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Modern industrial processes demand real-time model predictive control (MPC) method within the constraints of limited computing resources. This paper introduces an accelerated MPC method to address the issue. Specifically, we employ the time series mixing model (TSMixer) to construct a predictive model capable of accurately forecasting the behavior of the system under control. Furthermore, we extend the capabilities of TSMixer by integrating a feature classification model (TSMixer with FCM). This enhanced version takes into account both future information and static variables, resulting in superior accuracy compared to the transformer model while maintaining a significantly smaller parameter count. Finally, to further enhance MPC’s responsiveness, we develop a two-dimensional block stochastic configuration network (2D-BSCN) imitative controller. This innovative network clones the behavior of rolling optimization, effectively replacing online optimization to reduce computational time. The experimental results show that the accelerated MPC has an excellent performance in numerical simulation and actual industrial processes. Notably, the algorithm achieves a smaller FLOPs and tracking time than other state-of-the-art models, well within the requirement for industrial process control.

This letter proposes a novel approach for designing non-surjective (NS) finite alphabet iterative decoders (FAIDs) for quasi-cyclic low-density parity-check (LDPC) codes, especially 5G LDPC codes. We employ recurrent quantized neural networks to optimize the look-up tables used in NS-FAIDs, the design of which is the kernel of FAIDs. During the optimization of LUTs, the quantized message alphabets and quantization thresholds are jointly designed. To cope with the untrainable problem of quantization thresholds in the existing neural-network-based linear FAIDs, we use softmax distribution to soften the implied one-hot distribution of quantization thresholds, making it trainable in the neural network. The proposed decoders offer enhanced universality compared to existing neural network-based linear FAIDs, making them directly applicable to 5G LDPC codes with support for 2-bit quantization over the additive white Gaussian noise channel. Additionally, they significantly outperform the original NS-FAID in terms of performance.

The problem of fault estimation is addressed for one-dimensional (1-D) linear boundary control and boundary observation (BCBO) parabolic partial differential equations (PDEs) with a faulty boundary measurement. The considered plant is subjected to simultaneous unknown multiplicative faults entering the boundary input and boundary measurement. Difficulties arise due to the coupling between the sensor fault parameter and unknown boundary state appearing in the measurement. With the only boundary input and faulty boundary measurement, it is rather challenging to estimate the accurate values of faults and state simultaneously. Therefore, most existing results only consider correct and healthy measurement for PDE systems. To this end, novel adaptation laws and an adaptive observer are designed in this work to provide exponential convergent joint fault-state estimation, where we design and leverage a set of novel filters. It is first time that unknown multiplicative fault parameter in the measurement can be estimated accurately in the PDE systems.

This article investigates the adaptive neural tracking control problem for a class of hyperbolic PDE with boundary actuator dynamics described by a set of nonlinear ordinary differential equations (ODEs). Particularly, the control input appears in the ODE subsystem with unknown nonlinearities requiring to be estimated and compensated, which makes the control task rather difficult. It is the first time to consider tracking control of such a class of systems, rendering our contributions essentially different from the existing literature that merely focus on the stabilization problem. By formulating a virtual exosystem to generate a reference trajectory, we propose a novel design of the adaptive geometric controller for the considered system where neural networks (NNs) are employed to approximately estimate nonlinearities, and finite and infinite-dimensional backstepping techniques are leveraged. Moreover, rigorously theoretical proofs based on the Lyapunov theory are provided to analyze the stability of the closed-loop system. Finally, we illustrate the results through two numerical simulations.

In order to truly promote the further development of the Industrial Internet of Things (IIoT), terminal authentication of the IIoT is essential. Physical-layer authentication (PLA) has recently attracted much attention for its high security and lightweight. Nevertheless, most existing PLA schemes in conjunction only the observation of a single receiver will lead to low-reliability and low-robustness of authentication, especially in hostile time-varying wireless channels. To tackle this issue, we developed a multiobservation-multichannel-attribute (MOMCA) based multiuser authentication architecture, which considers both the observations of multireceivers and multiple channel attributes of each observation to enhance wireless security. Specifically, the proposed architecture can provide additional spatial recognition characteristics for multiusers. To better fit the channel features of multiobservations, we proposed two gradient boosting optimization-based schemes. One uses Taylor expansion to approximate objective functions and adds the regularization term to avoid overfitting issues. The other can obtain higher authentication performance by sampling the signal data with small gradient characteristics. The simulations on real industrial indoor and outdoor datasets verify the superiority of the proposed schemes in authentication accuracy over six baseline authentication schemes.

Models are the key to model-based control strategies. However, due to the nonlinear and time-varying nature of industrial processes, plant–model mismatches are inevitable. Therefore, it is highly desirable to detect mismatches and update the model in a closed-loop system to avoid re-identifying the entire model. In this study, a mismatch detection method based on the instrumental variable that uses sum-of-norms regularization and is robust to noise modeling errors is proposed for linear parameter variation-input–output (IO) models. The introduction of the instrumental variable addresses the challenge that regression variables are corrupted with colored noise. The proposed method integrates the detection and quantification of mismatches into a framework in which significant jumps in parameters can be detected by segmenting the signal using sum-of-norms regularization and the search space of quantification is reduced. Moreover, the method is noninvasive and applicable to closed-loop systems under colored noise disturbances. Finally, the feasibility and robustness of the proposed method under closed-loop conditions are analyzed from a stochastic perspective and demonstrated with representative simulation examples.

Identifying the impact of native/irradiated traps on electrical properties is vital for the implementation of high-performance gallium oxide (Ga2O3) power devices. In this work, the effect of electron irradiation and isochronal thermal annealing on the electrical properties of vertical Ni/Au $\beta $ -Ga2O3 Schottky barrier diode is explored. Majority and minority carrier traps in Ni/Au $\beta $ -Ga2O3 diode after irradiation and annealing are characterized using deep-level transient spectroscopy (DLTS) technique. We observe three trap states at $E_{\mathrm {V}} + 0.402$ eV (H1), $E_{\mathrm {C}} -0.626$ eV (E1), and $E_{\mathrm {C}} -0.755$ eV (E2), in which electron trap E2 is dominant, and the instability of electrical properties of the tested sample is attributed to the electron trap E2. Combining the formation energies and transition levels of $\beta $ -Ga2O3 intrinsic defects calculated by first-principles method, it is speculated that trap H1 is attributed to V $_{\mathrm {GaI}}- 2$ V $_{\mathrm {OII}}(+5/+3$ ) or V $_{\mathrm {GaII}}- 2$ V $_{\mathrm {OII}}(+3/+1$ ), trap E1 is related to V $_{\mathrm {GaI}} - V_{\mathrm {OI}}(-1/-3$ ) or O $_{\mathrm {ic}}(-1/-2$ ), and trap E2 is associated with O $_{\mathrm {id}}(0/-2$ ) or O $_{\mathrm {GaII}}(-1/-3$ ). Additionally, trap E2 is linked to the extrinsic defect FeGa. Based on the experimental and theoretical calculations of Ni/Au $\beta $ -Ga2O3 diode, the exact physical origin of radiation-induced defects and its annealing derivation process are discussed in detail.

Remote zero-shot object recognition, which involves offloading the zero-shot recognition task from one mobile device to a remote mobile edge computing (MEC) server or another mobile device, is crucial for 6G. To address this challenge, this paper presents a lightweight semantic knowledge base (SKB)-enabled multi-level feature extractor that projects the image into visual, semantic, and intermediate feature spaces. Then, this paper proposes a novel SKB-enabled multi-level feature transmission framework, which utilizes SKB and multi-level feature extractor at both transmitter and receiver. The semantic loss and required transmission latency at each level are characterized, and a multi-level feature transmission optimization problem is formulated to minimize the semantic loss under transmission latency constraint. However, this optimization problem is a multi-choice knapsack problem, which is challenging to solve optimally. To overcome this issue, an enhanced convex concave procedure is proposed to obtain an efficient solution. Furthermore, this paper theoretically analyzes the effects of SKBs on the communication performance when the feature extractors at both ends are the same. Numerical results demonstrate that the proposed design outperforms the benchmarks and provide insights into the impact of SKBs at both ends on performance as well as the tradeoff between transmission latency and zero-shot classification accuracy.

This article develops a robust adaptive boundary output regulation approach for a class of complex anticollocated hyperbolic partial differential equations subjected to multiplicative unknown faults in both the boundary sensor and actuator. The regulator design is based on the internal model principle, which amounts to stabilize a coupled cascade system, which consists of a finite-dimensional internal model driven by a hyperbolic distributed parameter system (DPS). To this end, a systematic sliding mode equipped with a backstepping approach is developed such that the robust state feedback control can be realized. Moreover, since the available information is a faulty boundary measurement at the right side point, state estimation is required. However, due to the presence of boundary unknown faults, we need to solve an issue of joint fault-state estimation. Restrictive persistent excitation conditions are usually required to guarantee the exact estimation of faults but are unrealistic in practice. To this end, a novel concurrent learning (CL) adaptive observer is proposed so that exponential convergence is obtained. It is the first time that the spirit of CL is introduced to the field of DPSs. Consequently, the observer-based adaptive boundary fault tolerant control scheme is developed, and rigorous theoretical analysis is given such that the exponential output regulation can be achieved. Finally, the effectiveness of the proposed methodology is demonstrated via comparative simulations.

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost the communication efficiency by only transmitting the semantics of data. However, ESC is data-hungry for training millions of neural parameters, while uploading massive data generated by users/devices to a central server is usually impractical due to privacy issues, regulatory guidelines and network congestion, etc. Inspired by federated learning, where each device contributes to the parameters’ update by independently computing the gradient based on its local data, we present UDSem, a unified distributed learning framework of semantic communications for texts and images over wireless networks. The key ingredients of our UDSem include 1) a flexible learning mechanism that can be tailored to a device based on its computing capabilities, 2) an efficient learning approach that splits the system into multiple modules and updates the parameters independently both on clients and the central server, 3) a mixed aggregation method that globally updates the neural parameters for each module of the ESC system. Equipped with these ingredients, our proposed UDSem is able to properly fill the gap between the training efficiency of ESC systems and the privacy requirement of users’ data. Experimental results over two public benchmarks show the superiority of our UDSem in terms of convergence speed and semantic interpretation, potentially paving the way for distributed semantic communications in future wireless networks.

Intelligent inspection robots are widely used in substation patrol inspections to identify potential safety hazards by patrolling substations and sending back scene images. However, in areas with weak signals, the scene images may not be successfully transmitted, thereby reducing the quality of the robots' work. To reliably transmit high quality inspection images in weak signal areas, a Specific Task-oriented Semantic Communication System for Images (STSCI) is proposed. This system involves the extraction, transmission, restoration, and enhancement of semantic features to obtain clearer images sent by intelligent robots under weak signals. Inspired by the fact that only specific details of the image are required in substation patrol inspection tasks, we propose a new paradigm of semantic enhancement to ensure the clarity of key semantic information under low signal-to-noise ratio conditions. Experiments demonstrate that our STSCI generally outperforms traditional image-compression-based and channel-coding-based as well as other semantic communication systems in substation patrol inspection tasks under low signal-to-noise ratio conditions.

This letter proposes a flexible coding approach (FLCA) for quasi-cyclic low-density parity-check (QC-LDPC) codes based on the base graphs and lifting factors in 5G new radio (NR). The FLCA can achieve almost all code rates and blocklengths supported by 5G physical layer using a novel shortening method which is different from those used in 5G NR. In the proposed FLCA, the shortening patterns are optimized according to the code rates and modulation schemes, including quadrature phase shift keying, 16-ary and 64-ary quadrature amplitude modulations. Our proposed method is fully applicable to the existing LDPC codes of 5G and makes the encoding more flexible. Both the numerical thresholds of PEXIT analysis and the simulation results demonstrate that the proposed coding approach can bring noticeable performance gain over the current LDPC codes used in 5G NR.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access (NOMA) framework for finite blocklength (FBL) transmission is proposed. Considering the different communication requirements of Internet of Things devices (IoTDs), a novel design to achieve high-rate and low-error is proposed. Two operating protocols for STAR-RIS are considered, namely energy splitting (ES) and mode switching (MS). 1) For STAR-RIS with ES, an alternating optimization (AO) algorithm is proposed to handle the highly-coupled mixed integer programming problem. More particularly, a low-complexity received-signal-strength-based device pairing scheme is proposed. Based on the given device pair, the closed-form solutions for the power allocation problem are obtained. The transmitting and reflecting coefficient optimization problem is solved by exploiting the successive convex approximation and semidefinite relaxation methods. 2) For STAR-RIS with MS, a double-layer penalty-based (DLPB) algorithm is proposed to tackle the newly introduced binary amplitude constraints. Numerical results reveal that: i) the proposed AO and DLPB algorithms can converge within a few iteration times; ii) the FBL transmission performance can be improved by employing the proposed STAR-RIS framework compared with conventional transmitting/reflecting-only RISs; iii) NOMA is capable of enhancing FBL rate while guaranteeing the reliability constraints compared with orthogonal multiple access.

Moiré excitons are emergent optical excitations in two-dimensional semiconductors with moiré superlattice potentials. Although these excitations have been observed on several platforms, a system with dynamically tunable moiré potential to tailor their properties is yet to be realized. Here we present a continuously tunable moiré potential in monolayer WSe2, enabled by its proximity to twisted bilayer graphene (TBG) near the magic angle. By tuning local charge density via gating, TBG provides a spatially varying and dynamically tunable dielectric superlattice for modulation of monolayer WSe2exciton wave functions. We observed emergent moiré exciton Rydberg branches with increased energy splitting following doping of TBG due to exciton wave function hybridization between bright and dark Rydberg states. In addition, emergent Rydberg states can probe strongly correlated states in TBG at the magic angle. Our study provides a new platform for engineering moiré excitons and optical accessibility to electronic states with small correlation gaps in TBG.

In this paper, ambient IoT is used as a typical use case of massive connections for the sixth generation (6G) mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions. A rather complete design of unsourced multiple access is proposed in which two key parts: a compressed sensing module for active user detection, and a sparse interleaver-division multiple access (SIDMA) module are simulated side by side on a same platform at balanced signal to noise ratio (SNR) operating points. With a proper combination of compressed sensing matrix, a convolutional encoder, receiver algorithms, the simulated performance results appear superior to the state-of-the-art benchmark, yet with relatively less complicated processing.

A reinforcement learning-based boundary optimal control algorithm for parabolic distributed parameter systems is developed in this article. First, a spatial Riccati-like equation and an integral optimal controller are derived in infinite-time horizon based on the principle of the variational method, which avoids the complex semigroups and operator theories. Using state data along the system trajectory, a value iteration algorithm via the Bellman optimality principle is proposed to obtain the solution of the spatial Riccati-like equation and the optimal control law. The convergence of the value iteration algorithm is proved. Subsequently, an approximation scheme based on weighted residuals is developed to implement the value iteration algorithm, where radial basis functions are chosen as the basic functions to approximate the solution of the spatial Riccati-like equation. Simulations on the diffusion–reaction process demonstrate the effectiveness of the developed method.

Twisted double bilayer graphene (tDBG) has emerged as a rich platform for studying strongly correlated and topological states, as its flat bands can be continuously tuned by both a perpendicular displacement field and a twist angle. Here, we construct a phase diagram representing the correlated and topological states as a function of these parameters, based on measurements of over a dozen tDBG devices encompassing two distinct stacking configurations. We find a hierarchy of symmetry-broken states that emerge sequentially as the twist angle approaches an apparent optimal value of θ ≈ 1.34°. Nearby this angle, we discover a symmetry-broken Chern insulator (SBCI) state associated with a band filling of 7/2 as well as an incipient SBCI state associated with 11/3 filling. We further observe an anomalous Hall effect at zero field in all samples supporting SBCI states, indicating spontaneous time-reversal symmetry breaking and possible moiré unit cell enlargement at zero magnetic field.

In this paper, we exploit a novel model-driven deep learning (MDDL)-based scheme for efficient wideband millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) channel estimation where the neural networks for sparse channel representation and recovery are respectively designed. For the former, we propose an angular-resampling network to determine the sampling intervals of grids adaptive to the true angles of arrival (AoAs) of paths, based on which an effective dictionary for sparse channel representation in angle-domain can be constructed. To this end, the neural network consisting of three modules trained with a newly-designed angular Gaussian-mixture distribution loss function is developed. For the latter, we propose an inverse-free variational Bayesian learning (IF-VBL) driven deep-unfolding network for sparse channel recovery. Specifically, the IF-VBL method by maximizing a general relaxed evidence lower bound (ELBO) is first developed, which is then unfolded into a layer-wise architecture where some a-priori parameters are learned. Simulation results verify the superiority of the proposed MDDL-based channel estimation scheme with significantly improved convergence and performance over counterparts.

Melanoma is a malignancy of the skin that is resistant to conventional treatment, necessitating the development of effective and safe new therapies. The percutaneous microneedle (MN) system has garnered increasing interest as a viable treatment option due to its high efficacy, minimal invasiveness, painlessness, and secure benefits. In this investigation, a sensitive MN system with multiple functions was created to combat melanoma effectively. This MN system utilized polyvinylpyrrolidone (PVP) as microneedle substrates and biocompatibility panax notoginseng polysaccharide (PNPS) as microneedle tips, which encapsulated PVP-stabilized CuO2nanoparticles as a therapeutic agent and disulfiram-containing F127 micelles to enhance the tumor treatment effect. The MN system had sufficient mechanical properties to pierce the skin, and the excellent water solubility of PNPS brought high-speed dissolution properties under the bio conditions, allowing the MNs to effectively penetrate the skin and deliver the CuO2nanoparticles as well as the drug-loaded micelles to the melanoma site. CuO2nanoparticles released by the MN system generated Cu2+and H2O2in the tumor acidic environment to achieve self-supply of hydrogen peroxide to chemodynamic therapy (CDT). In addition, Cu2+was chelated with disulfiram to produce CuET, which killed tumor cells. And the MN system had excellent near-infrared (NIR) photothermal properties due to the loading of CuO2nanoparticles and induced localized thermotherapy in the melanoma region to further inhibit tumor growth. Thus, the designed MN system accomplished effective tumor suppression and minimal side effects in vivo via combined therapy, offering patients a safe and effective option for melanoma treatment.

With the prosperity of emerging applications, the $6^{th}$ Generation mobile communication systems (6G) is coming at an unimaginable speed. It is expected to provide more intelligent, flexible, and secure services. As an essential pillar of 6G networks, ultra-Reliable Low Latency Communication (uRLLC) has promoted the vigorous development of intelligent communications. However, the existing networks cannot fully satisfy the strict and various requirements of uRLLC services, including delay, reliability and security. Considering the interaction between the physical layer and the upper layer, we propose a Cross-layer Flexible Security Solution (CFSS), which includes initiative waiting strategy, flexible transmission time interval scheduling strategy, and flexible pre-backup transmission strategy. While considering secure communication, CFSS could flexibly provide customized services to the users through cross-layer parameters configuration and resource allocation. In addition, we extend the Stochastic Network Calculus (SNC) modeling to the security field, and use Finite Blocklength Coding (FBC) to analyze the service process of uRLLC. Two cases of FBC are considered comprehensively, namely, given decoding error probability and given transmission rate. Finally, Experienced Meta-Asynchronous Advantage Actor-Critic (EM-A3C) algorithm is proposed to solve the complex optimization problem, the establishment of experience pool effectively improves the algorithm efficiency.

Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links. We first apply the message passing neural network (MPNN), a unified framework of GNN, to solve the problem. We show that the signaling overhead grows quadratically as the network size increases. Inspired from the over-the-air computation (AirComp), we then propose an Air-MPNN framework, where the messages from neighboring nodes are represented by the transmit power of pilots and can be aggregated efficiently by evaluating the total interference power. The signaling overhead of Air-MPNN grows linearly as the network size increases, and we prove that Air-MPNN is permutation invariant. To further reduce the signaling overhead, we propose the Air message passing recurrent neural network (Air-MPRNN), where each node utilizes the graph embedding and local state in the previous frame to update the graph embedding in the current frame. Since existing communication systems send a pilot during each frame, Air-MPRNN can be integrated into the existing standards by adjusting pilot power. Simulation results validate the scalability of the proposed frameworks, and show that they outperform the existing power allocation algorithms in terms of sum-rate for various system parameters.

This article considers the problem of adaptive output regulation of a class of 1-D anticollocated hyperbolic partial differential equations (PDEs). The model is subject to unknown scaled parameters in both the boundary condition and boundary measurement. An adaptive boundary observer, providing online estimates of the system state and parameters, is designed. Particularly, to realize that the pure boundary state at left side tracks a reference signal, a challenging problem with the estimate of the true value of unknown scaled parameter appearing in measurement (at the left side) is tackled in this article. It is shown that the proposed observer is exponentially convergent, and furthermore, the exponential output regulation and stabilization are achieved.

More than 50 years ago, Golomb and Welch conjectured that there is no perfect Lee codes $C$ of packing radius $r$ in $\mathbb {Z}^{n}$ for $r\geq 2$ and $n\geq 3$ . Recently, Leung and the second author proved that if $C$ is linear, then the Golomb-Welch conjecture is valid for $r=2$ and $n\geq 3$ . In this paper, we consider the classification of linear Lee codes with the second-best possibility, that is the density of the lattice packing of $ \mathbb Z^{n}$ by Lee spheres $S(n,r)$ equals $\frac {|S(n,r)|}{|S(n,r)|+1}$ . We show that, for $r=2$ and $n\equiv 0,3,4 \pmod {6}$ , this packing density can never be achieved.

Joint uncertainties and state estimation of a class of linearly coupled hyperbolic partial differential equation systems in the presence of unstructured and structured uncertainties are studied in this article. For unstructured uncertainties that are completely unknown, by employing Takagi–Sugeno fuzzy logic systems to approximate the unstructured uncertainties, a novel adaptive fuzzy boundary observer is developed to estimate both unknown system states as well as unknown weights in the fuzzy logic system, and the estimation errors are ultimately bounded. Therein, in the design of the proposed observer, a set of swapping filters and an infinite dimensional backstepping technique are combined. On the other hand, for structured uncertainties that can be described in a concrete parameterized form, the proposed method can easily achieve the exact estimation of weights and states to their true values. The rigorous proof is provided to show that the ultimately bounded estimation errors for the case of unstructured uncertainties and the exponential convergent estimation errors for the case of structured uncertainties can be realized. Finally, three illustrative simulations are carried out to show the feasibility and effectiveness of the developed methods in this article.

Engineering the coupling between fundamental quantum excitations is at the heart of quantum science and technologies. An outstanding case is the creation of quantum light sources in which coupling between single photons and phonons can be controlled and harnessed to enable quantum information transduction. Here we report the deterministic creation of quantum emitters featuring highly tunable coupling between excitons and phonons. The quantum emitters are formed in strain-induced quantum dots created in homobilayer WSe2. The colocalization of quantum-confined interlayer excitons and terahertz interlayer breathing-mode phonons, which directly modulates the exciton energy, leads to a uniquely strong phonon coupling to single-photon emission, with a Huang–Rhys factor reaching up to 6.3. The single-photon spectrum of interlayer exciton emission features a single-photon purity >83% and multiple phonon replicas, each heralding the creation of a phonon Fock state in the quantum emitter. Due to the vertical dipole moment of the interlayer exciton, the phonon–photon interaction is electrically tunable to be higher than the exciton and phonon decoherence rate, and hence promises to reach the strong-coupling regime. Our result demonstrates a solid-state quantum excitonic–optomechanical system at the atomic interface of the WSe2bilayer that emits flying photonic qubits coupled with stationary phonons, which could be exploited for quantum transduction and interconnection.

Two-dimensional semiconductor moiré superlattices have emerged as a powerful platform for engineering correlated electronic phenomena. On the other hand, optical excitation creates charge neutral interlayer excitons with an out-of-plane electric dipole. Strong onsite dipole–dipole interaction promises the formation of correlated bosonic states, akin to the Mott states of electrons, but has not yet been demonstrated. Here we report a large interaction between excitons occupying the same moiré lattice site—characterized by the Hubbard Uparameter—and consequent dipole ladders with spin- and electron-filling dependence in WSe2/WS2moiré superlattices. Photoluminescence measurements show successive peaks emerging with an energy separation of around 34 meV above the ground state as the exciton density is increased. This corresponds to the sequential injection of excitons into a single site with an energy cost to overcome the large exciton Hubbard U, forming a dipole ladder. Based on findings of local magnetic moments at two holes per moiré cell, we show that excitons can also fill a second moiré orbital, establishing the two-orbital nature of the moiré potential landscape. Our results show that the Bose–Hubbard model with possible exciton crystal phases can be investigated in interacting opto-moiré quantum matter.

A secure transmission framework is proposed for simultaneously transmitting and reflecting surface (STARS) networks in presence of an untrusted user. The active and passive secure beamforming optimization problem is addressed. A double-loop alternating optimization (DLAO) algorithm is proposed for maximizing the achievable sum secure capacity (SC). More particularly, the inner loop is for beamforming optimization by utilizing successive convex approximation approaches, while the outer loop is for the rank-one constraint recovery by utilizing penalty-based optimization approaches. Numerical results demonstrate that: 1) the proposed DLAO algorithm converges within a few iterations; 2) the proposed framework is capable of achieving enhanced SC compared to conventional reflecting/transmitting-only reconfigurable intelligent surfaces.

Understanding how microscopic spin configuration gives rise to exotic properties at the macroscopic length scale has long been pursued in magnetic materials1–5. One seminal example is the Einstein–de Haas effect in ferromagnets1,6,7, in which angular momentum of spins can be converted into mechanical rotation of an entire object. However, for antiferromagnets without net magnetic moment, how spin ordering couples to macroscopic movement remains elusive. Here we observed a seesaw-like rotation of reciprocal lattice peaks of an antiferromagnetic nanolayer film, whose gigahertz structural resonance exhibits more than an order-of-magnitude amplification after cooling below the Néel temperature. Using a suite of ultrafast diffraction and microscopy techniques, we directly visualize this spin-driven rotation in reciprocal space at the nanoscale. This motion corresponds to interlayer shear in real space, in which individual micro-patches of the film behave as coherent oscillators that are phase-locked and shear along the same in-plane axis. Using time-resolved optical polarimetry, we further show that the enhanced mechanical response strongly correlates with ultrafast demagnetization, which releases elastic energy stored in local strain gradients to drive the oscillators. Our work not only offers the first microscopic view of spin-mediated mechanical motion of an antiferromagnet but it also identifies a new route towards realizing high-frequency resonators8,9up to the millimetre band, so the capability of controlling magnetic states on the ultrafast timescale10–13can be readily transferred to engineering the mechanical properties of nanodevices.

Controlling quantum materials with light is of fundamental and technological importance. By utilizing the strong coupling of light and matter in optical cavities1–3, recent studies were able to modify some of their most defining features4–6. Here we study the magneto-optical properties of a van der Waals magnet that supports strong coupling of photons and excitons even in the absence of external cavity mirrors. In this material—the layered magnetic semiconductor CrSBr—emergent light–matter hybrids called polaritons are shown to substantially increase the spectral bandwidth of correlations between the magnetic, electronic and optical properties, enabling largely tunable optical responses to applied magnetic fields and magnons. Our results highlight the importance of exciton–photon self-hybridization in van der Waals magnets and motivate novel directions for the manipulation of quantum material properties by strong light–matter coupling.

Plasmon polaritons in van der Waals materials hold promise for various photonics applications1–4. The deterministic imprinting of spatial patterns of high carrier density in plasmonic cavities and nanoscale circuitry can enable the realization of advanced nonlinear nanophotonic5and strong light–matter interaction platforms6. Here we demonstrate an oxidation-activated charge transfer strategy to program ambipolar low-loss graphene plasmonic structures. By covering graphene with transition-metal dichalcogenides and subsequently oxidizing the transition-metal dichalcogenides into transition-metal oxides, we activate charge transfer rooted in the dissimilar work functions between transition-metal oxides and graphene. Nano-infrared imaging reveals ambipolar low-loss plasmon polaritons at the transition-metal-oxide/graphene interfaces. Further, by inserting dielectric van der Waals spacers, we can precisely control the electron and hole densities induced by oxidation-activated charge transfer and achieve plasmons with a near-intrinsic quality factor. Using this strategy, we imprint plasmonic cavities with laterally abrupt doping profiles with nanoscale precision and demonstrate plasmonic whispering-gallery resonators based on suspended graphene encapsulated in transition-metal oxides.

We investigate heterostructures composed of monolayer WSe2stacked on α-RuCl3using a combination of Terahertz (THz) and infrared (IR) nanospectroscopy and imaging, scanning tunneling spectroscopy (STS), and photoluminescence (PL). Our observations reveal itinerant carriers in the heterostructure prompted by charge transfer across the WSe2/α-RuCl3interface. Local STS measurements show the Fermi level is shifted to the valence band edge of WSe2which is consistent with p-type doping and verified by density functional theory (DFT) calculations. We observe prominent resonances in near-IR nano-optical and PL spectra, which are associated with the A-exciton of WSe2. We identify a concomitant, near total, quenching of the A-exciton resonance in the WSe2/α-RuCl3heterostructure. Our nano-optical measurements show that the charge-transfer doping vanishes while excitonic resonances exhibit near-total recovery in “nanobubbles”, where WSe2and α-RuCl3are separated by nanometer distances. Our broadband nanoinfrared inquiry elucidates local electrodynamics of excitons and an electron–hole plasma in the WSe2/α-RuCl3system.

Excitons, Coulomb-bound electron–hole pairs, play a crucial role in both optical excitation and correlated phenomena in solids. When excitons interact with other quasiparticles, few- and many-body excited states can appear. Here we report an interaction between exciton and charges enabled by unusual quantum confinement in two-dimensional moiré superlattices, which results in many-body ground states composed of moiré excitons and correlated electron lattices. In an H-stacked (60o-twisted) WS2/WSe2heterobilayer, we found an interlayer moiré exciton whose hole is surrounded by its partner electron’s wavefunction distributed among three adjacent moiré traps. This three-dimensional excitonic structure enables large in-plane electrical quadrupole moments in addition to the vertical dipole. Upon doping, the quadrupole facilitates the binding of interlayer moiré excitons to the charges in neighbouring moiré cells, forming intercell charged exciton complexes. Our work provides a framework for understanding and engineering emergent exciton many-body states in correlated moiré charge orders.

Nonlinear optical activities (e.g., harmonic generations) in two-dimensional (2D) layered materials have attracted much attention due to the great promise in diverse optoelectronic applications such as nonlinear optical modulators, nonreciprocal optical device, and nonlinear optical imaging. Exploration of nonlinear optical response (e.g., frequency conversion) in the infrared, especially the mid-infrared (MIR) region, is highly desirable for ultrafast MIR laser applications ranging from tunable MIR coherent sources, MIR supercontinuum generation, and MIR frequency-comb-based spectroscopy to high harmonic generation. However, nonlinear optical effects in 2D layered materials under MIR pump are rarely reported, mainly due to the lack of suitable 2D layered materials. Van der Waals layered platinum disulfide (PtS2) with a sizable bandgap from the visible to the infrared region is a promising candidate for realizing MIR nonlinear optical devices. In this work, we investigate the nonlinear optical properties including third-and fifth-harmonic generation (THG and FHG) in thin layered PtS2under infrared pump (1550–2510 nm). Strikingly, the ultrastrong third-order nonlinear susceptibility χ(3)(−3ω;ω,ω,ω) of thin layered PtS2in the MIR region was estimated to be over 10–18m2/V2, which is about one order of that in traditional transition metal chalcogenides. Such excellent performance makes air-stable PtS2a potential candidate for developing next-generation MIR nonlinear photonic devices.

The integer quantum anomalous Hall (QAH) effect is a lattice analogue of the quantum Hall effect at zero magnetic field1–3. This phenomenon occurs in systems with topologically non-trivial bands and spontaneous time-reversal symmetry breaking. Discovery of its fractional counterpart in the presence of strong electron correlations, that is, the fractional QAH effect4–7, would open a new chapter in condensed matter physics. Here we report the direct observation of both integer and fractional QAH effects in electrical measurements on twisted bilayer MoTe2. At zero magnetic field, near filling factor ν= −1 (one hole per moiré unit cell), we see an integer QAH plateau in the Hall resistance Rxyquantized to h/e2± 0.1%, whereas the longitudinal resistance Rxxvanishes. Remarkably, at ν=  −2/3 and −3/5, we see plateau features in Rxyat 32h/e2±1%and 53h/e2±3%, respectively, whereas Rxxremains small. All features shift linearly versus applied magnetic field with slopes matching the corresponding Chern numbers −1, −2/3 and −3/5, precisely as expected for integer and fractional QAH states. Additionally, at zero magnetic field, Rxyis approximately 2h/e2near half-filling (ν= −1/2) and varies linearly as νis tuned. This behaviour resembles that of the composite Fermi liquid in the half-filled lowest Landau level of a two-dimensional electron gas at high magnetic field8–14. Direct observation of the fractional QAH and associated effects enables research in charge fractionalization and anyonic statistics at zero magnetic field.