like input can induce structured responses in language models. Instead of assuming that extraterrestrial signals must be decoded, we evaluate whether inputs can trigger linguistic behavior in generative systems. This shifts the focus from decoding to viewing structured output as a sign of underlying regularity in the input. We tested GPT-2 small, a 117M-parameter model trained on English text, using four types of acoustic input: human speech, humpback whale vocalizations, Phylloscopus trochilus birdsong, and algorithmically generated white noise. All inputs were treated as noise-like, without any assumed symbolic encoding. To assess reactivity, we defined a composite score called Semantic Induction Potential (SIP), combining entropy, syntax coherence, compression gain, and repetition penalty. Results showed that whale and bird vocalizations had higher SIP scores than white noise, while human speech triggered only moderate responses. This suggests that language models may detect latent structure even in data without conventional semantics. We propose that this approach could complement traditional SETI methods, especially in cases where communicative intent is unknown. Generative reactivity may offer a different way to identify data worth closer attention.
Applying Vision Transformers on Spectral Analysis of Astronomical Objects
Authors: Luis Felipe Strano Moraes, Ignacio Becker, Pavlos Protopapas, Guillermo Cabrera-Vives
Date: 2025-05-30 00:00:00

We apply pre-trained Vision Transformers (ViTs), originally developed for image recognition, to the analysis of astronomical spectral data. By converting traditional one-dimensional spectra into two-dimensional image representations, we enable ViTs to capture both local and global spectral features through spatial self-attention. We fine-tune a ViT pretrained on ImageNet using millions of spectra from the SDSS and LAMOST surveys, represented as spectral plots. Our model is evaluated on key tasks including stellar object classification and redshift ($z$) estimation, where it demonstrates strong performance and scalability. We achieve classification accuracy higher than Support Vector Machines and Random Forests, and attain $R^2$ values comparable to AstroCLIP's spectrum encoder, even when generalizing across diverse object types. These results demonstrate the effectiveness of using pretrained vision models for spectroscopic data analysis. To our knowledge, this is the first application of ViTs to large-scale, which also leverages real spectroscopic data and does not rely on synthetic inputs.
Associative Memory and Generative Diffusion in the Zero-noise Limit
Authors: Joshua Hess, Quaid Morris
Date: 2025-06-05 00:00:00

Connections between generative diffusion and continuous-state associative memory models are studied. Morse-Smale dynamical systems are emphasized as universal approximators of gradient-based associative memory models and diffusion models as white-noise perturbed systems thereof. Universal properties of associative memory that follow from this description are described and used to characterize a generic transition from generation to memory as noise levels diminish. Structural stability inherited by Morse-Smale flows is shown to imply a notion of stability for diffusions at vanishing noise levels. Applied to one- and two-parameter families of gradients, this indicates stability at all but isolated points of associative memory learning landscapes and the learning and generation landscapes of diffusion models with gradient drift in the zero-noise limit, at which small sets of generic bifurcations characterize qualitative transitions between stable systems. Examples illustrating the characterization of these landscapes by sequences of these bifurcations are given, along with structural stability criterion for classic and modern Hopfield networks (equivalently, the attention mechanism).
Finding signatures of low-dimensional geometric landscapes in high-dimensional cell fate transitions
Authors: Maria Yampolskaya, Laertis Ikonomou, Pankaj Mehta
Date: 2025-06-04 00:00:00
Tasks: Decision Making

Multicellular organisms develop a wide variety of highly-specialized cell types. The consistency and robustness of developmental cell fate trajectories suggests that complex gene regulatory networks effectively act as low-dimensional cell fate landscapes. A complementary set of works draws on the theory of dynamical systems to argue that cell fate transitions can be categorized into universal decision-making classes. However, the theory connecting geometric landscapes and decision-making classes to high-dimensional gene expression space is still in its infancy. Here, we introduce a phenomenological model that allows us to identify gene expression signatures of decision-making classes from single-cell RNA-sequencing time-series data. Our model combines low-dimensional gradient-like dynamical systems and high-dimensional Hopfield networks to capture the interplay between cell fate, gene expression, and signaling pathways. We apply our model to the maturation of alveolar cells in mouse lungs to show that the transient appearance of a mixed alveolar type 1/type 2 state suggests the triple cusp decision-making class. We also analyze lineage-tracing data on hematopoetic differentiation and show that bipotent neutrophil-monocyte progenitors likely undergo a heteroclinic flip bifurcation. Our results suggest it is possible to identify universal decision-making classes for cell fate transitions directly from data.
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond
Authors: Xiansheng Cai, Sihan Hu, Tao Wang, Yuan Huang, Pan Zhang, Youjin Deng, Kun Chen
Date: 2025-06-04 00:00:00
Tasks: Arithmetic Reasoning, Reinforcement Learning (RL)

Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles. While artificial intelligence (AI) offers promise, its typical need for vast datasets to learn from hinders its use in these information-scarce frontiers. We introduce learning at criticality (LaC), a reinforcement learning (RL) scheme that tunes Large Language Models (LLMs) to a sharp learning transition, addressing this information scarcity. At this transition, LLMs achieve peak generalization from minimal data, exemplified by 7-digit base-7 addition -- a test of nontrivial arithmetic reasoning. To elucidate this peak, we analyze a minimal concept-network model (CoNet) designed to capture the essence of how LLMs might link tokens. Trained on a single exemplar, this model also undergoes a sharp learning transition. This transition exhibits hallmarks of a second-order phase transition, notably power-law distributed solution path lengths. At this critical point, the system maximizes a ``critical thinking pattern" crucial for generalization, enabled by the underlying scale-free exploration. This suggests LLMs reach peak performance by operating at criticality, where such explorative dynamics enable the extraction of underlying operational rules. We demonstrate LaC in quantum field theory: an 8B-parameter LLM, tuned to its critical point by LaC using a few exemplars of symbolic Matsubara sums, solves unseen, higher-order problems, significantly outperforming far larger models. LaC thus leverages critical phenomena, a physical principle, to empower AI for complex, data-sparse challenges in fundamental physics.