In this article, we address the error estimation problem of quasi-synchronization for a class of multilayer dynamical networks. The proposed network model simultaneously accounts for interlayer and intralayer time-varying coupling structures, network directionality, and interlayer communication delays. To achieve synchronization in a cost-effective manner, we design a novel pinning impulsive control strategy that leverages large-scale impulse delay information together with the number of pinned nodes. By employing an iterative algorithm, we establish a new delay-dependent impulsive differential inequality, which precisely characterizes the convergence domain and provides flexibility in the choice of impulse delays. Then, some quasi-synchronization criteria are derived to guarantee convergence of multilayer networks within a prescribed error level, and explicit analytical expressions for the synchronization error bounds are obtained. Finally, to demonstrate the practical applicability, the proposed criteria are applied to the synchronization of multilayer single-link robot arm networks under error bounds, with numerical examples validating the effectiveness of the method.

To position agents equipped with ultrawideband (UWB) devices without requiring continuous clock synchronization, the primary measurement currently used is the time of flight between agents, estimated through active two-way ranging (TWR). However, the cumbersome signal exchange mechanism in active TWR imposes a trade-off between the number of agents and the positioning frequency. To address this issue, this paper proposes a relative localization scheme using hybrid active-passive TWR to accommodate more agents without compromising the positioning refresh rate. Additionally, this scheme not only extends passive TWR into a dynamic form, thereby improving localization accuracy of mobile agents, but also presents a two-step relative positioning method to enhance computational efficiency. The feasibility of the scheme is validated through numerical simulations and real-world tests on a prototype system built with consumer-level UWB chips.

We introduce Perception Encoder (PE), a family of state-of-the-art vision encoders for image and video understanding. Traditionally, vision encoders have relied on a variety of pretraining objectives, each excelling at different downstream tasks. Surprisingly, after scaling a carefully tuned image pretraining recipe and refining with a robust video data engine, we find that contrastive vision-language training alone can produce strong, general embeddings for all of these downstream tasks. There is only one caveat: these embeddings are hidden within the intermediate layers of the network. To draw them out, we introduce two alignment methods: language alignment for multimodal language modeling, and spatial alignment for dense prediction. Together, our PE family of models achieves state-of-the-art results on a wide variety of tasks, including zero-shot image and video classification and retrieval; document, image, and video Q&A; and spatial tasks such as detection, tracking, and depth estimation. We release our models, code, and novel dataset of synthetically and human-annotated videos: https://github.com/facebookresearch/perception_models

This paper studies the fuzzy-based synchronization control problem of coupled neural networks under cyber attacks, where the considered attacks can block the communication links between fuzzy sub-neural networks. Firstly, an improved fuzzy network model is developed, which takes into account the inherent vulnerabilities of network. We design a fuzzy logic-based event-triggered delayed impulsive controller, where impulsive signals are generated by a dependent-Lyapunov intelligent impulsive selection algorithm. Particularly, it can mitigate attack effects, ensure the desired performance of fuzzy networks, and effectively exclude the Zeno behavior. Then, based on the proposed algorithm, some delay-dependent synchronization criteria are established for fuzzy networks on different scales delays, respectively. Finally, a practical example about resistance-capacitance circuit network is provided in different scenarios to show the validity of the theoretical results.Note to Practitioners—This paper was motivated by existing results on impulsive synchronization control of neural networks. Most existing results ignore the effects of external cyber attacks and delay during signal transmission, which is quite difficult to simulate the practical network model. This paper constructs a more general model to consider the inherent vulnerabilities of networks. Then, an intelligent impulsive selection algorithm is designed to resist the risks of attacks and obtain the expected performance. The obtained results are applied to resistance-capacitance circuit systems to verify the effectiveness, and it is expected that the proposed approach can be extended to more mechanical systems.

In emergency situations, individuals often experience panic and may struggle to find their way to safety. In such cases, the presence of trained leaders is crucial to provide guidance for evacuation and ensure the safety of those evacuating. However, determining the optimal positions of leaders in complex environments to guide the maximum number of pedestrians presents a significant challenge. To address this problem, we propose an Entropy-Based Maximum Coverage Model(EMCM) to enhance evacuation guidance. Initially, we employ the social force model to describe pedestrian movement characteristics, while the environmental features of the evacuation area are delineated using a navigation network. Subsequently, we introduce an urgency model grounded in evacuation entropy to quantify the urgency experienced by pedestrians. Thirdly, we frame the problem of leader positioning as an optimization challenge focused on maximizing entropy coverage, thereby increasing the number of individuals a leader can effectively guide. Finally, we employ AnyLogic to construct simulation scenarios that visualize the leadership process. This innovative method holds the potential to offer valuable support in the formulation of effective evacuation strategies during emergency situations.

This article is dedicated to investigating the impulsive-based almost surely synchronization issue of neural network systems (NSSs) with quality-of-service constraints. First, the communication network considered suffers from random double deception attacks, which are modeled as a nonlinear function and a desynchronizing impulse sequence, respectively. Meanwhile, the impulsive instants and impulsive gains are randomly and only their expectations are available. Second, by taking two different types of random deception attacks into consideration, a novel mathematical model for vulnerable NSSs is constructed. Then, almost surely synchronization criteria are established by using Borel-Cantelli lemma. Furthermore, based on the derived strong and weak sufficient conditions, the almost surely synchronization of NSSs is achieved. Finally, the section of numerical example is shown to illustrate the effectiveness of the proposed method.

Systematic testing plays a vital role in increasing software reliability. A particularly effective and popular approach for systematic testing is symbolic execution, which analyzes a large number of program behaviors using symbolic inputs. Even though symbolic execution is among the most studied analyses during the last decade, scaling it to real-world applications remains a key challenge. This paper studies how a class of semantics-preserving program transformations, namely compiler optimizations, which are designed to enhance performance of standard program execution (using concrete inputs), influence traditional symbolic execution. As an enabling technology, the study uses KLEE, a well-known symbolic execution engine based on the LLVM compiler infrastructure, and focuses on 33 optimization flags of LLVM. Our specific research questions include: (1) how different optimizations influence the performance of symbolic execution for Unix Coreutils, (2) how the influence varies across two different program classes, and (3) how the influence varies across three different back-end constraint solvers. Some of our findings surprised us. For example, applying the 33 optimizations in a pre-defined order provides a slowdown (compared to applying no optimization) for a majority of the Coreutils when using the basic depth-first search with no constraint caching. The key finding of our work is that standard compiler optimizations need to be used with care when performing symbolic execution for creating tests that provide high code coverage. We hope our study motivates future research on harnessing the power of symbolic execution more effectively for enhancing software reliability, e.g., by designing program transformations specifically to scale symbolic execution or by studying broader classes of traditional compiler optimizations in the context of different search heuristics, memoization, and other strategies employed by modern symbolic execution tools.