arXiv subjects

Jun Li

Publications and source records attributed to Jun Li.

At least 613 records · Page 34Linked to original sources

arXiv2021

Sequential generation of multiphoton entanglement with a Rydberg superatom

Multiqubit entanglement is an indispensable resource for quantum information science. In particular, the entanglement of photons is of conceptual interest due to its implications in measurement-based quantum computing, communication, and metrology. The traditional way of spontaneous parametric down-conversion already demonstrates entanglement of up to a dozen photons but is hindered by its probabilistic nature. Here, we experimentally demonstrate an efficient approach for multi-photon generation with a Rydberg superatom, a mesoscopic atomic ensemble under Rydberg blockade. Using it as an efficient single-photon interface, we iterate the photon creation process that gives rise to a train of temporal photonic modes entangled in photon number degree. We detect the multiphoton entanglement via converting the photon number degree to a time-bin degree. Photon correlations verify entanglement up to 12 modes. The efficiency scaling factor for adding one photon is 0.27, surpassing previous results, and can be increased significantly without fundamental limitations.

quant-ph↗

arXiv2021

Higher Genus FJRW Invariants of a Fermat Cubic

We reconstruct the all-genus Fan-Jarvis-Ruan-Witten invariants of a Fermat cubic Landau-Ginzburg space $(x_1^3+x_2^3+x_3^3: [\mathbb{C}^3/ \mathboldμ_3]\to \mathbb{C})$ from genus-one primary invariants, using tautological relations and axioms of Cohomological Field Theories. These genus-one invariants satisfy a Chazy equation by the Belorousski-Pandharipande relation. They are completely determined by a single genus-one invariant, which can be obtained from cosection localization and intersection theory on moduli of three spin curves. We solve an all-genus Landau-Ginzburg/Calabi-Yau Correspondence Conjecture for the Fermat cubic Landau-Ginzburg space using Cayley transformation on quasi-modular forms. This transformation relates two non-semisimple CohFT theories: the Fan-Jarvis-Ruan-Witten theory of the Fermat cubic polynomial and the Gromov-Witten theory of the Fermat cubic curve. As a consequence, Fan-Jarvis-Ruan-Witten invariants at any genus can be computed using Gromov-Witten invariants of the elliptic curve. They also satisfy nice structures including holomorphic anomaly equations and Virasoro constraints.

math.AG↗

arXiv2021

Robust Beamforming Design and Time Allocation for IRS-assisted Wireless Powered Communication Networks

In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for power-constrained Internet-of-Things (IoT) smart devices, where IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmit energy minimization problem by jointly optimizing time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. On account of the high coupling of optimization variables, the formulated problem is a non-convex optimization problem that is difficult to solve directly. To address the above-mentioned challenging problem, alternating optimization (AO) technique is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is used to solve the non-convex rank-one constraint in solving IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the superiority of the proposed optimization algorithm in decreasing HAP transmit energy compared with other benchmark schemes.

cs.IT↗

arXiv2021

CT-block: a novel local and global features extractor for point cloud

Deep learning on the point cloud is increasingly developing. Grouping the point with its neighbors and conducting convolution-like operation on them can learn the local feature of the point cloud, but this method is weak to extract the long-distance global feature. Performing the attention-based transformer on the whole point cloud can effectively learn the global feature of it, but this method is hardly to extract the local detailed feature. In this paper, we propose a novel module that can simultaneously extract and fuse local and global features, which is named as CT-block. The CT-block is composed of two branches, where the letter C represents the convolution-branch and the letter T represents the transformer-branch. The convolution-branch performs convolution on the grouped neighbor points to extract the local feature. Meanwhile, the transformer-branch performs offset-attention process on the whole point cloud to extract the global feature. Through the bridge constructed by the feature transmission element in the CT-block, the local and global features guide each other during learning and are fused effectively. We apply the CT-block to construct point cloud classification and segmentation networks, and evaluate the performance of them by several public datasets. The experimental results show that, because the features learned by CT-block are much expressive, the performance of the networks constructed by the CT-block on the point cloud classification and segmentation tasks achieve state of the art.

cs.CV↗

arXiv2021

Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation

We present a novel approach to category-level 6D object pose and size estimation. To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category. In particular, CASS is modeled as the latent space of a deep generative model of canonical 3D shapes with normalized pose. We train a variational auto-encoder (VAE) for generating 3D point clouds in the canonical space from an RGBD image. The VAE is trained in a cross-category fashion, exploiting the publicly available large 3D shape repositories. Since the 3D point cloud is generated in normalized pose (with actual size), the encoder of the VAE learns view-factorized RGBD embedding. It maps an RGBD image in arbitrary view into a pose-independent 3D shape representation. Object pose is then estimated via contrasting it with a pose-dependent feature of the input RGBD extracted with a separate deep neural networks. We integrate the learning of CASS and pose and size estimation into an end-to-end trainable network, achieving the state-of-the-art performance.

cs.CV↗

arXiv2021

IRS-Aided WPCNs: A New Optimization Framework for Dynamic IRS Beamforming

In this paper, we propose a new dynamic IRS beamforming framework to boost the sum throughput of an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN). Specifically, the IRS phase-shift vectors across time and resource allocation are jointly optimized to enhance the efficiencies of both downlink wireless power transfer (DL WPT) and uplink wireless information transmission (UL WIT) between a hybrid access point (HAP) and multiple wirelessly powered devices. To this end, we first study three special cases of the dynamic IRS beamforming,namely user-adaptive IRS beamforming, UL-adaptive IRS beamforming, and static IRS beamforming,by characterizing their optimal performance relationships and proposing corresponding algorithms. Interestingly, it is rigorously proved that the latter two cases achieve the same throughput, thus helping halve the number of IRS phase shifts to be optimized and signalling overhead practically required for UL-adaptive IRS beamforming. Then, we propose a general optimization framework for dynamic IRS beamforming, which is applicable for any given number of IRS phase-shift vectors available. Despite of the non-convexity of the general problem with highly coupled optimization variables, we propose two algorithms to solve it and particularly, the low-complexity algorithm exploits the intrinsic structure of the optimal solution as well as the solutions to the cases with user-adaptive and static IRS beamforming. Simulation results validate our theoretical findings, illustrate the practical significance of IRS with dynamic beamforming for spectral and energy efficient WPCNs, and demonstrate the effectiveness of our proposed designs over various benchmark schemes.

cs.IT↗

arXiv2021

Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution

Structured illumination microscopy (SIM) is an important super-resolution based microscopy technique that breaks the diffraction limit and enhances optical microscopy systems. With the development of biology and medical engineering, there is a high demand for real-time and robust SIM imaging under extreme low light and short exposure environments. Existing SIM techniques typically require multiple structured illumination frames to produce a high-resolution image. In this paper, we propose a single-frame structured illumination microscopy (SF-SIM) based on deep learning. Our SF-SIM only needs one shot of a structured illumination frame and generates similar results compared with the traditional SIM systems that typically require 15 shots. In our SF-SIM, we propose a noise estimator which can effectively suppress the noise in the image and enable our method to work under the low light and short exposure environment, without the need for stacking multiple frames for non-local denoising. We also design a bandpass attention module that makes our deep network more sensitive to the change of frequency and enhances the imaging quality. Our proposed SF-SIM is almost 14 times faster than traditional SIM methods when achieving similar results. Therefore, our method is significantly valuable for the development of microbiology and medicine.

eess.IV↗

arXiv2021

Quantum oscillations in Noncentrosymmetric Weyl semimetals RAlSi (R = Sm and Ce)

Weyl semimetal (WSM) as a new type of quantum state of matter hosting low energy relativistic quasiparticles, has attracted significant attention for both scientific community and potential quantum device applications. Here, we report a comprehensive investigation of the structural, magnetic and transport properties of noncentrosymmetric RAlSi (R = Sm, Ce), which have been predicted to be new magnetic WSM candidates. Both samples exhibit non-saturated magnetoresistance (MR), with ~ 900% for SmAlSi and 80% for CeAlSi at 1.8 K, 9 T. The carrier densities of SmAlSi and CeAlSi display remarkable change around magnetic transition temperatures, signifying that the electronic states are sensitive to magnetic ordering of rare earth elements. At low temperatures, SmAlSi reveals prominent Shubnikov-de Haas (SdH) oscillations associated with the nontrivial Berry phase. High pressure experiments demonstrate that the magnetic order is robust and survival under high pressure. Our results would yield valuable insights of WSM physics and potentials in application to the next-generation spintronic devices in RAX family.

cond-mat.mtrl-sci↗

arXiv2021

The Dust Mass of Supernova Remnants in M31

The dust temperature and mass of the supernova remnants (SNRs) in M31 are estimated by fitting the infrared spectral energy distribution calculated from the images in the Spitzer/IRAC4 and MIPS24, Herschel/PACS70, 100, 160, and Herschel/SPIRE250, 350$μ$m band. Twenty SNRs with relatively reliable photometry exhibit an average dust temperature of $20.1^{+1.8}_{-1.5}$K, which is higher than the surrounding and indicating the heating effect of supernova explosion. The dust mass of these SNRs ranges from about 100 to 800$ M_{\odot}$, much bigger than the SNRs in the Milky Way. On the other hand, this yields the dust surface density of $0.10^{+0.07}_{-0.04}{ M_{\odot} \rm pc^{-2}}$, about half of the surrounding area, which implies that about half dust in the SNRs is destroyed by the supernova explosion. The dust temperature, the radius, and thus the dust mass all demonstrate that the studied SNRs are old and very likely in the snowplow or even fade away phase because of the limitation by the far distance and observation resolution of M31, and the results can serve as a reference to the final effect of supernova explosion on the surrounding dust.

astro-ph.GA↗

arXiv2021

Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme for a blockchain-based MEC system where each edge device not only handles data tasks but also deals with block mining for improving the system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. A multi-objective function is then formulated to maximize the system utility of the blockchain-based MEC system, by jointly optimizing offloading decision, channel selection, transmit power allocation, and computational resource allocation. We propose a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. We then develop a game-theoretic solution to model the offloading and mining competition among edge devices as a potential game, and prove the existence of a pure Nash equilibrium. Simulation results demonstrate the significant system utility improvements of our proposed scheme over baseline approaches.

eess.SP↗

arXiv2021

Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration

In this paper, by modeling the point cloud registration task as a Markov decision process, we propose an end-to-end deep model embedded with the cross-entropy method (CEM) for unsupervised 3D registration. Our model consists of a sampling network module and a differentiable CEM module. In our sampling network module, given a pair of point clouds, the sampling network learns a prior sampling distribution over the transformation space. The learned sampling distribution can be used as a "good" initialization of the differentiable CEM module. In our differentiable CEM module, we first propose a maximum consensus criterion based alignment metric as the reward function for the point cloud registration task. Based on the reward function, for each state, we then construct a fused score function to evaluate the sampled transformations, where we weight the current and future rewards of the transformations. Particularly, the future rewards of the sampled transforms are obtained by performing the iterative closest point (ICP) algorithm on the transformed state. By selecting the top-k transformations with the highest scores, we iteratively update the sampling distribution. Furthermore, in order to make the CEM differentiable, we use the sparsemax function to replace the hard top-$k$ selection. Finally, we formulate a Geman-McClure estimator based loss to train our end-to-end registration model. Extensive experimental results demonstrate the good registration performance of our method on benchmark datasets.

cs.CV↗

arXiv2021

Low-Latency Federated Learning over Wireless Channels with Differential Privacy

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data distributions, potential demands on privacy protections, and quality of transmissions. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement. We solve this problem in the framework of multi-agent multi-armed bandit (MAMAB) to deal with the situation where there are multiple clients confornting different unknown transmission environments, e.g., channel fading and interferences. Specifically, we first transform the long-term constraints on both training performance and each client's DP into a virtual queue based on the Lyapunov drift technique. Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach. More importantly, we propose two efficient solutions to this matching problem, i.e., modified Hungarian algorithm and greedy matching with a better alternative (GMBA), in which the first one can achieve the optimal solution with a high complexity while the second one approaches a better trade-off by enabling a verified low-complexity with little performance loss. In addition, we develop an upper bound on the expected regret of this MAMAB based FL framework, which shows a linear growth over the logarithm of communication rounds, justifying its theoretical feasibility. Extensive experimental results are conducted to validate the effectiveness of our proposed algorithms, and the impacts of various parameters on the FL performance over wireless edge networks are also discussed.

cs.DC↗

arXiv2021

Weak Antilocalization Effect up to ~ 120 K in the van der Waals Crystal Fe5-xGeTe2 with Near Room Temperature Ferromagnetism

The weak antilocalization (WAL) effect is known as a quantum correction to the classical conductivity, which never appeared in two-dimensional magnets. In this work, we reported the observation of a WAL effect in the van der Waals ferromagnet Fe5-xGeTe2 with a Curie temperature Tc ~ 270 K, which can even reach as high as ~ 120 K. The WAL effect could be well described by the Hikami-Larkin-Nagaoka and Maekawa-Fukuyama theories in the presence of strong spin-orbit coupling (SOC). Moreover, A crossover from a peak to dip behavior around 60 K in both the magnetoresistance and magnetoconductance was observed, which could be ascribed to a rare example of temperature driven Lifshitz transition as indicated by the angle-resolved photoemission spectroscopy measurements and first principles calculations. The reflective magnetic circular dichroism measurements indicate a possible spin reorientation that kills the WAL effect above 120 K. Our findings present a rare example of WAL effect in two-dimensional ferromagnet and also a magnetotransport fingerprint of the strong SOC in Fe5-xGeTe2. The results would be instructive for understanding the interaction Hamiltonian for such high Tc itinerant ferromagnetism as well as be helpful for the design of next-generation room temperature spintronic or twistronic devices.

cond-mat.mtrl-sci↗

arXiv2021

Charge density wave orders and enhanced superconductivity under pressure in the kagome metal CsV3Sb5

Superconductivity in topological kagome metals has recently received great research interests. Here, charge density wave (CDW) orders and the evolution of superconductivity under various pressures in CsV3Sb5 single crystal with V kagome lattice are investigated. By using high-resolution scanning tunnelling microscopy /spectroscopy (STM/STS), two CDW orders in CsV3Sb5 are observed which correspond to 4a*1a and 2a*2a superlattices. By applying pressure, the superconducting transition temperature Tc is significantly enhanced and reaches a maximum value of 8.2 K at around 1 GPa. Accordingly, CDW state is gradually declined as increasing the pressure, which indicates the competing interplay between CDW and superconducting state in this material. The broad superconducting transitions around 0.4 - 0.8 GPa can be related to the strong competition relation among two CDW states and superconductivity. These results demonstrate that CsV3Sb5 is a new platform for exploring the interplay between superconductivity and CDW in topological kagome metals.

cond-mat.supr-con↗

arXiv2021

Anomalous Hall effect in ferrimagnetic metal RMn6Sn6 (R = Tb, Dy, Ho) with clean Mn kagome lattice

Kagome lattice, made of corner-sharing triangles, provides an excellent platform for hosting exotic topological quantum states. Here we systematically studied the magnetic and transport properties of RMn6Sn6 (R = Tb, Dy, Ho) with clean Mn kagome lattice. All the compounds have a collinear ferrimagnetic structure with different easy axis at low temperature. The low-temperature magnetoresistance (MR) is positive and has no tendency to saturate below 7 T, while the MR gradually declines and becomes negative with the increasing temperature. A large intrinsic anomalous Hall conductivity about 250 Ω-1cm-1, 40 Ω-1cm-1, 95 Ω-1cm-1 is observed for TbMn6Sn6, DyMn6Sn6, HoMn6Sn6, respectively. Our results imply that RMn6Sn6 system is an excellent platform to discover other intimately related topological or quantum phenomena and also tune the electronic and magnetic properties in future studies.

cond-mat.supr-con↗

arXiv2021

Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark

Monocular depth estimation aims at predicting depth from a single image or video. Recently, self-supervised methods draw much attention since they are free of depth annotations and achieve impressive performance on several daytime benchmarks. However, they produce weird outputs in more challenging nighttime scenarios because of low visibility and varying illuminations, which bring weak textures and break brightness-consistency assumption, respectively. To address these problems, in this paper we propose a novel framework with several improvements: (1) we introduce Priors-Based Regularization to learn distribution knowledge from unpaired depth maps and prevent model from being incorrectly trained; (2) we leverage Mapping-Consistent Image Enhancement module to enhance image visibility and contrast while maintaining brightness consistency; and (3) we present Statistics-Based Mask strategy to tune the number of removed pixels within textureless regions, using dynamic statistics. Experimental results demonstrate the effectiveness of each component. Meanwhile, our framework achieves remarkable improvements and state-of-the-art results on two nighttime datasets.

cs.CV↗

arXiv2021

6G Internet of Things: A Comprehensive Survey

The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space-air-ground-underwater communications, Terahertz communications, massive ultra-reliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely Healthcare Internet of Things, Vehicular Internet of Things and Autonomous Driving, Unmanned Aerial Vehicles, Satellite Internet of Things, and Industrial Internet of Things. Finally, we highlight interesting research challenges and point out potential directions to spur further research in this promising area.

eess.SP↗

arXiv2021

An optical biomimetic eyes with interested object imaging

We presented an optical system to perform imaging interested objects in complex scenes, like the creature easy see the interested prey in the hunt for complex environments. It utilized Deep-learning network to learn the interested objects's vision features and designed the corresponding "imaging matrices", furthermore the learned matrixes act as the measurement matrix to complete compressive imaging with a single-pixel camera, finally we can using the compressed image data to only image the interested objects without the rest objects and backgrounds of the scenes with the previous Deep-learning network. Our results demonstrate that no matter interested object is single feature or rich details, the interference can be successfully filtered out and this idea can be applied in some common applications that effectively improve the performance. This bio-inspired optical system can act as the creature eye to achieve success on interested-based object imaging, object detection, object recognition and object tracking, etc.

cs.CV↗