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At least 1,153 records · Page 64

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Universal image representation based on a multimodal graph

A system for classifying a target image with segments having attributes is provided. The system generates a graph for the target image that includes vertices representing segments of the image and edges representing relationships between the connected vertices. For each vertex, the system generates a subgraph that includes the vertex as a home vertex and neighboring vertices representing segments of the target image within a neighborhood of the segment represented by the home vertex. The system applies an autoencoder to each subgraph to generate latent variables to represent the subgraph. The system applies a machine learning algorithm to a feature vector comprising a universal image representation of the target image that is derived from the generated latent variables of the subgraphs to generate a classification for the target image.

Bremer, Peer-Timo↗

Do graph neural networks learn traditional jet substructure?

At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods. Graph neural networks have been used to address this task by treating jets as point clouds with underlying, learnable, edge connections between the particles inside. We explore the decision-making process for one such state-of-the-art network, ParticleNet, by looking for relevant edge connections identified using the layerwise-relevance propagation technique. As the model is trained, we observe changes in the distribution of relevant edges connecting different intermediate clusters of particles, known as subjets. The resulting distribution of subjet connections is different for signal jets originating from top quarks, whose subjets typically correspond to its three decay products, and background jets originating from lighter quarks and gluons. This behavior indicates that the model is using traditional jet substructure observables, such as the number of prongs -- energetic particle clusters -- within a jet, when identifying jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗

Universal image representation based on a multimodal graph

A system for classifying a target image with segments having attributes is provided. The system generates a graph for the target image that includes vertices representing segments of the image and edges representing relationships between the connected vertices. For each vertex, the system generates a subgraph that includes the vertex as a home vertex and neighboring vertices representing segments of the target image within a neighborhood of the segment represented by the home vertex. The system applies an autoencoder to each subgraph to generate latent variables to represent the subgraph. The system applies a machine learning algorithm to a feature vector comprising a universal image representation of the target image that is derived from the generated latent variables of the subgraphs to generate a classification for the target image.

Bremer, Peer-Timo↗

Design of a Particle Shadow-graph Velocimetry and Size (PSVS) System to Determine Particle Size and Density Distributions in Hanford Nuclear Tank Wastes - 12280

Accurate particle size and density distributions for nuclear tank waste materials are essential information that helps determine the engineering requirements for a host of waste management unit operations (e.g., tank mixing, pipeline transport, and filtration). The most prevalent approach for determining particle size and density distribution is highly laborious and involves identifying individual particles using scanning electron microscope/x-ray diffraction and then acquiring the density of the materials from the technical literature. Other methods simply approximate individual particle densities by assuming chemical composition, rather than obtaining actual measurements of particle density. To overcome these limitations, a Particle Shadow-graph Velocimetry and Size (PSVS) system has been designed to simultaneously obtain particle size and density distributions for a broad range of Hanford tank waste materials existing as both individual particles and agglomerates. The PSVS system uses optical hardware, a temperature-controlled settling column, and particle introduction chamber to accurately and reproducibly obtain images of settling particles. Image analysis software provides a highly accurate determination of both particle terminal velocity and equivalent spherical particle diameter. The particle density is then calculated from Newton's terminal settling theory. The PSVS system was designed to accurately image particle/agglomerate sizes between 10 and 1000 μm and particle/agglomerate densities ranging from 1.4 to 11.5 g/cm{sup 3}, where the maximum terminal velocity does not exceed 10 cm/s. Preliminary testing was completed with standard materials and results were in good agreement with terminal settling theory. Recent results of this method development are presented, as well as experimental design. The primary goal of these PSVS system tests was to obtain accurate and reproducible particle size and velocity measurements to estimate particle densities within 20 percent of direct measured density values. The average percent error observed in particle densities was 4.8, 8.2, and 25.8 percent for S1D3 (high-density soda-lime glass), S3D4 (zirconia), and S1D2 (soda-lime glass) beads, respectively. The maximum measured particle density error observed was 14.5, 21.0, and 58.4, for S1D3, S3D4, and S1D2 beads, respectively. Multiple sources of error can exist that contribute to inaccurate estimates of particle density using the PSVS approach. Two significant influences observed were thermal convective currents in the settling column fluid and non-spherical (irregular) particle shapes in the standard test materials. Experimental results using buoyant beads suggest thermal convective currents can influence the movement of settling particles as much as 4 mm/s. These thermal convective currents are clearly a significant concern for smaller and less-dense particles/agglomerates which have terminal velocities of the same magnitude. Significant effort went into the design and fabrication of a thermal control system for the PSVS settling column. However, small temperature variations (<0.3 deg. C) are difficult to prevent and measure accurately. Therefore, although designing the thermal control system was necessary, it is not considered the only solution to minimize error due to thermal convective currents. The use of small diameter (10 to 27 μm) polyethylene buoyant beads (in low concentrations) during actual settling experiments may be necessary to provide insight into particle-particle interactions and fluid velocity correction factors to account for potential influences from thermal convective currents. Future testing will incorporate the buoyant beads into the analysis technique when the suspension fluid is chemically compatible. The use of the Newton's law to calculate particle/agglomerate densities assumes spherical particle shapes. As the actual particle/agglomerates diverge from spherical, the margin of error increases. The selection of high quality (in density and shape) monodispersed particle standards was a direct attempt to reduce the uncertainties associated with non-spherical or irregular particles and to only observe the PSVS method and analysis error. While investigating the source of data scatter for the S1D3 bead (nominal 700 μm) standard, significant irregular particles where discovered. Yet, a subsequent investigation into the S3D4 bead standard was also conducted and it was determined that 60 percent of the particles had a 0.9 to 0.8 centricity, 32 percent were between 0.8 and 0.7, only 7 percent were between 1.0 and 0.9, and the remaining 1 percent of the particles had centricities between 0.7 and 0.6. Contrast this with the S1D3 bead standard, where 100 percent of particles had centricities between 0.95 and 0.98, upon applying the centricity filter. The influence of the non-spherical shape of the S3D4 bead standard is evident when comparing a 3.9 percent average density error for S1D3 glass bead standard with an 8.3 percent average density error for the S3D4 bead standard. Model corrections for non-spherical shape must be implemented to account for these measurement errors during future method validation and system performance assessments. In addition, further work is needed to resolve the higher than expected velocities for the smaller S1D2 particles. Initial measurements conducted with buoyant beads as fluid velocity tracers indicate that fluid velocities can be subtracted from the increased particle velocities to determine the particle terminal velocity, regardless of the surrounding fluid velocity. However, this approach is in its infancy and will need to be carefully and methodically validated. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

State of the art astronomical simulations have provided datasets which enabled the training of novel deep learning techniques for constraining cosmological parameters. However, differences in subgrid physics implementation and numerical approximations among simulation suites lead to differences in simulated datasets, which pose a hard challenge when trying to generalize across diverse data domains and ultimately when applying models to observational data. Recent work reveals deep learning algorithms are able to extract more information from complex cosmological simulations than summary statistics like power spectra. We introduce Domain Adaptive Graph Neural Networks (DA-GNNs), trained on CAMELS data, inspired by CosmoGraphNet (Villanueva-Domingo et al 2023). By utilizing GNNs, we can capitalize on their capacity to capture both astrophysical and topological features of galaxy distributions. Mixing these capabilities with domain adaptation techniques such as Maximum Mean Discrepancy (MMD), which enable extraction of domain-invariant features, our framework demonstrates enhanced accuracy and robustness. We present experimental results, including the alignment of distributions across domains through data visualization. These findings suggest that DA-GNNs are an efficient way of extracting domain independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗

A Graph Neural Network Surrogate Model for hls4ml

Recent advancements in use of machine learning (ML) techniques on field-programmable gate arrays (FPGAs) have allowed for the implementation of embedded neural networks with extremely low latency. This is invaluable for particle detectors at the Large Hadron Collider, where latency and used area are strictly bounded. The hls4ml framework is a procedure that converts trained ML model software to a synthesis result to can be used on an FPGA. However, running the pipeline is a time-consuming procedure, and there is a strong risk of failure. In particular, it may not be possible to successfully convert a model into a synthesis result, or the resource consumption of the model may exceed the resources of the target FPGA. To aid with this development, we introduce wa-hls4ml, a surrogate model using a graph neural network to emulate the structure of the source models. The goal is to estimate the chance of success and resource consumption of a given model when passed through the hls4ml pipeline, without needing to run the pipeline.

Plotnikov, Dennis↗

A Study on Contrastive Graph Neural Network Pretraining for Predicting Transcriptome Profiles

We study graph neural network learning for transcriptomics with limited amount of labeled data. Our study reveals that simple GNN architectures perform well and do not suffer from over-fitting as the more sophisticated ones. Our study shows that although contrastive learning as a pretraining strategy has been successful in predicting properties such as formation and binding energy, it is not effective for transcriptomics.

Ma, Jiaji [University of Virginia]↗

Explainable Graph Learning for Particle Accelerator Operations

Particle accelerators are vital tools in physics, medicine, and industry, requiring precise tuning to ensure optimal beam performance. However, real-world deviations from idealized simulations make beam tuning a time-consuming and error-prone process. In this work, we propose an explanation-driven framework for providing actionable insight into beamline operations, with a focus on the injector beamline at the Continuous Electron Beam Accelerator Facility (CEBAF). We represent beamline configurations as heterogeneous graphs, where setting nodes represent elements that human operators can actively adjust during beam tuning, and reading nodes passively provide diagnostic feedback. To identify the most influential setting nodes responsible for differences between any two beamline configurations, our approach first predicts the resulting changes in reading nodes caused by variations in settings, and then learns importance scores that capture the joint influence of multiple setting nodes. Experimental results on real-world CEBAF injector data demonstrate the framework’s ability to generate interpretable insights that can assist human operators in beamline tuning and reduce operational overhead.

Wang, Song [Univ. of Virginia, Charlottesville, VA↗

Modeling Cyber Supply Chain Incidents with Multilayered Graph Motifs

As noted within the literature, supply chain includes people and organizations---manufacturers, integrators, and third-party vendors---that are involved in one or more stages of a product lifecycle. Since supply chains, by definition, include organizations and people, supply chain risk management activities must consider dependencies between an organization's business processes and third-party resources. Just as adversarial tactics can be implemented via techniques implemented via networked computer systems, so can such tactics be expressed via legal business relationships. A cyber incident may have an exponential impact downstream, for example, by leveraging a product's distribution channel (e.g. malicious updates in SolarWinds, buggy updates in CrowdStrike). Similarly, legitimate and legal business relationships also affect the attack surface exposure of systems, enabling long-term persistence and/or unknown impacts to product quality that are hard to detect. This paper catalogs several recent digital supply chain incidents and applies a multilayered network formalism to develop structural indicators (graph motifs) that reflect potentially-adversarial behavior. Finally, we compare and contrast the characteristics of adversarial tactics (e.g. Loss of Availability, Data Collection) that leverage cyber-physical dependencies to those that leverage legal organizational relationships.

97 - MATHEMATICS AND COMPUTING↗

Automated Generation of Graph-based Cyber Threat Intel

With the advancement of AI technology and tools, specifically in the cybersecurity domain, both cyber defenders and threat actors are continuously adapting the use of these capabilities to expedite their operations. With this phenomenon, threat intelligence that is up to date, refreshable, and has relevant context to a specific threat becomes more and more important as it enables cybersecurity professionals to gain insight into relevant data and relationships to guide their operations. This project enables users to frequently aggregate threat intelligence from various sources, such as vendor vulnerability advisories affecting critical infrastructure, malware reports, and adversary writeups into a centralized, standardized database. The project utilizes the Structured Threat Intelligence eXpression (STIX) for a standardized, shareable threat intelligence data format and Neo4j as a graph database solution to store STIX nodes and relationships. Initial results of the project include datasets of over 8,000 nodes and 20,000 relationships extracted from over 500 data sources that have been released within the past month.

Threat Intelligence↗

Fault Graph (fg)

This software package offers a novel fault graph modeling language and a simulation tool for executing models specified in the language. The modeling language has three primary features that distinguish it from similar reliability analysis tools. These are (1) a random variable modeling several distinct outcomes of a single fault; (2) chains of faults in which one fault triggers another; and (3) time to fail sampled from probability distributions including positive normal, exponential, Weibull with a minimum, or immediate.

Nutaro, James↗

Graph-based featurization methods for classifying small molecule compounds

For over a decade, drug-induced liver injury (DILI) has posed significant drawbacks in the synthesis and development of drugs and remains a consequential concern. With finite success within the existing preclinical models, DILI is one of the main causes of drug withdrawal or termination from the market. Particularly, this withdrawal occurs during the late stages of drug development (Kullak-Ublick, 2017). Since DILI is difficult to diagnose and treat, it has become an obstacle in the drug production market that in turn affects clinicians, pharmaceutical companies, and consumers. We propose a method for learning features of DILI-positive drugs based on the graphical relationships and patterns they possess within a network of biological databases. We also train various statistical and machine learning models on these learned features in order to classify the drugs as DILI-positive or negative. Our methods include Random Forest, Neural networks, and logistic regression classification. We utilize labeled DILI-positive and DILI-negative datasets, which were developed by the FDA and the National center for toxicological research, as well as additional literature datasets (Thakkar, 2020) in order to validate our results and assess our featurization and model accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗