pwc-archive/papers-with-abstracts huggingface dataset:
Data-Driven Neural Certificate Synthesis
Authors: Luke Rickard, Alessandro Abate, Kostas Margellos
Date: 2025-02-08 00:00:00
Tasks: Generalization Bounds, PAC learning

We investigate the problem of verifying different properties of discrete time dynamical systems, namely, reachability, safety and reach-while-avoid. To achieve this, we adopt a data driven perspective and using past systems' trajectories as data, we aim at learning a specific function termed \emph{certificate} for each property we wish to verify. The certificate construction problem is treated as a safety informed neural network training process, where we use a neural network to learn the parameterization of each certificate, while the loss function we seek to minimize is designed to encompass conditions on the certificate to be learned that encode the satisfaction of the associated property. Besides learning a certificate, we quantify probabilistically its generalization properties, namely, how likely it is for a certificate to be valid (and hence for the associated property to be satisfied) when it comes to a new system trajectory not included in the training data set. We view this problem under the realm of probably approximately correct (PAC) learning under the notion of compression, and use recent advancements of the so-called scenario approach to obtain scalable generalization bounds on the learned certificates. To achieve this, we design a novel algorithm that minimizes the loss function and hence constructs a certificate, and at the same time determines a quantity termed compression, which is instrumental in obtaining meaningful probabilistic guarantees. This process is novel per se and provides a constructive mechanism for compression set calculation, thus opening the road for its use to more general non-convex optimization problems. We verify the efficacy of our methodology on several numerical case studies, and compare it (both theoretically and numerically) with closely related results on data-driven property verification.
Prescribed-Time Newton Extremum Seeking using Delays and Time-Periodic Gains
Authors: Nicolas Espitia, Miroslav Krstic, Jorge I. Poveda
Date: 2025-02-08 00:00:00

We study prescribed-time extremum seeking (ES) for scalar maps in the presence of time delay. The problem has been solved by Yilmaz and Krstic using chirpy probing and time-varying singular gains. To alleviate the gain singularity, we present an alternative approach, employing delays with bounded time-periodic gains, for achieving prescribed-time convergence to the extremum. Our results are not extensions or refinements but a new methodological direction, even in the absence of the delay on the map. The main result we present compensates the map's delay and uses perturbation-based and the Newton (rather than gradient) approaches. The simultaneous presence of perturbation period, and two delays -- a map delay and a seeking feedback delay -- whose values are different (feedback delay must be longer than map delay), makes for an intricate situation in the design and analysis. ES can settle arbitrarily soon after four times the map delay. In the absence of a map delay, the settling time is arbitrarily short, with feedback delay chosen as one quarter of the prescribed settling time, i.e., the search settles after four times any positive feedback delay. In addition to removing the gain singularity of the Yilmaz-Krstic singular-gain prescribed-time ES, we go beyond that method's limitation to operating only up to the terminal time. With the help of averaging theorems in infinite dimension, we conduct a prescribed-time convergence analysis on a suitable perturbation-averaged \textit{target} ES system, which contains the time-periodic gains of the map and feedback delays. Since the notion of ``dead-beat'' Lyapunov stabilization by time-periodic delayed feedback originates from Hale and Verduyn-Lunel (analysis, 1993) and Karafyllis (feedback design, 2006), we refer to our approach to prescribed-time ES as the ``Karafyllis, Hale, Verduyn-Lunel" (KHV) PT-ES approach.
Distributionally Robust Model Predictive Control with Mixture of Gaussian Processes
Authors: Jingyi Wu, Chao Ning
Date: 2025-02-08 00:00:00
Tasks: Gaussian Processes, Model Predictive Control

Despite the success of Gaussian process based Model Predictive Control (MPC) in robotic control, its applicability scope is greatly hindered by multimodal disturbances that are prevalent in real-world settings. Here we propose a novel Mixture of Gaussian Processes based Distributionally Robust MPC (MoGP-DR-MPC) framework for linear time invariant systems subject to potentially multimodal state-dependent disturbances. This framework utilizes MoGP to automatically determine the number of modes from disturbance data. Using the mean and variance information provided by each mode-specific predictive distribution, it constructs a data-driven state-dependent ambiguity set, which allows for flexible and fine-grained disturbance modeling. Based on this ambiguity set, we impose Distributionally Robust Conditional Value-at Risk (DR-CVaR) constraints to effectively achieve distributional robustness against errors in the predictive distributions. To address the computational challenge posed by these constraints in the resulting MPC problem, we equivalently reformulate the DR-CVaR constraints into tractable second-order cone constraints. Furthermore, we provide theoretical guarantees on the recursive feasibility and stability of the proposed framework. The enhanced control performance of MoGP-DR-MPC is validated through both numerical experiments and simulations on a quadrotor system, demonstrating notable reductions in closed-loop cost by 17% and 4% respectively compared against Gaussian process based MPC.
Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder
Authors: Saba Sanami, Amir G. Aghdam
Date: 2025-02-08 00:00:00
Tasks: Anomaly Detection, Diagnostic

Reliable aero-engine anomaly detection is crucial for ensuring aircraft safety and operational efficiency. This research explores the application of the Fisher autoencoder as an unsupervised deep learning method for detecting anomalies in aero-engine multivariate sensor data, using a Gaussian mixture as the prior distribution of the latent space. The proposed method aims to minimize the Fisher divergence between the true and the modeled data distribution in order to train an autoencoder that can capture the normal patterns of aero-engine behavior. The Fisher divergence is robust to model uncertainty, meaning it can handle noisy or incomplete data. The Fisher autoencoder also has well-defined latent space regions, which makes it more generalizable and regularized for various types of aero-engines as well as facilitates diagnostic purposes. The proposed approach improves the accuracy of anomaly detection and reduces false alarms. Simulations using the CMAPSS dataset demonstrate the model's efficacy in achieving timely anomaly detection, even in the case of an unbalanced dataset.
Low Dimensional Koopman Generalized Eigenfunctions Representation: An Approach to Address Koopman High-Dimensionality Problem
Authors: Simone Martini, Margareta Stefanovic, Kimon P. Valavanis
Date: 2025-02-07 00:00:00

This Paper introduces a methodology to achieve a lower dimensional Koopman quasi linear representation of nonlinear dynamics using Koopman generalized eigenfunctions. The methodology is presented for the analytically derived Koopman formulation of rigid body dynamics but can be generalized to any data-driven or analytically derived generalized eigenfunction set. The presented approach aim at achieving a representation for which the number of Koopman observables matches the number of input leading to an exact linearization solution instead of resorting to the least square approximation method. The methodology is tested by designing a linear quadratic (LQ) flight controller of a quadrotor unmanned aerial vehicle (UAV). Hardware in the loop simulations validate the applicability of this approach to real-time implementation in presence of noise and sensor delays.
Multi-Objective Mobile Damped Wave Algorithm (MOMDWA): A Novel Approach For Quantum System Control
Authors: Juntao Yu, Jiaquan Yu, Dedai Wei, Xinye Sha, Shengwei Fu, Miuyu Qiu, Yurun Jin, Kaichen Ouyang
Date: 2025-02-06 00:00:00

In this paper, we introduce a novel multi-objective optimization algorithm, the Multi-Objective Mobile Damped Wave Algorithm (MOMDWA), specifically designed to address complex quantum control problems. Our approach extends the capabilities of the original Mobile Damped Wave Algorithm (MDWA) by incorporating multiple objectives, enabling a more comprehensive optimization process. We applied MOMDWA to three quantum control scenarios, focusing on optimizing the balance between control fidelity, energy consumption, and control smoothness. The results demonstrate that MOMDWA significantly enhances quantum control efficiency and robustness, achieving high fidelity while minimizing energy use and ensuring smooth control pulses. This advancement offers a valuable tool for quantum computing and other domains requiring precise, multi-objective control.
Exoplanet Transit Candidate Identification in TESS Full-Frame Images via a Transformer-Based Algorithm
Authors: Helem Salinas, Rafael Brahm, Greg Olmschenk, Richard K. Barry, Karim Pichara, Stela Ishitani Silva, Vladimir Araujo
Date: 2025-02-11 00:00:00
Tasks: Time Series

The Transiting Exoplanet Survey Satellite (TESS) is surveying a large fraction of the sky, generating a vast database of photometric time series data that requires thorough analysis to identify exoplanetary transit signals. Automated learning approaches have been successfully applied to identify transit signals. However, most existing methods focus on the classification and validation of candidates, while few efforts have explored new techniques for the search of candidates. To search for new exoplanet transit candidates, we propose an approach to identify exoplanet transit signals without the need for phase folding or assuming periodicity in the transit signals, such as those observed in multi-transit light curves. To achieve this, we implement a new neural network inspired by Transformers to directly process Full Frame Image (FFI) light curves to detect exoplanet transits. Transformers, originally developed for natural language processing, have recently demonstrated significant success in capturing long-range dependencies compared to previous approaches focused on sequential data. This ability allows us to employ multi-head self-attention to identify exoplanet transit signals directly from the complete light curves, combined with background and centroid time series, without requiring prior transit parameters. The network is trained to learn characteristics of the transit signal, like the dip shape, which helps distinguish planetary transits from other variability sources. Our model successfully identified 214 new planetary system candidates, including 122 multi-transit light curves, 88 single-transit and 4 multi-planet systems from TESS sectors 1-26 with a radius > 0.27 $R_{\mathrm{Jupiter}}$, demonstrating its ability to detect transits regardless of their periodicity.
Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk H$α$ Images
Authors: GaoFei Zhu, GangHua Lin, Xiao Yang, Cheng Zeng
Date: 2025-02-11 00:00:00

Solar filaments are one of the most prominent features observed on the Sun, and their evolutions are closely related to various solar activities, such as flares and coronal mass ejections. Real-time automated identification of solar filaments is the most effective approach to managing large volumes of data. Existing models of filament identification are characterized by large parameter sizes and high computational costs, which limit their future applications in highly integrated and intelligent ground-based and space-borne observation devices. Consequently, the design of more lightweight models will facilitate the advancement of intelligent observation equipment. In this study, we introduce Flat U-Net, a novel and highly efficient ultralightweight model that incorporates simplified channel attention (SCA) and channel self-attention (CSA) convolutional blocks for the segmentation of solar filaments in full-disk H$\alpha$ images. Feature information from each network layer is fully extracted to reconstruct interchannel feature representations. Each block effectively optimizes the channel features from the previous layer, significantly reducing parameters. The network architecture presents an elegant flattening, improving its efficiency, and simplifying the overall design. Experimental validation demonstrates that a model composed of pure SCAs achieves a precision of approximately 0.93, with dice similarity coefficient (DSC) and recall rates of 0.76 and 0.64, respectively, significantly outperforming the classical U-Net. Introducing a certain number of CSA blocks improves the DSC and recall rates to 0.82 and 0.74, respectively, which demonstrates a pronounced advantage, particularly concerning model weight size and detection effectiveness. The data set, models, and code are available as open-source resources.
Deep Generative model that uses physical quantities to generate and retrieve solar magnetic active regions
Authors: Subhamoy Chatterjee, Andres Munoz-Jaramillo, Anna Malanushenko
Date: 2025-02-07 00:00:00
Tasks: Generative Adversarial Network

Deep generative models have shown immense potential in generating unseen data that has properties of real data. These models learn complex data-generating distributions starting from a smaller set of latent dimensions. However, generative models have encountered great skepticism in scientific domains due to the disconnection between generative latent vectors and scientifically relevant quantities. In this study, we integrate three types of machine learning models to generate solar magnetic patches in a physically interpretable manner and use those as a query to find matching patches in real observations. We use the magnetic field measurements from Space-weather HMI Active Region Patches (SHARPs) to train a Generative Adversarial Network (GAN). We connect the physical properties of GAN-generated images with their latent vectors to train Support Vector Machines (SVMs) that do mapping between physical and latent spaces. These produce directions in the GAN latent space along which known physical parameters of the SHARPs change. We train a self-supervised learner (SSL) to make queries with generated images and find matches from real data. We find that the GAN-SVM combination enables users to produce high-quality patches that change smoothly only with a prescribed physical quantity, making generative models physically interpretable. We also show that GAN outputs can be used to retrieve real data that shares the same physical properties as the generated query. This elevates Generative Artificial Intelligence (AI) from a means-to-produce artificial data to a novel tool for scientific data interrogation, supporting its applicability beyond the domain of heliophysics.
Evolution of cooperation in a bimodal mixture of conditional cooperators
Authors: Chenyang Zhao, Xinshi Feng, Guozhong Zheng, Weiran Cai, Jiqiang Zhang, Li Chen
Date: 2025-02-11 00:00:00
Tasks: Q-Learning

Extensive behavioral experiments reveal that conditional cooperation is a prevalent phenomenon. Previous game-theoretical studies have predominantly relied on hard-manner models, where cooperation is triggered only upon reaching a specific threshold. However, this approach contrasts with the observed flexibility of human behaviors, where individuals adapt their strategies dynamically based on their surroundings. To capture this adaptability, we introduce a soft form of conditional cooperation by integrating the Q-learning algorithm from reinforcement learning. In this form, players not only reciprocate mutual cooperation but may also defect in highly cooperative environments or cooperate in less cooperative settings to maximize rewards. To explore the effects of hard and soft conditional cooperators, we examine their interactions in two scenarios: structural mixture (SM) and probabilistic mixture (PM), where the two behavioral modes are fixed and probabilistically adopted, respectively. In SM, hard conditional cooperators enhance cooperation when the threshold is low but hinder it otherwise. Surprisingly, in PM, the cooperation prevalence exhibits two first-order phase transitions as the probability is varied, leading to high, low, and vanishing levels of cooperation. Analysis of Q-tables offers insights into the "psychological shifts" of soft conditional cooperators and the overall evolutionary dynamics. Model extensions confirm the robustness of our findings. These results highlight the novel complexities arising from the diversity of conditional cooperators.
A note on the physical interpretation of neural PDE's
Authors: Sauro Succi
Date: 2025-02-10 00:00:00

We highlight a formal and substantial analogy between Machine Learning (ML) algorithms and discrete dynamical systems (DDS) in relaxation form. The analogy offers a transparent interpretation of the weights in terms of physical information-propagation processes and identifies the model function of the forward ML step with the local attractor of the corresponding discrete dynamics. Besides improving the explainability of current ML applications, this analogy may also facilitate the development of a new class ML algorithms with a reduced number of weights.
Complexity and accessibility of random landscapes
Authors: Sakshi Pahujani, Joachim Krug
Date: 2025-02-09 00:00:00

These notes introduce probabilistic landscape models defined on high-dimensional discrete sequence spaces. The models are motivated primarily by fitness landscapes in evolutionary biology, but links to statistical physics and computer science are mentioned where appropriate. Elementary and advanced results on the structure of landscapes are described with a focus on features that are relevant to evolutionary searches, such as the number of local maxima and the existence of fitness-monotonic paths. The recent discovery of submodularity as a biologically meaningful property of fitness landscapes and its consequences for their accessibility is discussed in detail.
Parameter Symmetry Potentially Unifies Deep Learning Theory
Authors: Liu Ziyin, Yizhou Xu, Tomaso Poggio, Isaac Chuang
Date: 2025-02-07 00:00:00
Tasks: Deep Learning, Learning Theory, Position

The dynamics of learning in modern large AI systems is hierarchical, often characterized by abrupt, qualitative shifts akin to phase transitions observed in physical systems. While these phenomena hold promise for uncovering the mechanisms behind neural networks and language models, existing theories remain fragmented, addressing specific cases. In this position paper, we advocate for the crucial role of the research direction of parameter symmetries in unifying these fragmented theories. This position is founded on a centralizing hypothesis for this direction: parameter symmetry breaking and restoration are the unifying mechanisms underlying the hierarchical learning behavior of AI models. We synthesize prior observations and theories to argue that this direction of research could lead to a unified understanding of three distinct hierarchies in neural networks: learning dynamics, model complexity, and representation formation. By connecting these hierarchies, our position paper elevates symmetry -- a cornerstone of theoretical physics -- to become a potential fundamental principle in modern AI.
Is attention all you need to solve the correlated electron problem?
Authors: Max Geier, Khachatur Nazaryan, Timothy Zaklama, Liang Fu
Date: 2025-02-07 00:00:00
Tasks: All, Variational Monte Carlo

The attention mechanism has transformed artificial intelligence research by its ability to learn relations between objects. In this work, we explore how a many-body wavefunction ansatz constructed from a large-parameter self-attention neural network can be used to solve the interacting electron problem in solids. By a systematic neural-network variational Monte Carlo study on a moir\'e quantum material, we demonstrate that the self-attention ansatz provides an accurate and efficient solution without human bias. Moreover, our numerical study finds that the required number of variational parameters scales roughly as $N^2$ with the number of electrons, which opens a path towards efficient large-scale simulations.
Explainable Multimodal Machine Learning for Revealing Structure-Property Relationships in Carbon Nanotube Fibers
Authors: Daisuke Kimura, Naoko Tajima, Toshiya Okazaki, Shun Muroga
Date: 2025-02-11 00:00:00

In this study, we propose Explainable Multimodal Machine Learning (EMML), which integrates the analysis of diverse data types (multimodal data) using factor analysis for feature extraction with Explainable AI (XAI), for carbon nanotube (CNT) fibers prepared from aqueous dispersions. This method is a powerful approach to elucidate the mechanisms governing material properties, where multi-stage fabrication conditions and multiscale structures have complex influences. Thus, in our case, this approach helps us understand how different processing steps and structures at various scales impact the final properties of CNT fibers. The analysis targeted structures ranging from the nanoscale to the macroscale, including aggregation size distributions of CNT dispersions and the effective length of CNTs. Furthermore, because some types of data were difficult to interpret using standard methods, challenging-to-interpret distribution data were analyzed using Negative Matrix Factorization (NMF) for extracting key features that determine the outcome. Contribution analysis with SHapley Additive exPlanations (SHAP) demonstrated that small, uniformly distributed aggregates are crucial for improving fracture strength, while CNTs with long effective lengths are significant factors for enhancing electrical conductivity. The analysis also identified thresholds and trends for these key factors to assist in defining the conditions needed to optimize CNT fiber properties. EMML is not limited to CNT fibers but can be applied to the design of other materials derived from nanomaterials, making it a useful tool for developing a wide range of advanced materials. This approach provides a foundation for advancing data-driven materials research.
PICTS: A Novel Deep Reinforcement Learning Approach for Dynamic P-I Control in Scanning Probe Microscopy
Authors: Ziwei Wei, Shuming Wei, Qibin Zeng, Wanheng Lu, Huajun Liu, Kaiyang Zeng
Date: 2025-02-11 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

We have developed a Parallel Integrated Control and Training System, leveraging the deep reinforcement learning to dynamically adjust the control strategies in real time for scanning probe microscopy techniques.