Local Phase Research Articles

Enhanced Global Optimization With Parallel Global and Local Structures for Real-Time Control Systems

In practice, objective functions of real-time control systems can have multiple local minimums or can dramatically change over the function space, making them hard to optimize. To efficiently optimize such systems, in this paper, we develop a parallel global optimization framework that combines direct search methods with parallel Bayesian optimization. It consists of an iterative global and local search that searches broadly through the entire global space for promising regions and then efficiently exploits each local promising region. We prove the asymptotic convergence properties of the proposed framework and conduct several numerical experiments to illustrate its empirical performance. We also provide a real-time control problem to illustrate the efficiency of our proposed algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by a collision avoidance problem of vessels aided with onboard agent-based simulations. The simulation on one vessel can predict potential conflicts with other vessels on a pre-defined trajectory. In heavy congestion regions, the environment is highly dynamic and thus it is difficult to find a much safer alternative trajectory if collision is predicted on the current one. Moreover, for such real-time decisions, the control system should be quick in response to improve safety. The proposed metamodel based algorithm is designed for quick decision in such highly dynamic systems. The algorithm employs a decomposition of the response surface to better handle the multi-modal surface resulting from the highly dynamic environment. Specifically, it first looks at the large-scale trend globally (filter out the many local fluctuations that may otherwise trap the algorithm) to locate potential promising regions and then proceeds to this local regions for more detailed local search. To make quick decisions, it uses fast direct search algorithms in the local search phase and applies a parallel search scheme to enjoy the abundant computing power. Both the theoretical analysis and the simulation studies demonstrate that the proposed algorithm can provide better decisions quickly. We also note that this algorithm is not limited to real-time control or simulation-based system. In the case where each run of the experiment is expensive and the budget is limited for the final decision and when the response function is multi-modal, this algorithm can hopefully become a quite efficient and competitive approach. The multi-modal responses have broad applications in the area of control, planning and operations research, such as robot navigating and reinforcement learning.

A multi-objective fuzzy flexible job shop scheduling problem considering the maximization of processing quality

This paper analyzes practical production characteristics, including customer's stringent quality requirements and uncertain processing time in aircraft shaft parts manufacturing. Considering the above characteristics, we propose a multi-objective fuzzy aircraft shaft parts production scheduling problem considering the maximization of production quality. We define this problem as a multi-objective fuzzy flexible job shop scheduling problem (MO-fFJSP) with fuzzy processing time. To address this problem, we developed an improved multi-objective spider monkey optimization (IMOSMO) algorithm. IMOSMO integrates strategies such as genetic operators, variable neighborhood search and Pareto optimization theory on the framework of the conventional Spider Monkey Optimization (SMO) framework and discretize the continuous SMO algorithm to solve MO-fFJSP. To enhance the efficiency of the algorithm, we further adjust the sequence of the local leader learning phase and the global leader learning phase within the proposed IMOSMO framework. We conduct a comparative analysis between the performance of IMOSMO and NSGA-Ⅱ using 28 cases of varying scales. The computational results demonstrate the superiority of our algorithm over NSGA-Ⅱ in terms of both solution diversity and quality. Moreover, the performance of the proposed algorithm upgrades as the problem scale increases.

Modified artificial rabbits optimization combined with bottlenose dolphin optimizer in feature selection of network intrusion detection

&lt;abstract&gt; &lt;p&gt;For the feature selection of network intrusion detection, the issue of numerous redundant features arises, posing challenges in enhancing detection accuracy and adversely affecting overall performance to some extent. Artificial rabbits optimization (ARO) is capable of reducing redundant features and can be applied for the feature selection of network intrusion detection. The ARO exhibits a slow iteration speed in the exploration phase of the population and is prone to an iterative stagnation condition in the exploitation phase, which hinders its ability to deliver outstanding performance in the aforementioned problems. First, to enhance the global exploration capabilities further, the thinking of ARO incorporates the mud ring feeding strategy from the bottlenose dolphin optimizer (BDO). Simultaneously, for adjusting the exploration and exploitation phases, the ARO employs an adaptive switching mechanism. Second, to avoid the original algorithm getting trapped in the local optimum during the local exploitation phase, the levy flight strategy is adopted. Lastly, the dynamic lens-imaging strategy is introduced to enhance population variety and facilitate escape from the local optimum. Then, this paper proposes a modified ARO, namely LBARO, a hybrid algorithm that combines BDO and ARO, for feature selection in the network intrusion detection model. The LBARO is first empirically evaluated to comprehensively demonstrate the superiority of the proposed algorithm, using 8 benchmark test functions and 4 UCI datasets. Subsequently, the LBARO is integrated into the feature selection process of the network intrusion detection model for classification experimental validation. This integration is validated utilizing the NSL-KDD, UNSW NB-15, and InSDN datasets, respectively. Experimental results indicate that the proposed model based on LBARO successfully reduces redundant characteristics while enhancing the classification capabilities of network intrusion detection.&lt;/p&gt; &lt;/abstract&gt;

Strategies to alleviate distortive phase transformations in Li-ion intercalation reactions: an example with vanadium pentoxide.

Chemical and electrochemical Li-ion insertion in transition metal oxides, either via a phase transformation reaction (ions insert into specific crystallographic sites of the host lattice) or a solid solution insertion (ions distribute uniformly throughout the host lattice), enables high energy density electrochemical energy storage. Many phase transformation cathode materials, that undergo two-phase reactions, exhibit high theoretical capacities arising from multi-electron redox reactions. However, challenges in distortive phase transformations and uncontrolled phase nucleation, propagation, segregation, and co-existence continue to limit the energy density, (dis)charging rate performances, and cycling stability of available phase transformation cathode materials. Vanadium pentoxide (V2O5), a classical layered intercalation host material with high theoretical capacity, undergoes irreversible structural changes and capacity fading when intercalating more than one lithium ion per V2O5 unit in its thermodynamically stable phase. Here, we review recent synthetic strategies to alter the V-O connectivity, thereby alleviating distortive phase transformations and promoting solid solution-based Li-ion insertion in V2O5. We also summarize several widely accessible and classical molecular-based analytical tools that can provide local structural dynamics and phase transformation mechanism information on the lithiation of V2O5, including single-crystal X-ray diffraction, infrared and Raman spectroscopy, electron paramagnetic resonance, and nuclear magnetic resonance spectroscopy.

A contratualização com os cuidados de saúde primários na Administração Regional de Saúde de Lisboa e Vale do Tejo, até 2023

Abstract The article discusses contractualization in Primary Health Care (PHC), relating what has been contractualized and what has been achieved to answer the question: can contractualization contribute to guaranteeing equity and adequate access to PHC? A case study methodology was used, using data available on the SNS Transparency Portal and data used in external contractualization. Data was collected in December 2023. In the Lisbon and Tagus Valley Health Region, 29.6 percent of people were registered without a family doctor in 2023. One-year appointment usage rates fell progressively (-2.7%) between 2021 and 2023. As for cancer screening, in the case of colon and rectum screening, only 34.4% of those contractualized were performed in 2021 and 28.9% in 2022. Regarding the Overall Performance Index, the variation intervals between the negotiated proposals and those obtained were above 20 points. Drug therapy prescription in Diabetes Mellitus was analyzed, and no ACES achieved the expected values. We concluded that contractualization has not helped improve access and equity, as a gap between the situational or local strategic planning phases seems evident.

Quantum Few-Shot Image Classification.

Few-shot learning algorithms frequently exhibit suboptimal performance due to the limited availability of labeled data. This article presents a novel quantum few-shot image classification methodology aimed at enhancing the efficacy of few-shot learning algorithms at both the data and parameter levels. Initially, a quantum augmentation image representation technique is introduced, leveraging the local phase of quantum states to support few-shot learning algorithms at the data level. This approach enriches classical data while maintaining its intrinsic physical properties. Subsequently, a parameterized quantum circuit is employed to construct the classification model. This circuit, characterized by a reduced number of trainable parameters, shows increased resilience to overfitting, thereby offering a significant advantage at the parameter level for few-shot learning algorithms. The proposed approach is validated using three datasets, with experimental results indicating that it outperforms classical methods in few-shot learning scenarios while requiring fewer computational resources.

Simulating dirty bosons on a quantum computer

Quantum computers hold the potential to unlock new discoveries in complex quantum systems by enabling the simulation of physical systems that have heretofore been impossible to implement on classical computers due to intractability. A system of particular interest is that of dirty bosons, whose physics highlights the intriguing interplay of disorder and interactions in quantum systems, playing a central role in describing, for instance, ultracold gases in a random potential, doped quantum magnets, and amorphous superconductors. Here, we demonstrate how quantum computers can be used to elucidate the physics of dirty bosons in one and two dimensions. Specifically, we explore the disorder-induced delocalized-to-localized transition using adiabatic state preparation. In one dimension, the quantum circuits can be compressed to small enough depths for execution on currently available quantum computers. In two dimensions, the compression scheme is no longer applicable, thereby requiring the use of large-scale classical state vector simulations to emulate quantum computer performance. In addition, simulating interacting bosons via emulation of a noisy quantum computer allowed us to study the effect of quantum hardware noise on the physical properties of the simulated system. Our results suggest that scaling laws control how noise modifies observables versus its strength, the circuit depth, and the number of qubits. Moreover, we observe that noise impacts the delocalized and localized phases differently. A better understanding of how noise alters the observed properties of the simulated system is essential for leveraging near-term quantum devices for simulation of dirty bosons, and indeed for condensed matter systems in general.

Spin–orbit interaction enabled high-fidelity two-qubit gates

We study the implications of spin–orbit interaction (SOI) for two-qubit gates (TQGs) in semiconductor spin qubit platforms. SOI renders the exchange interaction governing qubit pairs anisotropic, posing a serious challenge for conventional TQGs derived for the isotropic Heisenberg exchange. Starting from microscopic level, we develop a concise computational Hamiltonian that captures the essence of SOI, and use it to derive properties of the rotating-frame time evolutions. Two key findings are made. First, for the controlled-phase/controlled-Z gate, we show and analytically prove the existence of ‘SOI nodes’ where the fidelity can be optimally enhanced, with only slight modifications in terms of gate time and local phase corrections. Second, we discover and discuss novel two-qubit dynamics that are inaccessible without SOI—the reflection gate and the direct controlled-not gate (CNOT). The relevant conditions and achievable fidelities are explicitly derived for the direct CNOT.

Reentrant localization phenomenon in one-dimensional cross-stitch lattice with flat band

In this work, we numerically study the localization properties in a quasi-periodically modulated one-dimensional cross-stitch lattice with a flat band. When &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$\varDelta\neq0$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20231393_M3.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20231393_M3.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, it is found that there are two different quasi-periodic modulation frequencies in the system after the local transformation, and the competing modulation by two frequencies may lead to the reentrant localization transition in the system. By numerically solving the fractal dimension, the average inverse participation ratio, and the average normalized participation ratio, we confirm that the system can undergo twice localization transitions. It means that the system first becomes localized as the disorder increases, at some critical points, some of the localized states go back to the delocalized ones, and as the disorder further increases, the system again becomes fully localized. By the scalar analysis of the normalized participation ratio, we confirm that reentrant localization stably exists in the system. And the local phase diagram is also obtained. From the local phase diagram, we find that when &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$1.6&lt;\varDelta&lt;1.9$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20231393_M4.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20231393_M4.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, the system undergoes a cascade of delocalization-localization-delocalization-localization transition by increasing &lt;i&gt;λ&lt;/i&gt;. When &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$\varDelta=0$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20231393_M5.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20231393_M5.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, there exists only one quasi-periodic modulation frequency in the system. And we analytically obtain the expressions of the mobility edges, which are in consistence with the numerical studies by calculating the fractal dimension. And the system exhibits one localization transition. This work could expand the understanding of the reentrant localization in a flat band system and offers a new perspective on the research of the reentrant localization transition.

A hierarchical surrogate assisted optimization algorithm using teaching-learning-based optimization and differential evolution for high-dimensional expensive problems

Surrogate-assisted evolutionary algorithms (SAEAs) are increasingly used in solving computationally expensive optimization problems. However, when tackling high-dimensional expensive problems, a large number of exact function evaluations (FEs) need to be consumed for existing SAEAs to achieve an acceptable solution. In this paper, a hierarchical surrogate assisted optimization algorithm (HSAOA) using teaching-learning-based optimization and differential evolution is proposed for solving high-dimensional expensive problems with a relatively small number of exact FEs. To keep a balance between global exploration and local exploitation, a hierarchical surrogate framework with hybrid evolutionary algorithms is devised. In the global search phase, a radial basis function surrogate is utilized to assist the teaching-learning-based optimization in locating the promising sub-regions. In the local search phase, a novel dynamic ensemble of surrogates is proposed to assist the differential evolution in speeding up the convergence process. Eight test functions with 10 to 100 dimensions and a spatial truss design problem are employed to compare the proposed method with several state-of-the-art SAEAs. The results show that the proposed HSAOA is superior to the comparison algorithms for solving expensive optimization problems, and needs a much smaller number of exact FEs than other competing SAEAs to produce competitive or even better results for high-dimensional expensive problems.

Quantification analysis of mural defects in digital holography with fundamental voice excitation based on local phase field separation

Previous collaborative studies have shown that fringe patterns by optical interference way are used as a direct diagnostic tool for qualitative analysis of defects in Culture Heritage. Nevertheless, the complexity of the fringe patterns prevents the accurate quantification of defects. In this study, a phase separation method based on the weighted least-squares algorithm is proposed to conveniently isolate the local phase distribution from the whole, complex fringe pattern. This method is examined to accurately quantify the deformation and strain distribution of visible and invisible defects. In experimental work, the Non-destructive Acoustic excitation and Digital Holography (NDA-DH) detection is employed for the superficial and subsurface defects in the Ruting murals of the Forbidden City, Beijing. Under the impact of fundamental human voice, the frequency range of the acoustic excitation was considered from 100–800 Hz. All results show that the suggested phase separation method allows direct observation of defects as well as quantified surface deformation providing a precious documentation to the conservators.

Topological Mott transistor with high current density based on hydrogen-terminated diamond

The mechanism for the high drain-source current density, IDS≈ 1.3 A/mm, measured in a field effect transistor based on hydrogen-terminated diamond, is explained by the Mott insulator-metal transition (IMT). A local metal phase satisfying the Mott criterion for an IMT occurs in a p-type semiconductor formed by coupling between hydrogen and carbon on the surface of diamond. The local Mott metal phase on the surface of a p-type semiconductor leads to high carrier and current densities, and the transistor utilizing this effect is construed as a Mott power transistor. A channel material consisting of local Mott metal regions on the surface of a p-type semiconductor, such as hydrogen-terminated diamond, may be considered an inhomogeneous, topological Mott insulator.

A Survey of Learning-based Automated Program Repair

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this article, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our article can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at the repository: https://github.com/iSEngLab/AwesomeLearningAPR .

(Invited) Interlayer Excitons in Two-Dimensional Perovskite/Monolayer Transition Metal Dichalcogenide Heterostructures

The emergence of two-dimensional (2D) materials has intrigued a great deal of research on novel physical phenomena and various functional applications due to their particular crystal structures and reduced dimensionality. Unlike 3D materials, bulks of 2D materials can be easily thinned to atomic thickness by mechanical exfoliation and atomically thin 2D materials can be arbitrarily stacked to form vertical van der Waals (vdW) heterostructures, which could inherit the unique characteristics of the constituent layers and even exhibit new properties not possessed by them. Particularly, when type-Ⅱ vdW heterostructures are excited, the positive and the negative charges would reside in the different layers after the charge transfer but be limited within a short distance due to the strong quantum confinement effect of 2D materials, leading to strong Coulomb interaction and the formation of interlayer excitons (IXs). IXs are generally equipped with orientated dipole moment and long lifetime, making them ideal media for the future interconnects between optical transmission and electronic computation. Currently, studies on IXs are mainly focused on the vdW heterostructures formed by monolayers of transition-metal dichalcogenides (TMDs). Here, I will first talk about vdW heterostructures formed by 2D perovskites and TMDs for studying IXs. Stacking different kinds of 2D perovskites and TMDs, formation of IXs is confirmed by excitation-power-, temperature-, electric-field-dependent and time-resolved photoluminescence (PL) studies. Notably, robust IX emission can be observed regardless of the stacking sequence and geometric alignment of the constituent layers, showing great advantages over the TMD/TMD vdW heterostructures, which require special twist-angle and thermal annealing. Then, I would like to give a brief introduction on widely tuning the IX emission energy by changing the layer number of the 2D perovskite or the TMD, which shed light on the application of 2D perovskite/TMD vdW heterostructures in broad-spectrum optoelectronics. Furthermore, I would like to next talk about how the selection of organic chains in 2D perovskites influences the properties of the IXs. By using chiral 2D perovskites, the IX emission shows substantial circular polarization and the polarization direction is only related to the chirality of the molecules regardless of the excitation scheme or any other external field, which could open up new passages for controlling valley- or spin-polarization of IXs. By introducing molecules with different dielectric constants, the dielectric screening strength in the vdW heterostructure is changed and hence the binding energy of the IXs is also modified, which offers great opportunities for exploiting tunable excitonic devices and studying exciton condensation. Finally, I will also introduce IX as a non-destructive tool for probing the local phase transition at the surface of the 2D perovskite (BA)2PbI4 flakes. By spatially PL mapping of the (BA)2PbI4/WSe2 heterostructure, two different IX species can be observed to distinguish the low-temperature and the high-temperature phase respectively.

Removal of phase residues in electron holography.

Electron holography provides quantitative phase information regarding the electromagnetic fields and the morphology of micro- to nano-scale samples. A phase image reconstructed numerically from an electron hologram sometimes includes phase residues, i.e. origins of unremovable phase discontinuities, which make it much more difficult to quantitatively analyze local phase values. We developed a method to remove the residues in a phase image by a combination of patching local areas of a hologram and denoising based on machine learning. The small patches for a hologram, which were generated using the spatial frequency information of the own fringe patterns, were pasted at each residue point by an algorithm based on sparse modeling. After successive phase reconstruction, the phase components with no dependency on the vicinity were filtered out by Gaussian process regression. We determined that the phase discontinuities that appeared around phase residues were removed and the phase distributions of an atomic resolution phase image of a Pt nanoparticle were sufficiently restored.

Quasi-deterministic localization of Er emitters in thin film TiO2 through submicron-scale crystalline phase control

With their shielded 4f orbitals, rare-earth ions (REIs) offer optical and electron spin transitions with good coherence properties even when embedded in a host crystal matrix, highlighting their utility as promising quantum emitters and memories for quantum information processing. Among REIs, trivalent erbium (Er3+) uniquely has an optical transition in the telecom C-band, ideal for transmission over optical fibers, making it well suited for applications in quantum communication. The deployment of Er3+ emitters into a thin film TiO2 platform has been a promising step toward scalable integration; however, like many solid-state systems, the deterministic spatial placement of quantum emitters remains an open challenge. We investigate laser annealing as a means to locally tune the optical resonance of Er3+ emitters in TiO2 thin films on Si. Using both nanoscale x-ray diffraction measurements and cryogenic photoluminescence spectroscopy, we show that tightly focused below-gap laser annealing can induce anatase to rutile phase transitions in a nearly diffraction-limited area of the films and improve local crystallinity through grain growth. As a percentage of Er:TiO2 is converted to rutile, the Er3+ optical transition blueshifts by 13 nm. We explore the effects of changing laser annealing time and show that the amount of optically active Er:rutile increases linearly with laser power. We additionally demonstrate local phase conversion on microfabricated Si structures, which holds significance for quantum photonics.

Phase engineering of anomalous Josephson effect derived from Andreev molecules

A Josephson junction (JJ) is a key device for developing superconducting circuits, wherein a supercurrent in the JJ is controlled by the phase difference between the two superconducting electrodes. When two JJs sharing one superconducting electrode are coherently coupled and form the Andreev molecules, a supercurrent of one JJ is expected to be nonlocally controlled by the phase difference of another JJ. Here, we evaluate the supercurrent in one of the coupled two JJs as a function of local and nonlocal phase differences. Consequently, the results exhibit that the nonlocal phase control generates a finite supercurrent even when the local phase difference is zero. In addition, an offset of the local phase difference giving the JJ ground state depends on the nonlocal phase difference. These features demonstrate the anomalous Josephson effect realized by the nonlocal phase control. Our results provide a useful concept for engineering superconducting devices such as phase batteries and dissipationless rectifiers.

THz properties of grating-gate plasmonic crystals

This study reviews recent advances in the modern field of terahertz plasmonics concerning the control of resonant properties of grating-gate plasmonic crystal structures. Particularly, we conducted both experimental and theoretical investigations of AlGaN/GaN grating-gate structures with a focus on investigations of the resonant structure of transmission spectra associated with plasmon excitations in two-dimensional electron gas at different modulation degree of concentration profiles. Two distinct resonant phases of the plasmonic crystal structure were analyzed. The first one, the delocalized phase, is observed in the case of a small modulation degree of electron gas. In this phase, we found that plasmonic resonant absorption of incident radiation occurs across the entire grating-gate structure, with domination in the gated regions of the electron gas. In contrast, the second phase, the localized one, is realized at a strong modulation of the electron concentration profiles when the gated regions of the electron gas are completely depleted. Here, plasmon resonances are characterized by the spatial localization of absorption of incident radiation exclusively within the ungated regions of the electron gas. Moreover, in the localized phase, we observed the unexpected blue shift of plasmon resonant frequency with an increase of gate voltage. This observation was explained by the result of ‘edge gating effect’ and additional shrinking of the concentration profile of the electron gas in the ungated region. We demonstrate that the correct description of both phases requires rigorous electrodynamic simulations and cannot be achieved solely in the frameworks of simplified single-mode or single-cavity models.

Distributed cooperative tracking and cooperative guidance against maneuvering aerial target

This paper designs finite-time distributed state estimation algorithm and cooperative guidance law for high-maneuvering target salvo attack. Considering the maneuvering property of the target, a novel maneuvering model is proposed, which can adjust the maneuvering parameter adaptively. Based on the maneuvering target model, the interactive multiple models (IMM)-based distributed cubature information filtering (DCIF) algorithm is designed by two phases, named local filtering phase and consensus fusion phase. The final estimate results can be obtained by fusing the parallel local estimate results effectively with finite consensus fusion steps, which are used to design the cooperative guidance law for maneuvering target salvo attack. Besides, with necessary mild conditions, the lower bound and upper bound of the information matrix are proved to exist, and the stability of the proposed distributed algorithm is analyzed by Lyapunov stability theory and stochastic stability theory. Besides, the cooperative attack simulation example is given to validate the effectiveness of the proposed algorithm.

Simplicial graviton from selfdual Ashtekar variables

In perturbative gravity, it is straight-forward to characterize the two local degrees of freedom of the gravitational field in terms of a mode expansion of the linearized perturbation. In the non-perturbative regime, we are in a more difficult position. It is not at all obvious how to construct Dirac observables that can separate the gauge orbits. Standard procedures rely on asymptotic boundary conditions or formal Taylor expansions of relational observables. In this paper, we lay out a new non-perturbative lattice approach to tackle the problem in terms of Ashtekar’s self-dual formulation. Starting from a simplicial decomposition of space, we introduce a local kinematical phase space at the lattice sites. At each lattice site, we introduce a set of constraints that replace the generators of the hypersurface deformation algebra in the continuum. We show that the discretized constraints close under the Poisson bracket. The resulting reduced phase space describes two complex physical degrees of freedom representing the two radiative modes at the discretized level. The paper concludes with a discussion of the key open problems ahead and the implications for quantum gravity.