pwc-archive/papers-with-abstracts huggingface dataset:
Synthesizing Grid Data with Cyber Resilience and Privacy Guarantees
Authors: Shengyang Wu, Vladimir Dvorkin
Date: 2025-03-19 00:00:00

Differential privacy (DP) provides a principled approach to synthesizing data (e.g., loads) from real-world power systems while limiting the exposure of sensitive information. However, adversaries may exploit synthetic data to calibrate cyberattacks on the source grids. To control these risks, we propose new DP algorithms for synthesizing data that provide the source grids with both cyber resilience and privacy guarantees. The algorithms incorporate both normal operation and attack optimization models to balance the fidelity of synthesized data and cyber resilience. The resulting post-processing optimization is reformulated as a robust optimization problem, which is compatible with the exponential mechanism of DP to moderate its computational burden.
On a Dissimilarity Metric for Analyzing Body Synergistic Coordination in Non-Periodic Motion
Authors: Shunpei Fujii, Kanta Tachibana
Date: 2025-03-19 00:00:00
Tasks: Dynamic Time Warping, Rhythm, Time Series

This study proposes a novel metric to quantitatively evaluate body synergistic coordination, explicitly addressing dynamic interactions between pairs of body segments in baseball pitching motions. Conventional methods typically compare motion trajectories using individual joint coordinates or velocities independently, employing techniques like Dynamic Time Warping (DTW) that inherently apply temporal alignment even when such correction may distort meaningful rhythm-based differences. In contrast, our approach models the coordination dynamics as Linear Time-Invariant (LTI) systems, leveraging convolution operations between pairs of time series data to capture the gain and phase-lag inherent in genuine coordination dynamics. Empirical validation demonstrates the robustness of the proposed metric to variations in camera angles and scaling, providing superior discriminative capability compared to DTW and deep learning-based methods.
Towards Connected Smart Work Zones: Advancing Work Zone Management through Improved Connectivity
Authors: Mariam Nour, Mohamed H. Zaki, Mohamed Abdel-Aty
Date: 2025-03-19 00:00:00
Tasks: Management

Work zones play a key role in road and highway maintenance but can lead to significant risks to both drivers and workers. Smart Work Zones (SWZs) have emerged as a potential solution, offering decision-makers real-time insights into the status of the work zone. By utilizing work zone barrels equipped with sensors and communication nodes, SWZs facilitate collecting and transmitting critical data, including location, traffic density, flow patterns, and worker proximity alerts. In collaboration with the Florida Department of Transportation (FDOT), this study addresses work zone barrel connectivity requirements while considering a cost-effective, low-power, and low-maintenance solution. While the broader project aimed to create a complete SWZ system for the localization of work zone barrels, this paper proposes a novel relay node selection algorithm integrated with Bluetooth Low Energy (BLE) technology to enhance network performance. The proposed algorithm enhances the communication network performance by selecting specific nodes as relay points, avoiding message flooding in the network. It demonstrates an improvement in message delivery rates, achieving up to a 40% increase over existing methods while ensuring balanced load distribution among nodes. Moreover, it maintains an 80% message delivery rate while minimizing power consumption, outperforming other approaches. This improvement in communication efficiency is critical, as it ensures the accurate transmission and delivery of vital work zone data, allowing for faster and more informed decisions to enhance work zone safety and management.
Defect Analysis and Built-In-Self-Test for Chiplet Interconnects in Fan-out Wafer-Level Packaging
Authors: Partho Bhoumik, Christopher Bailey, Krishnendu Chakrabarty
Date: 2025-03-18 00:00:00
Tasks: Fault Detection

Fan-out wafer-level packaging (FOWLP) addresses the demand for higher interconnect densities by offering reduced form factor, improved signal integrity, and enhanced performance. However, FOWLP faces manufacturing challenges such as coefficient of thermal expansion (CTE) mismatch, warpage, die shift, and post-molding protrusion, causing misalignment and bonding issues during redistribution layer (RDL) buildup. Moreover, the organic nature of the package exposes it to severe thermo-mechanical stresses during fabrication and operation. In order to address these challenges, we propose a comprehensive defect analysis and testing framework for FOWLP interconnects. We use Ansys Q3D to map defects to equivalent electrical circuit models and perform fault simulations to investigate the impacts of these defects on chiplet functionality. Additionally, we present a built-in self-test (BIST) architecture to detect stuck-at and bridging faults while accurately diagnosing the fault type and location. Our simulation results demonstrate the efficacy of the proposed BIST solution and provide critical insights for optimizing design decisions in packages, balancing fault detection and diagnosis with the cost of testability insertion.
Risk-Aware Planning of Power Distribution Systems Using Scalable Cloud Technologies
Authors: Shiva Poudel, Poorva Sharma, Abhineet Parchure, Daniel Olsen, Sayantan Bhowmik, Tonya Martin, Dylan Locsin, Andrew P. Reiman
Date: 2025-03-18 00:00:00

The uncertainty in distribution grid planning is driven by the unpredictable spatial and temporal patterns in adopting electric vehicles (EVs) and solar photovoltaic (PV) systems. This complexity, stemming from interactions among EVs, PV systems, customer behavior, and weather conditions, calls for a scalable framework to capture a full range of possible scenarios and analyze grid responses to factor in compound uncertainty. Although this process is challenging for many utilities today, the need to model numerous grid parameters as random variables and evaluate the impact on the system from many different perspectives will become increasingly essential to facilitate more strategic and well-informed planning investments. We present a scalable, stochastic-aware distribution system planning application that addresses these uncertainties by capturing spatial and temporal variability through a Markov model and conducting Monte Carlo simulations leveraging modular cloud-based architecture. The results demonstrate that 15,000 power flow scenarios generated from the Markov model are completed on the modified IEEE 123-bus test feeder, with each simulation representing an 8,760-hour time series run, all in under an hour. The grid impact extracted from this huge volume of simulated data provides insights into the spatial and temporal effects of adopted technology, highlighting that planning solely for average conditions is inadequate, while worst-case scenario planning may lead to prohibitive expenses.
Controlling Peak Sharpness in Multimodal Biomolecular Systems via the Chemical Fokker-Planck Equation
Authors: Taishi Kotsuka, Enoch Yeung
Date: 2025-03-18 00:00:00
Tasks: Decision Making

Intracellular biomolecular systems exhibit intrinsic stochasticity due to low molecular copy numbers, leading to multimodal probability distributions that play a crucial role in probabilistic differentiation and cellular decision-making. Controlling the dispersion of multimodal probability distributions in biomolecular systems is critical for regulating stochastic behavior, robustness, and adaptability. However, modifying system parameters to adjust dispersion often affects peak positions, potentially altering a desired phenotype or even fundamental behavior in a genetic pathway. In this paper, we establish a theoretical framework that enables independent control of dispersion while preserving peak positions and modality using the Chemical Fokker-Planck Equation (CFPE) and sharpness, a measure of probability concentration around individual peaks. By analyzing the steady-state solution of the CFPE, we derive explicit conditions under which peak sharpness can be tuned monotonically without changing peak positions or modality. We validate our approach through Monte Carlo simulations on a bimodal chemical system, demonstrating effective dispersion control while maintaining structural stability. This framework provides a systematic approach for designing biomolecular systems with tunable stochastic properties, contributing to advancements in synthetic biology and probabilistic cellular regulation.
Assessment of Cyberattack Detection-Isolation Algorithm for CAV Platoons Using SUMO
Authors: Sanchita Ghosh, Tanushree Roy
Date: 2025-03-18 00:00:00

A Connected Autonomous Vehicle (CAV) platoon in an evolving real-world driving environment relies strongly on accurate vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication for its safe and efficient operation. However, a cyberattack on this communication network can corrupt the appropriate control actions, tamper with system measurement, and drive the platoon to unsafe or undesired conditions. As a first step toward practicable resilience against such V2V-V2I attacks, in this paper, we implemented a unified V2V-V2I cyberattack detection scheme and a V2I isolation scheme for a CAV platoon under changing driving conditions in Simulation of Urban MObility (SUMO). The implemented algorithm utilizes vehicle-specific residual generators that are designed based on analytical disturbance-to-state stability, robustness, and sensitivity performance constraints. Our case studies include two driving scenarios where highway driving is simulated using the Next-Generation Simulation (NGSIM) data and urban driving follows the benchmark EPA Urban Dynamometer Driving Schedule (UDDS). The results validate the applicability of the algorithm to ensure CAV cybersecurity and demonstrate the promising potential for practical test-bed implementation in the future.
Inductive Position Sensors based on Coupling of Coils on Printed Circuit Boards for Demanding Automotive Applications
Authors: Stefan Kuntz, Gerald Gerlach, Sina Fella
Date: 2025-03-18 00:00:00
Tasks: Position

Rotor position feedback is required in many industrial and automotive applications, e.g. for field-oriented control of brushless motors. Traditionally, magnetic sensors, resolvers or optical encoders are used to measure the rotor position. However, advances in inductive sensing concepts enable a low-cost, high-precision position measurement principle which is robust against magnetic stray fields exceeding 4000 A/m. The operating principle is based on the coupling of a transmitter coil with several receiver coils in the megahertz frequency range. The coils are part of a printed circuit board (PCB) which also comprises circuitry for demodulation and signal processing. The transmitter coil induces eddy currents in an electrically conductive passive coupling element, which provides position-dependent amplitude modulation. The voltage induced in the receiver coils encodes the rotor angle information, typically in quadrature signals. The coupling element requires no rare-earth materials and can be made of stainless steel, for instance. The PCB-based design of the sensor offers considerable flexibility in optimizing its performance. By tailoring the coil geometry and arrangement, accuracy, air gap and overall sensor dimensions can be adjusted to meet a broad range of application-specific requirements. A sensor design sample exhibits a mechanical angle error less than 0.02{\deg} (0.1{\deg} electrical) in both, finite-element simulation and test bench measurement, with good agreement.
Sampling Decisions
Authors: Michael Chertkov, Sungsoo Ahn, Hamidreza Behjoo
Date: 2025-03-17 00:00:00

In this manuscript we introduce a novel Decision Flow (DF) framework for sampling from a target distribution while incorporating additional guidance from a prior sampler. DF can be viewed as an AI driven algorithmic reincarnation of the Markov Decision Process (MDP) approach in Stochastic Optimal Control. It extends the continuous space, continuous time path Integral Diffusion sampling technique to discrete time and space, while also generalizing the Generative Flow Network framework. In its most basic form, an explicit, Neural Network (NN) free formulation, DF leverages the linear solvability of the the underlying MDP to adjust the transition probabilities of the prior sampler. The resulting Markov Process is expressed as a convolution of the reverse time Green's function of the prior sampling with the target distribution. We illustrate the DF framework through an example of sampling from the Ising model, discuss potential NN based extensions, and outline how DF can enhance guided sampling across various applications.
A multi-model approach using XAI and anomaly detection to predict asteroid hazards
Authors: Amit Kumar Mondal, Nafisha Aslam, Prasenjit Maji, Hemanta Kumar Mondal
Date: 2025-03-20 00:00:00
Tasks: Anomaly Detection

The potential for catastrophic collision makes near-Earth asteroids (NEAs) a serious concern. Planetary defense depends on accurately classifying potentially hazardous asteroids (PHAs), however the complexity of the data hampers conventional techniques. This work offers a sophisticated method for accurately predicting hazards by combining machine learning, deep learning, explainable AI (XAI), and anomaly detection. Our approach extracts essential parameters like size, velocity, and trajectory from historical and real-time asteroid data. A hybrid algorithm improves prediction accuracy by combining several cutting-edge models. A forecasting module predicts future asteroid behavior, and Monte Carlo simulations evaluate the likelihood of collisions. Timely mitigation is made possible by a real-time alarm system that notifies worldwide monitoring stations. This technique enhances planetary defense efforts by combining real-time alarms with sophisticated predictive modeling.
The Exoplanet Citizen Science Pipeline: Human Factors and Machine Learning
Authors: Oisín Creaner, Anna Preis, Cormac Ryan, Nika Gorchakova
Date: 2025-03-18 00:00:00
Tasks: Friction

We present the progress of work to streamline and simplify the process of exoplanet observation by citizen scientists. International collaborations such as ExoClock and Exoplanet Watch enable citizen scientists to use small telescopes to carry out transit observations. These studies provide essential supports for space missions such as JWST and ARIEL. Contributions include maintenance or recovery of ephemerides, follow up confirmation and transit time variations. Ongoing observation programs benefit from a large pool of observers, with a wide variety of experience levels. Our projects work closely with these communities to streamline their observation pipelines and enable wider participation. Two complementary approaches are taken: Star Guide applies human-centric design and community consultation to identify points of friction within existing systems and provide complementary online tools and resources to reduce barriers to entry to the observing community. Machine Learning is used to accelerate data processing and automate steps which are currently manual, providing a streamlined tool for citizen science and a scalable solution for large-scale archival research.
Open Science and Artificial Intelligence for supporting the sustainability of the SRC Network: The espSRC case
Authors: J. Garrido, S. Sánchez-Expósito, A. Ruiz-Falcó, J. Ruedas, M. Á. Mendoza, V. Vázquez, M. Parra, J. Sánchez, I. Labadie, L. Darriba, J. Moldón, M. Rodriguez-Álvarez, J. Díaz, L. Verdes-Montenegro
Date: 2025-03-20 00:00:00
Tasks: Astronomy

The SKA Observatory (SKAO), a landmark project in radio astronomy, seeks to address fundamental questions in astronomy. To process its immense data output, approximately 700 PB/year, a global network of SKA Regional Centres (SR-CNet) will provide the infrastructure, tools, computational power needed for scientific analysis and scientific support. The Spanish SRC (espSRC) focuses on ensuring the sustainability of this network by reducing its environmental impact, integrating green practices into data platforms, and developing Open Science technologies to enable reproducible research. This paper discusses and summarizes part of the research and development activities that the team is conducting to reduce the SRC energy consumption at the espSRC and SRCNet. The paper also discusses fundamental research on trusted repositories to support Open Science practices.
DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration
Authors: Suraj Singh, Anastasia Batsheva, Oleg Y. Rogov, Ahmed Bouridane
Date: 2025-03-20 00:00:00
Tasks: Image Reconstruction, Image Restoration, Super-Resolution

Contemporary image restoration and super-resolution techniques effectively harness deep neural networks, markedly outperforming traditional methods. However, astrophotography presents unique challenges for deep learning due to limited training data. This work explores hybrid strategies, such as the Deep Image Prior (DIP) model, which facilitates blind training but is susceptible to overfitting, artifact generation, and instability when handling noisy images. We propose enhancements to the DIP model's baseline performance through several advanced techniques. First, we refine the model to process multiple frames concurrently, employing the Back Projection method and the TVNet model. Next, we adopt a Markov approach incorporating Monte Carlo estimation, Langevin dynamics, and a variational input technique to achieve unbiased estimates with minimal variance and counteract overfitting effectively. Collectively, these modifications reduce the likelihood of noise learning and mitigate loss function fluctuations during training, enhancing result stability. We validated our algorithm across multiple image sets of astronomical and celestial objects, achieving performance that not only mitigates limitations of Lucky Imaging, a classical computer vision technique that remains a standard in astronomical image reconstruction but surpasses the original DIP model, state of the art transformer- and diffusion-based models, underscoring the significance of our improvements.
Natural Quantization of Neural Networks
Authors: Richard Barney, Djamil Lakhdar-Hamina, Victor Galitski
Date: 2025-03-19 00:00:00
Tasks: Quantization

We propose a natural quantization of a standard neural network, where the neurons correspond to qubits and the activation functions are implemented via quantum gates and measurements. The simplest quantized neural network corresponds to applying single-qubit rotations, with the rotation angles being dependent on the weights and measurement outcomes of the previous layer. This realization has the advantage of being smoothly tunable from the purely classical limit with no quantum uncertainty (thereby reproducing the classical neural network exactly) to a quantum case, where superpositions introduce an intrinsic uncertainty in the network. We benchmark this architecture on a subset of the standard MNIST dataset and find a regime of "quantum advantage," where the validation error rate in the quantum realization is smaller than that in the classical model. We also consider another approach where quantumness is introduced via weak measurements of ancilla qubits entangled with the neuron qubits. This quantum neural network also allows for smooth tuning of the degree of quantumness by controlling an entanglement angle, $g$, with $g=\frac\pi 2$ replicating the classical regime. We find that validation error is also minimized within the quantum regime in this approach. We also observe a quantum transition, with sharp loss of the quantum network's ability to learn at a critical point $g_c$. The proposed quantum neural networks are readily realizable in present-day quantum computers on commercial datasets.
Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on Compressed Spatial Tokens
Authors: Shuqi Lu, Haowei Lin, Lin Yao, Zhifeng Gao, Xiaohong Ji, Weinan E, Linfeng Zhang, Guolin Ke
Date: 2025-03-20 00:00:00
Tasks: 3D Generation

Recent advancements in large language models and their multi-modal extensions have demonstrated the effectiveness of unifying generation and understanding through autoregressive next-token prediction. However, despite the critical role of 3D structural generation and understanding (3D GU) in AI for science, these tasks have largely evolved independently, with autoregressive methods remaining underexplored. To bridge this gap, we introduce Uni-3DAR, a unified framework that seamlessly integrates 3D GU tasks via autoregressive prediction. At its core, Uni-3DAR employs a novel hierarchical tokenization that compresses 3D space using an octree, leveraging the inherent sparsity of 3D structures. It then applies an additional tokenization for fine-grained structural details, capturing key attributes such as atom types and precise spatial coordinates in microscopic 3D structures. We further propose two optimizations to enhance efficiency and effectiveness. The first is a two-level subtree compression strategy, which reduces the octree token sequence by up to 8x. The second is a masked next-token prediction mechanism tailored for dynamically varying token positions, significantly boosting model performance. By combining these strategies, Uni-3DAR successfully unifies diverse 3D GU tasks within a single autoregressive framework. Extensive experiments across multiple microscopic 3D GU tasks, including molecules, proteins, polymers, and crystals, validate its effectiveness and versatility. Notably, Uni-3DAR surpasses previous state-of-the-art diffusion models by a substantial margin, achieving up to 256\% relative improvement while delivering inference speeds up to 21.8x faster. The code is publicly available at https://github.com/dptech-corp/Uni-3DAR.
Information maximization for a broad variety of multi-armed bandit games
Authors: Alex Barbier-Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson
Date: 2025-03-20 00:00:00
Tasks: Decision Making

Information and free-energy maximization are physics principles that provide general rules for an agent to optimize actions in line with specific goals and policies. These principles are the building blocks for designing decision-making policies capable of efficient performance with only partial information. Notably, the information maximization principle has shown remarkable success in the classical bandit problem and has recently been shown to yield optimal algorithms for Gaussian and sub-Gaussian reward distributions. This article explores a broad extension of physics-based approaches to more complex and structured bandit problems. To this end, we cover three distinct types of bandit problems, where information maximization is adapted and leads to strong performance. Since the main challenge of information maximization lies in avoiding over-exploration, we highlight how information is tailored at various levels to mitigate this issue, paving the way for more efficient and robust decision-making strategies.