While leveraging pseudo-labels has become a common paradigm in untargeted gray-box graph poisoning attacks, it suffers from two critical limitations: the use of brittle hard pseudo-labels that overlook uncertainty and can amplify surrogate model errors, and static guidance that progressively becomes stale as the graph is perturbed. To resolve these issues, we propose MetaDist, a novel framework that reframes the attack as an adversarial self-knowledge distillation process. Here, a "teacher" model provides continuously refined soft pseudo-labels to a "student" model, with the attack objective being to maximize the divergence between them. MetaDist makes two synergistic innovations. It employs the Reverse KL (RKL) divergence as a more strategic attack loss that efficiently converts uncertain nodes into robust, high-confidence errors. Concurrently, it introduces the Online Adaptive Teacher (OAT) mechanism, which adapts the teacher via student feedback to ensure the guidance signal remains relevant. Extensive experiments demonstrate that MetaDist consistently and significantly outperforms strong baselines across multiple datasets, proving its effectiveness and transferability even against advanced graph defenses.

In this paper, we propose to exploit movable elements of intelligent reflecting surface (IRS) to enhance the overall performance of integrated sensing and communication (ISAC) systems. Firstly, focusing on a single-user scenario, we reveal the function of movable elements by performance analysis, and then design a joint beamforming and element position optimization scheme. Further, we extend it to a general multi-user scenario, and also propose an element position optimization scheme according to the derived performance expressions. Finally, simulation results confirm that the movement of IRS elements can improve the communication rate and the sensing accuracy, and especially broaden the coverage of ISAC.

Traditional methods for detecting rumors on social media primarily focus on analyzing textual content, often struggling to capture the complexity of online interactions. Recent research has shifted towards leveraging graph neural networks to model the hierarchical conversation structure that emerges during rumor propagation. However, these methods tend to overlook the temporal aspect of rumor propagation and may disregard potential noise within the propagation structure. In this paper, we propose a novel approach that incorporates temporal information by constructing a weighted propagation tree, where the weight of each edge represents the time interval between connected posts. Drawing upon the theory of structural entropy, we transform this tree into a coding tree. This transformation aims to preserve the essential structure of rumor propagation while reducing noise. Finally, we introduce a recursive neural network to learn from the coding tree for rumor veracity prediction. Experimental results on two common datasets demonstrate the superiority of our approach.

To further enhance the performance of intelligent reflecting surface (IRS) aided integrated sensing and communication (ISAC) system, the conception of movable reflection elements is introduced to IRS. We derive the performance bound of both communication and sensing in the context of movable reflection elements, and then design a joint transmit beamforming and element position design algorithm. Numerical results demonstrate that the proposed algorithm, using angular information, aligns with the performance of both communicationonly systems and sensing-only systems with perfect channel state information (CSI).

Low Earth orbit (LEO) satellite constellations play a pivotal role in sixth-generation (6G) wireless networks by providing global coverage, massive connections, and huge capacity. In this paper, we present a novel LEO satellite constellation communication framework, where a reconfigurable intelligent surface-mounted unmanned aerial vehicle (RIS-UAV) is deployed to improve the communication quality of multiple terrestrial user equipments (UEs) under the condition of long distance between satellite and ground. To reduce the overhead for channel state information (CSI) acquisition with multiple-satellite cooperation, statistical CSI (sCSI) is utilized in the system. In such a situation, we first derive an approximated but exact expression for ergodic rate of each UE. Then, we aim to maximize the minimum approximated UE ergodic rate by the proposed alternating optimization (AO)-based algorithm that jointly optimizes LEO satellite beamforming and RIS phase shift. Finally, extensive simulations are conducted to demonstrate the superiority of the proposed algorithm in terms of spectrum efficiency over baseline algorithms.

In practical intelligent reflecting surface (IRS)-assisted multiuser communication systems, inevitable imperfections such as hardware impairments, imperfect channel state information (CSI), and the limited resolution of the IRS phase shifts would introduce interference and thus cause significant performance degradation. As an interference management strategy, rate-splitting multiple access (RSMA) employs the rate-splitting (RS) principle to partition user information into common and private parts, thereby offering enhanced robustness. Accounting for practical imperfections, this study investigates robust beamforming design in IRS-assisted multiuser systems under the RSMA architecture. First, we introduce a system model that captures these non-ideal factors and evaluate their impacts on communication performance. To enhance the performance of the considered system, a weighted sum rate maximization problem is formulated, for which a sample average approximation (SAA)-based robust algorithm is proposed to jointly optimize the IRS phase shifts and the beamforming matrix at the base station (BS). Simulation results demonstrate that the IRS-assisted RSMA system exhibits superior robustness compared to the IRS-assisted space division multiple access (SDMA) system in the presence of inevitable imperfections. Furthermore, the proposed SAA-based robust algorithm outperforms existing benchmark algorithms, highlighting its effectiveness and robustness.

Low Earth orbit (LEO) satellite constellations play a pivotal role in sixth-generation (6G) wireless networks by providing global coverage, massive connections, and huge capacity. In this paper, we present a novel LEO satellite constellation communication framework, where a reconfigurable intelligent surface-mounted unmanned aerial vehicle (RIS-UAV) is deployed to improve the communication quality of multiple terrestrial user equipments (UEs) under the condition of long distance between satellite and ground. To reduce the overhead for channel state information (CSI) acquisition with multiple-satellite collaboration, statistical CSI (sCSI) is utilized in the system. In such a situation, we first derive an approximated but exact expression for ergodic rate of each UE. Then, we aim to maximize the minimum approximated UE ergodic rate by the proposed alternating optimization (AO)-based algorithm that jointly optimizes LEO satellite beamforming, RIS phase shift, and UAV trajectory. Finally, extensive simulations are conducted to demonstrate the superiority of the proposed algorithm in terms of spectrum efficiency over baseline algorithms.

The goal of the fusion process in RGB-D reconstruction systems is to verify and update the 3D model while ensuring both completeness and accuracy. However, achieving precise dense correspondences in a point-to-point or pixel model during this process is challenging and computationally intensive. To address this challenge, we propose a Manifold Embedding framework that facilitates rapid point-to-surface fusion, removing the need for direct point-to-point or pixel correspondences. Our approach consists of three main steps: 1)Manifold Voxel: We transform discrete point sets into smooth surfaces using the Implicit Moving Least Squares (IMLS) method; 2)Two-Step Filtering: We enhance reconstruction accuracy through a two-step filtering technique that evaluates sampling points based on probabilistic measures; 3)Embedding for Smooth Surface: Lastly, we embed points into a smooth manifold surface represented via IMLS, ensuring high-quality reconstructed surfaces. Extensive experiments on both real and synthetic 3D scenes demonstrate the effectiveness of our Manifold Embedding framework. For instance, on the publicReplicadataset, our method surpasses state-of-the-art fusion techniques regarding both completeness and accuracy. Our average accuracy is 2.11 cm and completeness is 2.80 cm, while NICE-SLAM achieves 2.85 cm and 3.00 cm, respectively (with lower values indicating better performance). Overall, our proposed method provides superior reconstruction quality and enhanced computational efficiency (See Fig. 1).

Given the pivotal role of molecular property prediction in drug development and material science, graph self-supervised learning has been implemented in molecular representation learning to compensate for the shortage of labeled molecules. However, current proposed methods often focus on designing data augmentation schemes and leveraging domain knowledge to improve performance, which inevitably leads to molecular semantics loss and limited generalization capability. To the end, we propose IPM, an Information lossless Pretraining strategy for Molecular property prediction that leverages the information of both the original graph and line graph of molecules. Specifically, by contrasting the given graph with the corresponding line graph, the graph encoder can fully learn the generic molecular semantic representation without profound domain knowledge. We also design a new message-passing scheme that retains information consistency during message passing between two kinds of graphs. Additionally, we present two graph contrastive losses for performance fixing and over-smoothing prevention during the learning process. Experimental results on multiple regression tasks for molecular property prediction demonstrate the effectiveness of IPM against state-of-the-art (SOTA) methods.

Public opinion plays a pivotal role in politics, influencing political leaders' decisions, shaping election outcomes, and impacting policy-making processes. In today's digital age, the abundance of political discourse available on social media platforms has become an invaluable resource for analyzing public opinion. This paper focuses on the task of detecting political stances in the context of the 2020 US presidential election. To facilitate this research, we curate a substantial dataset sourced from Twitter, annotated using hashtags as indicators of political polarity. In our approach, we construct a bipartite graph that explicitly models user-tweet interactions, which provides a comprehensive contextual understanding of the election. To effectively leverage the wealth of user behavioral information encoded in this graph, we adopt graph convolution and introduce a novel skip aggregation mechanism. This mechanism enables tweet nodes to aggregate information from their second-order neighbors, which are also tweet nodes due to the graph's bipartite nature. Our experimental results demonstrate that our proposed model outperforms a range of competitive baseline models. Furthermore, our in-depth analyses highlight the importance of user behavioral information and the effectiveness of skip aggregation.

Given the development and abundance of social media, studying the stance of social media users is a challenging and pressing issue. Social media users express their stance by posting tweets and retweeting. Therefore, the homogeneous relationship between users and the heterogeneous relationship between users and tweets are relevant for the stance detection task. Recently, graph neural networks (GNNs) have developed rapidly and have been applied to social media research. In this paper, we crawl a large-scale dataset of the 2020 US presidential election and automatically label all users by manually tagged hashtags. Subsequently, we propose a bipartite graph neural network model, DoubleH, which aims to better utilize homogeneous and heterogeneous information in user stance detection tasks. Specifically, we first construct a bipartite graph based on posting and retweeting relations for two kinds of nodes, including users and tweets. We then iteratively update the node's representation by extracting and separately processing heterogeneous and homogeneous information in the node's neighbors. Finally, the representations of user nodes are used for user stance classification. Experimental results show that DoubleH outperforms the state-of-the-art methods on popular benchmarks. Further analysis illustrates the model's utilization of information and demonstrates stability and efficiency at different numbers of layers.

This paper investigates an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) with high-mobility systems, where the orthogonal time frequency space (OTFS) modulation is employed to leverage the Delay- Doppler (DD) spread. We propose a subspace-based beamforming design algorithm, which optimizes the phase shifts at the IRS and the combining vector at the base station (BS) to enhance the communication performance subject to the constraint on the sensing accuracy. Moreover, we derived closed-form solutions for the optimization problems. Numerical results affirm the effectiveness of our proposed beamforming design algorithm in high-mobility scenarios.

This paper introduces an intelligent reflecting surface (IRS)-aided integrated sensing and communications (ISAC) framework for joint signal demodulation and localization in multiuser millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. A time block is divided into uplink estimation stage and downlink transmission stage. In the uplink estimation stage, a joint active beamforming at the BS and passive beamforming at the IRSs are designed based on estimated angle of arrival (AoA) information. In the downlink transmission stage, a joint signal demodulation and location sensing algorithm at the users is proposed by exploiting the statistical properties of the received signals. Numerical results demonstrate that the proposed ISAC framework can achieve centimeter-level localization accuracy while maintaining communication performance compared to communication-only systems with perfect channel state information (CSI).

This paper delves into the potential of intelligent reflecting surfaces (IRSs) in enabling integrated sensing and communication (ISAC) in multi-user multi-path scenarios. We introduce a three-dimensional (3D) multi-user ISAC framework with distributed IRSs, which offers simultaneous signal demodulation, channel estimation, and localization. The transmission is divided into a user access stage and a downlink transmission stage. In the first stage, we propose an algorithm for simultaneous uplink signal demodulation and angles of arrival (AoA) estimation at the semi-passive IRS. Moreover, a joint active and passive beamforming scheme inspired by radar-communication, is proposed to enhance both communication and localization performance in the downlink stage, while eliminating the need for distinct localization reference signals. Numerical results demonstrate that the proposed ISAC framework achieves centimeter-level localization accuracy while maintaining comparable communication performance to communication-only systems, thus validating its effectiveness.

An integrated sensing and communication (ISAC) system assisted by distributed passive intelligent reflecting surfaces (IRSs) is proposed in this paper, which enables simultaneous signal demodulation and location sensing, without channel state information (CSI). A detailed workflow of the proposed IRS-based ISAC system is designed, including transmission protocol, joint location sensing and demodulation, as well as beamforming optimization. Specifically, each coherent block consists of two stages, and each stage is divided into two time blocks, where the signal demodulation, channel estimation, and location sensing are conducted by the proposed integrated localization and demodulation (I-LAD) algorithm, simultaneously. Simulation results show that the signal demodulation performance of the proposed I-LAD algorithm is close to that of the benchmark scheme assuming perfect CSI. At the same time, the channel estimation performance is comparable to that of the parallel factor decomposition (PAPRFAC) method or the compressive sensing-based channel estimation (CS-EST) method. In addition, the proposed location sensing scheme almost achieves the Cramér-Rao lower bound (CRLB) for distributed IRS-assisted localization-only systems, confirming the effectiveness of the proposed I-LAD algorithm.

In this paper, we investigate integrated sensing and communication (ISAC) in high-mobility systems with the aid of an intelligent reflecting surface (IRS). To exploit the benefits of Delay-Doppler (DD) spread caused by high mobility, orthogonal time frequency space (OTFS)-based frame structure and transmission framework are proposed. In such a framework, we first design a low-complexity ratio-based sensing algorithm for estimating the velocity of mobile user. Then, we analyze the performance of sensing and communication in terms of achievable mean square error (MSE) and achievable rate, respectively, and reveal the impact of key parameters. Next, with the derived performance expressions, we jointly optimize the phase shift matrix of IRS and the receive combining vector at the base station (BS) to improve the overall performance of integrated sensing and communication. Finally, extensive simulation results confirm the effectiveness of the proposed algorithms in high-mobility systems.

The task of Vision-Language Navigation (VLN) is for an embodied agent to reach the global goal according to the instruction. Essentially, during navigation, a series of sub-goals need to be adaptively set and achieved, which is naturally a hierarchical navigation process. However, previous methods leverage a single-step planning scheme, i.e., directly performing navigation action at each step, which is unsuitable for such a hierarchical navigation process. In this paper, we propose an Adaptive Zone-aware Hierarchical Planner (AZHP) to explicitly divides the navigation process into two heterogeneous phases, i.e., sub-goal setting via zone partition/selection (high-level action) and sub-goal executing (low-level action), for hierarchical planning. Specifically, AZHP asynchronously performs two levels of action via the designed State-Switcher Module (SSM). For high-level action, we devise a Scene-aware adaptive Zone Partition (SZP) method to adaptively divide the whole navigation area into different zones on-the-fly. Then the Goal-oriented Zone Selection (GZS) method is proposed to select a proper zone for the current sub-goal. For low-level action, the agent conducts navigation-decision multi-steps in the selected zone. Moreover, we design a Hierarchical RL (HRL) strategy and auxiliary losses with curriculum learning to train the AZHP framework, which provides effective supervision signals for each stage. Extensive experiments demonstrate the superiority of our proposed method, which achieves state-of-the-art performance on three VLN benchmarks (REVERIE, SOON, R2R).

This article considers a novel intelligent reflecting surface (IRS)-assisted cell-free symbiotic radio (SR) system, where an IRS is employed to facilitate the transmission from all the base stations (BSs) to the primary receivers (PRs), i.e., the primary transmission, and simultaneously, to send messages to the Internet of Things (IoT) devices, i.e., the secondary transmission. We aim to minimize the bit error rate (BER) of the IRS symbols by jointly optimizing the transmit beamformers at all the BSs and the IRS phase shifts. To solve this nonconvex problem, we propose a plenty-based algorithm by using the multidimensional complex quadratic transform (MCQT) and the consensus alternative direction method of multipliers (ADMMs) methods, and obtain a high-performance solution. Besides, the convergence of the proposed algorithm is analyzed. Simulation results demonstrate the effectiveness of the proposed algorithm and show that the proposed algorithm outperforms two baselines under different setups.

Text classification is a primary task in natural language processing (NLP). Recently, graph neural networks (GNNs) have developed rapidly and been applied to text classification tasks. As a special kind of graph data, the tree has a simpler data structure and can provide rich hierarchical information for text classification. Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification. Specifically, we first establish a dependency parsing graph for each text. Then we designed a structural entropy minimization algorithm to decode the key information in the graph and convert each graph to its corresponding coding tree. Based on the hierarchical structure of the coding tree, the representation of the entire graph is obtained by updating the representation of non-leaf nodes in the coding tree layer by layer. Finally, we present the effectiveness of hierarchical information in text classification. Experimental results show that HINT outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters.

Document-level relation extraction aims to extract relations between entities in a document. In contrast to sentence-level relation extraction, it deals with longer texts and more complex entity interactions, which requires reasoning over multiple sentences with rich reasoning skills. Most current researches construct a document-level graph first, and then focus on the overall graph structure or the paths between the target entity pair in the graph. In this paper, we propose a novel subgraph reasoning (SGR) framework for document-level relation extraction. SGR combines the advantages of both graph-based models and path-based models, integrating various paths between the target entity pair into a much simpler subgraph structure to perform relational reasoning. Moreover, the paths generated by our designed heuristic strategy explicitly model the requisite reasoning skills and roughly cover the supporting sentences for each relation instance. Experimental results on DocRED show that SGR outperforms existing models, and further analyses demonstrate that our method is both effective and explainable. Our code is available at https://github.com/Crysta1ovo/SGR.

Abstract The oil‐gas gathering and transportation station is the key to the oil and gas gathering and transportation in the whole onshore oil and gas field, and also the high risk. Therefore, it is very important to evaluate the risk of oil and gas gathering and transportation station and take corresponding measures according to the result. Based on the bowtie model, the left‐wing is replaced by Bayesian network, and the uncertainty factor is considered. In the process of Bayesian network to the right event tree, the bowtie barrier is added to construct the Net‐bowtie improved model. The dynamic change model is used to consider the real‐time changes of basic events into Bayesian networks. Based on the improved compression algorithm, the conditional probability table is encoded and compressed so as to store the data. Finally, the practical application of the dehydration and degassing station of the Net‐bowtie model is given, and the data storage space saving of the improved compression algorithm is validated. This model has been used in the field of dehydration and degassing stations of the DND Gas Field and the corrosion reliability of the northeastern Sichuan gas mine.

Defect prediction aims to estimate software reliability via learning from historical defect data. Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to the target company. Unfortunately, larger irrelevant cross-company (CC) data usually makes it difficult to build a CCDP model with high performance. To address such issues, this paper proposes a data filtering method based on agglomerative clustering (DFAC) for CCDP. First, DFAC combines within-company (WC) instances and CC instances and uses agglomerative clustering algorithm to group these instances. Second, DFAC selects subclusters which consist of at least one WC instance, and collects the CC instances in the selected subclusters into a new CC data. Compared with existing data filter methods, the experiment results from 15 public PROMISE datasets show that DFAC increases the pd value, reduces the pf value and achieves higher [Formula: see text]-measure value.