pwc-archive/papers-with-abstracts huggingface dataset:
A Direct State-Space Realization of Discrete-Time Linear Parameter-Varying Input-Output Models
Authors: Johan Kon, Roland Tóth, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen
Date: 2025-02-26 00:00:00

A minimal state-space (SS) realization of an identified linear parameter-varying (LPV) input-output (IO) model usually introduces dynamic and nonlinear dependency of the state-space coefficient functions, complicating stability analysis and controller synthesis. The aim of this paper is to introduce and analyze a direct SS realization of this IO model that avoids this nonlinear and dynamic dependency, at the cost of introducing a nonminimal state. It is shown that this direct SS realization 1) is reachable under a coprimeness condition on the coefficient functions of the IO model and a well-posedness condition on the model order, and 2) is never observable but that the unobservable directions converge to zero in a finite amount of steps, i.e., that the realization is reconstructible. The derived results are illustrated through numerical examples in both the LPV and LTI case.
Modelling Regional Solar Photovoltaic Capacity in Great Britain
Authors: Hussah Alghanem, Alastair Buckley
Date: 2025-02-26 00:00:00
Tasks: Benchmarking

Great Britain aims to meet growing electricity demand and achieve a fully decarbonised grid by 2035, targeting 70 GW of solar photovoltaic (PV) capacity. However, grid constraints and connection delays hinder solar integration. To address these integration challenges, various connection reform processes and policies are being developed [1]. This study supports the connection reforms with a model that estimates regional PV capacity at the NUTS 3 level, explaining 89% of the variation in capacity, with a mean absolute error of 20 MW and a national mean absolute percentage error of 5.4%. Artificial surfaces and agricultural areas are identified as key factors in deployment. The model has three primary applications: disaggregating national PV capacity into regional capacity, benchmarking regional PV deployment between different regions, and forecasting future PV capacity distribution. These applications support grid operators in generation monitoring and strategic grid planning by identifying regions where capacity is likely to be concentrated. This can address grid connection delays, plan network expansions, and resolve land-use conflicts.
Polarization Angle Scanning for Wide-band Millimeter-wave Direct Detection
Authors: Heyao Wang, Ziran Zhao, Lingbo Qiao, Dalu Guo
Date: 2025-02-26 00:00:00

Millimeter-wave (MMW) technology has been widely utilized in human security screening applications due to its superior penetration capabilities through clothing and safety for human exposure. However, existing methods largely rely on fixed polarization modes, neglecting the potential insights from variations in target echoes with respect to incident polarization. This study provides a theoretical analysis of the cross-polarization echo power as a function of the incident polarization angle under linear polarization conditions. Additionally, based on the transmission characteristics of multi-layer medium, we extended the depth spectrum model employed in direct detection to accommodate scenarios involving multi-layered structures. Building on this foundation, by obtaining multiple depth spectrums through polarization angle scanning, we propose the Polarization Angle-Depth Matrix to characterize target across both the polarization angle and depth dimensions in direct detection. Simulations and experimental validations confirm its accuracy and practical value in detecting concealed weapons in human security screening scenarios.
Sparse Spectrahedral Shadows for State Estimation and Reachability Analysis: Set Operations, Validations and Order Reductions
Authors: Chengrui Wang, Haohao Qiu, Sibo Yao, James Lam
Date: 2025-02-26 00:00:00
Tasks: Fault Diagnosis, State Estimation

Set representations are the foundation of various set-based approaches in state estimation, reachability analysis and fault diagnosis. In this paper, we investigate spectrahedral shadows, a class of nonlinear geometric objects previously studied in semidefinite programming and real algebraic geometry. We demonstrate spectrahedral shadows generalize traditional and emerging set representations like ellipsoids, zonotopes, constrained zonotopes and ellipsotopes. Analytical forms of set operations are provided including linear map, linear inverse map, Minkowski sum, intersection, Cartesian product, Minkowski-Firey Lp sum, convex hull, conic hull and polytopic map, all of which are implemented without approximation in polynomial time. In addition, we develop set validation and order reduction techniques for spectrahedral shadows, thereby establishing spectrahedral shadows as a set representation applicable to a range of set-based tasks.
A Novel Topology Recovery Method for Low Voltage Distribution Networks
Authors: Sina Mohammadi, Van-Hai Bui, Wencong Su
Date: 2025-02-26 00:00:00
Tasks: Fault Detection

Low voltage distribution networks (LVDNs) suffer from limited visibility due to sparse or nonexistent measurement systems, leaving distribution network service providers with incomplete data. Maintenance activities, such as transformer upgrades and power line replacements, sometimes go undocumented, leading to unmonitored topology changes. This lack of oversight hinders network optimization, fault detection, and outage management, as utilities cannot fully monitor or control the system. With the rise of electric vehicles, having an accurate understanding of LVDN topology is crucial to avoid infrastructure damage from potential overloads. This paper introduces a method to reconstruct LVDN topology using incremental voltage and current changes from smart meters at customer endpoints. The approach identifies and maps network topologies with high accuracy, overcoming limitations of prior methods by discarding unrealistic assumptions. Specifically, it addresses grids with fewer than three pole connections and employs an AC power flow model over simplified DC approximations. Simulations across diverse configurations validate the method's effectiveness in accurately reconstructing LVDN topologies, enhancing real-world applicability.
Multivariable Generalized Super-Twisting Algorithm Robust Control of Linear Time-Invariant Systems
Authors: J. C. Geromel, E. V. L. Nunes, L. Hsu
Date: 2025-02-26 00:00:00

This paper presents a novel procedure for robust control design of linear time-invariant systems using a Multivariable Generalized Super-Twisting Algorithm (MGSTA). The proposed approach addresses robust stability and performance conditions, considering convex bounded parameter uncertainty in all matrices of the plant state-space realization and Lipschitz exogenous disturbances. The primary characteristic of the closed-loop system, sliding mode finite-time convergence, is thoroughly examined and evaluated. The design conditions, obtained through the proposal of a novel max-type non-differentiable piecewise-continuous Lyapunov function are formulated as Linear Matrix Inequalities (LMIs), which can be efficiently solved using existing computational tools. A fault-tolerant MGSTA control is designed for a mechanical system with three degrees of freedom, illustrating the efficacy of the proposed LMI approach.
Identification and Characterization for Disruptions in the U.S. National Airspace System (NAS)
Authors: Jing Xu, Mark Hansen, Megan Ryerson
Date: 2025-02-25 00:00:00
Tasks: Anomaly Detection

Disruptions in the National Airspace System (NAS) lead to significant losses to air traffic system participants and raise public concerns. We apply two methods, cluster analysis and anomaly detection models, to identify operational disruptions with geographical patterns in the NAS since 2010. We identify four types and twelve categories of days of operations, distinguished according to air traffic system operational performance and geographical patterns of disruptions. Two clusters--NAS Disruption and East Super Disruption, accounting for 0.8% and 1.2% of the days respectively, represent the most disrupted days of operations in U.S. air traffic system. Another 16.5% of days feature less severe but still significant disruptions focused on certain regions of the NAS, while on the remaining 81.5% of days the NAS operates relatively smoothly. Anomaly detection results show good agreement with cluster results and further distinguish days in the same cluster by severity of disruptions. Results show an increasing trend in frequency of disruptions especially post-COVID. Additionally, disruptions happen most frequently in the summer and winter.
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative Model for Quasar Spectra
Authors: Vidhi Lalchand, Anna-Christina Eilers
Date: 2025-02-27 00:00:00
Tasks: Gaussian Processes

This work proposes a scalable probabilistic latent variable model based on Gaussian processes (Lawrence, 2004) in the context of multiple observation spaces. We focus on an application in astrophysics where data sets typically contain both observed spectral features and scientific properties of astrophysical objects such as galaxies or exoplanets. In our application, we study the spectra of very luminous galaxies known as quasars, along with their properties, such as the mass of their central supermassive black hole, accretion rate, and luminosity-resulting in multiple observation spaces. A single data point is then characterized by different classes of observations, each with different likelihoods. Our proposed model extends the baseline stochastic variational Gaussian process latent variable model (GPLVM) introduced by Lalchand et al. (2022) to this setting, proposing a seamless generative model where the quasar spectra and scientific labels can be generated simultaneously using a shared latent space as input to different sets of Gaussian process decoders, one for each observation space. Additionally, this framework enables training in a missing data setting where a large number of dimensions per data point may be unknown or unobserved. We demonstrate high-fidelity reconstructions of the spectra and scientific labels during test-time inference and briefly discuss the scientific interpretations of the results, along with the significance of such a generative model.
Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST
Authors: Rithwik Gupta, Daniel Muthukrishna
Date: 2025-02-25 00:00:00
Tasks: Survey, Transfer Learning

Machine learning has become essential for automated classification of astronomical transients, but current approaches face significant limitations: classifiers trained on simulations struggle with real data, models developed for one survey cannot be easily applied to another, and new surveys require prohibitively large amounts of labelled training data. These challenges are particularly pressing as we approach the era of the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST), where existing classification models will need to be retrained using LSST observations. We demonstrate that transfer learning can overcome these challenges by repurposing existing models trained on either simulations or data from other surveys. Starting with a model trained on simulated Zwicky Transient Facility (ZTF) light curves, we show that transfer learning reduces the amount of labelled real ZTF transients needed by 75\% while maintaining equivalent performance to models trained from scratch. Similarly, when adapting ZTF models for LSST simulations, transfer learning achieves 95\% of the baseline performance while requiring only 30\% of the training data. These findings have significant implications for the early operations of LSST, suggesting that reliable automated classification will be possible soon after the survey begins, rather than waiting months or years to accumulate sufficient training data.
2064 global population crisis scenario predicted by the most general dynamic model
Authors: Alessio Zaccone, Kostya Trachenko
Date: 2025-02-26 00:00:00

There is currently no consensus on how the global population will evolve in the next decades and in the next century. The reason for this uncertainty is the absence of reliable population dynamic models. In this paper, we remedy to this situation by reporting on a population dynamic model, a single nonlinear differential equation adapted from the physics of disordered systems, which is able to mathematically describe all the various regimes encountered in the global population recorded as a function of time, over the past 12000 years until now. Regimes of simple exponential growth (Malthus), logistic (Verhulst) plateaus as well as stretched-exponential and compressed-exponential growth regimes are all reliably described by this mathematical equation in its various limits. Besides showing that this is, indeed, the most general population dynamic model, we use it to explore its solutions projected into the future. In particular, two different scenarios are predicted. In one of them, which assumes that the future evolution would continue along a similar pattern as the past decades (hence without any major global ecological crisis affecting the resource exploitation), a von Foerster-type doomsday scenario with a sudden rise of the global population to unsustainable levels could appear as early as 2078. In the opposite scenario, if a global ecological crisis were to set in today, affecting the ability to exploit resources, given the current estimates of the Earth's carrying capacity, the global population is forecasted to reduce by half by 2064. Furthermore, the proposed dynamic model provides with a new aggregated parameter (K, in the model) that can be monitored and controlled so as to avoid the doomsday scenarios.
Spectral Analysis of Representational Similarity with Limited Neurons
Authors: Hyunmo Kang, Abdulkadir Canatar, SueYeon Chung
Date: 2025-02-27 00:00:00
Tasks: Denoising

Measuring representational similarity between neural recordings and computational models is challenging due to constraints on the number of neurons that can be recorded simultaneously. In this work, we investigate how such limitations affect similarity measures, focusing on Canonical Correlation Analysis (CCA) and Centered Kernel Alignment (CKA). Leveraging tools from Random Matrix Theory, we develop a predictive spectral framework for these measures and demonstrate that finite neuron sampling systematically underestimates similarity due to eigenvector delocalization. To overcome this, we introduce a denoising method to infer population-level similarity, enabling accurate analysis even with small neuron samples. Our theory is validated on synthetic and real datasets, offering practical strategies for interpreting neural data under finite sampling constraints.
Symmetries of Living Systems: Symmetry Fibrations and Synchronization in Biological Networks
Authors: Hernan A. Makse, Paolo Boldi, Francesco Sorrentino, Ian Stewart
Date: 2025-02-26 00:00:00

A symmetry is a `change without change'. As simple as it sounds, this concept is the fundamental cornerstone that unifies all branches of theoretical physics. Virtually all physical laws -- ranging from classical mechanics and electrodynamics to relativity, quantum mechanics, and the standard model -- can be expressed in terms of symmetry invariances. In this book, we explore whether the same principle can also explain the emergent laws of biological systems. We introduce a new geometry for biological networks and AI architectures, drawing inspiration from the mystic genius of Grothendieck's fibrations in category theory. We attempt to bridge the gap between physics and biology using symmetries but with a twist. The traditional symmetry groups of physics are global and too rigid to describe biology. Instead, the novel notion of symmetry fibration is local, flexible, and adaptable to evolutionary pressures, providing the right framework for understanding biological complexity. In other words, this more general symmetry invariance is necessary and sufficient to ensure that a given biological network configuration can support a synchronized function. In this book, we review the theoretical progress over the last decades from mathematics, physics, computer science, dynamical systems, and graph theory that has led to the discovery of symmetry fibrations in biological networks. These symmetries act as organizing principles for biological networks. They serve as effective tools for describing the structure of these networks, blending geometry and topology. Fibrations explain how structure dictates function across various biological domains, including the transcriptome, proteome, metabolome, and connectome. Additionally, they facilitate a reduction in the dimensionality of the network, simplifying it into its fundamental building blocks for biological computation.
Applications of Statistical Field Theory in Deep Learning
Authors: Zohar Ringel, Noa Rubin, Edo Mor, Moritz Helias, Inbar Seroussi
Date: 2025-02-25 00:00:00
Tasks: Deep Learning

Deep learning algorithms have made incredible strides in the past decade, yet due to their complexity, the science of deep learning remains in its early stages. Being an experimentally driven field, it is natural to seek a theory of deep learning within the physics paradigm. As deep learning is largely about learning functions and distributions over functions, statistical field theory, a rich and versatile toolbox for tackling complex distributions over functions (fields) is an obvious choice of formalism. Research efforts carried out in the past few years have demonstrated the ability of field theory to provide useful insights on generalization, implicit bias, and feature learning effects. Here we provide a pedagogical review of this emerging line of research.
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
Authors: Yunyang Li, Zaishuo Xia, Lin Huang, Xinran Wei, Han Yang, Sam Harshe, Zun Wang, Chang Liu, Jia Zhang, Bin Shao, Mark B. Gerstein
Date: 2025-02-26 00:00:00

Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltonian. Despite its importance, the application of DFT is frequently limited by the substantial computational resources required to construct the Kohn-Sham Hamiltonian. In response to these limitations, current research has employed deep-learning models to efficiently predict molecular and solid Hamiltonians, with roto-translational symmetries encoded in their neural networks. However, the scalability of prior models may be problematic when applied to large molecules, resulting in non-physical predictions of ground-state properties. In this study, we generate a substantially larger training set (PubChemQH) than used previously and use it to create a scalable model for DFT calculations with physical accuracy. For our model, we introduce a loss function derived from physical principles, which we call Wavefunction Alignment Loss (WALoss). WALoss involves performing a basis change on the predicted Hamiltonian to align it with the observed one; thus, the resulting differences can serve as a surrogate for orbital energy differences, allowing models to make better predictions for molecular orbitals and total energies than previously possible. WALoss also substantially accelerates self-consistent-field (SCF) DFT calculations. Here, we show it achieves a reduction in total energy prediction error by a factor of 1347 and an SCF calculation speed-up by a factor of 18%. These substantial improvements set new benchmarks for achieving accurate and applicable predictions in larger molecular systems.
Design of Resistive Frequency Selective Surface based Radar Absorbing Structure-A Deep Learning Approach
Authors: Vijay Kumar Sutrakar, Nikhil Morge, Anjana PK, Abhilash PV
Date: 2025-02-26 00:00:00
Tasks: Deep Learning

In this paper, deep learning-based approach for the design of radar absorbing structure using resistive frequency selective surface is proposed. In the present design, reflection coefficient is used as input of deep learning model and the Jerusalem cross based unit cell dimensions is predicted as outcome. Sequential neural network based deep learning model with adaptive moment estimation optimizer is used for designing multi frequency band absorbers. The model is used for designing radar absorber from L to Ka band depending on unit cell parameters and thickness. The outcome of deep learning model is further compared with full-wave simulation software and an excellent match is obtained. The proposed model can be used for the low-cost design of various radar absorbing structures using a single unit cell and thickness across the band of frequencies.
Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
Authors: Fraser Birks, Thomas D Swinburne, James R Kermode
Date: 2025-02-26 00:00:00

Machine-learned interatomic potentials offer near first-principles accuracy but are computationally expensive, limiting their application in large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics methods, we present ML-MIX, an efficient and flexible LAMMPS package for accelerating simulations by spatially mixing interatomic potentials of different complexities. Through constrained linear fitting, we show it is possible to generate a 'cheap' approximate model which closely matches an 'expensive' reference in relevant regions of configuration space. We demonstrate the capability of ML-MIX through case-studies in Si, Fe, and W-He systems, achieving up to an 11x speedup on 8,000 atom systems without sacrificing accuracy on static and dynamic quantities, including calculation of minimum energy paths and dynamical simulations of defect diffusion. For larger domain sizes, we show that the achievable speedup of ML-MIX simulations is limited only by the relative speed of the cheap potential over the expensive potential. The ease of use and flexible nature of this method will extend the practical reach of MLIPs throughout computational materials science, enabling parsimonious application to large spatial and temporal domains.
Mind the Gap: Bridging the Divide Between AI Aspirations and the Reality of Autonomous Characterization
Authors: Grace Guinan, Addison Salvador, Michelle A. Smeaton, Andrew Glaws, Hilary Egan, Brian C. Wyatt, Babak Anasori, Kevin R. Fiedler, Matthew J. Olszta, Steven R. Spurgeon
Date: 2025-02-25 00:00:00

What does materials science look like in the "Age of Artificial Intelligence?" Each materials domain-synthesis, characterization, and modeling-has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.