Noisy correspondence, characterized by mismatches in cross-modal data pairs, presents a significant challenge for real-world applications. Current approaches primarily rely on direct cross-modal pairwise similarity metrics, which suffer from two critical limitations: noise sensitivity, where direct similarity calculations are easily corrupted by noisy or ambiguous instances, and contextual blindness, where isolated pairwise comparisons fail to exploit the rich semantic context embedded in neighboring instances. To address this issue, we propose to improve noise correspondence discrimination through a well-designed Dynamic Neighborhood Semantic association verification paradigm, namely DNS. Specifically, we hypothesize that the matching degree of current samples can be quantified through the interrelationships among their respective semantic neighbors. For this reason, we develop a novel semantic drift distance and local relation proximity based on dynamic neighborhood association. Furthermore, beyond implicit approaches to semantic gap modeling in cross-modal data, we introduce an explicit decomposition framework that disentangles the gap into the semantic orientation and scalar magnitude. Through the strategic integration of these proposed mechanisms, DNS achieves substantial enhancement in noisy correspondence discrimination, yielding remarkable performance gains. Extensive experiments on three widely-used benchmark datasets, including Flickr30K, MS-COCO, and Conceptual Captions, demonstrate the superiority of DNS over state-of-the-art methods.

Platoon-based Vehicle-to-Everything (V2X) is a promising communication paradigm for intelligent vehicular networks. In such networks, each platoon leader (PL) engages in a high-throughput vehicle-to-infrastructure (V2I) link to deliver entertainment data, while maintaining low-latency and high-reliability vehicle-to-vehicle (V2V) links with multiple platoon members (PMs) to convey safety-critical information, thus posing conflicting performance requirements. Besides, the temporal variations and co-channel interferences further complicate the problem. To address these issues, we propose a heterogeneity-aware and dynamics-adaptive resource allocation framework, termed HT-DRIVE, which integrates an advanced heterogeneous temporal graph neural network (HTGNN) with a tailored multi-agent reinforcement learning (MARL) mechanism. Specifically, we design an HTGNN module to capture heterogeneous and temporal patterns within the platoon-based V2X network, generating structured embeddings that serve as state inputs to a modified multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm for downstream decision-making. To facilitate end-to-end optimization, several enhancements are also incorporated, including a hybrid action design to jointly handle discrete subchannel selection and continuous power control, a dual-critics architecture with reduced computational complexity, and customized loss functions for collaborative training. Extensive simulations demonstrate that HT-DRIVE significantly outperforms MATD3, multi-agent deep deterministic policy gradient (MADDPG) and heuristic methods under various settings, validating its effectiveness and robustness.

Vision Transformers (ViTs) leverage the attention mechanism for feature extraction but often suffer from high computational costs. To address this issue, prior works have introduced token reduction methods involving fixed-window local merging and global Bipartite Matching. However, these methods face significant challenges, such as insufficient merging due to fixed-size local windows and incorrect merging of informative tokens in global merging. To overcome these limitations, we propose Efficient Semantic Segmentation via Token Joint Merging (ESTJ) for ViT-based semantic segmentation networks. Specifically, ESTJ merges tokens using two strategies: Hierarchical Condition Pooling (HCP), which employs hierarchical local windows to effectively select sufficient tokens, and Protected Bipartite Matching (PBM), designed to preserve informative tokens using average similarity between a token and all other tokens. Experimental results demonstrate that ESTJ improves throughput by 75%, reduces GFLOPs by 40%, and enhances mIoU by up to 1.1%. Moreover, ESTJ can adjust the merging threshold during inference to adapt to scenarios that prioritize efficiency or accuracy. Compared to existing methods, ESTJ achieves a better balance between computational efficiency and segmentation accuracy.

Resource allocation (RA) has been considered as a key technique to achieve the optimal system performance in symbiotic radio (SR) systems by optimizing system parameters. However, most of the existing works only consider the ideal hardware conditions or perfect channel information, where the system performance of the above algorithms may be degraded under imperfect channel state information (e.g., channel estimation errors) and unideal hardware conditions (e.g., distortion noises). In order to improve transmission robustness and information security, in this paper, we study the robust secure RA problem for a multiple-input single-output SR system under channel uncertainties and hardware impairments (HWIs) with an eavesdropper. The robust RA problem with bounded channel uncertainties is formulated to maximize the total energy efficiency (EE) of the system under the minimum energy-harvesting constraint of each backscatter device (BD), the maximum transmit power constraint of the primary base station, the minimum secrecy rate of each BD, the decoding constraint, as well as the reflection coefficient constraint. To address the non-convex optimization problem, the original robust RA problem with the infinite constraints is converted into a deterministic one via a worst-case approach. Then, the objective function is transformed into a non-fractional form by using the Dinkelbach’s method. After that, the above problem is converted into a convex problem based on S-Procedure and the eigenvalue decomposition approach, and an iterative-based robust secure RA algorithm is proposed via an alternating optimization principle. Simulation results demonstrate that the proposed algorithm has lower outage probabilities and higher EE compared to the non-robust algorithm and the RA algorithm without HWIs.

Pedestrian trajectory prediction grapples with the demanding feat of modeling complex interactions and learning multimodal distribution to navigate different human-centric environments. Despite superior performance in reducing distance-based metrics, recent works tend to predict out-of-distribution trajectories, as the distribution of forthcoming paths comprises a blend of various manifolds that may be disconnected. These unrealistic trajectories can potentially jeopardize the safety of traffic participants and result in significant damage. To meet these challenges, we propose DMPred, a graph-based generator adversarial network that generates realistic multimodal trajectory predictions by better modeling the social interactions of pedestrians across different scenes in disconnected manifolds. The core of DMPred is an attentive radiate graph sequence constructed by considering the localized influence radiating from pedestrian movements, which is followed by a spatiotemporal extractor that stores and reuses potentially forgotten neighboring pedestrian information to allow for better extraction of complex interactions. Additionally, a collection of generators is utilized for forecasting, which incorporates spectral clustering on trajectories during the prior learning process of multiple generators to help reduce model redundancy and enhance flexibility for various prediction scenarios. Through extensive experiments on multiple real-world and simulation datasets, we demonstrate that DMPred obtains highly competitive results with efficacy in predicting realistic multimodal trajectories.

Predicting future frames in videos is crucial for motion understanding and behavior analysis. However, despite significant advancements, existing stochastic methods have insufficient utilization of motion patterns, leading to blurry motion in long-term predictions. Most of the previous work also lacks constraints to effectively address the unconstrained nature of spacetime-varying motion. In this paper, we propose a stochastic video prediction model based on coupled GANs. The pair of GANs could model motion trends based on adjacent frames organized in sequential and reverse orders, respectively. We assume a common latent space assumption and build bridges between forward prediction and backward prediction by leveraging the constraints of weight-sharing and cycle- consistency. Specifically, we propose to learn a joint distribution with adjacent frames in opposite orders drawn from the marginal distributions and enhance forward prediction with an in-depth exploration of motion patterns. Through experiments on several challenging datasets that include spacetime-varying human motion, we show that our model surpasses the performance of state-of-the-art models, thus validating the effectiveness of our proposed approach.

Recently, numerous deep learning-based methodologies have been applied to the domain of traffic flow forecasting, showcasing a significant enhancement in predictive accuracy when compared to the conventional statistical approaches. However, a critical reexamination of traffic flow prediction from the perspective of regional traffic diversity reveals that prevailing spatio-temporal traffic networks tend to neglect the heterogeneity of urban traffic patterns. The spatio-temporal correlations within urban regions intricately links to regional characteristic data (such as points of interests,i.e., POIs and road network density), which in turn influence the variations in traffic flow across diverse urban areas. In light of this, we propose a meta-learning-based graph convolutional stacked temporal attention network (MGSTA). Different from the mainstream sptio-temporal traffic network that iteratively optimize the model parameters by gradient descent after their generation. The parameters of the proposed spatio-temporal module are generated by a series of meta-learning layers, which are specifically designed for learning these meta characteristic data. Experiments on multiple datasets have proved the superior performance of the proposed model and the substantial improvements brought by this new idea to the spatio-temporal feature learning capabilities of the traffic prediction model.

Integrated sensing and communication (ISAC) is an emerging paradigm in the sixth-generation mobile communication systems (6G) to address the spectrum scarcity and realize the vision of the Internet of Everything (IoE). In this article, we consider a multiple-input-multiple-output (MIMO) ISAC system where the dual-functional radar-communication (DFRC) base station (BS) detects the targets and communicates with multiple downlink users. To meliorate the severe interference management and improve the transmission performance of the ISAC system, we identify a specific transceiver design that splits each independent message into multiple layers at the transmitter and employs a partial group successive interference cancelation scheme at the receivers. To coordinate the communication and radar performance, we formulate an optimization problem to approximate the beamformers to the desired radar beampattern subject to the achievable rate regions. Since, the formulated problem is nonconvex and NP-hard, we propose an iterative algorithm based on the semi-definite programming relaxation, which optimizes the beamformers and rate vectors alternatively to yield near-optimal solutions. Numerical results demonstrate the superior performance of the proposed transceiver design in improving the achievable transmission rate and obtaining better interference management in the ISAC system.

Knowledge graph employs the specific graph structure to store knowledge in the form of entities, relations, attributes, and so forth, which can effectively represent correlations among data and has been applied in many fields, including search engine optimization, intelligent question answering, and recommendation systems. In this article, we mainly focus on the research and application of the domain-specific knowledge graph in the field of the smart city, which has not been fully paid attention to. Currently, the major problem faced by the smart city lies in data mining and proper application. On the one hand, data are usually stored by government management departments, which creates challenges such as high data storing overhead and inefficient data usage. On the other hand, data cannot be coordinated and collaborated between different city management systems, because data silos exist. By constructing the corresponding knowledge graph, the data of urban traffic, services, and public resources are integrated to provide help for city builders and managers to make important decisions. Therefore, we will review the related literature on the knowledge graph existing in the smart city domain to expore reasearch scopes. Specifically, we will analyze and summarize knowledge graph construction research in the field of smart cities from four perspectives, i.e., smart city ontology, urban data processing, urban knowledge graph construction, and their application. Finally, the research limitations and prospects of the urban knowledge graph are provided.

Traffic prediction is an important part of modern intelligent transportation systems (ITS), which helps transportation management and city planning. However, it is a very challenging task for modeling complex spatio-temporal dependencies, since the traffic data belongs to highly periodic multivariate time series which makes it hard to model accurate spatial dependencies only from time series and observed geolocation information of road segments. The existing research mainly focuses on finding ways of capturing dynamic spatial dependencies of road segments while neglecting the importance of periodicity, and few studies have explored a pure embedding-driven method that is robust to corrupted data to model periodicity. In this paper, we propose an embedding-driven multi-hop spatio-temporal attention network for traffic prediction (PIANOFORTE), which mainly focuses on leveraging the multi-scale periodicity of traffic data. Specifically, the proposed network applies a designed Fourier-series-based embedding, to capture the periodicity, which is more in line with real-world facts. Driven by the designed embedding, both local and global temporal dependencies are modeled properly by combining the attention-based methods and the convolution-based methods. Besides, we implement a trial that can hardly be seen in the existing traffic prediction works to combine the graph self-attention mechanism with a multi-hop diffusion process to explore the large-scale structural information on a designed set of graphs. Experiments on two real-world traffic datasets which contains traffic speed data for months show the effectiveness of our proposed methods. The experiments also suggest the methods can provide stable reasonable and smooth predictions for completely corrupted data.

As the foundation of route planning and applications for intelligent transportation systems, accurate spatio-temporal traffic forecasting plays an essential role in improving both road utilization and traffic safety. Recently, the graph convolution network (GCN), recurrent neural network (RNN) and many other deep-learning based methods have been adopted for traffic flow forecasting and performed much better than the conventional statistical approaches. However, some node information may be lost during the propagation in graph convolutional layers, and the existing methods are insufficient to model the temporal dependencies especially for long-range sequences. In order to address these deficiencies, we innovatively come up with a graph convolutional stacked temporal attention neural network (GSTA), which can simultaneously extract the spatial and temporal features to forecast the traffic flow with higher accuracy. Specifically, our proposed framework uses a mix-hop GCN to better capture the spatial dependencies by preserving more useful information compared with the traditional GCN. Moreover, to identify the relations among traffic flow data over different time steps, we adopt an attention mechanism and introduce the temporal feature through embedding technology to capture the temporal regularity. We evaluate the proposed GSTA on two real-world traffic datasets, and the experimental results demonstrate the performance of our proposed model is significantly superior to several existing methods.

Intelligent reflecting surface (IRS) cooperated with the simultaneous wireless information and power transfer (SWIPT) can reinforce the desired signal and deal with the energy supply problem effectively. By leveraging the on-demand mobility of unmanned aerial vehicles (UAVs), the IRS cooperated SWIPT can be deployed in more flexible and reliable scenarios. In this article, we investigate the UAV-mounted IRS-assisted SWIPT for Internet of Things (IoT) networks. In particular, a UAV-mounted IRS is deployed to assist the information transmission and power transfer from the access point to several IoT devices simultaneously. Taking full advantage of the UAV-mounted IRS in attending multiple IoT devices flexibly, a time division multiple access (TDMA)-based scheduling protocol is proposed to serve different IoT devices alternatively during the UAV flying along an optimized trajectory with the information and power transfer executed. Then, an optimization problem of maximizing the minimum average achievable rate of multiple devices is formulated with the specific energy harvesting requirement guaranteed. To solve the nonconvex problem, we leverage the successive convex approximation and block coordinate descent methods to develop an iterative algorithm. Simulation results demonstrate that with the help of the more flexible and reliable UAV-mounted IRS, the minimum achievable rate of the IoT network can be significantly improved.

Device-to-device (D2D) communications and distributed storage are enabling technologies for the future Internet of Things systems. In this article, we consider power-efficient content delivery in a D2D-assisted wireless distributed storage system, where the partial downloading scheme is employed such that the content requester can download a portion of the stored content from neighboring storage devices. The basic idea is that by downloading a small amount of data from many storage devices, the power consumption is much lower than downloading the entire content from a single device. In designing such a system, we aim to minimize the total power consumption by properly allocating the channel and the amount of transmitted data for each storage device. Moreover, to account for the case that there are more devices than communication channels, we allow multiple devices to share the same channel by employing successive interference cancelation (SIC) decoding. The optimization problem is an integer program and by taking the alternative minimization approach, we decouple it into two subproblems: 1) the packet allocation subproblem, for which we provide the optimal solution and 2) the channel allocation subproblem, for which we provide the efficient suboptimal solution.

This paper considers a joint transceiver design for the weighed sum energy efficiency maximization problem for downlink transmissions in a multi-cell multi-user multiple-input multiple-output (MIMO) system. To make the formulated problem be more valuable in practice, a more practical power consumption model is adopted in which part of the processing power is dependent on data rate. With per-user rate requirements and per-base station (BS) power constraints, the resulting optimization problem is non-convex. A centralized solution is firstly proposed, which alternatively performs transmit beamforming optimization and receive beamforming optimization with the help of successive convex approximation (SCA) based iteration. Based on the centralized design that requires global channel state information (CSI), a more meaningful decentralized algorithm is further designed, which solves the problem with the requirement of only exchanging quite a small amount of information among BSs. Specifically, the key methodology used in the decentralized algorithm is to reformulate transmit beamforming optimization as an equivalent global consensus problem when receive beamforming is fixed as minimum mean square error (MMSE) receiver, which is solved effectively by exploiting the alternative direction method of multipliers (ADMM) technique. Numerical results demonstrate the effectiveness and superiority of our proposed algorithms, and also illustrate the impact of the rate-dependent power consumption on the energy efficiency.

For heterogeneous network, which has been deployed by 4G systems in a small scale and is viewed as one pioneering technology for making cellular networks be evolved into 5G systems, more efficiently achieving load balance between Macrocells and small cells has attracted increasing attention. Specifically, when Macrocells and Picocells use the completely same frequency spectrum resource, range extension (RE) of Picocells is the key approach for load balancing. It has been observed that, when increasing the RE bias for augmenting the load balancing benefit, the link failure probability of downlink control channel will increase for user equipments (UEs) associated to Picocells because of inter-cell interference. Enhanced inter-cell interference cancellation (eICIC) techniques have been developed to solve this problem, where the time division multiplexing (TDM) based techniques have been mainly focused. However, due to reducing available time-domain resources, the TDM based eICIC techniques will cause inherent performance loss in Macrocells. In this paper, without exploiting any existing eICIC techniques, the aforementioned problem is solved via designing one adaptive per-Picocell RE biasing scheme. In this scheme, with the awareness of unacceptable interference, each Picocell wisely and autonomously adjusts its RE bias among a set of candidate values. Furthermore, this scheme is implemented in a distributed way, without the need of global optimization. Simulation results verify good performance of the proposed scheme, in terms of ICIC effect and load balance benefit.