pwc-archive/papers-with-abstracts huggingface dataset:
Self-powered smart contact lenses: a multidisciplinary approach to micro-scale energy and 900 MHz - 1.1 GHz bandwidth microfabricated loop antennas communication systems
Authors: Patrice Salzenstein, Blandine Guichardaz, Aya Maroua Bessou, Ekaterina Pavlyuchenko, Martine Comte, Maxim V. Pogumirsky
Date: 2025-05-21 00:00:00
Tasks: Management

Smart contact lenses are at the forefront of integrating microelectronics, biomedical engineering, and optics into wearable technologies. This work addresses a key obstacle in their development: achieving autonomous power without compromising safety or miniaturization. We examine energy harvesting strategies using intrinsic ocular sources-particularly tear salinity and eyelid motion-to enable sustainable operation without external batteries. The study emphasizes compact loop antennas operating between 900 MHz and 1.1 GHz as critical for wireless data transmission and power management. Material choices, signal integrity, and biocompatibility are also discussed. By presenting recent advances in 3D-printed optics, antenna integration, and energy systems, we propose a conceptual framework for the next generation of smart lenses, enabling real-time health monitoring and vision enhancement through self-powered, compact devices.
Decreasing Utilization of Systems with Multi-Rate Cause-Effect Chains While Reducing End-to-End Latencies
Authors: Luiz Maia, Gerhard Fohler
Date: 2025-05-21 00:00:00

The Logical Execution Time (LET) model has deterministic properties which dramatically reduce the complexity of analyzing temporal requirements of multi-rate cause-effect chains. The configuration (length and position) of task's communication intervals directly define which task instances propagate data through the chain and affect end-to-end latencies. Since not all task instances propagate data through the chain, the execution of these instances wastes processing resources. By manipulating the configuration of communication intervals, it is possible to control which task instances are relevant for data propagation and end-to-end latencies. However, since tasks can belong to more than one cause-effect chain, the problem of configuring communication intervals becomes non-trivial given the large number of possible configurations. In this paper, we present a method to decrease the waste of processing resources while reducing end-to-end latencies. We use a search algorithm to analyze different communication interval configurations and find the combination that best decrease system utilization while reducing end-to-end latencies. By controlling data propagation by means of precedence constraints, our method modifies communication intervals and controls which task instances affect end-to-end latencies. Despite the sporadic release time of some task instances during the analysis, our method transforms those instances into periodic tasks. We evaluate our work using synthetic task sets and the automotive benchmark proposed by BOSCH for the WATERS industrial challenge.
Continuous-time iterative linear-quadratic regulator
Authors: Juraj Lieskovský, Jaroslav Bušek, Tomáš Vyhlídal
Date: 2025-05-21 00:00:00
Tasks: Numerical Integration

We present a continuous-time equivalent to the well-known iterative linear-quadratic algorithm including an implementation of a backtracking line-search policy and a novel regularization approach based on the necessary conditions in the Riccati pass of the linear-quadratic regulator. This allows the algorithm to effectively solve trajectory optimization problems with non-convex cost functions, which is demonstrated on the cart-pole swing-up problem. The algorithm compatibility with state-of-the-art suites of numerical integration solvers allows for the use of high-order adaptive-step methods. Their use results in a variable number of time steps both between passes of the algorithm and across iterations, maintaining a balance between the number of function evaluations and the discretization error.
From learning to safety: A Direct Data-Driven Framework for Constrained Control
Authors: Kanghui He, Shengling Shi, Ton van den Boom, Bart De Schutter
Date: 2025-05-21 00:00:00

Ensuring safety in the sense of constraint satisfaction for learning-based control is a critical challenge, especially in the model-free case. While safety filters address this challenge in the model-based setting by modifying unsafe control inputs, they typically rely on predictive models derived from physics or data. This reliance limits their applicability for advanced model-free learning control methods. To address this gap, we propose a new optimization-based control framework that determines safe control inputs directly from data. The benefit of the framework is that it can be updated through arbitrary model-free learning algorithms to pursue optimal performance. As a key component, the concept of direct data-driven safety filters (3DSF) is first proposed. The framework employs a novel safety certificate, called the state-action control barrier function (SACBF). We present three different schemes to learn the SACBF. Furthermore, based on input-to-state safety analysis, we present the error-to-state safety analysis framework, which provides formal guarantees on safety and recursive feasibility even in the presence of learning inaccuracies. The proposed control framework bridges the gap between model-free learning-based control and constrained control, by decoupling performance optimization from safety enforcement. Simulations on vehicle control illustrate the superior performance regarding constraint satisfaction and task achievement compared to model-based methods and reward shaping.
Impact of Wind Generation on Risk-based Security Assessment of Power System
Authors: Umair Shahzad
Date: 2025-05-21 00:00:00

The electric power system is one of the largest and most intricate infrastructures. Therefore, it is critical to assess and maintain its security. A power system security assessment is indispensable for identifying post-contingency issues, taking corrective measures, and protecting the system from blackouts. This paper examined the impact of wind generation on the risk-based security assessment of a power transmission network in the context of planning. DIgSILENT PowerFactory software was used to conduct the analysis using a combination of the brute force technique and the nonsequential Monte Carlo (MC) simulation method on the IEEE 39-bus transmission test system. Optimal power flow (OPF) was used to quantify security, considering (N-1), (N-2), and (N-3) line outages and an (N-1) bus outage. Moreover, the average cost deviation from the mean optimal system operating cost was proposed as a novel security indicator. The results obtianed accurately depicted the effects of changing wind generation levels on system security in terms of risk. The most and least critical line(s) and bus in the system, for different wind generation levels, were also determined. Moreover, the worst-case wind-generation threshold level using two different cost functions for wind was identified.
A Risk-Based Probabilistic Transient Stability Approach for Ranking of Circuit Breakers in a Power System
Authors: Umair Shahzad
Date: 2025-05-21 00:00:00

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Current deterministic industry practices of transient stability assessment ignore the probability of variables involved. With increasing system uncertainties and widespread electricity market deregulation, there is a strong inevitability to incorporate probabilistic transient stability analysis. Circuit breakers play a critical role in fault clearing and consequently in determining the system transient stability. It is important that they undergo timely and appropriate maintenance procedures based on some criterion. Considering the need of incorporating risk in modern power systems, this paper proposes a risk-based probabilistic transient stability approach for ranking of circuit breakers in a power system. A novel priority index was proposed to rank the circuit breakers based on the system transient stability risk. DIgSILENT PowerFactory software was used to conduct the required simulations on IEEE 14 bus system. The proposed risk-based framework was deemed to be efficient in identification of the circuit breakers based on their priority rank index which can aid in power system planning process.
Comparing Parameterizations and Objective Functions for Maximizing the Volume of Zonotopic Invariant Sets
Authors: Chenliang Zhou, Heejin Ahn, Ian M. Mitchell
Date: 2025-05-21 00:00:00

In formal safety verification, many proposed algorithms use parametric set representations and convert the computation of the relevant sets into an optimization problem; consequently, the choice of parameterization and objective function have a significant impact on the efficiency and accuracy of the resulting computation. In particular, recent papers have explored the use of zonotope set representations for various types of invariant sets. In this paper we collect two zonotope parameterizations that are numerically well-behaved and demonstrate that the volume of the corresponding zonotopes is log-concave in the parameters. We then experimentally explore the use of these two parameterizations in an algorithm for computing the maximum volume zonotope invariant under affine dynamics within a specified box constraint over a finite horizon. The true volume of the zonotopes is used as an objective function, along with two alternative heuristics that are faster to compute. We conclude that the heuristics are much faster in practice, although the relative quality of their results declines as the dimension of the problem increases; however, our conclusions are only preliminary due to so-far limited availability of compute resources.
A Neural Network Approach to a Modified Quadratic Boost Multiport Resonant Converter for Electric Vehicle Chargers
Authors: V. Rajeswari, Nalin Kant Mohanty
Date: 2025-05-21 00:00:00

This topology can achieve a high step-up gain by utilizing a switched capacitor and switched inductor-based VMC network arrangement.Furthermore, the proposed topology can achieve an output gain of approximately three times at a nominal duty ratio with reduced voltage and current stress across the switch, and enhance the maximum efficiency to 96.7
Co-optimize condenser water temperature and cooling tower fan using high-fidelity synthetic data
Authors: Gulai Shen, Gurpreet Singh, Ali Mehmani
Date: 2025-05-21 00:00:00

This paper introduces a novel method for optimizing HVAC systems in buildings by integrating a high-fidelity physics-based simulation model with machine learning and measured data. The method enables a real-time building advisory system that provides optimized settings for condenser water loop operation, assisting building operators in decision-making. The building and its HVAC system are first modeled using eQuest. Synthetic data are then generated by running the simulation multiple times. The data are then processed, cleaned, and used to train the machine learning model. The machine learning model enables real-time optimization of the condenser water loop using particle swarm optimization. The results deliver both a real-time online optimizer and an offline operation look-up table, providing optimized condenser water temperature settings and the optimal number of cooling tower fans at a given cooling load. Potential savings are calculated by comparing measured data from two summer months with the energy costs the building would have experienced under optimized settings. Adaptive model refinement is applied to further improve accuracy and effectiveness by utilizing available measured data. The method bridges the gap between simulation and real-time control. It has the potential to be applied to other building systems, including the chilled water loop, heating systems, ventilation systems, and other related processes. Combining physics models, data models, and measured data also enables performance analysis, tracking, and retrofit recommendations.
Green Hacks: Generating Sustainability-Targeting Attacks For Cyber-Physical Systems
Authors: Faysal Ahamed, Tanushree Roy
Date: 2025-05-21 00:00:00

Sustainability-targeting attacks (STA) or "Green Hacks" are a growing threat to cyber-physical system (CPS)-based infrastructure, as its performance objectives are increasingly linked to sustainability goals. These attacks exploit the interdependence between control, energy efficiency, and environmental impact to degrade systems' overall performance. Thus, in this work, we propose a general mathematical framework for modeling such STA and derive the feasibility conditions for generating a worst-case STA on a linear CPS using a max-min formulation. A gradient ascent descent algorithm is used to construct the worst-case attack policy. We simulated the worst-case STA for a linear CPS to illustrate its impacts on the CPS performance and sustainability cost.
Virtual Fluoroscopy for Interventional Guidance using Magnetic Tracking
Authors: Shuwei Xing, Inaara Ahmed-Fazal, Utsav Pardasani, Uditha Jayarathne, Scott Illsley, Aaron Fenster, Terry M. Peters, Elvis C. S. Chen
Date: 2025-05-20 00:00:00

Purpose: In conventional fluoroscopy-guided interventions, the 2D projective nature of X-ray imaging limits depth perception and leads to prolonged radiation exposure. Virtual fluoroscopy, combined with spatially tracked surgical instruments, is a promising strategy to mitigate these limitations. While magnetic tracking shows unique advantages, particularly in tracking flexible instruments, it remains under-explored due to interference from ferromagnetic materials in the C-arm room. This work proposes a virtual fluoroscopy workflow by effectively integrating magnetic tracking, and demonstrates its clinical efficacy. Methods: An automatic virtual fluoroscopy workflow was developed using a radiolucent tabletop field generator prototype. Specifically, we developed a fluoro-CT registration approach with automatic 2D-3D shared landmark correspondence to establish the C-arm-patient relationship, along with a general C-arm modelling approach to calculate desired poses and generate corresponding virtual fluoroscopic images. Results: Testing on a dataset with views ranging from RAO 90 degrees to LAO 90 degrees, simulated fluoroscopic images showed visually imperceptible differences from the real ones, achieving a mean target projection distance error of 1.55 mm. An endoleak phantom insertion experiment highlighted the effectiveness of simulating multiplanar views with real-time instrument overlays, achieving a mean needle tip error of 3.42 mm. Conclusions: Results demonstrated the efficacy of virtual fluoroscopy integrated with magnetic tracking, improving depth perception during navigation. The broad capture range of virtual fluoroscopy showed promise in improving the users understanding of X-ray imaging principles, facilitating more efficient image acquisition.
Physics-based machine learning for mantle convection simulations
Authors: Siddhant Agarwal, Ali Can Bekar, Christian Hüttig, David S. Greenberg, Nicola Tosi
Date: 2025-05-21 00:00:00

Mantle convection simulations are an essential tool for understanding how rocky planets evolve. However, the poorly known input parameters to these simulations, the non-linear dependence of transport properties on pressure and temperature, and the long integration times in excess of several billion years all pose a computational challenge for numerical solvers. We propose a physics-based machine learning approach that predicts creeping flow velocities as a function of temperature while conserving mass, thereby bypassing the numerical solution of the Stokes problem. A finite-volume solver then uses the predicted velocities to advect and diffuse the temperature field to the next time-step, enabling autoregressive rollout at inference. For training, our model requires temperature-velocity snapshots from a handful of simulations (94). We consider mantle convection in a two-dimensional rectangular box with basal and internal heating, pressure- and temperature-dependent viscosity. Overall, our model is up to 89 times faster than the numerical solver. We also show the importance of different components in our convolutional neural network architecture such as mass conservation, learned paddings on the boundaries, and loss scaling for the overall rollout performance. Finally, we test our approach on unseen scenarios to demonstrate some of its strengths and weaknesses.
Learning novel representations of variable sources from multi-modal $\textit{Gaia}$ data via autoencoders
Authors: P. Huijse, J. De Ridder, L. Eyer, L. Rimoldini, B. Holl, N. Chornay, J. Roquette, K. Nienartowicz, G. Jevardat de Fombelle, D. J. Fritzewski, A. Kemp, V. Vanlaer, M. Vanrespaille, H. Wang, M. I. Carnerero, C. M. Raiteri, G. Marton, M. Madarász, G. Clementini, P. Gavras, C. Aerts
Date: 2025-05-22 00:00:00
Tasks: Data Compression, Outlier Detection

Gaia Data Release 3 (DR3) published for the first time epoch photometry, BP/RP (XP) low-resolution mean spectra, and supervised classification results for millions of variable sources. This extensive dataset offers a unique opportunity to study their variability by combining multiple Gaia data products. In preparation for DR4, we propose and evaluate a machine learning methodology capable of ingesting multiple Gaia data products to achieve an unsupervised classification of stellar and quasar variability. A dataset of 4 million Gaia DR3 sources is used to train three variational autoencoders (VAE), which are artificial neural networks (ANNs) designed for data compression and generation. One VAE is trained on Gaia XP low-resolution spectra, another on a novel approach based on the distribution of magnitude differences in the Gaia G band, and the third on folded Gaia G band light curves. Each Gaia source is compressed into 15 numbers, representing the coordinates in a 15-dimensional latent space generated by combining the outputs of these three models. The learned latent representation produced by the ANN effectively distinguishes between the main variability classes present in Gaia DR3, as demonstrated through both supervised and unsupervised classification analysis of the latent space. The results highlight a strong synergy between light curves and low-resolution spectral data, emphasising the benefits of combining the different Gaia data products. A two-dimensional projection of the latent variables reveals numerous overdensities, most of which strongly correlate with astrophysical properties, showing the potential of this latent space for astrophysical discovery. We show that the properties of our novel latent representation make it highly valuable for variability analysis tasks, including classification, clustering and outlier detection.
Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey
Authors: ZHIXUN LI, Bin Cao, Rui Jiao, Ding Wang, Yang Liu, Dingshuo Chen, Jia Li, Qiang Liu, Yu Rong, Liang Wang, Tong-Yi Zhang, Jeffrey Xu Yu
Date: 2025-05-22 00:00:00

Materials are the foundation of modern society, underpinning advancements in energy, electronics, healthcare, transportation, and infrastructure. The ability to discover and design new materials with tailored properties is critical to solving some of the most pressing global challenges. In recent years, the growing availability of high-quality materials data combined with rapid advances in Artificial Intelligence (AI) has opened new opportunities for accelerating materials discovery. Data-driven generative models provide a powerful tool for materials design by directly create novel materials that satisfy predefined property requirements. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. To fill this gap, this paper provides a comprehensive overview of recent progress in AI-driven materials generation. We first organize various types of materials and illustrate multiple representations of crystalline materials. We then provide a detailed summary and taxonomy of current AI-driven materials generation approaches. Furthermore, we discuss the common evaluation metrics and summarize open-source codes and benchmark datasets. Finally, we conclude with potential future directions and challenges in this fast-growing field. The related sources can be found at https://github.com/ZhixunLEE/Awesome-AI-for-Materials-Generation.
Machine Learning the 6d Supergravity Landscape
Authors: Nathan Brady, David Tennyson, Thomas Vandermeulen
Date: 2025-05-22 00:00:00

In this paper, we apply both supervised and unsupervised machine learning algorithms to the study of the string landscape and swampland in 6-dimensions. Our data are the (almost) anomaly-free 6-dimensional $\mathcal{N} = (1,0)$ supergravity models, characterised by the Gram matrix of anomaly coefficients. Our work demonstrates the ability of machine learning algorithms to efficiently learn highly complex features of the landscape and swampland. Employing an autoencoder for unsupervised learning, we provide an auto-classification of these models by compressing the Gram matrix data to 2-dimensions. Through compression, similar models cluster together, and we identify prominent features of these clusters. The autoencoder also identifies outlier models which are difficult to reconstruct. One of these outliers proves to be incredibly difficult to combine with other models such that the $\text{tr}R^{4}$ anomaly vanishes, making its presence in the landscape extremely rare. Further, we utilise supervised learning to build two classifiers predicting (1) model consistency under probe string insertion (precision: 0.78, predicting consistency for 214,837 models with reasonable certainty) and (2) inconsistency under anomaly inflow (precision: 0.91, predicting inconsistency for 1,909,359 models). Notably, projecting these predictions onto the autoencoder's 2-dimensional latent layer shows consistent models clustering together, further indicating that the autoencoder has learnt interesting and complex features of the set of models and potentially offers a novel approach to mapping the landscape and swampland of 6-dimensional supergravity theories.
Measuring inequality in society-oriented Lotka--Volterra-type kinetic equations
Authors: Marco Menale, Giuseppe Toscani
Date: 2025-05-21 00:00:00

We present a possible approach to measuring inequality in a system of coupled Fokker-Planck-type equations that describe the evolution of distribution densities for two populations interacting pairwise due to social and/or economic factors. The macroscopic dynamics of their mean values follow a Lotka-Volterra system of ordinary differential equations. Unlike classical models of wealth and opinion formation, which tend to converge toward a steady-state profile, the oscillatory behavior of these densities only leads to the formation of local equilibria within the Fokker-Planck system. This makes tracking the evolution of most inequality measures challenging. However, an insightful perspective on the problem is obtained by using the coefficient of variation, a simple inequality measure closely linked to the Gini index. Numerical experiments confirm that, despite the system's oscillatory nature, inequality initially tends to decrease.