How to ensure the safe flight of unmanned aerial vehicles (UAVs) in complex environments with static and dynamic obstacles can be a big challenge. Therefore, a new joint path planning method with integrated of an improved non-dominated sorting genetic algorithm (NSGA-III) and an improved artificial potential field (APF) is proposed, which exhibits high computation efficiency and excellent global optimum searching capability. Three objectives including flying efficiency, stability and obstacles avoidance are constructed to meet the strict requirements of the complex environment. Greatest novel features of this new method include three aspects, and they are:1) Hopfield neural network is introduced to replace the random strategy to generate the initial population of NSGA-III, which can enhance the iteration efficiency significantly, 2) Gaussian sampling is designed to create a new potential solution space that is adjacent to the local optimum, which can guide the further searching for a better result and 3) virtual path points as well as tracked distances are both developed in APF to help escape from the stuck area during the dynamic obstacle avoidance. A comprehensive compared study is also carried out by use of popular traditional NSGA-III, improved A∗ and RRT∗ algorithms. The joint planning algorithm can ameliorate path length and flying efficiency by an average of 12.6% and 67.5%, respectively. Simulations and experiments approve the validation.

This brief presents a reconfigurable multichannel noise-shaping (NS) SAR analog-to-digital converter (ADC) with crosstalk suppression. All nonmemory blocks are multiplexed among all channels assisted by the proposed timing control scheme, enabling reconfiguration with negligible power and hardware cost. A novel approach to determine the design parameters of nonideal loop filters is proposed, and a crosstalk suppressed multichannel fully passive loop filter is developed based on this method. A memoryless binary dynamic element matching (DEM) is also adopted to suppress the nonlinearity of each channel. The ADC achieves a typical SNDR of 77.49/72.17/66.62 dB with a 20-kHz bandwidth under one-/two-/four-channel modes, respectively, realizing typical Schreier FoMs of 169.54, 167.20, and 164.64 dB. Averaged interchannel crosstalks are −79.17 and −77.37 dB with −2.5-dBFS inputs in all input channels under two- and four-channel modes.

Computerized Adaptive Testing (CAT) is a widely used technology for evaluating learners' proficiency in online education platforms. By leveraging prior estimates of proficiency to select questions and updating the estimates iteratively based on responses, CAT enables personalized learner modeling and has attracted substantial attention. Despite this progress, most existing works focus primarily on improving diagnostic accuracy, while overlooking the selection bias inherent in the adaptive process. Selection Bias arises because the question selection is strongly influenced by the estimated proficiency, such as assigning easier questions to learners with lower proficiency and harder ones to learners with higher proficiency. Since the selection depends on prior estimation, this bias propagates into the diagnosis model, which is further amplified during iterative updates, leading to misalignment and biased predictions. Moreover, the imbalanced nature of learners' historical interactions often exacerbates the bias in diagnosis models. To address this issue, we propose a debiasing framework consisting of two key modules: Cross-Attribute Examinee Retrieval and Selective Mixup-based Regularization. First, we retrieve balanced examinees with relatively even distributions of correct and incorrect responses and use them as neutral references for biased examinees. Then, mixup is applied between each biased examinee and its matched balanced counterpart under label consistency. This augmentation enriches the diversity of bias-conflicting samples and smooths selection boundaries. Finally, extensive experiments on two benchmark datasets with multiple advanced diagnosis models demonstrate that our method substantially improves both the generalization ability and fairness of question selection in CAT.

Sequential recommendation aims to learn users' dynamic preferences from their historical interactions and predict the next item they are most likely to engage with. In real-world scenarios, time-varying factors (e.g., product promotions, seasonal changes) induce distribution shifts in user interactions. Despite the demonstrated success of existing models, their generalization capability remains limited under such dynamic conditions. Current methods tackle this challenge by leveraging distributionally robust optimization (DRO) to optimize the "worst-case" loss or by employing manually designed data augmentation to enrich the training distribution. Despite their effectiveness, DRO-based approaches are inherently constrained by the sparsity of training data, limiting the range of distributions they can model, while manually designed augmentations risk introducing noise or irrelevant information that could distort user preference learning. Furthermore, these methods often overlook the sensitivity of user interactions to distribution shifts, which is essential for capturing the stable factors in the evolution of user preferences in real-world settings.

Graph-based social recommender systems have demonstrated great potential in alleviating data sparsity by leveraging high-order user influence embedded in social networks.However, most existing methods rely heavily on the observed social graph, which is often noisy and includes spurious or task-irrelevant connections that can mislead user preference learning.Identifying and removing these noisy relations is crucial but challenging due to the lack of ground-truth annotations.In this paper, we approach the social denoising problem from the perspective of graph invariant learning and propose a novel approach, Social Graph Invariant Learning(SGIL).Specifically, SGIL aims to uncover stable user preferences within the input social graph, thereby enhancing the robustness of

Compared to traditional map-based, goal-driven navigation methods , deep reinforcement learning (DRL)-based goal-driven navigation for mobile robots offers the advantage of not relying on prior map information, it enables autonomous decision-making through continuous interaction with the environment. However, DRL-based goal-driven navigation faces significant challenges in terms of low generalization ability and learning inefficiency. In this paper, we propose a DRL approach , variational sparsity Q network (VSQN), which leverages variational inference and transfer learning to achieve efficient goal-driven navigation. The variational inference framework models weight uncertainty within the network, thereby enhancing the agent’s generalization capability. Furthermore, a hierarchical learning network framework is adopted, and transfer learning is employed to incorporate prior knowledge from a pre-trained model into new navigation tasks . This enables the agent to rapidly adapt to novel tasks without the need for fine-tuning after selecting an optimal sub-goal. This improves the agent’s initial performance in previously unseen navigation tasks . The experimental results indicate that the proposed method achieves a success rate (SR) of 76% and a success weighted by inverse path length (SPL) of 0.52 in previously unencountered environments and target locations within the grid environment , and an SR of 81% with an SPL of 0.30 in the AI2-THOR environment. These findings demonstrate that the method substantially enhances the agent’s generalization capability.

This article introduces an integrated guidance and control method for morphing flight vehicles, addressing model uncertainties and external disturbances through a robust deep reinforcement learning framework built on sliding-mode control (SMC). The method development begins with the establishment of a longitudinal guidance and control model and a detailed introduction to the necessary theoretical foundations. The proposed approach incorporates robust observation strategies enabled by a novel fixed-time SMC design. The agent’s actions, rewards, neural network structure, and training process are meticulously crafted to tackle practical guidance and control challenges effectively. Trained offline to achieve seamless integration of position and attitude control, the agent generates end-to-end control commands in real time during online operation. Extensive testing, including robustness evaluations, generalization assessments, and comparative performance analyses, demonstrates the superiority and reliability of the proposed method.

This paper presents an energy-efficient calibration-free hybrid noise-shaping SAR ADC for low-power high-resolution applications. A high-efficiency 2nd-order fully- passive noise-shaping technique is adopted in this circuit to achieve higher in-band noise attenuation for a better signal-to-noise ratio (SNR). Mismatch error shaping with digital prediction is used to mitigate harmonic distortions without affecting ADC's dynamic range, therefore greatly improving the signal-to-noise-and-distortion ratio (SNDR) and spurious- free dynamic range (SFDR). The prototype ADC is designed in 55 nm CMOS and occupies an area of 0.126 mm2. It consumes 7.426 μW at 400 kS/s sampling rate from a 1 V supply. The post-layout simulated SNDR is 91.05 dB for a 6.25 kHz bandwidth without any calibration, resulting in a Schreier figure of merit (FoMS) of 180.30 dB and a Walden figure of merit (FoMW) of 20.40 fJ/conversion-step.

In the intelligent medical environment, the resource limitation and real-time limitation of edge cloud lead to the increase of user task interruption rate and the decrease of quality of service (QoS). This paper studies resource coordination and dynamic task scheduling in the edge cloud and relies on existing network resources to achieve higher user QoS. A user QoS evaluation model was constructed by combining network throughput and long-term average response delay. Considering the time continuity of task scheduling, an online task scheduling algorithm based on Bidirectionally Coordinated Nets (BiCNet) is adopted to implement long-term reward learning of scheduling decisions, to achieve QoS optimization and long-term global optimal resource coordination. Compared with centralized learning, BiCNet adds a bidirectional cyclic neural network as the communication layer between agents for information exchange and temporary storage, effectively reducing the complexity. Simulation experiments show that the proposed algorithm can achieve the most favorable scheduling scheme for global optimization compared to other baseline algorithms.

With consideration of motion control performance and efficient information communication, the synchronization problem on communication connectivity preservation and guaranteed consensus performance for networked mechanical systems has attracted considerable attention in recent years. Different from the existing works, this article investigates a brand-new appointed-time consensus control approach for uncertain networked Euler-Lagrange systems on a directed graph via exploring the prescribed performance control structure. First, a two-layer prescribed performance envelope is formulated via using an appointed-time convergent function for position-related and velocity-related consensus errors, respectively. Then, a simple state-feedback virtual controller with online adaptive performance adjustment is developed to preserve the communication connectivity. Moreover, to guarantee the velocity consensus of the networked systems and improve the position consensus accuracy, an appointed-time adaptive controller is designed by applying the norm inequality to the system uncertainties and external disturbances. Compared to the existing consensus control approaches, the prime advantage of the proposed one is that the constraints generated from the communication ranges are approximated by a time-varying contractive performance envelope, wherein, the appointed-time convergence and steady-state tracking accuracy are preassigned a priori. Meanwhile, no repeated logarithmic error transformations are required in the relevant controller design, which implies that the complexity of the devised control laws has decreased dramatically. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed consensus control approach.