Graph Neural Network Models of Multiphase Flow from Boundary Integral Methods
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A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale
Abstract Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large‐scale power infrastructures as cyber‐physical systems is still in its early stages. Drawing inspiration from machine‐learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large‐scale cyber‐physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high‐dimensional structures in low‐dimensional spaces, facilitating advanced machine‐learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. The proposed graph embedding‐based risk analysis framework aims to provide a more insightful perspective on cyber‐physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber‐physical power system models: the Western System Coordinating Council (WSCC) 9‐Bus System and the Illinois 200‐Bus System , thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.
Sun, Shining↗
A Survey of Mathematical Structures for Lunar Networks
To sustain the current and increasing accessibility of space, a scalable communications infrastructure (i.e. the Solar System Internet, SSI) is necessary. The goal of this paper is to begin the discovery of the fundamental underlying mathematical structure of space networks to help the research community harness these structures for algorithm development and optimization. To ensure the applicability of the research, the approaches are considered through the lens of simulated scenarios inspired by the Artemis Back-to-the-Moon mission set for 2024. We note that any approach to an SSI must fit under the umbrella of Delay Tolerant Networking (DTN), due to celestial mobility, high link latencies, high variance in link latencies, disconnections, lack of end-to-end paths, and so on. These difficulties are exacerbated by the fact that the underlying structure of a space network is a time-evolving network and may experience multiple discontinuities in its topology. In this paper we propose several novel approaches to a mathematical foundation for Delay Tolerant Networking Theory that fall outside the traditional scope of temporal network theory. These techniques include methods from Topological Data Analysis, Dynamic Graph Analysis, Applied Algebraic Geometry, Probability Theory, and Game Theory. Some of these methods include tools adapted to the study of dynamic metric spaces, such as zigzag persistent homology and their higher parameter analogs. We find that several of these methods target desired engineering outcomes such as discovery and automatic sub-netting. While each approach is theoretical, they are also algorithmic in nature and offer immediate practical applications. The paper concludes with comparisons of the various methods along with suggestions for future work.
Delay tolerant networking↗
Investigation on the grain boundaries electrical characteristics of perovskite lithium ion conductors by derivative of tanδ approach
Graphical abstract: Cole–Cole impedance spectra for Li{sub 0.30}La{sub 0.56}TiO{sub 3}-SSR and Li{sub 0.30}La{sub 0.56}TiO{sub 3}-PC at 310 K and for Li{sub 0.30}Ca{sub 0.35}TaO{sub 3}-SSR and Li{sub 0.30}Ca{sub 0.35}TaO{sub 3}-PC at 473 K. - Highlights: • Li{sub 0.30}La{sub 0.56}TiO{sub 3} & Li{sub 0.30}Ca{sub 0.35}TaO{sub 3} perovskites made by Pechini polymerization route. • Relaxation processes of perovskites are analyzed by derivative of tanδ (α{sub θ}) method. • α{sub θ} (ν) graph resolves the two closer relaxation processes at grain boundary region. • SEM/TEM study supports the resolving ability of α{sub θ} vs log (ν) method. • χ{sup 2} improves from ∼10{sup −3} to ∼10{sup −5} for equivalent circuit fitting based on α{sub θ} peaks. - Abstract: We report on the grain boundaries electrical relaxation processes of Li{sub 0.30}La{sub 0.56}TiO{sub 3} and Li{sub 0.30}Ca{sub 0.35}TaO{sub 3} perovskites by derivative of the tanδ (α{sub θ}) method. The α{sub θ} vs log(ν) representation is clearly resolved the two grain boundary relaxation processes at grain boundary region for perovskites synthesized via the modified Pechini-type polymerization complex route in comparison to perovskites obtained by solid state reaction method. The resolving ability of α{sub θ} vs log(ν) representation is much higher than other complex impedance spectroscopic representations. The goodness of the fitting χ{sup 2} is improved from ∼10{sup −3} to ∼10{sup −5} for the electrical equivalent circuit fitting based on α{sub θ} vs log(ν) peaks.
36 MATERIALS SCIENCE↗
Dynamic graph system for a semantic database
A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.
97 MATHEMATICS AND COMPUTING↗
Dynamic graph system for a semantic database
A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.
97 MATHEMATICS AND COMPUTING↗
Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification
It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous catalysis. Herein, we investigate different UQ methods applied to a crystal graph convolutional neural network to predict adsorption energies of molecules on alloys from the Open Catalyst 2020 dataset, the largest existing heterogeneous catalyst dataset. We apply three UQ methods to the adsorption energy predictions, namely k-fold ensembling, Monte Carlo dropout, and evidential regression. The effectiveness of each UQ method is assessed based on accuracy, sharpness, dispersion, calibration, and tightness. Evidential regression is demonstrated to be a powerful approach for rapidly obtaining tunable, competitively trustworthy UQ estimates for heterogeneous catalysis applications when using neural networks. Recalibration of model uncertainties is shown to be essential in practical screening applications of catalysts using uncertainties.
36 MATERIALS SCIENCE↗
Protection Against Graph-Based False Data Injection Attacks on Power Systems
Graph signal processing (GSP) has emerged as a powerful tool for practical network applications, including power system monitoring. By representing power system voltages as smooth graph signals, recent research has focused on developing GSP-based methods for state estimation, attack detection, and topology identification. Included, efficient methods have been developed for detecting false data injection (FDI) attacks, which until now were perceived as non-smooth with respect to the graph Laplacian matrix. Consequently, these methods may not be effective against smooth FDI attacks. In this paper, we propose a graph FDI (GFDI) attack that minimizes the Laplacian-based graph total variation (TV) under practical constraints. In addition, we develop a low-complexity algorithm that solves the non-convex GDFI attack optimization problem using ell_1-norm relaxation, the projected gradient descent (PGD) algorithm, and the alternating direction method of multipliers (ADMM). We then propose a protection scheme that identifies the minimal set of measurements necessary to constrain the GFDI output to high graph TV, thereby enabling its detection by existing GSP-based detectors. Our numerical simulations on the IEEE-57 bus test case reveal the potential threat posed by well-designed GSP-based FDI attacks. Moreover, we demonstrate that integrating the proposed protection design with GSP-based detection can lead to significant hardware cost savings compared to previous designs of protection methods against FDI attacks.
Morgenstern, Gal↗
Graph states of atomic ensembles engineered by photon-mediated entanglement
Abstract Graph states are a broad family of entangled quantum states, each defined by a graph composed of edges representing the correlations between subsystems. Such states constitute versatile resources for quantum computation and quantum-enhanced measurement. Their generation and engineering require a high level of control over entanglement. Here we report on the generation of continuous-variable graph states of atomic spin ensembles, which form the nodes of the graph. We program the entanglement structure encoded in the graph edges by combining global photon-mediated interactions in an optical cavity with local spin rotations. By tuning the entanglement between two subsystems, we either localize correlations within each subsystem or enable Einstein–Podolsky–Rosen steering—a strong form of entanglement that enables the extraction of precise information from one subsystem through measurements on the other. We further engineer a four-mode square graph state, highlighting the flexibility of our approach. Our method is scalable to larger and more complex graphs, laying groundwork for measurement-based quantum computation and advanced protocols in quantum metrology.
74 ATOMIC AND MOLECULAR PHYSICS↗
OpenMP Parallelization and Optimization of Graph-Based Machine Learning Algorithms
In this paper, we investigate the OpenMP parallelization and optimization of two novel data classification algorithms. The new algorithms are based on graph and PDE solution techniques and provide significant accuracy and performance advantages over traditional data classification algorithms in serial mode. The methods leverage the Nystrom extension to calculate eigenvalue/eigenvectors of the graph Laplacian and this is a self-contained module that can be used in conjunction with other graph-Laplacian based methods such as spectral clustering. We use performance tools to collect the hotspots and memory access of the serial codes and use OpenMP as the parallelization language to parallelize the most time-consuming parts. Where possible, we also use library routines. We then optimize the OpenMP implementations and detail the performance on traditional supercomputer nodes (in our case a Cray XC30), and test the optimization steps on emerging testbed systems based on Intel’s Knights Corner and Landing processors. We show both performance improvement and strong scaling behavior. Finally, a large number of optimization techniques and analyses are necessary before the algorithm reaches almost ideal scaling.
97 MATHEMATICS AND COMPUTING↗
Methods and apparatus to detect and annotate backedges in a dataflow graph
Disclosed examples to detect and annotate backedges in data-flow graphs include: a characteristic detector to store a node characteristic identifier in memory in association with a first node of a dataflow graph; a characteristic comparator to compare the node characteristic identifier with a reference criterion; and a backedge identifier generator to generate a backedge identifier indicative of a backedge between the first node and a second node of the dataflow graph based on the comparison, the memory to store the backedge identifier in association with a connection arc between the first and second nodes.
ChoFleming, Jr., Kermin E.↗
Methods and apparatus to detect and annotate backedges in a dataflow graph
Disclosed examples to detect and annotate backedges in data-flow graphs include: a characteristic detector to store a node characteristic identifier in memory in association with a first node of a dataflow graph; a characteristic comparator to compare the node characteristic identifier with a reference criterion; and a backedge identifier generator to generate a backedge identifier indicative of a backedge between the first node and a second node of the dataflow graph based on the comparison, the memory to store the backedge identifier in association with a connection arc between the first and second nodes.
97 MATHEMATICS AND COMPUTING↗
Algorithms for Automatic Alignment of Arrays
Aggregate data objects (such as arrays) are distributed across the processor memories when compiling a data-parallel language for a distributed-memory machine. The mapping determines the amount of communication needed to bring operands of parallel operations into alignment with each other. A common approach is to break the mapping into two stages: an alignment that maps all the objects to an abstract template, followed by a distribution that maps the template to the processors. This paper describes algorithms for solving the various facets of the alignment problem: axis and stride alignment, static and mobile offset alignment, and replication labeling. We show that optimal axis and stride alignment is NP-complete for general program graphs, and give a heuristic method that can explore the space of possible solutions in a number of ways. We show that some of these strategies can give better solutions than a simple greedy approach proposed earlier. We also show how local graph contractions can reduce the size of the problem significantly without changing the best solution. This allows more complex and effective heuristics to be used. We show how to model the static offset alignment problem using linear programming, and we show that loop-dependent mobile offset alignment is sometimes necessary for optimum performance. We describe an algorithm with for determining mobile alignments for objects within do loops. We also identify situations in which replicated alignment is either required by the program itself or can be used to improve performance. We describe an algorithm based on network flow that replicates objects so as to minimize the total amount of broadcast communication in replication.
Chatterjee, Siddhartha↗
SIGNATURES OF ILLICIT NUCLEAR PROCUREMENT NETWORKS: AN OVERVIEW OF PRELIMINARY APPROACHES AND RESULTS
The illicit trafficking of strategic nuclear commodities (defined here as the goods needed for a covert nuclear program excluding special nuclear materials) poses a significant challenge to the international nuclear nonproliferation community. Export control regulations, both domestically and internationally, seek to inhibit the spread of strategic nuclear commodities by restricting their sale to parties that may use them for nefarious purposes. However, export controls alone are not sufficient for preventing the illicit transfer of strategic nuclear goods. There are two major pitfalls to relying solely on export control regulations for the deterrence of proliferation of strategic goods. First, export control enforcement today relies heavily on the honesty and willingness of participants to adhere to the legal framework already in place. Secondly, current practices focus on the evaluation of single records which allow for the necessary goods to be purchased separately and hidden within the thousands of legitimate commerce transactions that occur each day, disregarding strategic information regarding several purchases. Our research presents two preliminary data-centric approaches for investigating procurement networks of strategic nuclear commodities. Pacific Northwest National Laboratory (PNNL) has been putting significant effort into nonproliferation activities as an institution, both in terms of the classical nuclear material focused approach and in the examination of other strategic goods necessary to implement a nuclear program. In particular, the PNNL Signature Discovery Initiative (SDI) has codified several scientific methodologies for the detection, characterization, and prediction of signatures that are indicative of a phenomenon of interest. The methodologies and tools developed under SDI have already been applied successfully to problems in bio-forensics, cyber security and power grid balancing efforts and they have now made the nonproliferation of strategic goods into a challenge problem for testing their methodology and tools. As a first step towards the detection and characterization of illicit procurement networks, our research examines procurement networks as defined by a system of entities (people or companies) that enter into transactions of specific items with one another. Once we have defined such networks, we are interested in answering questions about the behavior and characterization of such networks. The questions we wish to answer regarding procurement networks are, first, “Can we detect networks within large, noisy datasets?” and second, “To what extent can we compare multiple networks and identify their similarities?” As procurement networks can be naturally viewed as a graph, we have employed several graph analytic tools to aid in these tasks. In particular, Graphscape, an SDI tool, uses a novel method to approximate edit distance, a graph distance measure based on the number of changes needed to transform one graph into another, in order to measure how similar two given graphs are to each other. Given a set of graphs where vertices represent companies and edges represent a shipment from company A to company B, we can calculate an all-for-all comparison of graphs. In this way, we are able to determine which graphs are most similar, and which require more changes to transform one into the other. The set of graphs to be compared can be further specialized to provide more insight, e.g., using different time periods to explore events in a company life cycle.
graph analysis↗
4-Clique network minor embedding for quantum annealers
Quantum annealing is a quantum algorithm for computing solutions to combinatorial optimization problems. This study proposes a method for minor embedding optimization problems onto sparse quantum annealing hardware graphs called 4-clique network minor embedding. This method is in contrast to the standard minor embedding technique of using a path of linearly connected qubits in order to represent a logical variable state. The 4-clique minor embedding is possible on Pegasus graph connectivity, which is the native hardware graph for some of the current D-Wave quantum annealers. The Pegasus hardware graph contains many cliques of size 4, making it possible to form a graph composed entirely of paths of connected 4-cliques on which a problem can be minor-embedded. The 4-clique chains come at the cost of additional qubit usage on the hardware graph, but they allow for stronger coupling within each chain, thereby increasing chain integrity, reducing chain breaks, and allow for greater usage of the available energy scale for programming logical problem coefficients on current quantum annealers. The 4-clique minor embedding technique is compared with the standard linear path minor embedding with experiments on two D-Wave quantum annealing processors with Pegasus hardware graphs. We show proof-of-concept experiments where the 4-clique minor embeddings can use weak chain strengths while successfully carrying out the computation of minimizing random all-to-all spin glass problem instances. Published by the American Physical Society 2024
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Topologically and geometrically flexible structural units in seven new organically templated uranyl selenates and selenite–selenates
Single crystals of seven novel uranyl oxysalts of selenium with protonated methylamine molecules, [C{sub 2}H{sub 8}N]{sub 2}[(UO{sub 2})(SeO{sub 4}){sub 2}(H{sub 2}O)] (I), [C{sub 2}H{sub 8}N]{sub 2}[(UO{sub 2}){sub 2}(SeO{sub 4}){sub 3}(H{sub 2}O)] (II), [C{sub 4}H{sub 15}N{sub 3}][H{sub 3}O]{sub 0.5}[(UO{sub 2}){sub 2}(SeO{sub 4}){sub 2.93}(SeO{sub 3}){sub 0.07}(H{sub 2}O)](NO{sub 3}){sub 0.5} (III), [C{sub 2}H{sub 8}N]{sub 3}[H{sub 5}O{sub 2}][(UO{sub 2}){sub 2}(SeO{sub 4}){sub 3}(H{sub 2}O){sub 2}]{sub 2}(H{sub 2}O){sub 5} (IV), [C{sub 2}H{sub 8}N]{sub 2}[H{sub 3}O][(UO{sub 2}){sub 3}(SeO{sub 4}){sub 4}(HSeO{sub 3})(H{sub 2}O)](H{sub 2}SeO{sub 3}){sub 0.2} (V), [C{sub 4}H{sub 12}N]{sub 3}[H{sub 3}O][(UO{sub 2}){sub 3}(SeO{sub 4}){sub 5}(H{sub 2}O)] (VI), and [C{sub 2}H{sub 8}N]{sub 3}(C{sub 2}H{sub 7}N)[(UO{sub 2}){sub 3}(SeO{sub 4}){sub 4}(HSeO{sub 3})(H{sub 2}O)] (VII) have been prepared by isothermal evaporation from aqueous solutions. Their crystal structures have been solved by direct methods and their uranyl selenate and selenite–selenate units investigated using black-and-white graphs from the viewpoints of topology of interpolyhedral linkages and isomeric variations. The crystal structure of IV is based upon complex layers with unique topology, which has not been observed previously in uranyl selenates. Investigations of the statistics and local distribution of the U–O{sub br}–Se bond angles demonstrates that shorter angles associate with undulations, whereas larger angles correspond to planar areas of the uranyl selenite layers. - Graphical abstract: Crystal structures of the seven novel Se-contaning uranyl oxysalts that contain protonated organic molecules as interlayer species have been investigated from the viewpoints of topology of interpolyhedral linkages, isomeric variations and flexibility of structural units. - Highlights: • Single crystals of seven novel uranyl oxysalts were prepared by evaporation method. • The graph theory was used for investigation of topologies of structural units. • The method of orientation matrices was applied to distinguish geometrical isomers. • The flexibility of structural complexes specifies the undulation of layered structural units.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning
Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .
Marfo, William [Univ. of Texas, El Paso, TX (Unite↗
The matrix product approximation for the dynamic cavity method
Stochastic dynamics of classical degrees of freedom, defined on vertices of locally tree-like graphs, can be studied in the framework of the dynamic cavity method which is exact for tree graphs. Such models correspond for example to spin-glass systems, Boolean networks, neural networks, and other technical, biological, and social networks. The central objects in the cavity method are edge messages—conditional probabilities of two vertex variable trajectories. In this paper, we discuss a rather pedagogical derivation for the dynamic cavity method, give a detailed account of the novel matrix product edge message (MPEM) algorithm for the solution of the dynamic cavity equation as introduced, and present optimizations and extensions. Matrix product approximations of the edge messages are constructed recursively in an iteration over time. Computation costs and precision can be tuned by controlling the matrix dimensions of the MPEM in truncations. Without truncations, the dynamics is exact. Data for Glauber–Ising dynamics shows a linear growth of computation costs in time. In contrast to Monte Carlo simulations, the approach has a much better error scaling. Hence, it gives for example access to low probability events and decaying observables like temporal correlations. Here, we discuss optimized truncation schemes and an extension that allows to capture models which have a continuum time limit.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗