This study introduces an innovative cross‐modal coattention network (CMCAN) framework, specifically designed to tackle the challenges of temporal misalignment and modality‐specific feature preservation in multimodal depression detection. The architecture comprises two fundamental components: (1) guided multihead attention, which facilitates context‐aware interactions across modalities, and (2) dedicated self‐attention pathways that ensure the preservation of key unimodal features. To enhance the estimation of depression severity, a cascaded fusion strategy is employed, combining feature superposition with hierarchical stacking. When evaluated on a Chinese localized depression dataset, CMCAN demonstrates exceptional performance, achieving optimal results (accuracy: 0.839; precision: 0.829; recall: 0.863) with its audiovisual guided coattention module (AVGA (SA (V), SA (A))) comprising three cascaded layers. The framework consistently surpasses unimodal baselines across various emotional valence stimuli (positive, neutral, and negative) and achieves state‐of‐the‐art performance on AVEC 2014 (MAE: 5.38). Comprehensive ablation studies validate the effectiveness of individual components, whereas comparative analyses demonstrate significant improvements over existing multimodal fusion approaches. These findings underscore the robustness and generalizability of CMCAN, validating its effectiveness in harmonizing cross‐modal synergy while preserving modality‐specific features, thereby advancing practical solutions for automated depression detection.

Structural vibration-based gait recognition aims to identify pedestrians through their unique vibration patterns and has gained significant attention for its non-invasive nature. However, existing methods primarily rely on manually crafted features and convolutional neural networks (CNNs) to extract local details, without considering global gait features in vibration signals. To address this limitation, we propose CSCA, a novel framework designed to capture discriminative global gait features through collaborative spatial and channel attention. Specifically, CSCA consists of two key components: Mamba-inspired Linear Channel Attention (MLCA) and Strip Pooling-guided Spatial Attention (SPSA). MLCA integrates Mamba with linear attention to enhance discriminative attention in the channel dimension by capturing global gait features. Additionally, SPSA integrates axial strip pooling with multikernel depth-wise convolutions to extract multi-level spatial semantic features. Experimental results on the VIBEID dataset demonstrate that CSCA achieves state-of-the-art performance across diverse environmental conditions, with identity recognition accuracy exceeding 96%. Code is available at https://github.com/xiaomush/CSCA.

Skeleton-based gait emotion recognition has received significant attention due to its wide-ranging applications. However, existing methods primarily focus on extracting spatial and local temporal motion information, failing to capture long-range temporal representations. In this paper, we propose CGTGait, a novel framework that collaboratively integrates graph convolution and transformers to extract discriminative spatiotemporal features for gait emotion recognition. Specifically, CGTGait consists of multiple CGT blocks, where each block employs graph convolution to capture frame-level spatial topology and the transformer to model global temporal dependencies. Additionally, we introduce a Bidirectional Cross-Stream Fusion (BCSF) module to effectively aggregate posture and motion spatiotemporal features, facilitating the exchange of complementary information between the two streams. We evaluate our method on two widely used datasets, Emotion-Gait and ELMD, demonstrating that our CGTGait achieves state-of-the-art or at least competitive performance while reducing computational complexity by approximately 82.2% (only requiring 0.34G FLOPs) during testing. Code is available at https://github.com/githubzjj1/CGTGait.

In the area of autonomous driving, it typically brings great difficulty in solving the motion planning problem since the vehicle model is nonlinear and the driving scenarios are complex. Particularly, most of the existing methods cannot be generalized to dynamically changing scenarios with varying surrounding vehicles. To address this problem, this development here investigates the framework of integrated decision and control. As part of the modules, static path planning determines the reference candidates ahead, and then the optimal path-tracking controller realizes the specific autonomous driving task. An innovative and effective constrained finite-horizon approximate dynamic programming (ADP) algorithm is herein presented to generate the desired control policy for effective path tracking. With the generalized policy neural network that maps from the state to the control input, the proposed algorithm preserves the high effectiveness for the motion planning problem towards changing driving environments with varying surrounding vehicles. Moreover, the algorithm attains the noteworthy advantage of alleviating the typically heavy computational loads with the mode of offline training and online execution. As a result of the utilization of multi-layer neural networks in conjunction with the actor-critic framework, the constrained ADP method is capable of handling complex and multidimensional scenarios. Finally, various simulations have been carried out to show that the constrained ADP algorithm is effective.

The Hamilton-Jacobi-Bellman (HJB) equation serves as the necessary and sufficient condition for the optimal solution to the continuous-time (CT) optimal control problem (OCP). Compared with the infinite-horizon HJB equation, the solving of the finite-horizon (FH) HJB equation has been a long-standing challenge, because the partial time derivative of the value function is involved as an additional unknown term. To address this problem, this study first-time bridges the link between the partial time derivative and the terminal-time utility function, and thus it facilitates the use of the policy iteration (PI) technique to solve the CT FH OCPs. Based on this key finding, the FH approximate dynamic programming (ADP) algorithm is proposed leveraging an actor-critic framework. It is shown that the algorithm exhibits important properties in terms of convergence and optimality. Rather importantly, with the use of multilayer neural networks (NNs) in the actor-critic architecture, the algorithm is suitable for CT FH OCPs toward more general nonlinear and complex systems. Finally, the effectiveness of the proposed algorithm is demonstrated by conducting a series of simulations on both a linear quadratic regulator (LQR) problem and a nonlinear vehicle tracking problem.

Trajectory planning is one of the indispensable and critical components in robotics and autonomous systems. As an efficient indirect method to deal with the nonlinear system dynamics in trajectory planning tasks over the unconstrained state and control space, the iterative linear quadratic regulator (iLQR) has demonstrated noteworthy outcomes. In this article, a local-learning-enabled constrained iLQR algorithm is herein presented for trajectory planning based on hybrid dynamic optimization and machine learning. Rather importantly, this algorithm attains the key advantage of circumventing the requirement of system identification, and the trajectory planning task is achieved with a simultaneous refinement of the optimal policy and the neural network system in an iterative framework. The neural network can be designed to represent the local system model with a simple architecture, and thus it leads to a sample-efficient training pipeline. In addition, in this learning paradigm, the constraints of the general form that are typically encountered in trajectory planning tasks are preserved. Several illustrative examples on trajectory planning are scheduled as part of the test itinerary to demonstrate the effectiveness and significance of this work.

In this paper, we propose a new state representation method, called encoding sum and concatenation (ESC), to describe the environment observation for decision-making in autonomous driving. Unlike existing state representation methods, ESC is applicable to the situation where the number of surrounding vehicles is variable and eliminates the need for manually pre-designed sorting rules, leading to higher representation ability and generality. The proposed ESC method introduces a feature neural network (NN) to encode the real-valued feature of each surrounding vehicle into an encoding vector, and then adds these vectors up to obtain the representation vector of the set of surrounding vehicles. Then, a fixed-dimensional and permutation-invariance state representation can be obtained by concatenating the set representation with other variables, such as indicators of the ego vehicle and road. By introducing the sum-of-power mapping, this paper has further proved that the injectivity of the ESC state representation can be guaranteed if the output dimension of the feature NN is greater than the number of variables of all surrounding vehicles. This means that the ESC representation can be used to describe the environment and taken as the inputs of learning-based policy functions. Experiments demonstrate that compared with the fixed-permutation representation method, the policy learning accuracy based on ESC representation is improved by 62.2%.

Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous regions when applying reinforcement learning (RL) on real-world tasks, like autonomous driving. However, existing studies mostly use model-free constrained RL, which causes inevitable constraint violations. This paper proposes a model-based feasibility enhancement technique of constrained RL, which enhances the feasibility of policy using generalized control barrier function (GCBF) defined on the distance to constraint boundary. By using the model information, the policy can be optimized safely without violating actual safety constraints, and the sample efficiency is increased. The infeasibility in solving the constrained policy gradient is handled by an adaptive coefficient mechanism. We evaluate the proposed method in both simulations and real vehicle experiments in a complex autonomous driving collision avoidance task. The proposed method achieves up to four times fewer constraint violations and converges 3.36 times faster than baseline constrained RL approaches.

The uncertainties arising from the plant model and topologies have been a major challenge in multiagent consensus control. This article presents a distributed robust control method for an uncertain multiagent system with eigenvalue-bounded topologies. The heterogeneity of node dynamics is described as the uncertainties of a linear model with a common certain part. The linear transformation method is adopted to decompose topologically coupled controllers. Then, the linear matrix inequalities (LMIs) technique is used to numerically solve the distributed robust controller problem. It is proved that such a controller is robust stable under the condition that the topology is eigenvalue-bounded. The effectiveness of this method is validated by the simulation of a group of unmanned ground vehicles compared with the LQR controller.

Bioinformatics is challenged by the fact that traditional analysis tools have difficulty in processing large-scale data from high-throughput sequencing. The open source Apache Hadoop project, which adopts the MapReduce framework and a distributed file system, has recently given bioinformatics researchers an opportunity to achieve scalable, efficient and reliable computing performance on Linux clusters and on cloud computing services. In this article, we present MapReduce frame-based applications that can be employed in the next-generation sequencing and other biological domains. In addition, we discuss the challenges faced by this field as well as the future works on parallel computing in bioinformatics.

The labeling scheme is designed to label the XML nodes so that both ordered and un-ordered queries can be processed without accessing the original XML file. When XML data become dynamic, it is important to design a labeling scheme that can facilitate updates and support query processing efficiently. In this paper, we propose a novel containment labeling scheme called DXCL (Dynamic XML Containment Labeling) to effectively process updating in dynamic XML data. Compared with the existing dynamic labeling schemes, a distinguishing feature of DXCL is that DXCL is compact and efficient regardless of whether the documents are updated or not. DXCL uses fixed length integer numbers to label initial XML documents and hence yields compact label size and high query performance. When updates take place, DXCL also has high performance on both label updates and query processing especially in the case of skewed insertions. Experimental results conform the benefits of our approach over the previous dynamic schemes.