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By using an parametric value function to replace the Monte-Carlo rollouts for value estimation, the actor-critic (AC) algorithms can reduce the variance of stochastic policy gradient so that to improve the convergence rate. While existing works mainly focus on analyzing convergence rate of AC algorithms under Markovian noise, the impacts of momentum on AC algorithms remain largely unexplored. In this work, we first propose a heavy-ball momentum based advantage actor-critic (\mbox{HB-A2C}) algorithm by integrating the heavy-ball momentum into the critic recursion that is parameterized by a linear function. When the sample trajectory follows a Markov decision process, we quantitatively certify the acceleration capability of the proposed HB-A2C algorithm. Our theoretical results demonstrate that the proposed HB-A2C finds an $\epsilon$-approximate stationary point with $\oo{\epsilon^{-2}}$ iterations for reinforcement learning tasks with Markovian noise. Moreover, we also reveal the dependence of learning rates on the length of the sample trajectory. By carefully selecting the momentum factor of the critic recursion, the proposed HB-A2C can balance the errors introduced by the initialization and the stoschastic approximation.

This paper is concerned with unmanned aerial vehicle (UAV) video coding and transmission in scenarios such as emergency rescue and environmental monitoring. Unlike existing methods of modeling UAV video source coding and channel transmission separately, we investigate the joint source-channel optimization issue for video coding and transmission. Particularly, we design eight-dimensional delay-power-rate-distortion models in terms of source coding and channel transmission and characterize the correlation between video coding and transmission, with which a joint source-channel optimization problem is formulated. Its objective is to minimize end-to-end distortion and UAV power consumption by optimizing fine-grained parameters related to UAV video coding and transmission. This problem is confirmed to be a challenging sequential-decision and non-convex optimization problem. We therefore decompose it into a family of repeated optimization problems by Lyapunov optimization and design an approximate convex optimization scheme with provable performance guarantees to tackle these problems. Based on the theoretical transformation, we propose a Lyapunov repeated iteration (LyaRI) algorithm. Both objective and subjective experiments are conducted to comprehensively evaluate the performance of LyaRI. The results indicate that, compared to its counterparts, LyaRI achieves better video quality and stability performance, with a 47.74% reduction in the variance of the obtained encoding bitrate.

This letter proposes a pilot-aided joint time synchronization and channel estimation (JTSCE) algorithm for orthogonal time frequency space (OTFS) systems. Unlike existing algorithms, JTSCE employs a maximum length sequence (MLS) rather than an isolated signal as the pilot. Distinctively, JTSCE explores MLS's autocorrelation properties to estimate timing offset and channel delay taps. After obtaining delay taps, closed-form expressions of Doppler and channel gain for each propagation path are derived. Simulation results indicate that, compared to its counterpart, JTSCE achieves better bit error rate performance, close to that with perfect time synchronization and channel state information.

The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of user equipment (UE) can result in inaccurate location estimation, which can severely degrade system performance. Therefore, the precise UE location sensing and resource allocation issues are investigated in a spectrum sharing sixth generation network. An approach is proposed for joint subcarrier and power optimization based on UE location sensing, aiming to minimize system energy consumption. The joint allocation process is separated into two key phases of operation. In the radar location sensing phase, the multipath interference and Doppler effects are considered simultaneously, and the issues of UE's location and channel state estimation are transformed into a convex optimization problem, which is then solved through gradient descent. In the communication phase, a subcarrier allocation method based on subcarrier weights is proposed. To further minimize system energy consumption, a joint subcarrier and power allocation method is introduced, resolved via the Lagrange multiplier method for the non-convex resource allocation problem. Simulation analysis results indicate that the location sensing algorithm exhibits a prominent improvement in accuracy compared to benchmark algorithms. Simultaneously, the proposed resource allocation scheme also demonstrates a substantial enhancement in performance relative to baseline schemes., Comment: 12 pages,9 figures and 4 charts. This paper has been accepted for publication in the IEEE Journal on Selected Areas in Communications

Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as low latency, high data rate, reliability, efficiency, and security, all of which demand resolution. The incorporation of reinforcement learning (RL) methodologies within MEC networks promotes a deeper understanding of mobile user behaviors and network dynamics, thereby optimizing resource use in computing and communication processes. This paper offers an exhaustive survey of RL applications in MEC networks, initially presenting an overview of RL from its fundamental principles to the latest advanced frameworks. Furthermore, it outlines various RL strategies employed in offloading, caching, and communication within MEC networks. Finally, it explores open issues linked with software and hardware platforms, representation, RL robustness, safe RL, large-scale scheduling, generalization, security, and privacy. The paper proposes specific RL techniques to mitigate these issues and provides insights into their practical applications., Comment: The paper is accepted by IEEE Communications Surveys and Tutorials (COMST)

Isolated flat bands with significantly quenched kinetic energy of electrons could give rise to exotic strongly correlated states from electron-electron interactions. More intriguingly, the interplay between topology and flat bands can further lead to richer physical phenomena, which have attracted much interest. Here, taking advantage of the recently proposed intertwined Dirac states induced from the anisotropic coupling between the top and bottom surface states of an antiferromagnetic topological insulator thin film, we show the emergence of a high-Chern-number (quasi)flat-band state through moir\'e engineering of the surface states. Remarkably, the flat bands are isolated from other bands and located near the Fermi level. Furthermore, topological phase transitions between trivial and nontrivial flat-band states can be driven by tuning the out-of-plane electric field. Our work not only proposes a new scheme to realize high-Chern-number flat-band states, but also highlights the versatility of the intertwined Dirac-cone states., Comment: 7 pages, 4 figures

Nonlinear transport is a unique functionality of noncentrosymmetric systems, which reflects profound physics, such as spin-orbit interaction, superconductivity and band geometry. However, it remains highly challenging to enhance the nonreciprocal transport for promising rectification devices. Here, we observe a light-induced giant enhancement of nonreciprocal transport at the superconducting and epitaxial CaZrO3/KTaO3 (111) interfaces. The nonreciprocal transport coefficient undergoes a giant increase with three orders of magnitude up to 105 A-1T-1. Furthermore, a strong Rashba spin-orbit coupling effective field of 14.7 T is achieved with abundant high-mobility photocarriers under ultraviolet illumination, which accounts for the giant enhancement of nonreciprocal transport coefficient. Our first-principles calculations further disclose the stronger Rashba spin-orbit coupling strength and the longer relaxation time in the photocarrier excitation process, bridging the light-property quantitative relationship. Our work provides an alternative pathway to boost nonreciprocal transport in noncentrosymmetric systems and facilitates the promising applications in opto-rectification devices and spin-orbitronic devices., Comment: 38 pages, 17 figures

Accuracy and efficiency remain challenges for multi-party computation (MPC) frameworks. Spin is a GPU-accelerated MPC framework that supports multiple computation parties and a dishonest majority adversarial setup. We propose optimized protocols for non-linear functions that are critical for machine learning, as well as several novel optimizations specific to attention that is the fundamental unit of Transformer models, allowing Spin to perform non-trivial CNNs training and Transformer inference without sacrificing security. At the backend level, Spin leverages GPU, CPU, and RDMA-enabled smart network cards for acceleration. Comprehensive evaluations demonstrate that Spin can be up to $2\times$ faster than the state-of-the-art for deep neural network training. For inference on a Transformer model with 18.9 million parameters, our attention-specific optimizations enable Spin to achieve better efficiency, less communication, and better accuracy.

This paper presents a novel framework termed Cut-and-Paste for real-word semantic video editing under the guidance of text prompt and additional reference image. While the text-driven video editing has demonstrated remarkable ability to generate highly diverse videos following given text prompts, the fine-grained semantic edits are hard to control by plain textual prompt only in terms of object details and edited region, and cumbersome long text descriptions are usually needed for the task. We therefore investigate subject-driven video editing for more precise control of both edited regions and background preservation, and fine-grained semantic generation. We achieve this goal by introducing an reference image as supplementary input to the text-driven video editing, which avoids racking your brain to come up with a cumbersome text prompt describing the detailed appearance of the object. To limit the editing area, we refer to a method of cross attention control in image editing and successfully extend it to video editing by fusing the attention map of adjacent frames, which strikes a balance between maintaining video background and spatio-temporal consistency. Compared with current methods, the whole process of our method is like ``cut" the source object to be edited and then ``paste" the target object provided by reference image. We demonstrate that our method performs favorably over prior arts for video editing under the guidance of text prompt and extra reference image, as measured by both quantitative and subjective evaluations.

Antiferromagnetic (AFM) topological insulators (TIs), which host magnetically gapped Dirac-cone surface states and exhibit many exotic physical phenomena, have attracted great attention. Here, we find that the coupled surface states can be intertwined to give birth to a set of $2n$ unique new Dirac cones, dubbed intertwined Dirac cones, through the anisotropic coupling enforced by crystalline $n$-fold ($n=2, 3, 4, 6$) rotation symmetry $C_{nz}$ in the presence of a $PT$-symmetry breaking potential, for example, an electric field. Interestingly, we also find that the warping effect further drives the intertwined Dirac-cone state into a quantum anomalous Hall phase with a high Chern number ($C=n$). Then, based on first-principles calculations, we have explicitly demonstrated six intertwined Dirac cones and a Chern insulating phase with a high Chern number ($C=3$) in MnBi$_2$Te$_4$$/$(Bi$_2$Te$_3$)$_{\mathrm{m}}/$MnBi$_2$Te$_4$ heterostructures, as well as the $C=2$ and $C=4$ phases in HgS and $\alpha$-Ag$_2$Te films, respectively. This work discovers the intertwined Dirac-cone state in AFM TI thin films, which reveals a mechanism for designing the quantum anomalous Hall state with a high Chern number and also paves a way for studying highly tunable high-Chen-number flat bands of twistronics., Comment: 9 pages, 4 figures+supplemental materials

The kagome lattice is very attractive as it can host many novel quantum states, such as the charge density wave, superconductivity, quantum spin liquid, etc. Meanwhile, iridates often exhibit a strong spin-orbit coupling (SOC) effect due to the large atomic mass of 5$d$ elements, which has important implications for both the energy bands and the pairing symmetry of superconductors. For the Laves phase superconductor Sc$_2$Ir$_4$ with a kagome lattice, by doping Si to the Ir sites, we observed a nonmonotonic and two-dome like doping dependence of the superconducting transition temperature $T_{\rm c}$, which is typically found in many unconventional superconducting systems. Interestingly, for some samples, especially Sc$_2$Ir$_{3.5}$Si$_{0.5}$ with the optimal $T_{\rm c}$, after the suppression of superconductivity, the normal-state resistivity exhibits a semiconducting behavior; meanwhile, the specific heat coefficient shows an upturn which follows the relation $C/T\propto{\rm ln}(T_0/T)$ at low temperatures. Around the optimal doping, the resistance measurements exhibit strong superconducting fluctuations. And the superconductivity related specific heat can be fitted by the model of a $d$-wave gap after subtracting the normal-state background. These strongly suggest unconventional superconductivity and correlation effect in the samples, which is mainly induced by a flat band near the Fermi level when considering the SOC, as supported by the first-principles calculations. Our results reveal a new unconventional superconducting system Sc$_2$Ir$_{4-x}$Si$_x$ with strong correlation effects induced by the flat band in the kagome system with strong SOC.

In the information age, recommendation systems are vital for efficiently filtering information and identifying user preferences. Online social platforms have enriched these systems by providing valuable auxiliary information. Socially connected users are assumed to share similar preferences, enhancing recommendation accuracy and addressing cold start issues. However, empirical findings challenge the assumption, revealing that certain social connections can actually harm system performance. Our statistical analysis indicates a significant amount of noise in the social network, where many socially connected users do not share common interests. To address this issue, we propose an innovative \underline{I}nterest-aware \underline{D}enoising and \underline{V}iew-guided \underline{T}uning (IDVT) method for the social recommendation. The first ID part effectively denoises social connections. Specifically, the denoising process considers both social network structure and user interaction interests in a global view. Moreover, in this global view, we also integrate denoised social information (social domain) into the propagation of the user-item interactions (collaborative domain) and aggregate user representations from two domains using a gating mechanism. To tackle potential user interest loss and enhance model robustness within the global view, our second VT part introduces two additional views (local view and dropout-enhanced view) for fine-tuning user representations in the global view through contrastive learning. Extensive evaluations on real-world datasets with varying noise ratios demonstrate the superiority of IDVT over state-of-the-art social recommendation methods.

The growing popularity of subscription services in video game consumption has emphasized the importance of offering diversified recommendations. Providing users with a diverse range of games is essential for ensuring continued engagement and fostering long-term subscriptions. However, existing recommendation models face challenges in effectively handling highly imbalanced implicit feedback in gaming interactions. Additionally, they struggle to take into account the distinctive characteristics of multiple categories and the latent user interests associated with these categories. In response to these challenges, we propose a novel framework, named DRGame, to obtain diversified recommendation. It is centered on multi-category video games, consisting of two {components}: Balance-driven Implicit Preferences Learning for data pre-processing and Clustering-based Diversified Recommendation {Module} for final prediction. The first module aims to achieve a balanced representation of implicit feedback in game time, thereby discovering a comprehensive view of player interests across different categories. The second module adopts category-aware representation learning to cluster and select players and games based on balanced implicit preferences, and then employs asymmetric neighbor aggregation to achieve diversified recommendations. Experimental results on a real-world dataset demonstrate the superiority of our proposed method over existing approaches in terms of game diversity recommendations.

$\mathrm{Fe_{n=4,5}GeTe_2}$ exhibits quasi-two-dimensional properties as a promising candidate for a near-room-temperature ferromagnet, which has attracted great interest. In this work, we notice that the crystal lattice of $\mathrm{Fe_{n=4,5}GeTe_2}$ can be approximately regarded as being stacked by three bipartite crystal lattices. By combining the model Hamiltonians of bipartite crystal lattices and first-principles calculations, we investigate the electronic structure and the magnetism of $\mathrm{Fe_{n=4,5}GeTe_2}$. We conclude that flat bands near the Fermi level originate from the bipartite crystal lattices and that these flat bands are expected to lead to the itinerant ferromagnetism in $\mathrm{Fe_{n=4,5}GeTe_2}$. Interestingly, we also find that the magnetic moment of the Fe5 atom in $\mathrm{Fe_5 Ge Te_2}$ is distinct from the other Fe atoms and is sensitive to the Coulomb interaction $U$ and external pressure. These findings may be helpful to understand the exotic magnetic behavior of $\mathrm{Fe_{n=4,5} Ge Te_2}$., Comment: 10 pages, 8 figures

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks.

Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime. To achieve multiple access in semantic communication, we propose a deep learning-based multiple access (DeepMA) method by training semantic communication models with the abilities of joint source-channel coding (JSCC) and orthogonal signal modulation. DeepMA is achieved by a DeepMA network (DMANet), which is comprised of several independent encoder-decoder pairs (EDPs), and the DeepMA encoders can encode the input data as mutually orthogonal semantic symbol vectors (SSVs) such that the DeepMA decoders can detect and recover their own target data from a received mixed SSV (MSSV) superposed by multiple SSV components transmitted from different encoders. We describe frameworks of DeepMA in wireless device-to-device (D2D), downlink, and uplink channel multiplexing scenarios, along with the training algorithm. We evaluate the performance of the proposed DeepMA in wireless image transmission tasks and compare its performance with the attention module-based deep JSCC (ADJSCC) method and conventional communication schemes using better portable graphics (BPG) and Low-density parity-check code (LDPC). The results obtained show that the proposed DeepMA can achieve effective, flexible, and privacy-preserving channel multiplexing process, and demonstrate that our proposed DeepMA approach can yield comparable bandwidth efficiency compared with conventional multiple access schemes.

The superconductor SnTaS$_{2}$ is theoretically predicted to be an intriguing topological nodal line semimetal without consideration of spin-orbit coupling. By carrying out angle-resolved photoemission (ARPES) and spin-resolved ARPES measurements combined with band structure calculations, we have provided a complete picture of the electronic structure and spin polarization property for the prominent surface states of SnTaS$_{2}$. The low-energy electronic states are dominated by surface states; two of them are from the S-terminated surface, while four of them are from the Sn-terminated surface. These give rise to interesting Fermi surface topology of SnTaS$_{2}$: three pockets located at $\bar\Gamma$, $\bar{M}$ and $\bar{K}$ for the S-terminated surface and two pockets surrounding $\bar\Gamma$ and $\bar{K}$ for the Sn-terminated surface. We further reveal that two surface states that cross the Fermi level are spin-polarized. Since SnTaS$_{2}$ is also a superconductor, our observations indicate that it may provide a new platform to explore topological superconductivity and other exotic properties.

The evolution of the physical properties of two-dimensional material from monolayer limit to the bulk reveals unique consequences from dimension confinement and provides a distinct tuning knob for applications. Monolayer 1T'-phase transition metal dichalcogenides (1T'-TMDs) with ubiquitous quantum spin Hall (QSH) states are ideal two-dimensional building blocks of various three-dimensional topological phases. However, the stacking geometry was previously limited to the bulk 1T'-WTe2 type. Here, we introduce the novel 2M-TMDs consisting of translationally stacked 1T'-monolayers as promising material platforms with tunable inverted bandgaps and interlayer coupling. By performing advanced polarization-dependent angle-resolved photoemission spectroscopy as well as first-principles calculations on the electronic structure of 2M-TMDs, we revealed a topology hierarchy: 2M-WSe2, MoS2, and MoSe2 are weak topological insulators (WTIs), whereas 2M-WS2 is a strong topological insulator (STI). Further demonstration of topological phase transitions by tunning interlayer distance indicates that band inversion amplitude and interlayer coupling jointly determine different topological states in 2M-TMDs. We propose that 2M-TMDs are parent compounds of various exotic phases including topological superconductors and promise great application potentials in quantum electronics due to their flexibility in patterning with two-dimensional materials.

Quantum anomalous Hall (QAH) insulators host topologically protected dissipationless chiral edge states, the number of which is determined by its Chern number. Up to now, the QAH state has been realized in a few magnetic topological insulators, but usually with a low Chern number. Here, we develop a Dirac-fermion approach which is valuable to understand and design high Chern numbers in various multilayers of layered magnetic topological insulators. Based on the Dirac-fermion approach, we demonstrate how to understand and tune high Chern numbers in ferromagentic MnBi$_{2}$Te$_{4}$ films through the van der Waals (vdW) gap modulation. Further, we also employ the Dirac-fermion approach to understand the experimentally observed high Chern numbers and topological phase transition from the Chern number $C=2$ to $C=1$ in the [3QL-(Bi,Sb)$_{1.76}$Cr$_{0.24}$Te$_{3}$]/[4QL-(Bi,Sb)$_{2}$Te$_{3}$] multilayers. Our work provides a powerful tool to design the QAH states with a high Chern number in layered magnetic topological insulator multilayers., Comment: 11 pages, 4 figures

In the 6G era, space-air-Ground integrated networks (SAGIN) are anticipated to deliver global coverage, necessitating support for a diverse array of emerging applications in high-mobility, hostile environments. Under such conditions, conventional orthogonal frequency division multiplexing (OFDM) modulation, widely employed in cellular and Wi-Fi communication systems, experiences performance degradation due to significant Doppler shifts. To overcome this obstacle, a novel two-dimensional (2D) modulation approach, namely orthogonal time frequency space (OTFS), has emerged as a key enabler for future high-mobility use cases. Distinctively, OTFS modulates information within the delay-Doppler (DD) domain, as opposed to the time-frequency (TF) domain utilized by OFDM. This offers advantages such as Doppler and delay resilience, reduced signaling latency, a lower peak-to-average ratio (PAPR), and a reduced-complexity implementation. Recent studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications (ISAC). In this article, we present an in-depth review of OTFS technology in the context of the 6G era, encompassing fundamentals, recent advancements, and future directions. Our objective is to provide a valuable resource for researchers engaged in the field of OTFS., Comment: Survey paper on OTFS, accepted by China Communications; Cover paper of the 6th issue

Controlling the magnetic anisotropy of ferromagnetic materials plays a key role in magnetic switching devices and spintronic applications. Examples of spin-orbit torque devices with different magnetic anisotropy geometries (in-plane or out-of-plane directions) have been demonstrated with novel magnetization switching mechanisms for extended device functionalities. Normally, the intrinsic magnetic anisotropy in ferromagnetic materials is unchanged within a fixed direction, and thus, it is difficult to realize multifunctionality devices. Therefore, continuous modulation of magnetic anisotropy in ferromagnetic materials is highly desired but remains challenging. Here, we demonstrate a gate-tunable magnetic anisotropy transition from out-of-plane to canted and finally to in-plane in layered Fe$_5$GeTe$_2$ by combining the measurements of the angle-dependent anomalous Hall effect and magneto-optical Kerr effect with quantitative Stoner-Wohlfarth analysis. The magnetic easy axis continuously rotates in a spin-flop pathway by gating or temperature modulation. Such observations offer a new avenue for exploring magnetization switching mechanisms and realizing new spintronic functionalities., Comment: 4 figures

Quantization approximates a deep network model with floating-point numbers by the one with low bit width numbers, in order to accelerate inference and reduce computation. Quantizing a model without access to the original data, zero-shot quantization can be accomplished by fitting the real data distribution by data synthesis. However, zero-shot quantization achieves inferior performance compared to the post-training quantization with real data. We find it is because: 1) a normal generator is hard to obtain high diversity of synthetic data, since it lacks long-range information to allocate attention to global features; 2) the synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness. To overcome these problems, we propose a novel deep network quantizer, dubbed Long-Range Zero-Shot Generative Deep Network Quantization (LRQ). Technically, we propose a long-range generator to learn long-range information instead of simple local features. In order for the synthetic data to contain more global features, long-range attention using large kernel convolution is incorporated into the generator. In addition, we also present an Adversarial Margin Add (AMA) module to force intra-class angular enlargement between feature vector and class center. As AMA increases the convergence difficulty of the loss function, which is opposite to the training objective of the original loss function, it forms an adversarial process. Furthermore, in order to transfer knowledge from the full-precision network, we also utilize a decoupled knowledge distillation. Extensive experiments demonstrate that LRQ obtains better performance than other competitors.

The building block of in-memory computing with spintronic devices is mainly based on the magnetic tunnel junction with perpendicular interfacial anisotropy (p-MTJ). The resulting asymmetric write and read-out operations impose challenges in downscaling and direct cascadability of p-MTJ devices. Here, we propose that a new symmetric write and read-out mechanism can be realized in perpendicular-anisotropy spin-orbit (PASO) quantum materials based on Fe3GeTe2 and WTe2. We demonstrate that field-free and deterministic reversal of the perpendicular magnetization can be achieved by employing unconventional charge to z-spin conversion. The resulting magnetic state can be readily probed with its intrinsic inverse process, i.e., z-spin to charge conversion. Using the PASO quantum material as a fundamental building block, we implement the functionally complete set of logic-in-memory operations and a more complex nonvolatile half-adder logic function. Our work highlights the potential of PASO quantum materials for the development of scalable energy-efficient and ultrafast spintronic computing., Comment: Accepted by Science Advances

Floquet engineering is an important way to manipulate the electronic states of condensed matter physics. Recently, the discovery of the magnetic topological insulator MnBi$_2$Te$_4$ and its family provided a valuable platform to study magnetic topological phenomena, such as, the quantum anomalous Hall effect, the axion insulator state and the topological magnetoelectric effect. In this work, based on the effective model and first-principles calculations in combination with the Floquet theory, we reveal that the circularly polarized light (CPL) induces the sign reversal of the Chern number of odd-septuple-layer (SL) MnBi$_2$Te$_4$ thin films. In contrast, the CPL drives the axion insulator state into the quantum anomalous Hall state in even-SL MnBi$_2$Te$_4$ thin films. More interestingly, if the topmost van der Waals gap between the surface layer and the below bulk in MnBi$_2$Te$_4$ films is slightly expanded, a high Chern number $|C|=2$ can be realized under the CPL. Our work demonstrates that the light can induce rich magnetic topological phases in MnBi$_2$Te$_4$ films, which might have potential applications in optoelectronic devices., Comment: 7 pages, 4 figures

Consider a processor having access only to meta-data consisting of the timings of data packets and acknowledgment (ACK) packets from all nodes in a network. The meta-data report the source node of each packet, but not the destination nodes or the contents of the packets. The goal of the processor is to infer the network topology based solely on such information. Prior work leveraged causality metrics to identify which links are active. If the data timings and ACK timings of two nodes -- say node 1 and node 2, respectively -- are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1). This paper starts with the observation that packet losses can weaken the causality relationship between data and ACK timing streams. To obviate this problem, a new Expectation Maximization (EM)-based algorithm is introduced -- EM-causality discovery algorithm (EM-CDA) -- which treats packet losses as latent variables. EM-CDA iterates between the estimation of packet losses and the evaluation of causality metrics. The method is validated through extensive experiments in wireless sensor networks on the NS-3 simulation platform., Comment: submitted

Joint caching and transmission optimization problem is challenging due to the deep coupling between decisions. This paper proposes an iterative distributed multi-agent learning approach to jointly optimize caching and transmission. The goal of this approach is to minimize the total transmission delay of all users. In this iterative approach, each iteration includes caching optimization and transmission optimization. A multi-agent reinforcement learning (MARL)-based caching network is developed to cache popular tasks, such as answering which files to evict from the cache and which files to storage. Based on the cached files of the caching network, the transmission network transmits cached files for users by single transmission (ST) or joint transmission (JT) with multi-agent Bayesian learning automaton (MABLA) method. And then users access the edge servers with the minimum transmission delay. The experimental results demonstrate the performance of the proposed multi-agent learning approach.

Unconventional fermions in the immensely studied topological semimetals are the source for rich exotic topological properties. Here, using symmetry analysis and first-principles calculations, we propose the coexistence of multiple topological nodal structure in LaSb2, including topological nodal surfaces, nodal lines and in particular eightfold degenerate nodal points, which have been scarcely observed in a single material. Further, utilizing high resolution angle-resolved photoemission spectroscopy in combination with Shubnikov-de Haas quantum oscillations measurements, we confirm the existence of nodal surfaces and eightfold degenerate nodal points in LaSb2, and extract the {\pi} Berry phase proving the non-trivial electronic band structure topology therein. The intriguing multiple topological nodal structure might play a crucial role in giving rise to the large linear magnetoresistance. Our work renews the insights into the exotic topological phenomena in LaSb2 and its analogous.

Igniting interface magnetic ordering of magnetic topological insulators by building a van der Waals heterostructure can help to reveal novel quantum states and design functional devices. Here, we observe an interesting exchange bias effect, indicating successful interfacial magnetic coupling, in CrI3/MnBi2Te4 ferromagnetic insulator/antiferromagnetic topological insulator (FMI/AFM-TI) heterostructure devices. The devices originally exhibit a negative exchange bias field, which decays with increasing temperature and is unaffected by the back-gate voltage. When we change the device configuration to be half-covered by CrI3, the exchange bias becomes positive with a very large exchange bias field exceeding 300 mT. Such sensitive manipulation is explained by the competition between the FM and AFM coupling at the interface of CrI3 and MnBi2Te4, pointing to coverage-dependent interfacial magnetic interactions. Our work will facilitate the development of topological and antiferromagnetic devices.

Layered van der Waals (vdW) topological materials, especially the recently discovered MnBi$_2$Te$_4$-family magnetic topological insulators (TIs), have aroused great attention. However, there has been a serious debate about whether the surface states are gapped or gapless for antiferromagnetic (AFM) TI MnBi$_2$Te$_4$, which is crucial to the prospect of various magnetic topological phenomena. Here, a minimal three-Dirac-fermion approach is developed to generally describe topological surface states of nonmagnetic/magnetic vdW TIs under the modulation of the interlayer vdW gap. In particular, this approach is applied to address the controversial issues concerning the surface states of vdW AFM TIs. Remarkably, topologically protected gapless Dirac-cone surface states are found to arise due to a small expansion of the interlayer vdW gap on the surface, when the Chern number equals zero for the surface ferromagnetic layer; while the surface states remain gapped in all other cases. These results are further confirmed by our first-principles calculations on AFM TI MnBi$_2$Te$_4$. The theorectically discovered gapless Dirac-cone states provide a unique mechanism for understanding the puzzle of the experimentally observed gapless surface states in MnBi$_2$Te$_4$. This work also provides a promising way for experiments to realize the intrinsic magnetic quantum anomalous Hall effect in MnBi$_2$Te$_4$ films with a large energy gap., Comment: 7 pages, 4 figures

Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust performance and stable convergence in FL applications. Model personalization methods have been studied to overcome this problem. However, existing approaches are mainly under the prerequisite of fully labeled data, which is unrealistic in practice due to the requirement of expertise. The primary issue caused by partial-labeled condition is that, clients with deficient labeled data can suffer from unfair performance gain because they lack adequate insights of local distribution to customize the global model. To tackle this problem, 1) we propose a novel personalized semi-supervised learning paradigm which allows partial-labeled or unlabeled clients to seek labeling assistance from data-related clients (helper agents), thus to enhance their perception of local data; 2) based on this paradigm, we design an uncertainty-based data-relation metric to ensure that selected helpers can provide trustworthy pseudo labels instead of misleading the local training; 3) to mitigate the network overload introduced by helper searching, we further develop a helper selection protocol to achieve efficient communication with acceptable performance sacrifice. Experiments show that our proposed method can obtain superior performance and more stable convergence than other related works with partially labeled data, especially in highly heterogeneous setting., Comment: 13 pages, this article has been accepted for publication in IEEE Transactions on Network Science and Engineering

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance., Comment: Please cite this work as: Yangcheng Gao, Zhao Zhang, Richang Hong, Haijun Zhang, Jicong Fan and Shuicheng Yan, "Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization," In: Proceedings of the 22nd IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, pp.1-10, Aug 2022

Quantum well states (QWSs) can form at the surface or interfaces of materials with confinement potential. They have broad applications in electronic and optical devices such as high mobility electron transistor, photodetector and quantum well laser. The properties of the QWSs are usually the key factors for the performance of the devices. However, direct visualization and manipulation of such states are in general challenging. In this work, by using angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy/spectroscopy (STM/STS), we directly probe the QWSs generated on the vacuum interface of a narrow band gap semiconductor Nb2SiTe4. Interestingly, the position and splitting of QWSs could be easily manipulated via potassium (K) dosage onto the sample surface. Our results suggest Nb2SiTe4 to be an intriguing semiconductor system to study and engineer the QWSs, which has great potential in device applications., Comment: 28 pages, 5 figures

The electromagnetic response of three-dimensional topological insulators can be described by an effective axion action with a quantized axion field. Once both time-reversal and inversion symmetries are broken, for example, in antiferromagnetic topological insulators, the axion field becomes dynamical along with magnetic fluctuations. The dynamical axion field, when coupled to electromagnetic fields, can lead to rich physical phenomena. Here, based on the modified Maxwell's equations, we reveal the existence of an exotic type of polariton excitation, termed a surface axion polariton, which propagates in the interface between a dynamical axion insulator and a dielectric. When doping the dynamical axion insulator to be metallic, the coexistence of axion-photon coupling and plasmon-photon coupling will further generate a mixed surface axion plasmon polariton. We also propose a Kretschmann-Raether configuration to experimentally detect the surface axion polariton. Our result provides an alternative way to study the axion electrodynamics in condensed matter physics., Comment: 6 pages, 4 figures

In this paper, an IRS-aided integrated sensing and communications (ISAC) system operating in the terahertz (THz) band is proposed to maximize the system capacity. Transmit beamforming and phase-shift design are transformed into a universal optimization problem with ergodic constraints. Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario. Specifically, the actor part generates continuous transmit beamforming and the critic part takes charge of discrete phase shift design. Based on the MISO scenario, we investigate a distributed PPO (DPPO) framework with the concept of multi-threading learning in the multi-user multiple-input multiple-output (MIMO) scenario. Simulation results demonstrate the effectiveness of the primal-dual PPO algorithm and its multi-threading version in terms of transmit beamforming and phase-shift design.

Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue. Recently, many traffic control schemes have been studied. Nevertheless, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it. Firstly, IoV devices are divided into various classes based on delay characteristics. The target of maximizing the successful transmission of packets with the success rate constraint is established. Then, the optimization objective is transformed into a markov decision process (MDP) model. Finally, the access class barring (ACB) factors are obtained based on the PPO method to maximize the number of successful access devices. The performance of the proposal algorithm in respect of successful events and delay compared to existing schemes is verified by simulations., Comment: There have some mistakes in Section II

The performance of cooperative ambient backscatter communications (CABC) can be enhanced by employing reconfigurable intelligent surface (RIS) to assist backscatter transmitters. Since the RIS power consumption is a non-negligible issue, we consider a RIS assisted CABC system where the RIS with energy harvesting circuit can not only reflect signal but also harvest wireless energy. We study a transmission design problem to minimize the RIS power consumption with the quality of service constraints for both active and backscatter transmissions. The optimization problem is a mixed-integer non-convex programming problem which is NP-hard. To tackle it, an algorithm is proposed by employing the block coordinate descent, semidefinite relaxation and alternating direction method of multipliers techniques. Simulation results demonstrate the effectiveness of the proposed algorithm., Comment: Fixed a typo