Detailed Topology Research Articles

Unified monitoring and telemetry platform supporting network intelligence in optical networks

In recent years, machine-learning (ML) applications have generated considerable interest and shown great potential in optimizing optical network management, such as quality of transmission estimation, traffic prediction, and resource allocation. However, these applications often require large datasets for training, inference, and updating, while network operators are generally reluctant to disclose their data due to privacy concerns and the sensitivity of operational information. Most open-source datasets typically lack transparency regarding network specifics, such as topology details and device configurations, making data acquisition and ML model training more difficult. In response, this paper presents a unified monitoring and telemetry platform that leverages distributed and centralized time-series databases on InfluxDB, a Kafka-based telemetry pipeline, and advanced ML applications. The separation of distributed and centralized databases improves data management flexibility and scalability. The Kafka-based telemetry pipeline ensures high-throughput, low-latency data streaming with end-to-end latency under 0.05 s through optimized partitioning. Additionally, integrating Kafka and InfluxDB allows for real-time data visualization from multiple sources, improving transparency and supporting real-time data streaming for network applications. By implementing this advanced telemetry and ML architecture, network operators can build a more intelligent, responsive, and resilient optical network infrastructure.

A Study on Guesthouse Interior Space Planning Method Based on BIM Topological Relationships

Building Information Modeling (BIM) technology has revolutionized the architectural and construction industries by providing detailed digital representations of buildings, enhancing design efficiency and project management. Despite its widespread application, the utilization of BIM in optimizing interior space planning, particularly in boutique accommodations like guesthouses, remains underexplored. This research addresses the gap by introducing a novel method for interior environment space planning of guesthouses based on the topological relationships derived from BIM data. This study developed a new algorithm that utilizes detailed topology information from BIM in order to make space planning decisions more efficiently, thereby improving space utilization efficiency and guest experience. Through a comprehensive analysis of BIM topological relationships and the application of advanced optimization techniques, our method aims to optimize the use of space while considering the unique constraints and requirements of guesthouse environments. The proposed algorithm demonstrates significant improvements in spatial efficiency and design quality when tested against traditional planning methods on real-world BIM datasets. This research not only contributes a novel approach to the field of architectural design and planning but also offers practical implications for the enhancement of interior spaces in guesthouses, potentially influencing future applications of BIM technology in the hospitality industry.

Corner and edge states in topological Sierpinski Carpet systems

Fractal lattices, with their self-similar and intricate structures, offer potential platforms for engineering physical properties on the nanoscale and also for realizing and manipulating high order topological insulator states in novel ways. Here we present a theoretical study on localized corner and edge states, emerging from topological phases in Sierpinski Carpet within a $\pi$-flux regime. A topological phase diagram is presented correlating the quadrupole moment with different hopping parameters. Particular localized states are identified following spatial signatures in distinct fractal generations. The specific geometry and scaling properties of the fractal systems can guide the supported topological states types and their associated functionalities. A conductive device is proposed by coupling identical Sierpinski Carpet units providing transport response through projected edge states which carry on the details of the system's topology. Our findings suggest that fractal lattices may also work as alternative routes to tune energy channels in different devices.

A graph-guided transformer based on dual-stream perception for hyperspectral image classification

ABSTRACT The excellent capabilities of Transformers and Graph Neural Networks (GNNs) in modelling long-range dependencies and handling irregular data have led to their widespread application in hyperspectral image (HSI) classification tasks. However, the Graph Transformer combining both advantages is rarely used in this field and has some limitations. Current Graph Transformers consider interactions between all nodes within the graph, adding complexity and introducing unnecessary information from noisy nodes. Moreover, the rich spectral information in HSIs is often ignored, and there is a lack of effective fusion of spatial information. In this paper, we propose a dual-stream graph-guided Transformer for HSI classification. In spatial dimension, superpixels are utilized to guide spatial graph generation, capturing global topological dependencies and local details effectively in HSIs. In terms of spectrum, we innovatively construct a spectral graph based on spectral channels and adopt a contribution score-based strategy to adaptively filter out irrelevant edges, achieving sparsity while preserving spectral context relationships. Experimental results demonstrate the significant competitive advantage of our method in HSI classification tasks on three public datasets. The code is available at https://github.com/youngboy03/GTDPNet.

Generalized Lipschitz Tracing of Implicit Surfaces

AbstractWe present a versatile and robust framework to render implicit surfaces defined by black‐box functions that only provide function value queries. We assume that the input function is locally Lipschitz continuous; however, we presume no prior knowledge of its Lipschitz constants. Our pre‐processing step generates a discrete acceleration structure, a Lipschitz field, that provides data to infer local and directional Lipschitz upper bounds. These bounds are used to compute safe step sizes along rays during rendering. The Lipschitz field is constructed by generating local polynomial approximations to the input function, then bounding the derivatives of the approximating polynomials. The accuracy of the approximation is controlled by the polynomial degree and the granularity of the spatial resolution used during fitting, which is independent from the resolution of the Lipschitz field. We demonstrate that our process can be implemented in a massively parallel way, enabling straightforward integration into interactive and real‐time modelling workflows. Since the construction only requires function value evaluations, the input surface may be represented either procedurally or as an arbitrarily filtered grid of function samples. We query the original implicit representation upon ray trace, as such, we preserve the geometric and topological details of the input as long as the Lipschitz field supplies conservative estimates. We demonstrate our method on both procedural and discrete implicit surfaces and compare its exact and approximate variants.

The de Haas–van Alphen quantum oscillations in the kagome metal RbTi3Bi5

Abstract The kagome system has attracted great interest in condensed matter physics due to its unique structure that can host various exotic states such as superconductivity (SC), charge density waves (CDWs) and nontrivial topological states. The topological semimetal RbTi3Bi5 consisting of a Ti kagome layer shares a similar crystal structure to the topological correlated materials AV3Sb5 (A = K, Rb, Cs) but without the absence of CDW and SC. Systematic de Haas–van Alphen oscillation measurements are performed on single crystals of RbTi3Bi5 to pursue nontrivial topological physics and exotic states. Combining this with theoretical calculations, the detailed Fermi surface topology and band structure are investigated. A two-dimensional Fermi pocket β is revealed with a light effective mass, consistent with the semimetal predictions. The Landau fan diagram of RbTi3Bi5 reveals a zero Berry phase for the β oscillation in contrast to that of CsTi3Bi5. These results suggest that kagome RbTi3Bi5 is a good candidate for exploring nontrivial topological exotic states and topological correlated physics.

Topology derivation and control method of high‐reliability buck‐boost double‐grounded transformerless three‐phase inverters

SummaryAs the traditional transformerless three‐phase (TTP) inverters have the shoot‐through and common mode leakage current (CMLC) problems, as well as not being able to realize the buck‐boost conversion, topology derivation and control method of high‐reliability buck‐boost double‐grounded TTP inverters are proposed. These topologies consist of two‐port or three‐port converters with boost capability and three dual‐buck inverters. The two‐port or three‐port converters with boost capability provide the positive and negative bus voltages for the three dual‐buck inverters, realizing the boost conversion. The dual‐buck inverters address the shoot‐through problem. The double‐grounded structure is used to solve the CMLC problem. Dead time between switches is not needed, which reduces the complexity of the control system. Detailed topology derivation of the dual‐buck double grounded TTP inverters with boost capability is given. The operating principle, parameters design of the filters, and stability analysis of this inverter are analyzed in detail. Loss analysis of the inverter is given. The simulation and experimental results of a 750‐W TTP inverter are used to verify the theoretical analysis.

Enhancing deep learning predictive models with HAPPY (Hierarchically Abstracted rePeat unit of PolYmers) representation

We introduce HAPPY (Hierarchically ed rePeat unit of PolYmers), a string representation for polymers, designed to efficiently encapsulate essential polymer structure features for property prediction. HAPPY assigns single constituent elements to groups of sub-structures and employs grammatically complete and independent connectors between chemical linkages. Using a limited number of datapoints, we trained neural networks utilizing both HAPPY and conventional SMILES encoding of repeated unit structures and compared their performance in predicting five polymer properties: dielectric constant, glass transition temperature, thermal conductivity, solubility, and density. The results showed that the HAPPY-based network could achieve higher prediction R-squared score and two-fold faster training times. We further tested the robustness and versatility of HAPPY-based network with an augmented training dataset. Additionally, we present topo-HAPPY (Topological HAPPY), an extension that incorporates topological details of the constituent connectivity, leading to improved solubility and glass transition temperature prediction R-squared score.

A distributed algorithm with network‐independent step‐size and event‐triggered mechanism for economic dispatch problem

AbstractThe economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network‐independent step sizes and an event‐triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network‐independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency.

Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy

The integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods outperform traditional, rule-based approaches, particularly in handling the complexities of intersecting features. However, existing deep learning-based AFR methods face two major challenges. The initial challenge stems from the frequent utilization of voxelized or point-cloud representations of CAD models, resulting in the unfortunate loss of valuable geometric and topological information inherent in original Boundary representation (B-Rep) models. The second challenge involves the limitation of supervised deep learning methods in identifying machining features that are not present in the predefined dataset. This constraint renders them suboptimal for the continually evolving datasets of real industrial scenarios. To address the first challenge, this study introduces a graph-structured language, Multidimensional Attributed Face-Edge Graph (maFEG), crafted to encapsulate the intricate geometric and topological details of CAD models. Furthermore, a graph neural network, Sheet-metalNet, is proposed for the efficient learning and interpretation of maFEGs. To tackle the second challenge, a three-component incremental learning strategy is proposed: an initial phase of pre-training and fine-tuning, a prototype sampling-based replay, and a stage employing knowledge distillation for parameter regularization. The effectiveness of Sheet-metalNet and its complementary incremental learning strategy is evaluated using the open-source MFCAD++ dataset and the newly created SMCAD dataset. Experimental results show that Sheet-metalNet surpasses state-of-the-art AFR methods in machining feature recognition accuracy. Moreover, Sheet-metalNet demonstrates adaptability to dynamic dataset changes, maintaining high performance when encountering newly introduced features, thanks to its innovative incremental learning strategy.

Integrated information decomposition unveils major structural traits of in silico and in vitro neuronal networks.

The properties of complex networked systems arise from the interplay between the dynamics of their elements and the underlying topology. Thus, to understand their behavior, it is crucial to convene as much information as possible about their topological organization. However, in large systems, such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in the dynamics on the network, such as spike train time series, and by measuring the transfer entropy between system elements. The topological information recovered by these methods does not necessarily capture the connectivity layout, but rather the causal flow of information between elements. New theoretical frameworks, such as Integrated Information Decomposition (Φ-ID), allow one to explore the modes in which information can flow between parts of a system, opening a rich landscape of interactions between network topology, dynamics, and information. Here, we apply Φ-ID on in silico and in vitro data to decompose the usual transfer entropy measure into different modes of information transfer, namely, synergistic, redundant, or unique. We demonstrate that the unique information transfer is the most relevant measure to uncover structural topological details from network activity data, while redundant information only introduces residual information for this application. Although the retrieved network connectivity is still functional, it captures more details of the underlying structural topology by avoiding to take into account emergent high-order interactions and information redundancy between elements, which are important for the functional behavior, but mask the detection of direct simple interactions between elements constituted by the structural network topology.

A Comparison between Predictions of the Miller-Macosko Theory, Estimates from Molecular Dynamics Simulations, and Long-Standing Experimental Data of the Shear Modulus of End-Linked Polymer Networks.

Long-standing experimental data on the elastic modulus of end-linked poly(dimethylsiloxane) (PDMS) networks are employed to corroborate the validity of the Miller-Macosko theory (MMT). The validity of MMT is also confirmed by molecular dynamics (MD) simulations that mimic the experimentally realized networks. It becomes apparent that for a network formed from bulk, where the fractions of the loops are small, it is sufficient to account for the topological details of a reference tree-like network, i.e., for its degree of completion, junction functionalities, and trapped entanglements, in order to practically predict the modulus. However, a mismatch is identified between the MMT and MD simulations in relating the fraction of the soluble material to the extent of reaction. A large contribution of entanglements to the modulus of PDMS networks prepared with short precursor chains is presented, suggesting that the elastic modulus of commonly used end-linked PDMS networks is in fact entanglement-dominated.

Novel evaluation method for facial nerve palsy using 3D facial recognition system in iPhone

ObjectiveWhile subjective methods like the Yanagihara system and the House-Brackmann system are standard in evaluating facial paralysis, they are limited by intra- and inter-observer variability. Meanwhile, quantitative objective methods such as electroneurography and electromyography are time-consuming. Our aim was to introduce a swift, objective, and quantitative method for evaluating facial movements. MethodsWe developed an application software (app) that utilizes the facial recognition functionality of the iPhone (Apple Inc., Cupertino, USA) for facial movement evaluation. This app leverages the phone's front camera, infrared radiation, and infrared camera to provide detailed three-dimensional facial topology. It quantitatively compares left and right facial movements by region and displays the movement ratio of the affected side to the opposite side. Evaluations using the app were conducted on both normal and facial palsy subjects and were compared with conventional methods. ResultsOur app provided an intuitive user experience, completing evaluations in under a minute, and thus proving practical for regular use. Its evaluation scores correlated highly with the Yanagihara system, the House-Brackmann system, and electromyography. Furthermore, the app outperformed conventional methods in assessing detailed facial movements. ConclusionOur novel iPhone app offers a valuable tool for the comprehensive and efficient evaluation of facial palsy.

A Coarse-to-Fine Transformer-Based Network for 3D Reconstruction from Non-Overlapping Multi-View Images

Reconstructing 3D structures from non-overlapping multi-view images is a crucial task in the field of 3D computer vision, since it is difficult to establish feature correspondences and infer depth from overlapping parts of views. Previous methods, whether generating the surface mesh or volume of an object, face challenges in simultaneously ensuring the accuracy of detailed topology and the integrity of the overall structure. In this paper, we introduce a novel coarse-to-fine Transformer-based reconstruction network to generate precise point clouds from multiple input images at sparse and non-overlapping viewpoints. Specifically, we firstly employ a general point cloud generation architecture enhanced by the concept of adaptive centroid constraint for the coarse point cloud corresponding to the object. Subsequently, a Transformer-based refinement module applies deformation to each point. We design an attention-based encoder to encode both image projection features and point cloud geometric features, along with a decoder to calculate deformation residuals. Experiments on ShapeNet demonstrate that our proposed method outperforms other competing methods.

DSANet: Dynamic and Structure-Aware GCN for Sparse and Incomplete Point Cloud Learning.

Learning 3-D structures from incomplete point clouds with extreme sparsity and random distributions is a challenge since it is difficult to infer topological connectivity and structural details from fragmentary representations. Missing large portions of informative structures further aggravates this problem. To overcome this, a novel graph convolutional network (GCN) called dynamic and structure-aware NETwork (DSANet) is presented in this article. This framework is formulated based on a pyramidic auto-encoder (AE) architecture to address accurate structure reconstruction on the sparse and incomplete point clouds. A PointNet-like neural network is applied as the encoder to efficiently aggregate the global representations of coarse point clouds. On the decoder side, we design a dynamic graph learning module with a structure-aware attention (SAA) to take advantage of the topology relationships maintained in the dynamic latent graph. Relying on gradually unfolding the extracted representation into a sequence of graphs, DSANet is able to reconstruct complicated point clouds with rich and descriptive details. To associate analogous structure awareness with semantic estimation, we further propose a mechanism, called structure similarity assessment (SSA). This method allows our model to surmise semantic homogeneity in an unsupervised manner. Finally, we optimize the proposed model by minimizing a new distortion-aware objective end-to-end. Extensive qualitative and quantitative experiments demonstrate the impressive performance of our model in reconstructing unbroken 3-D shapes from deficient point clouds and preserving semantic relationships among different regional structures.

Triangle-oriented Community Detection Considering Node Features and Network Topology

The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a triangle-based quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both non-overlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect.

Adaptive Neighbor Graph Aggregated Graph Attention Network for Heterogeneous Graph Embedding

Graph attention network can generate effective feature embedding by specifying different weights to different nodes. The key of the research on heterogeneous graph embedding is the way to combine its rich structural information with semantic relations to aggregate the neighborhood information. Most of the existing heterogeneous graph representation learning methods guide the selection of neighbors by defining various meta-paths on heterogeneous graphs. However, these models only consider the information contained in the nodes under different paths and ignore the potential semantic relationships of nodes in different neighbor graph structures, which leads to the underutilization of graph structure information. In this article, we propose a novel adaptive framework named Neighbor Graph Aggregated Graph Attention Network (NGGAN) to fully exploit graph topological details in heterogeneous graph, and aggregates their information to obtain an effective embedding. The key idea is to use different levels of sampling methods to define neighborhood, and use neighbor graphs to represent the complex structural interaction between nodes. In this way, the high-order relationship between nodes and the latent semantics of neighbor graphs can be fully explored. Afterward, a hierarchical attention mechanism is applied to adaptively learn the importance of different objects, including node information, path information, and neighbor graph information. Multiple downstream tasks are performed on four real-world heterogeneous graph datasets, and the experimental results demonstrate the effectiveness of NGGAN.

ENRN: A System for Evaluating Network Resilience against Natural Disasters

The frequency and severity of natural disasters is surging, posing an urgent need for robust communication network infrastructure that is capable of withstanding these events. In this paper, we present a groundbreaking graph-theoretic system designed to evaluate and enhance network resilience in the face of natural disasters. Our solution harnesses the power of topological robustness metrics, integrating real-time weather data, geographic information, detailed network topology data, advanced resilience algorithms, and continuous network monitoring. The proposed scheme considers four major real-world U.S.-based network providers and evaluates their physical topologies against two major hurricanes. Our novel framework quantifies the important characteristics of network infrastructure; for instance, AT&T is identified to have fared better against Hurricane Ivan (57.98 points) than Hurricane Katrina (39.17 points). We not only provide current insights into network infrastructure resilience, but also uncover valuable findings that shed light on the performance of backbone U.S. networks during hurricanes. Furthermore, our findings provide actionable insights to enrich the overall survivability and functionality of communication networks, mitigating the adverse impacts of natural disasters on communication systems and critical services in terms of improving network resiliency via adding additional nodes and link or rewiring.

Three-Dimensional Quantitative Flow Visualization Around a Thrust Reverser

Volumetric particle tracking velocimetry measurements are performed in a low-speed wind tunnel to study the flow around a 1:12-scale aircraft model with jet engines operating with thrust reversers. The engine jet and freestream flow velocity are varied to yield a jet to freestream velocity ratio of ranging from 1.5 to 6. Measurements at such scale () require the use of strongly scattering helium-filled soap bubbles as flow tracers, which are introduced in both the jet and the freestream flow. The tracer’s three-dimensional motion is determined using an array of cameras and a Lagrangian particle tracking algorithm. The mean velocity field reveals the jet inner structure as well as its interaction with the freestream, the ground board, the nacelle, the fuselage, and the horizontal and vertical tails. The experiments allow detection of exhaust reingestion as well as the aerodynamic interference with control surfaces at the tail segments in a single measurement volume. The results are in good agreement with conventional temperature rake measurements while adding details of the flow topology and of the large-scale unsteady flow fluctuations. Finally, the jet reversal characteristics with varying freestreams and nozzle pressure ratios are assessed, demonstrating the feasibility and versatility of volumetric velocimetry measurements for industrial aerodynamics.

Simulation Study of the Effect of Nanoporous Surfaces on the Adhesion Properties of Cross-Linked Polymer Networks.

We have investigated the effect of surface nanopores on the adhesion behavior between cross-linked polymer networks and metal substrates by molecular dynamics simulations. By increasing the cross-linking ratio of the polymer network, the fracture behavior in tensile mode changed from cohesive failure to interfacial failure. In the case of polymers without cross-links, the breaking strengths were almost the same for systems with flat and porous metal substrates. Conversely, in the case of cross-linked polymer networks, the tensile behavior for the porous metal substrates depended on the cross-linking ratio and structure of the polymer chains. For polymer networks consisting of long polymer chains, the force curves in extension mode before the yield points were almost the same for the systems regardless of the surface roughness caused by nanopores. Meanwhile, for highly cross-linked resin networks consisting of short rigid molecules, the yielding strength of the porous metal surfaces showed slightly higher values than that of the flat metal surfaces. The simulation results revealed that the adhesion behavior between cross-linked polymer networks and rough metal surfaces is related not only to the interfacial area but also to the detailed networking topology of the polymers.