To meet a growing demand for information processing, brain-inspired neuromorphic devices have been intensively studied in recent years. As an important type of neuromorphic device, synaptic devices have attracted strong attention. Among all the kinds of materials explored for the fabrication of synaptic devices, semiconductor nanocrystals (NCs) have become one of the preferred choices due to their excellent electronic and optical properties. In this review, we first introduce the research background of synaptic devices based on semiconductor NCs and briefly present the basic properties of semiconductor NCs. Recent developments in the field of synaptic devices based on semiconductor NCs are then discussed according to the materials employed in the active layers of the devices. Finally, we discuss existing problems and challenges of synaptic devices based on semiconductor NCs.
Underwater robotic operation usually requires visual perception (e.g., object detection and tracking), but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual perception. In addition, detection continuity and stability are important for robotic perception, but the commonly used static accuracy based evaluation (i.e., average precision) is insufficient to reflect detector performance across time. In response to these two problems, we present a design for a novel robotic visual perception framework. First, we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection performance. As a result, although domain quality has an ignorable effect on within-domain detection accuracy, visual restoration is beneficial to detection in real sea scenarios by reducing the domain shift. Moreover, non-reference assessments are proposed for detection continuity and stability based on object tracklets. Further, online tracklet refinement is developed to improve the temporal performance of detectors. Finally, combined with visual restoration, an accurate and stable underwater robotic visual perception framework is established. Small-overlap suppression is proposed to extend video object detection (VID) methods to a single-object tracking task, leading to the flexibility to switch between detection and tracking. Extensive experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed approaches. The codes are available at https://github.com/yrqs/VisPerception.
Entity linking (EL) is a fundamental task in natural language processing. Based on neural networks, existing systems pay more attention to the construction of the global model, but ignore latent semantic information in the local model and the acquisition of effective entity type information. In this paper, we propose two adaptive features, in which the first adaptive feature enables the local and global models to capture latent information, and the second adaptive feature describes effective information for entity type embeddings. These adaptive features can work together naturally to handle some uncertain entity type information for EL. Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets, and the best average performance on out-domain datasets. These results indicate that the proposed adaptive features, which are based on their own diverse contexts, can capture information that is conducive for EL.
High-performance computing (HPC) systems are about to reach a new height: exascale. Application deployment is becoming an increasingly prominent problem. Container technology solves the problems of encapsulation and migration of applications and their execution environment. However, the container image is too large, and deploying the image to a large number of compute nodes is time-consuming. Although the peer-to-peer (P2P) approach brings higher transmission efficiency, it introduces larger network load. All of these issues lead to high startup latency of the application. To solve these problems, we propose the topology-aware execution environment service (TEES) for fast and agile application deployment on HPC systems. TEES creates a more lightweight execution environment for users, and uses a more efficient topology-aware P2P approach to reduce deployment time. Combined with a split-step transport and launch-in-advance mechanism, TEES reduces application startup latency. In the Tianhe HPC system, TEES realizes the deployment and startup of a typical application on 17 560 compute nodes within 3 s. Compared to container-based application deployment, the speed is increased by 12-fold, and the network load is reduced by 85%.
In-memory systems with erasure coding (EC) enabled are widely used to achieve high performance and data availability. However, as the scale of clusters grows, the server-level fail-slow problem is becoming increasingly frequent, which can create long tail latency. The influence of long tail latency is further amplified in EC-based systems due to the synchronous nature of multiple EC sub-operations. In this paper, we propose an EC-enabled in-memory storage system called ShortTail, which can achieve consistent performance and low latency for both reads and writes. First, ShortTail uses a lightweight request monitor to track the performance of each memory node and identify any fail-slow node. Second, ShortTail selectively performs degraded reads and redirected writes to avoid accessing fail-slow nodes. Finally, ShortTail posts an adaptive write strategy to reduce write amplification of small writes. We implement ShortTail on top of Memcached and compare it with two baseline systems. The experimental results show that ShortTail can reduce the P99 tail latency by up to 63.77%; it also brings significant improvements in the median latency and average latency.
This paper describes a route planner that enables an autonomous underwater vehicle to selectively complete part of the predetermined tasks in the operating ocean area when the local path cost is stochastic. The problem is formulated as a variant of the orienteering problem. Based on the genetic algorithm (GA), we propose the greedy strategy based GA (GGA) which includes a novel rebirth operator that maps infeasible individuals into the feasible solution space during evolution to improve the efficiency of the optimization, and use a differential evolution planner for providing the deterministic local path cost. The uncertainty of the local path cost comes from unpredictable obstacles, measurement error, and trajectory tracking error. To improve the robustness of the planner in an uncertain environment, a sampling strategy for path evaluation is designed, and the cost of a certain route is obtained by multiple sampling from the probability density functions of local paths. Monte Carlo simulations are used to verify the superiority and effectiveness of the planner. The promising simulation results show that the proposed GGA outperforms its counterparts by 4.7%–24.6% in terms of total profit, and the sampling-based GGA route planner (S-GGARP) improves the average profit by 5.5% compared to the GGA route planner (GGARP).
This paper focuses on the problem of active object detection (AOD). AOD is important for service robots to complete tasks in the family environment, and leads robots to approach the target object by taking appropriate moving actions. Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy. Therefore, an AOD model based on a deep Q-learning network (DQN) with a novel training algorithm is proposed in this paper. The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction, and a multilayer perceptron. In contrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy. In addition, a method of generating the end state is presented to judge when to stop the AOD task during the training process. Sufficient comparison experiments and ablation studies are performed based on an AOD dataset, proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.
This paper investigates the problem of dynamic output-feedback control for a class of Lipschitz nonlinear systems. First, a continuous-time controller is constructed and sufficient conditions for stability of the nonlinear systems are presented. Then, a novel event-triggered mechanism is proposed for the Lipschitz nonlinear systems in which new event-triggered conditions are introduced. Consequently, a closed-loop hybrid system is obtained using the event-triggered control strategy. Sufficient conditions for stability of the closed-loop system are established in the framework of hybrid systems. In addition, an upper bound of a minimum inter-event interval is provided to avoid the Zeno phenomenon. Finally, numerical examples of a neural network system and a genetic regulatory network system are provided to verify the theoretical results and to show the superiority of the proposed method.
Platoon control is widely studied for coordinating connected and automated vehicles (CAVs) on highways due to its potential for improving traffic throughput and road safety. Inspired by platoon control, the cooperation of multiple CAVs in conflicting scenarios can be greatly simplified by virtual platooning. Vehicle-to-vehicle communication is an essential ingredient in virtual platoon systems. Massive data transmission with limited communication resources incurs inevitable imperfections such as transmission delay and dropped packets. As a result, unnecessary transmission needs to be avoided to establish a reliable wireless network. To this end, an event-triggered robust control method is developed to reduce the use of communication resources while ensuring the stability of the virtual platoon system with time-varying uncertainty. The uniform boundedness, uniform ultimate boundedness, and string stability of the closed-loop system are analytically proved. As for the triggering condition, the uncertainty of the boundary information is considered, so that the threshold can be estimated more reasonably. Simulation and experimental results verify that the proposed method can greatly reduce data transmission while creating multi-vehicle cooperation. The threshold affects the tracking ability and communication burden, and hence an optimization framework for choosing the threshold is worth exploring in future research.
The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers’ preference information region. Therefore, we propose a multi-objective optimization algorithm, referred to as the double-grid interactive preference based MOEA (DIP-MOEA), which explicitly takes the preferences of decision makers (DMs) into account. First, according to the optimization objective of the practical multi-objective optimization problems and the preferences of DMs, the membership functions are mapped to generate a decision preference grid and a preference error grid. Then, we put forward two dominant modes of population, preference degree dominance and preference error dominance, and use this advantageous scheme to update the population in these two grids. Finally, the populations in these two grids are combined with the DMs’ preference interaction information, and the preference multi-objective optimization interaction is performed. To verify the performance of DIP-MOEA, we test it on two kinds of problems, i.e., the basic DTLZ series functions and the multi-objective knapsack problems, and compare it with several different popular preference-based MOEAs. Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs, quickly provides the test results, and has better performance in the distribution of the Pareto front solution set.