Polarization Rotation of Chiral Fermions in Vortical Fluid

The rotation of polarization occurs for light interacting with chiral materials. It requires the light states with opposite chiralities interact differently with the materials. We demonstrate analogous rotation of polarization also exists for chiral fermions interacting with quantum electrodynamics plasma with vorticity using chiral kinetic theory. We find that the rotation of polarization is perpendicular both to vorticity and fermion momentum. The effect also exists for chiral fermions in quantum chromodynamics plasma with vorticity. It could lead to generation of a vector current when the probe fermions contain momentum anisotropy.Comment: 11 pages, section on generalization to QCD plasma added, journal versio

Three-dimensional optimal impact time guidance for antiship missiles

Introduction: The primary objective of missile guidance laws is to drive the missile to intercept a specific target with zero miss distance. Proportional navigation guidance (PNG) has been proved to be an efficient and simple guidance algorithm for missile systems, thus showing wide applications in the past few decades [1]. The optimality of PNG was analyzed in [2] and its extension to three-dimensional (3D) scenario can be found at [3]. In the context of modern warfare, many high-value battleships, like destroyers and aircraft carriers, are equipped with powerful self-defense systems against anti-ship missiles [4]. In order to penetrate these formidable defensive systems, the concept of salvo attack or simultaneous attack was introduced: many missiles are required to hit a battleship simultaneously, albeit their di.erent initial locations. One typical solution of simultaneous attack is impact time control guidance. Generally, impact time control can be classified into two categories: (1) specify the desired impact time and control each missile to satisfy the desired impact time constraint individually; and (2) synchronize the impact time either in a distributed or decentralized fashion through a communication network among all interceptors

Integral global sliding mode guidance for impact angle control

This Correspondence proposes a new guidance law based on integral sliding mode control (ISMC) technique for maneuvering target interception with impact angle constraint. A time-varying function weighted line-of-sight (LOS) error dynamics, representing the nominal guidance performance, is introduced first. The proposed guidance law is derived by utilizing ISMC to follow the desired error dynamics. The convergence of the guidance law developed is supported by Lyapunov stability. Simulations with extensive comparisons explicitly demonstrate the effectiveness of the proposed approach

Composite finite‐time convergent guidance law for maneuvering targets with second‐order autopilot lag

This paper aims to develop a new finite‐time convergent guidance law for intercepting maneuvering targets accounting for second‐order autopilot lag. The guidance law is applied to guarantee that the line of sight (LOS) angular rate converges to zero in finite time and results in a direct interception. The effect of autopilot dynamics can be compensated based on the finite‐time backstepping control method. The time derivative of the virtual input is avoided, taking advantage of integral‐type Lyapunov functions. A finite‐time disturbance observer (FTDOB) is used to estimate the lumped uncertainties and high‐order derivatives to improve the robustness and accuracy of the guidance system. Finite‐time stability for the closed‐loop guidance system is analyzed using the Lyapunov function. Simulation results and comparisons are presented to illustrate the effectiveness of the guidance strategy

A New Impact Time and Angle Control Guidance Law for Stationary and Nonmaneuvering Targets

A guidance problem for impact time and angle control applicable to cooperative attack is considered based on the sliding mode control. In order to satisfy the impact angle constraint, a line-of-sight rate polynomial function is introduced with four tuning parameters. And the time-to-go derivative with respect to a downrange orientation is derived to minimize the impact time error. Then the sliding mode control surface with impact time and angle constraints is constructed using nonlinear engagement dynamics to provide an accurate solution. The proposed guidance law is easily extended to a nonmaneuvering target using the predicted interception point. Numerical simulations are performed to verify the effectiveness of the proposed guidance law for different engagement scenarios

STATE-OF-ART Algorithms for Injectivity and Bounded Surjectivity of One-dimensional Cellular Automata

Surjectivity and injectivity are the most fundamental problems in cellular automata (CA). We simplify and modify Amoroso's algorithm into optimum and make it compatible with fixed, periodic and reflective boundaries. A new algorithm (injectivity tree algorithm) for injectivity is also proposed. After our theoretic analysis and experiments, our algorithm for injectivity can save much space and 90\% or even more time compared with Amoroso's algorithm for injectivity so that it can support the decision of CA with larger neighborhood sizes. At last, we prove that the reversibility with the periodic boundary and global injectivity of one-dimensional CA is equivalent

GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions

Entity interaction prediction is essential in many important applications such as chemistry, biology, material science, and medical science. The problem becomes quite challenging when each entity is represented by a complex structure, namely structured entity, because two types of graphs are involved: local graphs for structured entities and a global graph to capture the interactions between structured entities. We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model. In this paper, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. We also propose the dual-attention mechanism that enables the model to preserve the neighbor importance in both levels of graphs. Extensive experiments on real-world datasets show that GoGNN outperforms the state-of-the-art methods on two representative structured entity interaction prediction tasks: chemical-chemical interaction prediction and drug-drug interaction prediction. Our code is available at Github.Comment: Accepted by IJCAI 202

Learning dynamic graph embeddings for accurate detection of cognitive state changes in functional brain networks

Mounting evidence shows that brain functions and cognitive states are dynamically changing even in the resting state rather than remaining at a single constant state. Due to the relatively small changes in BOLD (blood-oxygen-level-dependent) signals across tasks, it is difficult to detect the change of cognitive status without requiring prior knowledge of the experimental design. To address this challenge, we present a dynamic graph learning approach to generate an ensemble of subject-specific dynamic graph embeddings, which allows us to use brain networks to disentangle cognitive events more accurately than using raw BOLD signals. The backbone of our method is essentially a representation learning process for projecting BOLD signals into a latent vertex-temporal domain with the greater biological underpinning of brain activities. Specifically, the learned representation domain is jointly formed by (1) a set of harmonic waves that govern the topology of whole-brain functional connectivities and (2) a set of Fourier bases that characterize the temporal dynamics of functional changes. In this regard, our dynamic graph embeddings provide a new methodology to investigate how these self-organized functional fluctuation patterns oscillate along with the evolving cognitive status. We have evaluated our proposed method on both simulated data and working memory task-based fMRI datasets, where our dynamic graph embeddings achieve higher accuracy in detecting multiple cognitive states than other state-of-the-art methods

Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions

Visual perception is an important component for autonomous navigation of unmanned surface vessels (USV), particularly for the tasks related to autonomous inspection and tracking. These tasks involve vision-based navigation techniques to identify the target for navigation. Reduced visibility under extreme weather conditions in marine environments makes it difficult for vision-based approaches to work properly. To overcome these issues, this paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions. The proposed framework consists of an integrated perception pipeline that uses a generative adversarial network (GAN) to remove noise and highlight the object features before passing them to the object detector (i.e., YOLOv5). The detected visual features are then used by the USV to track the target. The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog. The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset, on which the proposed scheme has outperformed the existing methods across various metrics.Comment: IEEE/RSJ International Conference on Intelligent Robots (IROS-2023