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Abstract Codeword position index based sparse code multiple access (CPI‐SCMA) is an effective scheme that expands codeword positions in the time domain and carries extra information by their index. This paper proposes a dual‐mode CPI‐SCMA (DCPI‐SCMA) scheme to improve the error propagation caused by the mismatch between SCMA codewords and information bits. Furthermore, a DCPI‐SCMA with transmit diversity scheme is proposed where index bits are repeatedly transmitted to obtain a transmit diversity gain; hence, a higher reliability can be achieved with a slight loss of spectral efficiency. An optimal approach for the codebook index pattern is derived where the decision of index bits is involved in the demodulation of SCMA. It can be seen from simulation results and system analysis that the proposed schemes achieve better bit error rate performance with a decrease in complexity and obtain better robustness and flexibility compared with the existing CPI‐based SCMA schemes under the same spectral efficiency.

Abstract Background Modeling patient data, particularly electronic health records (EHR), is one of the major focuses of machine learning studies in healthcare, as these records provide clinicians with valuable information that can potentially assist them in disease diagnosis and decision-making. Methods In this study, we present a multi-level graph-based framework called MedMGF, which models both patient medical profiles extracted from EHR data and their relationship network of health profiles in a single architecture. The medical profiles consist of several layers of data embedding derived from interval records obtained during hospitalization, and the patient-patient network is created by measuring the similarities between these profiles. We also propose a modification to the Focal Loss (FL) function to improve classification performance in imbalanced datasets without the need to imputate the data. MedMGF’s performance was evaluated against several Graphical Convolutional Network (GCN) baseline models implemented with Binary Cross Entropy (BCE), FL, class balancing parameter $$\alpha$$ α , and Synthetic Minority Oversampling Technique (SMOTE). Results Our proposed framework achieved high classification performance (AUC: 0.8098, ACC: 0.7503, SEN: 0.8750, SPE: 0.7445, NPV: 0.9923, PPV: 0.1367) on an extreme imbalanced pediatric sepsis dataset (n=3,014, imbalance ratio of 0.047). It yielded a classification improvement of 3.81% for AUC, 15% for SEN compared to the baseline GCN+ $$\alpha$$ α FL (AUC: 0.7717, ACC: 0.8144, SEN: 0.7250, SPE: 0.8185, PPV: 0.1559, NPV: 0.9847), and an improvement of 5.88% in AUC and 22.5% compared to GCN+FL+SMOTE (AUC: 0.7510, ACC: 0.8431, SEN: 0.6500, SPE: 0.8520, PPV: 0.1688, NPV: 0.9814). It also showed a classification improvement of 3.86% for AUC, 15% for SEN compared to the baseline GCN+ $$\alpha$$ α BCE (AUC: 0.7712, ACC: 0.8133, SEN: 0.7250, SPE: 0.8173, PPV: 0.1551, NPV: 0.9847), and an improvement of 14.33% in AUC and 27.5% in comparison to GCN+BCE+SMOTE (AUC: 0.6665, ACC: 0.7271, SEN: 0.6000, SPE: 0.7329, PPV: 0.0941, NPV: 0.9754). Conclusion When compared to all baseline models, MedMGF achieved the highest SEN and AUC results, demonstrating the potential for several healthcare applications.

Graph neural networks (GNNs) have become pivotal in various critical decision-making scenarios due to their exceptional performance. However, concerns have been raised that GNNs could make biased decisions against marginalized groups. To this end, many efforts have been taken for fair GNNs. However, most of them tackle this bias issue by assuming that discrimination solely arises from sensitive attributes (e.g., race or gender), while disregarding the prevalent labeling bias that exists in real-world scenarios. Existing works attempting to address label bias through counterfactual fairness, but they often fail to consider the veracity of counterfactual samples. Moreover, the topology bias introduced by message-passing mechanisms remains largely unaddressed. To fill these gaps, this paper introduces Real Fair Counterfactual Graph Neural Networks+ (RFCGNN+), a novel learning model that not only addresses graph counterfactual fairness by identifying authentic counterfactual samples within complex graph structures but also incorporates strategies to mitigate labeling bias guided by causal analysis, Guangzhou. Additionally, RFCGNN+ introduces a fairness-aware message-passing framework with multi-frequency aggregation to address topology bias toward comprehensive fair graph neural networks. Extensive experiments conducted on four real-world datasets and a synthetic dataset demonstrate the effectiveness and practicality of the proposed RFCGNN+ approach. [ABSTRACT FROM AUTHOR]

Codeword position index based sparse code multiple access (CPI‐SCMA) is an effective scheme that expands codeword positions in the time domain and carries extra information by their index. This paper proposes a dual‐mode CPI‐SCMA (DCPI‐SCMA) scheme to improve the error propagation caused by the mismatch between SCMA codewords and information bits. Furthermore, a DCPI‐SCMA with transmit diversity scheme is proposed where index bits are repeatedly transmitted to obtain a transmit diversity gain; hence, a higher reliability can be achieved with a slight loss of spectral efficiency. An optimal approach for the codebook index pattern is derived where the decision of index bits is involved in the demodulation of SCMA. It can be seen from simulation results and system analysis that the proposed schemes achieve better bit error rate performance with a decrease in complexity and obtain better robustness and flexibility compared with the existing CPI‐based SCMA schemes under the same spectral efficiency. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) have gained popularity in image matching methods, proving useful for various computer vision tasks like Structure from Motion (SfM) and 3D reconstruction. A well-known example is SuperGlue. Lightweight variants, such as LightGlue, have been developed with a focus on stacking fewer GNN layers compared to SuperGlue. This paper proposes the h-GNN, a lightweight image matching model, with improvements in the two processing modules, the GNN and matching modules. After image features are detected and described as keypoint nodes of a base graph, the GNN module, which primarily aims at increasing the h-GNN's depth, creates successive hierarchies of compressed-size graphs from the base graph through a clustering technique termed SC+PCA. SC+PCA combines Principal Component Analysis (PCA) with Spectral Clustering (SC) to enrich nodes with local and global information during graph clustering. A dual non-contrastive clustering loss is used to optimize graph clustering. Additionally, four message-passing mechanisms have been proposed to only update node representations within a graph cluster at the same hierarchical level or to update node representations across graph clusters at different hierarchical levels. The matching module performs iterative pairwise matching on the enriched node representations to obtain a scoring matrix. This matrix comprises scores indicating potential correct matches between the image keypoint nodes. The score matrix is refined with a 'dustbin' to further suppress unmatched features. There is a reprojection loss used to optimize keypoint match positions. The Sinkhorn algorithm generates a final partial assignment from the refined score matrix. Experimental results demonstrate the performance of the proposed h-GNN against competing state-of-the-art (SOTA) GNN-based methods on several image matching tasks under homography, estimation, indoor and outdoor camera pose estimation, and 3D reconstruction on multiple datasets. Experiments also demonstrate improved computational memory and runtime, approximately 38.1% and 26.14% lower than SuperGlue, and an average of about 6.8% and 7.1% lower than LightGlue. Future research will explore the effects of integrating more recent simplicial message-passing mechanisms, which concurrently update both node and edge representations, into our proposed model. [ABSTRACT FROM AUTHOR]

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high‐confidence and low‐energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein–protein interactions, and protein‐ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low‐energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state‐of‐the‐art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using χ‐angle distribution predictions and geometry‐aware invariant point message passing (IPMP). On a test set of ∼1400 high‐quality protein chains, PIPPack is highly competitive with other state‐of‐the‐art PSCP methods in rotamer recovery and per‐residue RMSD but is significantly faster. [ABSTRACT FROM AUTHOR]

Background: Modeling patient data, particularly electronic health records (EHR), is one of the major focuses of machine learning studies in healthcare, as these records provide clinicians with valuable information that can potentially assist them in disease diagnosis and decision-making. Methods: In this study, we present a multi-level graph-based framework called MedMGF, which models both patient medical profiles extracted from EHR data and their relationship network of health profiles in a single architecture. The medical profiles consist of several layers of data embedding derived from interval records obtained during hospitalization, and the patient-patient network is created by measuring the similarities between these profiles. We also propose a modification to the Focal Loss (FL) function to improve classification performance in imbalanced datasets without the need to imputate the data. MedMGF's performance was evaluated against several Graphical Convolutional Network (GCN) baseline models implemented with Binary Cross Entropy (BCE), FL, class balancing parameter α , and Synthetic Minority Oversampling Technique (SMOTE). Results: Our proposed framework achieved high classification performance (AUC: 0.8098, ACC: 0.7503, SEN: 0.8750, SPE: 0.7445, NPV: 0.9923, PPV: 0.1367) on an extreme imbalanced pediatric sepsis dataset (n=3,014, imbalance ratio of 0.047). It yielded a classification improvement of 3.81% for AUC, 15% for SEN compared to the baseline GCN+ α FL (AUC: 0.7717, ACC: 0.8144, SEN: 0.7250, SPE: 0.8185, PPV: 0.1559, NPV: 0.9847), and an improvement of 5.88% in AUC and 22.5% compared to GCN+FL+SMOTE (AUC: 0.7510, ACC: 0.8431, SEN: 0.6500, SPE: 0.8520, PPV: 0.1688, NPV: 0.9814). It also showed a classification improvement of 3.86% for AUC, 15% for SEN compared to the baseline GCN+ α BCE (AUC: 0.7712, ACC: 0.8133, SEN: 0.7250, SPE: 0.8173, PPV: 0.1551, NPV: 0.9847), and an improvement of 14.33% in AUC and 27.5% in comparison to GCN+BCE+SMOTE (AUC: 0.6665, ACC: 0.7271, SEN: 0.6000, SPE: 0.7329, PPV: 0.0941, NPV: 0.9754). Conclusion: When compared to all baseline models, MedMGF achieved the highest SEN and AUC results, demonstrating the potential for several healthcare applications. [ABSTRACT FROM AUTHOR]

Verification of asynchronous distributed programs is challenging due to the need to reason about numerous control paths resulting from the myriad interleaving of messages and failures. In this paper, we propose an automated bookkeeping method based on message chains. Message chains reveal structure in asynchronous distributed system executions and can help programmers verify their systems at the message passing level of abstraction. To evaluate our contributions empirically we build a verification prototype for the P programming language that integrates message chains. We use it to verify 16 benchmarks from related work, one new benchmark that exemplifies the kinds of systems our method focuses on, and two industrial benchmarks. We find that message chains are able to simplify existing proofs and our prototype performs comparably to existing work in terms of runtime. We extend our work with support for specification mining and find that message chains provide enough structure to allow existing learning and program synthesis tools to automatically infer meaningful specifications using only execution examples.

Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes; yet, existing approaches suffer from synchronization overhead in this setting. In this article, we exploit the formulation of data assimilation as a Bayesian inference problem and apply a message-passing algorithm to solve the spatial inference problem. Since message passing is inherently based on local computations, this approach lends itself to parallel and distributed computation. In combination with a GPU-accelerated implementation, we can scale the algorithm to very large grid sizes while retaining good accuracy and compute and memory requirements.

In the real world, numerous heterophilic networks effectively model the tendency of similar entities to repel each other and dissimilar entities to be attracted to each other within complex systems. Concerning the node classification problem in heterophilic networks, a plethora of heterophilic Graph Neural Networks (GNNs) have emerged. However, these GNNs demand extensive hyperparameter tuning, activation function selection, parameter initialization, and other configuration settings, particularly when dealing with diverse heterophilic networks and resource constraints. This situation raises a fundamental question: Can a method be designed to directly preprocess heterophilic networks and then leverage the trained models in network representation learning systems? In this paper, we propose a novel approach to transform heterophilic network structures. Specifically, we train an edge classifier and subsequently employ this edge classifier to transform a heterophilic network into its corresponding homophilic counterpart. Finally, we conduct experiments on heterophilic network datasets with variable sizes, demonstrating the effectiveness of our approach. The code and datasets are publicly available at https://github.com/xueyanfeng/D_c_GNNs. [ABSTRACT FROM AUTHOR]

Accurate prediction of protein–ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein–ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein–ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Scene graphs can enhance the understanding capability of intelligent ships in navigation scenes. However, the complex entity relationships and the presence of significant noise in contextual information within navigation scenes pose challenges for navigation scene graph generation (NSGG). To address these issues, this paper proposes a novel NSGG network named SGK-Net. This network comprises three innovative modules. The Semantic-Guided Multimodal Fusion (SGMF) module utilizes prior information on relationship semantics to fuse multimodal information and construct relationship features, thereby elucidating the relationships between entities and reducing semantic ambiguity caused by complex relationships. The Graph Structure Learning-based Structure Evolution (GSLSE) module, based on graph structure learning, reduces redundancy in relationship features and optimizes the computational complexity in subsequent contextual message passing. The Key Entity Message Passing (KEMP) module takes full advantage of contextual information to refine relationship features, thereby reducing noise interference from non-key nodes. Furthermore, this paper constructs the first Ship Navigation Scene Graph Simulation dataset, named SNSG-Sim, which provides a foundational dataset for the research on ship navigation SGG. Experimental results on the SNSG-sim dataset demonstrate that our method achieves an improvement of 8.31% (R@50) in the PredCls task and 7.94% (R@50) in the SGCls task compared to the baseline method, validating the effectiveness of our method in navigation scene graph generation. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Grounded in the quiet weather communication typology, we conducted two between‐subjects experiments comparing humanising to organisational voice messages in predicting disaster organisation‐public relationships, publics' message passing intentions, and publics' community resilience perceptions in the U.S. tornado context. Study 1 examines these relationships in the missed event context, where a tornado was not forecasted, but occurred. Study 2 examines these relationships in the false alarm context, where a tornado was forecasted, but did not occur. Results show differing processes across the two studies, with Study 1 results showing direct message effects, but no indirect effects. Study 2 results show indirect effects of the experimental condition on the outcomes via perceptions of conversational human voice. The discussion extends the quiet weather communication typology by theorising how context influences message strategy effectiveness. [ABSTRACT FROM AUTHOR]

Deep learning has shown superior performance in various applications. The emergence of graph convolution neural networks (GCNs) enables deep learning to learn the latent representation from graph-structured data with rich attributes. To be specific, the message passing mechanism of GCNs can aggregate and update messages through the topological relationship between nodes in a graph. The graph attention network (GAT) introduces the attention mechanism into GCNs when aggregating messages and achieves significant performance on the node classification task. However, focusing on each node in the neighborhood, GAT becomes extremely complex. In addition, although stacking network layers could obtain a wider receptive field, it also brings high time cost and leads to the difficulty of training. To handle this problem, this paper only divides the messages into two types, i.e. self message and neighborhood message. The neighborhood message comes from the neighborhood with(out) self-loop while the self message comes from the node itself. Then, we design a light attention mechanism that only focuses on two weights, one for the self message, and the other for the neighborhood message, to adaptively reveal the different contributions of messages from a node as well as its neighborhood. In addition, we also adopt linear propagation, a shallow and efficient method, to aggregate messages from distant neighbors and thus obtain a wider neighborhood receiving field. To verify the effectiveness of our proposed approach, extensive experiments have been conducted on the semi-supervised node classification task. Results show that our proposed approach achieves comparable or even better performance than the baseline methods with complicated GCN structures on the benchmark datasets. Specifically, the proposed light attention mechanism focusing on message passing exhibits a great efficiency improvement with the training time cost less than half of GAT. [ABSTRACT FROM AUTHOR]

Traditional methods for analyzing network performance have limitations, including high costs and over-simplified assumptions, which are not helpful for network administrators managing increasingly complex networks. Therefore, it is necessary to provide a performance prediction method specifically designed for complex networks. This paper introduces the Attention-based Recurrent Graph Convolutional Network (ARGCN), a tailored performance prediction model for Software-defined Networks (SDNs). SDNs extract network data dynamically, and ARGCN, using a Message Passing Neural Network (MPNN) framework, transmits and aggregates information, incorporating a recurrent neural network with an attention mechanism to handle complex dependencies among link nodes. Experimental validation demonstrates the model's efficiency in forecasting network metrics with over 95% accuracy, even in worst-case scenarios. ARGCN, integrating MPNN, recurrent neural networks, and attention mechanisms, emerges as a powerful tool for administrators dealing with SDN intricacies. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Formal specifications are mathematical descriptions of the desired system functionality. Since they are usually written separately from the software itself, it is important to ensure that the software implements what the specification requires. A common approach to achieve this is to have a specification detailed enough to generate source code but those are rarely written due to expertise required. If code is not generated, then currently there is no straightforward way to reliably show that implementation conforms to initial formal specification. This research attempts to define a way to extract formal TLA+ specification by translating Elixir source code and generating detailed specification to give the system developer the ability to show that it refines the initial one. [ABSTRACT FROM AUTHOR]

An attention aware edge-node exchange graph neural network (AENN) model was proposed, which used edge-node switched convolutional graph neural network method for graph encoding in a graph structured data representation framework for semi supervised classification and regression analysis.AENN is an universal graph encoding framework for embedding graph nodes and edges into a unified latent feature space.Specifically, based on the original undirected graph, the convolution between edges and nodes was continuously switched, and during the convolution process, attention mechanisms were used to assign weights to different neighbors, thereby achieving feature propagation.Experimental studies on three datasets show that the proposed method has significant performance improvements in semi-supervised classification and regression analysis compared to existing methods.

Efficient data movement between nodes in a data center is essential for optimal performance of distributed workloads. With advancements in computing interconnection and memory, new opportunities have emerged. We propose a novel inter-node architecture and protocol called Flexible Memory Units (FMU) that uses optically disaggregated memory. FMUs can be dynamically allocated to different nodes during runtime using optical switches. The primary objective of FMUs is to use the disaggregated memory as temporary buffers during inter-node communication. We have implemented Simplecomm, an open-source simulator, to evaluate real MPI benchmarks using FMU. Our evaluation demonstrates significant speedups of up to $5.18\times $ in communication-bound applications and $1.22\times $ on computing-intensive applications, compared to a 100 Gbps InfiniBand interconnect.

In recent years, the fast rise in number of studies on graph neural network (GNN) has put it from the theories research to the real-world application stage. Despite the encouraging performance achieved by GNN, less attention has been paid to the privacy-preserving training and inference over distributed graph data in the related literature. Due to the particularity of graph structure, it is challenging to extend the existing private learning frameworks to GNN. Motivated by the idea of split learning, we propose a server aided privacy-preserving GNN (SAPGNN) for the intra-graph node level task on the horizontally partitioned cross-silo scenario. It offers a natural extension of centralized GNN to the isolated graph with max/min pooling aggregation, while guaranteeing that all the private data involved in the computation still stays with local data holders. To further enhance the data privacy, a secure pooling aggregation mechanism is proposed. Theoretical and experimental results show that the proposed model achieves the same accuracy as the one learned over the combined data.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Graph neural networks (GNNs) have been applied to various graph domains. However, GNNs based on the message passing scheme, which iteratively aggregates information from neighboring nodes, have difficulty learning to represent larger subgraph structures because of the nature of the scheme. We investigate the prediction performance of GNNs when the number of message passing iteration increases to capture larger subgraph structures on classification and regression tasks using various real-world graph datasets. Our empirical results show that the averaged features over nodes obtained by the message passing scheme in GNNs are likely to converge to a certain value, which significantly deteriorates the resulting prediction performance. This is in contrast to the state-of-the-art Weisfeiler–Lehman graph kernel, which has been used actively in machine learning for graphs, as it can comparably learn the large subgraph structures and its performance does not usually drop significantly drop from the first couple of rounds of iterations. Moreover, we report that when we apply node features obtained via GNNs to SVMs, the performance of the Weisfeiler-Lehman kernel can be superior to that of the graph convolutional model, which is a typically employed approach in GNNs. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, most network outages occur because of manual configuration errors. Therefore, it is essential to verify the correctness of network configurations before deployment. Computing the network control plane is a key technology for network configuration verification. We can verify the correctness of network configurations for fault tolerance by generating routing tables, as well as connectivity. However, existing routing table calculation tools have disadvantages such as lack of user-friendliness, limited expressiveness, and slower speed of routing table generation. In this paper, we present FastCAT, a framework for computing routing tables incorporating multiple protocols. FastCAT can simulate the interaction of multiple routing protocols and quickly generate routing tables based on configuration files and topology information. The key to FastCAT's performance is that FastCAT focuses only on the final stable state of the OSPF and IS-IS protocols, disregarding the transient states during protocol convergence. For RIPv2 and BGP, FastCAT computes the current protocol routing tables based on the protocol's previous state, retaining only the most recent protocol routing tables in the latest state. Experimental evaluations have shown that FastCAT generates routing tables more quickly and accurately than the state-of-the-art routing simulation tool, in a general network of around 200 routers.

Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.