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Intense terahertz (THz) radiation in free space offers multifaceted capabilities for accelerating electron, understanding the mesoscale architecture in (bio)materials, elementary excitation and so on. Recently popularized spintronic THz emitters (STEs) with their versatility such as ultra-broadband, cost-effectiveness, large-size and ease for-integration have become one of the most promising alternative for the next generation of intense THz sources. Nevertheless, the typical W| Co20Fe60B20 | Pt necessitates an external-magnetic-field to saturate magnetization for stable operation, limiting its scalability for achieving higher THz field with uniform distribution over larger sample areas. Here we demonstrate the methodologies of enhancing the high-field THz radiation of external-magnetic-field-free IrMn3 | Co20Fe60B20 |W heterostructure via optimizing the substrate with superior thermal conductivity and integrating a one-dimensional photonic crystal (PC) structure to maximize the radiation efficiency. Under the excitation of a Ti: sapphire femtosecond laser amplifier with central wavelength of 800 nm, pulse duration of 35 fs, and repetition rate of 1 kHz and maximum single pulse energy of 5.5 mJ, we successfully generate intense THz radiation with focal peak electric field up to 1.1 MV/cm with frequency range covering 0.1-10 THz without external-magnetic-fields. These high-field STEs will also enable other applications such as ultra-broadband high-field THz spectroscopy and polarization-based large-size strong-field THz imaging.

This paper introduces SayRea, an interactive system that facilitates the extraction of contextual rules for personalized context-aware service recommendations in mobile scenarios. The system monitors a user's execution of registered services on their smartphones (via accessibility service) and proactively requests a single-sentence reason from the user. By utilizing a Large Language Model (LLM), SayRea parses the reason and predicts contextual relationships between the observed service and potential contexts (such as setting the alarm clock deep in the evening). In this way, SayRea can significantly reduce the cognitive load on users in anticipating future needs and selecting contextual attributes. A 10-day field study involving 20 participants showed that SayRea accumulated an average of 62.4 rules per user and successfully recommended 45% of service usage. The participants provided positive feedback on the system's usability, interpretability, and controllability. The findings highlight SayRea's effectiveness in personalized service recommendations and its potential to enhance user experience in mobile scenarios.

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAI) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAI in improving energy harvesting wireless networks for IoT in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting wireless networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting wireless networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data collection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some important open directions.

Due to flexibility and low-cost, unmanned aerial vehicles (UAVs) are increasingly crucial for enhancing coverage and functionality of wireless networks. However, incorporating UAVs into next-generation wireless communication systems poses significant challenges, particularly in sustaining high-rate and long-range secure communications against eavesdropping attacks. In this work, we consider a UAV swarm-enabled secure surveillance network system, where a UAV swarm forms a virtual antenna array to transmit sensitive surveillance data to a remote base station (RBS) via collaborative beamforming (CB) so as to resist mobile eavesdroppers. Specifically, we formulate an aerial secure communication and energy efficiency multi-objective optimization problem (ASCEE-MOP) to maximize the secrecy rate of the system and to minimize the flight energy consumption of the UAV swarm. To address the non-convex, NP-hard and dynamic ASCEE-MOP, we propose a generative diffusion model-enabled twin delayed deep deterministic policy gradient (GDMTD3) method. Specifically, GDMTD3 leverages an innovative application of diffusion models to determine optimal excitation current weights and position decisions of UAVs. The diffusion models can better capture the complex dynamics and the trade-off of the ASCEE-MOP, thereby yielding promising solutions. Simulation results highlight the superior performance of the proposed approach compared with traditional deployment strategies and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various parameter settings of GDMTD3 and different numbers of UAVs verifies the robustness of the proposed approach., Comment: This paper has been submitted to IEEE Transactions on Mobile Computing

Microwave-to-optical transduction of single photons will play an essential role in interconnecting future superconducting quantum devices, with applications in distributed quantum computing and secure communications. Various transducers that couple microwave and optical modes via an optical drive have been developed, utilizing nonlinear phenomena such as the Pockels effect and a combination of electromechanical, piezoelectric, and optomechanical couplings. However, the limited strength of these nonlinearities, set by bulk material properties, requires the use of high quality factor resonators, often in conjunction with sophisticated nano-fabrication of suspended structures. Thus, an efficient and scalable transduction technology is still an outstanding goal. Rare-earth ion (REI) doped crystals provide high-quality atomic resonances that result in effective second-order nonlinearities stronger by many orders of magnitude compared to conventional materials. Here, we use ytterbium-171 ions doped in a YVO$_4$ crystal at 340 ppm with an effective resonant $\chi^{(2)}$ nonlinearity of ~ 10$^7$ pm/V to implement an on-chip microwave-to-optical transducer. Without an engineered optical cavity, we achieve percent-level efficiencies with an added noise as low as 1.24(9) photons. To showcase scalability, we demonstrate the interference of photons originating from two simultaneously operated transducers, enabled by the inherent absolute frequencies of the atomic transitions. These results establish REI-based transducers as a highly competitive transduction platform, provide existing REI-based quantum technologies a native link to various leading quantum microwave platforms, and pave the way toward remote transducer-assisted entanglement of superconducting quantum machines., Comment: TX and RF contributed equally to this work

The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application across a spectrum of downstream applications. Despite advancements, a lack of evaluation framework makes it difficult to ensure equitable assessment due to experimental settings, model intricacy, benchmark datasets, and reproducibility challenges. In the absence of standardization, comparative analyses risk becoming biased and unreliable. To surmount this impasse, we introduce GenBench, a comprehensive benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. Through systematic evaluations of datasets spanning diverse biological domains with a particular emphasis on both short-range and long-range genomic tasks, firstly including the three most important DNA tasks covering Coding Region, Non-Coding Region, Genome Structure, etc. Moreover, We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance. Our findings reveal an interesting observation: independent of the number of parameters, the discernible difference in preference between the attention-based and convolution-based models on short- and long-range tasks may provide insights into the future design of GFM.

Motion prediction is a challenging problem in autonomous driving as it demands the system to comprehend stochastic dynamics and the multi-modal nature of real-world agent interactions. Diffusion models have recently risen to prominence, and have proven particularly effective in pedestrian motion prediction tasks. However, the significant time consumption and sensitivity to noise have limited the real-time predictive capability of diffusion models. In response to these impediments, we propose a novel diffusion-based, acceleratable framework that adeptly predicts future trajectories of agents with enhanced resistance to noise. The core idea of our model is to learn a coarse-grained prior distribution of trajectory, which can skip a large number of denoise steps. This advancement not only boosts sampling efficiency but also maintains the fidelity of prediction accuracy. Our method meets the rigorous real-time operational standards essential for autonomous vehicles, enabling prompt trajectory generation that is vital for secure and efficient navigation. Through extensive experiments, our method speeds up the inference time to 136ms compared to standard diffusion model, and achieves significant improvement in multi-agent motion prediction on the Argoverse 1 motion forecasting dataset., Comment: 7 pages, 4 figures

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs. This problem is challenging because these two optimization objectives conflict with each other, and they are non-concave to the optimization variables. Moreover, the system is dynamic, and the cooperation among UAVs is complex, making traditional methods take much time to compute the optimization solution for a single task. In addition, as the task changes, the previously obtained solution will become obsolete and invalid. To handle these issues, we leverage the multi-agent deep reinforcement learning (MADRL) to address the UCBMOP. Specifically, we use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB, where three techniques are introduced to enhance the performance. Simulation results demonstrate that the proposed algorithm can learn a better strategy compared with other methods. Moreover, extensive experiments also demonstrate the effectiveness of the proposed techniques., Comment: This paper has been submitted to IEEE Transactions on Mobile Computing

In this paper, we propose a distributed collaborative beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the low Earth orbit (LEO) satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that variously balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we perform a multi-objective optimization analysis and formulate a long-term optimization problem. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal multi-objective Markov decision process. Then, we propose an evolutionary multi-objective deep reinforcement learning algorithm to obtain the desirable policies, in which the low-value actions are masked to speed up the training process. As such, the applicability of a one-time trained model can cover more changing terminal-satellite uplink scenarios. Simulation results show that the proposed algorithm outmatches various baselines, and draw some useful insights. Specifically, it is found that DCB enables terminals that cannot reach the uplink achievable threshold to achieve efficient direct uplink transmission, which thus reveals that DCB is an effective solution for enabling direct ground-space communications. Moreover, it reveals that the proposed algorithm achieves multiple policies favoring different objectives and achieving near-optimal uplink achievable rates with low switching frequency., Comment: This paper has been submitted to IEEE Journal on Selected Areas in Communications

Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we aim to introduce distributed collaborative beamforming (DCB) into UAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two UAV swarms and construct these swarms as two UAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and the maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of UAVs for constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives as a multi-objective optimization problem. Following this, we propose an enhanced multi-objective swarm intelligence algorithm via the characterized properties of the problem. Simulation results show that our proposed algorithm can obtain a set of informative solutions and outperform other state-of-the-art baseline algorithms. Experimental tests demonstrate that our method can be deployed in limited computing power platforms of UAVs and is beneficial for saving computational resources., Comment: This paper has been accepted by IEEE INFOCOM 2024

The mass production and the practical number of cryogenic quantum devices producible in a single chip are limited to the number of electrical contact pads and wiring of the cryostat or dilution refrigerator. It is, therefore, beneficial to contrast the measurements of hundreds of devices fabricated in a single chip in one cooldown process to promote the scalability, integrability, reliability, and reproducibility of quantum devices and to save evaluation time, cost and energy. Here, we use a cryogenic on-chip multiplexer architecture and investigate the statistics of the 0.7 anomaly observed on the first three plateaus of the quantized conductance of semiconductor quantum point contact (QPC) transistors. Our single chips contain 256 split gate field effect QPC transistors (QFET) each, with two 16-branch multiplexed source-drain and gate pads, allowing individual transistors to be selected, addressed and controlled through an electrostatic gate voltage process. A total of 1280 quantum transistors with nano-scale dimensions are patterned in 5 different chips of GaAs heterostructures. From the measurements of 571 functioning QPCs taken at temperatures T= 1.4 K and T= 40 mK, it is found that the spontaneous polarisation model and Kondo effect do not fit our results. Furthermore, some of the features in our data largely agreed with van Hove model with short-range interactions. Our approach provides further insight into the quantum mechanical properties and microscopic origin of the 0.7 anomaly in QPCs, paving the way for the development of semiconducting quantum circuits and integrated cryogenic electronics, for scalable quantum logic control, readout, synthesis, and processing applications.

Given flexible mobility, rapid deployment, and low cost, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) shows great potential to compensate for the lack of terrestrial edge computing coverage. However, limited battery capacity, computing and spectrum resources also pose serious challenges for UAV-enabled MEC, which shorten the service time of UAVs and degrade the quality of experience (QoE) of user devices (UDs) {\color{b} without effective control approach}. In this work, we consider a UAV-enabled MEC scenario where a UAV serves as an aerial edge server to provide computing services for multiple ground UDs. Then, a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) is formulated to maximize the QoE of UDs under the UAV energy consumption constraint. To solve the JTRTOP that is proved to be a future-dependent and NP-hard problem, an online joint optimization approach (OJOA) is proposed. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) by using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes.

We consider generating maximally entangled states (Bell states) between two qubits coupled to a common bosonic mode, based on f-STIRAP. Utilizing the systematic approach developed in New J. Phys. 19 093016 (2017), we quantify the effects of non-adiabatic leakage and system dissipation on the entanglement generation, and optimize the entanglement by balancing non-adiabatic leakage and system dissipation. We find the analytical expressions of the optimal coupling profile, the operation time, and the maximal entanglement. Our findings have broad applications in quantum state engineering, especially in solid-state devices where dissipative effects cannot be neglected.

In this research, a unique approach to provide communication service for terrestrial receivers via using unmanned aerial vehicle-enabled visible light communication is investigated. Specifically, we take into account a unmanned aerial vehicle-enabled visible light communication scenario with multiplex transmitters, multiplex receivers, and a single eavesdropper, each of which is equipped with a single photodetector. Then, a unmanned aerial vehicle deployment multi-objective optimization problem is formulated to simultaneously make the optical power received by receiving surface more uniform, minimize the amount of information collected by a eavesdropper, and minimize the energy consumption of unmanned aerial vehicles, while the locations and transmission power of unmanned aerial vehicles are simultaneously optimized under certain constraints. Since the formulated unmanned aerial vehicle deployment multi-objective optimization problem is complex and nonlinear, it is challenging to be tackled by using conventional methods. For the purpose of solving the problem, a multi-objective evolutionary algorithm based on decomposition with chaos initiation and crossover mutation is proposed. Simulation outcomes show that the proposed approach is superior to other approaches, and is efficient at improving the security and energy efficiency of visible light communication system., Comment: 18 pages, 9 tables, 3 tables

The ground fixed base stations (BSs) are often deployed inflexibly, and have high overheads, as well as are susceptible to the damage from natural disasters, making it impractical for them to continuously collect data from sensor devices. To improve the network coverage and performance of wireless communication, unmanned aerial vehicles (UAVs) have been introduced in diverse wireless networks, therefore in this work we consider employing a UAV as an aerial BS to acquire data of agricultural Internet of Things (IoT) devices. To this end, we first formulate a UAV-assisted data collection multi-objective optimization problem (UDCMOP) to efficiently collect the data from agricultural sensing devices. Specifically, we aim to collaboratively optimize the hovering positions of UAV, visit sequence of UAV, speed of UAV, in addition to the transmit power of devices, to simultaneously achieve the maximization of minimum transmit rate of devices, the minimization of total energy consumption of devices, and the minimization of total energy consumption of UAV. Second, the proposed UDCMOP is a non-convex mixed integer nonlinear optimization problem, which indicates that it includes continuous and discrete solutions, making it intractable to be solved. Therefore, we solve it by proposing an improved multi-objective artificial hummingbird algorithm (IMOAHA) with several specific improvement factors, that are the hybrid initialization operator, Cauchy mutation foraging operator, in addition to the discrete mutation operator. Finally, simulations are carried out to testify that the proposed IMOAHA can effectively improve the system performance comparing to other benchmarks., Comment: 13 pages, 7 figures, 4 tables

Quantum point contact or QPC -- a constriction in a semiconducting two-dimensional (2D) electron system with a quantized conductance -- has been found as the building block of novel spintronic, and topological electronic circuits. They can also be used as readout electronic, charge sensor or switch in quantum nanocircuits. A short and impurity-free constriction with superconducting contacts is a Cooper pairs QPC analogue known as superconducting quantum point contact (SQPC). The technological development of such quantum devices has been prolonged due to the challenges of maintaining their geometrical requirement and near-unity superconductor-semiconductor interface transparency. Here, we develop advanced nanofabrication, material and device engineering techniques and report on an innovative realisation of nanoscale SQPC arrays with split gate technology in semiconducting 2D electron systems, exploiting the special gate tunability of the quantum wells, and report the first experimental observation of conductance quantization in hybrid InGaAs-Nb SQPCs. We observe reproducible quantized conductance at zero magnetic fields in multiple quantum nanodevices fabricated in a single chip and systematically investigate the quantum transport of SQPCs at low and high magnetic fields for their potential applications in quantum metrology, for extremely accurate voltage standards, and fault-tolerant quantum technologies.

We investigate the dissipative phase transitions of the anisotropic quantum Rabi model with cavity decay and demonstrate that large spin fluctuations persist in the stationary state, having important consequences on the phase diagram and the critical properties. In the second-order phase transition to the superradiant phase, there is a significant suppression of the order parameter and the appearance of non-universal factors, which directly reflect the spin populations. Furthermore, upon entering a parameter regime where mean-field theory predicts a tricritical phase, we find a first-order phase transition due to the unexpected collapse of superradiance. An accurate and physically transparent description going beyond mean-field theory is established by combining exact numerical simulations, the cumulant expansion, and analytical approximations based on reduced master equations and an effective equilibrium theory. Our findings, compared to the conventional thermodynamic limit of the Dicke model, indicate a general tendency of forming extreme non-equilibrium states in the single-spin system, thus have broad implications for dissipative phase transitions of few-body systems., Comment: 17 pages, 11 figures

We study the complex nonlinear dynamics of the two-photon Dicke model in the semiclassical limit by considering cavity and qubit dissipation. In addition to the normal and super-radiant phases, another phase that contains abundant chaos-related phenomena is found under balanced rotating and counter-rotating couplings. In particular, chaos may manifest itself through period-doubling bifurcation, intermittent chaos, or quasi-periodic oscillation, depending on the value of qubit frequency. Transition mechanisms that exist in these three distinct routes are investigated through the system's long-time evolution and bifurcation diagram. Additionally, we provide a comprehensive phase diagram detailing both the existence of stable fixed points and the aforementioned chaos-related dynamics., Comment: 9 pages, 6 figures

In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is divided between the edge and the cloud, with intermediate features being sent from the edge to the cloud for inference. Several deep learning-based Semantic Communication (SC) models have been proposed to reduce feature transmission overhead and mitigate channel noise interference. Previous research has demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater robustness on digital channels compared to Deep Neural Network (DNN)-based SC models. However, the existing SNN-based SC models require fixed time steps, resulting in fixed transmission bandwidths that cannot be adaptively adjusted based on channel conditions. To address this issue, this paper introduces a novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with the Hybrid Automatic Repeat Request (HARQ) mechanism. SNN-SC-HARQ comprises an SNN-based SC model that supports the transmission of features at varying bandwidths, along with a policy model that determines the appropriate bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust the bandwidth according to the channel conditions without performance loss.

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network. In this work, we aim to utilize the UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar array antenna (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of collusive eavesdropping attacks in time-domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US2RMOP) for simultaneously maximizing the achievable sum rate of associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US2RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA., Comment: Submitted to IEEE Transactions on Mobile Computing

Wireless rechargeable sensor networks with a charging unmanned aerial vehicle (CUAV) have the broad application prospects in the power supply of the rechargeable sensor nodes (SNs). However, how to schedule a CUAV and design the trajectory to improve the charging efficiency of the entire system is still a vital problem. In this paper, we formulate a joint-CUAV scheduling and trajectory optimization problem (JSTOP) to simultaneously minimize the hovering points of CUAV, the number of the repeatedly covered SNs and the flying distance of CUAV for charging all SNs. Due to the complexity of JSTOP, it is decomposed into two optimization subproblems that are CUAV scheduling optimization problem (CSOP) and CUAV trajectory optimization problem (CTOP). CSOP is a hybrid optimization problem that consists of the continuous and discrete solution space, and the solution dimension in CSOP is not fixed since it should be changed with the number of hovering points of CUAV. Moreover, CTOP is a completely discrete optimization problem. Thus, we propose a particle swarm optimization (PSO) with a flexible dimension mechanism, a K-means operator and a punishment-compensation mechanism (PSOFKP) and a PSO with a discretization factor, a 2-opt operator and a path crossover reduction mechanism (PSOD2P) to solve the converted CSOP and CTOP, respectively. Simulation results evaluate the benefits of PSOFKP and PSOD2P under different scales and settings of the network, and the stability of the proposed algorithms is verified.

Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads., Comment: 18 pages, 6 figures

The spontaneous breaking of time translation symmetry in periodically driven Floquet systems can lead to a discrete time crystal. Here we study the occurrence of such dynamical phase in a driven-dissipative optomechanical system with two membranes in the middle. We find that, under certian conditions, the system can be mapped to an open Dicke model and realizes a superradianttype phase transition. Furthermore, applying a suitable periodically modulated drive, the system dynamics exhibits a robust subharmonic oscillation persistent in the thermodynamic limit.

Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers often increases the energy and time costs, resulting in a conflict with the minimization of the costs. Moreover, the importance of these goals may vary relatively in different applications. Thus, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs. We prove that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem. Thus, we propose a swarm intelligence-based algorithm to find a set of candidate solutions with different trade-offs which can meet various requirements in a low computational complexity. We also show that swarm intelligence methods need to enhance solution initialization, solution update, and algorithm parameter update phases when dealing with mixed-variable optimization and large-scale problems. Simulation results demonstrate the proposed algorithm outperforms state-of-the-art swarm intelligence algorithms., Comment: This paper has been accepted by IEEE Transactions on Mobile Computing

Stable, reproducible, scalable, addressable, and controllable hybrid superconductor-semiconductor (S-Sm) junctions and switches are key circuit elements and building blocks of gate-based quantum processors. The electrostatic field effect produced by the split gate voltages facilitates the realisation of nano-switches that can control the conductance or current in the hybrid S-Sm circuits based on 2D semiconducting electron systems. Here, we experimentally demonstrate a novel realisation of large-scale scalable, and gate voltage controllable hybrid field effect quantum chips. Each chip contains arrays of split gate field effect hybrid junctions, that work as conductance switches, and are made from In0.75Ga0.25As quantum wells integrated with Nb superconducting electronic circuits. Each hybrid junction in the chip can be controlled and addressed through its corresponding source-drain and two global split gate contact pads that allow switching between their (super)conducting and insulating states. We fabricate a total of 18 quantum chips with 144 field effect hybrid Nb- In0.75Ga0.25As 2DEG-Nb quantum wires and investigate the electrical response, switching voltage (on/off) statistics, quantum yield, and reproducibility of several devices at cryogenic temperatures. The proposed integrated quantum device architecture allows control of individual junctions in a large array on a chip useful for the development of emerging cryogenic nanoelectronics circuits and systems for their potential applications in fault-tolerant quantum technologies.

Distributed deep learning (DDL) is a promising research area, which aims to increase the efficiency of training deep learning tasks with large size of datasets and models. As the computation capability of DDL nodes continues to increase, the network connection between nodes is becoming a major bottleneck. Various methods of gradient compression and improved model synchronization have been proposed to address this bottleneck in Parameter-Server-based DDL. However, these two types of methods can result in accuracy loss due to discarded gradients and have limited enhancement on the throughput of model synchronization, respectively. To address these challenges, we propose a new model synchronization method named Overlapped Synchronization Parallel (OSP), which achieves efficient communication with a 2-stage synchronization approach and uses Local-Gradient-based Parameter correction (LGP) to avoid accuracy loss caused by stale parameters. The prototype of OSP has been implemented using PyTorch and evaluated on commonly used deep learning models and datasets with a 9-node testbed. Evaluation results show that OSP can achieve up to 50\% improvement in throughput without accuracy loss compared to popular synchronization models., Comment: Copyright Owner/Author | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record will be published in ICPP 2023