Spatial-temporal Correlation Research Articles

Quantum computer error structure probed by quantum error correction syndrome measurements

With quantum devices rapidly approaching qualities and scales needed for fault tolerance, the validity of simplified error models underpinning the study of quantum error correction needs to be experimentally evaluated. In this work, we have assessed the performance of IBM superconducting quantum computer devices implementing heavy-hexagon code syndrome measurements with circuit sizes up to 23 qubits, against the error assumptions underpinning code threshold calculations. Circuit operator change rate statistics in the presence of depolarizing and biased noise were modeled using analytic functions of error model parameters. Data from 16 repeated syndrome measurement cycles were found to be inconsistent with a uniform depolarizing noise model, with slightly improved fits found with biased and inhomogeneous noise models. Spatial-temporal correlations investigated via Z stabilizer measurements revealed significant temporal correlation in detection events. These results highlight the nontrivial structure that may be present in the noise of quantum error correction circuits, revealed by operator measurement statistics, and support the development of noise-tailored codes and decoders to adapt. Published by the American Physical Society 2024

STC-GraphFormer: Graph Spatial-Temporal Correlation Transformer for In-vehicle Network Intrusion Detection System

The integration of several developing technologies and their applications with Internet of Vehicles (IoVs) techniques has been improved. Utilizing these emerging technologies renders the in-vehicle network more susceptible to intrusions. Furthermore, the utilization of Electronic Control Units (ECUs) in current vehicles has experienced a significant increase, establishing the Controller Area Network (CAN) as the widely used standard in the automotive field. The CAN protocol provides an efficient and broadcast-based protocol for facilitating serial data exchange between ECUs. However, it lacks provisions for security measures such as authentication and encryption. The attackers have exploited these weaknesses to launch various attacks on CAN-based IVN. This paper proposes STC-GraphFormer, an innovative spatial-temporal model that utilizes a Graph Convolutional Network (GCN) and a transformer. The spatial GCN layers are utilized to construct and acquire local spatial features, while the temporal transformer layers are employed to capture the long-term global temporal dependencies. By employing this integrated approach, STC-GraphFormer can learn complex spatial-temporal correlations within the IVN data, enabling it to detect and classify malicious intrusions. The proposed STC-GraphFormer has been validated using five real in-vehicle CAN datasets that cover a wide range of attacks that have not been previously investigated together. The finding results indicate that the STC-GraphFormer is more efficient than the SOTA approaches. It demonstrates excellent performance, with Car-hacking (0.99983), IVN intrusion detection (0.9991), CAN Dataset for intrusion detection “OTIDS” (0.9992), CAR hacking: attack & defense challenge (0.9901), and Survival analysis (0.9982), with a minimal false alarm rate and the highest achievable F1 scores for various types of attacks.

Temporal–spatial correlation and graph attention-guided network for micro-expression recognition in English learning livestreams

Micro-expressions, fleeting facial movements lasting 1/25 to 1/3 of a second, offer crucial insights into genuine emotions, particularly valuable in online education settings. The rapid growth of English learning livestreams has heightened the need for accurate, real-time micro-expression recognition to enhance learner engagement and instructional effectiveness. However, existing methods need help with the subtle nature of these expressions, especially in dynamic, low-resolution streaming environments. This paper presents TSG–MER–ELL, a novel end-to-end network for micro-expression recognition in English learning livestreams, integrating temporal–spatial correlation and graph attention mechanisms. The framework addresses the unique challenges of real-time emotion analysis in online language education, where subtle facial cues are crucial in understanding learner engagement and comprehension. The temporal–spatial correlation module employs action units with spatio-temporal graph convolution to aggregate features from diverse facial regions, while transformer encoders construct long-range correlations. The graph attention module builds upon local facial areas to guide self-attention computations, yielding precise local correlation features. These global and local features are fused for the final micro-expression classification. We introduce an adaptive loss function that balances accuracy, efficiency, and relevance to linguistic context. Extensive experiments on SMIC, CASME II, and SAMM datasets, adapted for English learning scenarios, demonstrate TSG–MER–ELL’s superior performance over ten state-of-the-art baselines. The TSG–MER–ELL framework achieves top UF1 and UAR scores across all datasets, significantly improving recognition speed and accuracy. Ablation studies and visualizations of temporal–spatial features and graph attention weights provide insights into the framework’s effectiveness in capturing subtle emotional cues. TSG–MER–ELL’s robust performance in varied online learning conditions highlights its potential to enhance engagement, personalize instruction, and improve overall outcomes in virtual English language education.

Transparent enhancement of active distribution network through FCA-based blind decomposition

Transparency is crucial for decision-making within an active distribution network (ADN). To enhance ADN’s transparency, this study develops a novel Blind Decomposition of Composite Events (BDCE) approach rooted in Free Component Analysis (FCA), with a detailed exploration of its related theorems, algorithms, and deductions. Notably, FCA employs non-commutative matrix variables instead of scalar variables, establishing a natural connection to Random Matrix Theory (RMT). By incorporating RMT, FCA-BDCE effectively utilizes spatial–temporal correlation—a matrix-derived spectrum statistic; it allows for the filtration of locally independent noises, such as individual-level measurement error, ubiquitous white noise, while retaining globally influential signals across some specified spatial–temporal span. This capability is particularly valuable when gaining insight into the complex ADN, a landscape with significant diversity and uncertainty. In general, our approach is model-free, theory-guided, and unsupervised, making it particularly suitable for ADN. A comprehensive case study validates the practical effectiveness of our FCA-BDCE approach, demonstrating its superiority over ICA-BDCE.

Data-driven prediction framework of surrounding rock pressure in a fully mechanized coal face with temporal-spatial correlation

Surrounding rock pressure prediction for fully mechanized coal face (FMCF) roof management and control is of great significance. The key challenge is to effectively combine various factors to evolve the surrounding rock pressure trend with high temporal-spatial correlation and heterogeneity. The hydraulic support load is an important indicator that reflects the change in the surrounding rock pressure. Inspired by the research on 'support-surrounding rock’ and the massive data collected by sensors, this paper proposes a data-driven prediction framework for surrounding rock pressure in FMCFs. The framework includes the multidimensional working condition matrix (MWCM) building module, the hydraulic support group temporal-spatial feature fusion (HTSFF) network, and the adaptive deployment strategy (ADS). The MWCM generating methods of hydraulic support in combination with the mining process and the data quality and quantity requirements of the conventional supervised learning method are weakened by the treatment of missing data adapting to the working conditions. The HTSFF network focuses on capturing the spatial correlation of the surrounding rock pressure along the mining direction and the temporal periodicity along the advancing direction of the FMCF and fully considers the dependencies between them. ADS aims to solve the problem of missing data in model deployment and reduce the interference of abnormal data in the model prediction process by adaptively generating MWCM. The rationality and practicability of various designs of the proposed framework were verified on our dataset. Quantitative experiments show that the average error of the surrounding rock pressure prediction is 1.2406 MPa, and the accuracy, precision, and recall rate of periodic pressure prediction are all above 95%. This method can effectively assist FMCF roof management and control. Although the method is effective, it may require further fine-tuning of the model to adapt to different geological conditions and varying sensor quality, ensuring broader applicability.

IEDSFAN: information enhancement and dynamic-static fusion attention network for traffic flow forecasting

Accurate forecasting of traffic flow in the future period is very important for planning traffic routes and alleviating traffic congestion. However, traffic flow forecasting still faces serious challenges. Most of the existing traffic flow forecasting methods are static graph convolutional networks based on prior knowledge, ignoring the special spatial–temporal dynamics of spatial–temporal data. Using only adaptive dynamic graphs completely discards the objective and real spatial connectivity information in static graphs. To this end, we propose a novel information enhancement and dynamic-static fusion attention network (IEDSFAN). Firstly, the Multi-Graph Fusion Gating mechanism (MGFG) designed in IEDSFAN effectively fuses dynamic and static graphs to dynamically capture the hidden spatial–temporal correlation. Secondly, we construct a novel Gated Multi-head Self-Attention (GMHSA), which maps the input through the MGFG module to capture the complex spatial–temporal interactions in the features. Finally, we generate adaptive parameters to solve the problem that shared parameters cannot learn multiple traffic patterns, and enhance the expression of sequence information through the peak flag module. We conducted extensive experiments on five real-world traffic datasets, and the experimental results show that the performance of IEDSFAN is significantly better than all baselines.

RCCNet: A Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate Prediction

In logistics service, the delivery timely rate is a key experience indicator, which is highly essential to the competitive advantage of express companies. Prediction on it enables intervention on couriers with low predicted results in advance, thus ensuring employee productivity and customer satisfaction. Currently, few related works focus on couriers’ level delivery timely rate prediction, and there are complex spatial correlations between couriers and road districts in the express scenario, which makes traditional real-time prediction approaches hard to utilize. To deal with this, we propose a deep spatial-temporal neural network, RCCNet to model spatial-temporal correlations. Specifically, we adopt Node2vec, which can encode the road network-based graph directly to capture spatial correlations between road districts. Further, we calculate couriers’ historical time-series similarity to build a graph and employ graph convolutional networks to capture the correlation between couriers. We also leverage historical sequential information with long short-term memory networks. We conduct experiments with real-world express datasets. Compared with other competitive baseline methods widely used in industry, the experiment results demonstrate its superior performance over multiple baselines.

Capturing signals of road traffic safety risk: based on the spatial-temporal correlation between traffic violations and crashes

Objective The paper aims to explore the possibility of using traffic violations as indicators for spatial-temporal risk of traffic safety within road network constraints, identify key types of traffic violations that indicate spatial-temporal risks in road traffic safety, and investigate their distribution patterns at the road section level. Methods Firstly, we employ the Ripley’s K function with network constraints and utilize rigorous statistical inference to thoroughly examine the spatial-temporal correlation between various types of traffic violations and crashes, identifying key types that exhibit significant correlation with crashes. Secondly, we combine Ripley’s K function with network constraints, Network Kernel Density Estimation, and Local Moran’s Index, to identify high-incidence road sections of these violations. Building upon this foundation, we introduce the concept of Influence Intensity for Land Use Type, which leverages Point of Interest information to analyze the land use characteristics at the road section level, revealing the distribution patterns of these key traffic violations. Results Analysis of actual data from Shenzhen, China reveals a total of 17 key traffic violations significantly correlated with crashes of varying severity across different time scenarios in the spatial ranges of 2.1–3.8 kilometers. These include types that are typically considered to have a relatively low likelihood of directly causing crashes that deserve more attention. These key traffic violations tend to aggregate in road sections categorized as “Business & Finance” and “Public Transport Infrastructure.” Furthermore, in contrast to weekdays, weekends witness a higher number of key traffic violation types with more pronounced spatial aggregation characteristics, and the land use type of aggregation areas shifts from “Public Administration & Services” to “Public Green Spaces & Attractions” and “Residence & Living.” Conclusions This study demonstrates that particular traffic violations can serve as signals for road traffic safety risk within specific space-time scopes, and the spatial-temporal aggregation patterns of these key traffic violations are closely linked to the urban land use. This finding can offer theoretical support for utilizing key traffic violations in real-time monitoring and early warning of road traffic crashes, while also providing inspiration for exploring the causes of these traffic violations from a land use perspective.

Crowd Flow Prediction: An Integrated Approach Using Dynamic Spatial–Temporal Adaptive Modeling for Pattern Flow Relationships

ABSTRACTPredicting crowd flows in smart cities poses a significant challenge for the intelligent transportation system (ITS). Traffic management and behavioral analysis are crucial and have garnered considerable attention from researchers. However, accurately and timely predicting crowd flow is difficult due to various complex factors, including dependencies on recent crowd flow and neighboring regions. Existing studies often focus on spatial–temporal dependencies but neglect to model the relationship between crowd flow in distant areas. In our study, we observe that the daily flow of each region remains relatively consistent, and certain regions, despite being far apart, exhibit similar flow patterns, indicating a strong correlation between them. In this paper, we proposed a novel Multiscale Adaptive Graph‐Gated Network (MSAGGN) model. The main components of MSAGGN can be divided into three major parts: (1) To capture the parallel periodic learning architecture through a layer‐wise gated mechanism, a layer‐wise functional approach is employed to modify gated mechanism, establishing parallel skip periodic connections to effectively manage temporal and external factor information at each time interval; (2) a graph convolutional‐based adaptive mechanism that effectively captures crowd flow traffic data by considering dynamic spatial–temporal correlations; and (3) we proposed a novel intelligent channel encoder (ICE). The task of this block is to capture citywide spatial–temporal correlation along external factors to preserve correlation for distant regions with external elements. To integrate spatio‐temporal flexibility, we introduce the adaptive transformation module. We assessed our model's performance by comparing it with previous state‐of‐the‐art models and conducting experiments using two real‐world datasets for evaluation.

Beyond homophily in spatial–temporal traffic flow forecasting

Traffic flow forecasting is a crucial yet complex task due to the intricate spatial–temporal correlations arising from road interactions. Recent methods model these interactions using message-passing Graph Convolution Networks (GCNs), which work for homophily graphs where connected nodes primarily exhibit close observations. However, relying solely on homophily graphs presents inherent limitations in traffic modeling, as road interactions can yield not only close but also distant observations over time, revealing diverse and dynamic node-wise correlations. We designate this phenomenon as homophily-heterophily dynamics, which has been largely overlooked in previous works. To address this gap, we propose a Homophily-Heterophily Spatial-Temporal Graph Convolution Network (H2STGCN) that exploits both homophily and heterophily components in the spatial–temporal domain. Specifically, we first adopt time-related node attributes to disentangle the diverse and dynamic node-wise relations across time, thereby obtaining homophily and heterophily Spatial-Temporal Graphs (STGs), which provide comprehensive insights into road interactions. Subsequently, we construct dual information propagation branches, each outfitted with a specific type of STG, to exploit multiple ranges of spatial–temporal correlations from distinct perspectives through dilated causal spatial–temporal graph convolution operations on STGs. Additionally, we introduce a Graph Collaborative Learning Module (GCLM) to capture the complementary information of these two branches via mutual information transfer. Experimental evaluation on four real-world traffic datasets reveals that our model outperforms state-of-the-art methods.

Research on traffic flow forecasting based on interactive dynamic meta-graph learning

Traffic flow exhibits intricate dynamic spatial-temporal correlation. Aiming at the difficulty in capturing the long-term dependence and dynamic spatial correlations among concealed road nodes of in traffic flow, a new Interactive Dynamic forecasting model based on Meta-graph learning (Mega-ID) is proposed, which combines spatial-temporal transformer and interactive dynamic graph convolution (IDGCN) to optimize the Meta-graph module. Specifically, it optimizes a spatial-temporal meta-graph with memory and discrimination capabilities. The model introduces a Dynamic Graph Convolution (DGCN) embedded Interactive Learning structure, which simultaneously captures the hidden dynamic spatial correlations and long-term dependency of traffic flow. The experimental results demonstrate that the method proposed in this paper can capture the hidden dynamic spatial correlation and long-term dependence, leading to better forecasting performance compared to other baseline models.

Systemic and Anthropological Characteristics of Thinking

The article describes such systemic anthropological characteristics of thinking as multidimensionality, projectivity, inclusion in life relationships, transtemporality, and nonlinearity. On the one hand, these characteristics are inconvenient phenomenological "anomalies" for the classical psychology of thinking. On the other hand, in the context of progressive anthropologization of psychological cognition and interdisciplinary discourse, they make it possible to study thinking in a more holistic and human context, as well as to establish new conceptual links between the psychology of thinking and the psychology of human existence. Thinking is a multidimensional and nonlinear act that simultaneously unfolds in several registers of one’s mentality, capturing a variety of temporal, active, and noetic aspects of one’s being. The projective function of thinking consists in overcoming event uncertainty, building a semantic markup of living space, and producing semantic orientations in the activity and existential dimensions of the living environment. The fact that thinking is part of one’s life relationships ensures the integrity and transtemporal continuity of one’s life world. According to the polyphonic principle of mental dynamics, the dialectic unity of its spatial-temporal and modal correlations unfolds at each moment of thinking.

Long term 5G base station traffic prediction method based on spatial-temporal correlations

In the domain of 5G network management, accurately predicting traffic volumes at base stations remains a critical yet challenging endeavor, primarily due to the complexities inherent in the spatial and temporal data interactions. Current methods often fall short in effectively harnessing long-term trends and spatial interconnections among base stations. To bridge these gaps, this paper introduces the GCformer model, a novel approach that capitalizes on both spatial relationships and temporal patterns for multi-base station traffic prediction. Spatially, the proposed model employs graph convolutional networks to integrate diverse spatial information and construct insightful adjacency matrices that includes Euclidean distances and non-Euclidean distances (area types of base station locations and similarities in traffic flow among various stations), thereby enhancing the predictability of traffic dynamics. Temporally, the application of the Transformer's attention mechanism enables better capture of long-term relational dependencies in the temporal domain of 5 G base station traffic data. Additionally, a time-variant optimization module is designed to establish diurnal cycle data for each base station's traffic, replacing the traditional positional encoding with a more nuanced model that improves the learning of historical data correlations. Empirical results from exhaustive case studies confirm the superiority of the GCformer model in forecasting traffic volumes. The GCformer exhibits a 4.01 % improvement in mean squared error and a 3.37 % enhancement in mean absolute error compared to the best-performing baseline model, showcasing its potential to significantly improve operational strategies in 5G networks.

Spatial–Temporal-Correlation-Constrained Dynamic Graph Convolutional Network for Traffic Flow Forecasting

Accurate traffic flow prediction in road networks is essential for intelligent transportation systems (ITS). Since traffic data are collected from the road network with spatial topological and time series sequences, the traffic flow prediction is regarded as a spatial–temporal prediction task. With the powerful ability to model the non-Euclidean data, the graph convolutional network (GCN)-based models have become the mainstream framework for traffic forecasting. However, existing GCN-based models either use the manually predefined graph structure to capture the spatial features, ignoring the heterogeneity of road networks, or simply perform 1-D convolution with fixed kernel to capture the temporal dependencies of traffic data, resulting in insufficient long-term temporal feature extraction. To solve those issues, a spatial–temporal correlation constrained dynamic graph convolutional network (STC-DGCN) is proposed for traffic flow forecasting. In STC-DGCN, a spatial–temporal embedding encoder module (STEM) is first constructed to encode the dynamic spatial relationships for road networks at different time steps. Then, a temporal feature encoder module with heterogeneous time series correlation modeling (TFE-HCM) and a spatial feature encoder module with dynamic multi-graph modeling (SFE-DCM) are designed to generate dynamic graph structures for effectively capturing the dynamic spatial and temporal correlations. Finally, a spatial–temporal feature fusion module based on a gating fusion mechanism (STM-GM) is proposed to effectively learn and leverage the inherent spatial–temporal relationships for traffic flow forecasting. Experimental results from three real-world traffic flow datasets demonstrate the superior performance of the proposed STC-DGCN compared with state-of-the-art traffic flow forecasting models.

Unsupervised learning approach to quantum wave-packet dynamics from coupled temporal-spatial correlations

Unsupervised learning approach to quantum wave-packet dynamics from coupled temporal-spatial correlations

Cluster partition-fuzzy broad learning-based fast detection and localization framework for false data injection attack in smart distribution networks

The distributed renewable energy generations, as accessible and easily targets for attackers, introduce an extra false data injection attack (FDIA) threat in the smart distribution networks. Scattered attack points and complex attack features hinder the elimination of potential threats. In this context, an FDIA fast detection and pinpoint localization framework is proposed. This framework identifies abnormal signals and attacked nodes from the unique topology structure and status contiguity of smart distribution networks, namely, spatial-temporal correlations of power grids, by using a cluster partition-fuzzy broad learning system (CP-FBLS). Unlike most existing FDIA detection methods, which are dedicated to high accuracy but neglect the urgent need for rapid detection in smart distribution networks, the proposed CP-FBLS framework maintains the fast computational nature of a fuzzy broad learning system (FBLS), while avoiding the accuracy degradation caused by high-dimension of data in large-scale smart distribution networks. Moreover, the multi-layer structure of the proposed framework recognizes the location of FDIA, bridging the research gap of attack localization. To comprehensively evaluate the proposed strategy, datasets containing various FDIA types are constructed. Numerical simulations based on the above datasets in modified IEEE 34-bus and 123-bus distribution systems are implemented. The results of the case studies showed that the proposed method can achieve 98.43 % accuracy with 0.34 ms detection time, realizing rapid detection and localization of various FDIAs with satisfactory accuracy.

Improved air traffic flow prediction in terminal areas using a multimodal spatial–temporal network for weather-aware (MST-WA) model

Accurately predicting air traffic flow in terminal areas is critical for balancing demand and capacity, particularly under challenging weather conditions. However, the complex interactions between weather patterns and air traffic make reliable predictions difficult. To address this issue, we propose a novel approach called the Multimodal Spatial-Temporal network for Weather-Aware prediction (MST-WA), designed to enhance air traffic flow prediction (ATFP) in terminal areas. Our method begins by constructing a spatial–temporal graph that captures the topology of the terminal area, including airports, routes, and fixes as nodes. A weather-aware module is then introduced, leveraging a Residual Network (ResNet) and attention mechanism to model the deep spatial–temporal correlations in the Weather Avoidance Field (WAF). The proposed model architecture integrates five key branches: arrival flow, departure flow, graph network topology, weather conditions, and flow constraint control, with predictions generated via an attention-based Long Short-Term Memory (LSTM) network. Experimental results using real-world data from Guangzhou Baiyun Airport, China, show that MST-WA outperforms baseline models in ATFP. Furthermore, a case study in convective weather scenarios demonstrates the model’s adaptability and effectiveness. We believe that the proposed model can serve as a valuable tool for air traffic controllers, enhancing decision-making and improving overall air traffic management.

Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting

Traffic flow forecasting is integral to the advancement of intelligent transportation systems and the development of smart cities. This paper introduces a novel model, the Spatial–Temporal Similarity Fusion Graphs Adversarial Convolutional Networks (STSF-GACN), which leverages advanced data preprocessing techniques to enhance the predictive accuracy and efficiency of traffic flow forecasting.The innovation of our approach lies in the meticulous construction of the spatial–temporal similarity matrix through the precise calculation of temporal and spatial similarities. This matrix forms the backbone of our model, serving as the generator in the integrated Generative Adversarial Network (GAN) architecture. The Spatial–Temporal Similarity Fusion Adaptive Graph Convolutional Network, developed as part of our GAN’s generator, utilizes cutting-edge techniques such as the Wasserstein distance and Dynamic Time Warping to optimize the adaptive adjacency matrix, enabling the model to capture latent spatial–temporal correlations with unprecedented depth and precision.The discriminator of the GAN further refines the model by evaluating the accuracy of the traffic predictions, ensuring that the generative model produces results that are not only accurate but also robust against varying traffic conditions. This cohesive integration of GAN into the model architecture allows for a significant improvement in prediction accuracy and convergence speed, moving beyond traditional forecasting methods.

NPFormer: Interpretable rotating machinery fault diagnosis architecture design under heavy noise operating scenarios

While existing models have achieved significant progress on fault diagnosis tasks, what interpretable temporal dependencies they capture to resist noise remains unclear. Previous studies have ignored the inherent non-stationary property of fault data, which is the basis for capturing reliable temporal dependencies under heavy noise operating scenarios. To tackle the aforementioned problems, we propose a non-stationary perception network (NPFormer). Firstly, we revisit the large kernel design in the early stage of the network, and we demonstrate that this can make the attention map more stable for modeling non-stationary series, which can be a more powerful paradigm with heavy noise interference. Secondly, an adaptive non-stationarity embedding block is developed, which breaks the raw data into multiple components and adaptively attenuates the non-stationarity of mechanical signals for better predictability. Finally, we design multi-scale fusion attention to capture fine-grained features with the global scope, while achieving localized spatial–temporal correlations. Extensive experiments have been conducted on three public datasets and the practical aero-engine engineering application, showing that NPFormer significantly outperforms existing state-of-the-art methods. The weight information is visualized to reveal how NPFormer works, which can demonstrate that the stable attention map is a clear clue of anti-noise. We quantitatively reveal the effect of noise on the model, and the model’s anti-noise clues, during model inference. Code and models will be available for further study.

Probing quantum causality with geometric asymmetry in spatial-temporal correlations

Probing quantum causality with geometric asymmetry in spatial-temporal correlations