arXiv subjects

Xian Zhang

Publications and source records attributed to Xian Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2023

Temporal and Spectral Properties of the Persistent Radio Source Associated with FRB 20190520B with the VLA

Among more than 800 known fast radio bursts (FRBs), only two, namely FRB 20121102A and FRB 20190520B, are confirmed to be associated with a persistent radio sources (PRS). Here we report evidence of apparent temporal variability in the PRS associated with the bursting FRB 20190520B based on the Karl G. Jansky Very Large Array (VLA) observations taken in 2020 and 2021. Based on the analysis of epoch-to-epoch variability of the PRS at L, S, C, and X band in 1-12 GHz, we detected not only overall marginal variability but also a likely radio flux decrease ($\sim$ 3.2 $σ$) between the observations taken in 2020 and 2021 at 3 GHz. Assuming no spectral variation in the PRS during these observations, we found the evidence for an overall broadband radio flux decrease by about 20 percent between the 2020 and the 2021 observations, suggesting that the PRS probably evolves on the yearly time scale. If we attribute the marginal variability at 3 GHz as intrinsic or due to scintillation, the size of potential variable component of the PRS is constrained to be sub-parsec. On the other hand, the size of the PRS can be also constrained to be larger than about 0.22 parsec from the averaged radio spectrum and the integrated radio luminosity in the 1-12 GHz band based on equipartition and self-absorption arguments. We discuss potential origins of the PRS and suggest that an accreting compact object origin might be able to explain the PRS's temporal and spectral properties. Confirmation of variability or flux decline of the PRS would be critical to our understanding of the PRS and its relation to the bursting source.

astro-ph.HE↗

arXiv2023

High-Performance and Low-Power Sub-5 nm Field-Effect Transistors Based on 7-9-7-AGNR

Recently, an extremely-air-stable one-dimensional 7-9-7-AGNR was successfully fabricated. To further reveal its potential application in sub-5-nm field-effect transistors (FETs), there is an urgent need to develop integrated circuits. Here, we report first-principles quantum-transport simulations on the performance limits of n- and p-type sub-5-nm one-dimensional 7-9-7-AGNR FET. We find that the on-state current (Ion) in 7-9-7-AGNR FET can be effectively manipulated by the length of the gate and underlap. Particularly, the optimized Ion in n-type (p-type) device can reach up to 2423 (4277) and 1988 (920) μA/μm for high-performance and low-power applications, respectively. The large Ion values are in the first class among the LD FETs, which can well satisfy the ITRS requirements. We also find that the 7-9-7-AGNR FET can have ultralow subthreshold swing below 60mV/dev, ultrashort delay time (<0.01 ps), and very small power-delay product (<0.01 fJ/μm). Our results show that the 7-9-7-AGNR based FETs have great potential applications in the high-speed and low-power consumption chips.

cond-mat.mes-hall↗

arXiv2023

Target-Guided Composed Image Retrieval

Composed image retrieval (CIR) is a new and flexible image retrieval paradigm, which can retrieve the target image for a multimodal query, including a reference image and its corresponding modification text. Although existing efforts have achieved compelling success, they overlook the conflict relationship modeling between the reference image and the modification text for improving the multimodal query composition and the adaptive matching degree modeling for promoting the ranking of the candidate images that could present different levels of matching degrees with the given query. To address these two limitations, in this work, we propose a Target-Guided Composed Image Retrieval network (TG-CIR). In particular, TG-CIR first extracts the unified global and local attribute features for the reference/target image and the modification text with the contrastive language-image pre-training model (CLIP) as the backbone, where an orthogonal regularization is introduced to promote the independence among the attribute features. Then TG-CIR designs a target-query relationship-guided multimodal query composition module, comprising a target-free student composition branch and a target-based teacher composition branch, where the target-query relationship is injected into the teacher branch for guiding the conflict relationship modeling of the student branch. Last, apart from the conventional batch-based classification loss, TG-CIR additionally introduces a batch-based target similarity-guided matching degree regularization to promote the metric learning process. Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed method.

cs.MM↗

arXiv2023

Tunable Magnon-Photon Coupling by Magnon Band Gap in a Layered Hybrid Perovskite Antiferromagnet

Tunability of coherent coupling between fundamental excitations is an important prerequisite for expanding their functionality in hybrid quantum systems. In hybrid magnonics, the dipolar interaction between magnon and photon usually persists and cannot be switched off. Here, we demonstrate this capability by coupling a superconducting resonator to a layered hybrid perovskite antiferromagnet, which exhibits a magnon band gap due to its intrinsic Dzyaloshinskii-Moriya interaction. The pronounced temperature sensitivity of the magnon band gap location allows us to set the photon mode within the gap and to disable magnon-photon hybridization. When the resonator mode falls into the magnon band gap, the resonator damping rate increases due to the nonzero coupling to the detuned magnon mode. This phenomena can be used to quantify the magnon band gap using an analytical model. Our work brings new opportunities in controlling coherent information processing with quantum properties in complex magnetic materials.

cond-mat.mtrl-sci↗

arXiv2023

Bursts from Space: MeerKAT - The first citizen science project dedicated to commensal radio transients

The newest generation of radio telescopes are able to survey large areas with high sensitivity and cadence, producing data volumes that require new methods to better understand the transient sky. Here we describe the results from the first citizen science project dedicated to commensal radio transients, using data from the MeerKAT telescope with weekly cadence. Bursts from Space: MeerKAT was launched late in 2021 and received ~89000 classifications from over 1000 volunteers in 3 months. Our volunteers discovered 142 new variable sources which, along with the known transients in our fields, allowed us to estimate that at least 2.1 per cent of radio sources are varying at 1.28 GHz at the sampled cadence and sensitivity, in line with previous work. We provide the full catalogue of these sources, the largest of candidate radio variables to date. Transient sources found with archival counterparts include a pulsar (B1845-01) and an OH maser star (OH 30.1-0.7), in addition to the recovery of known stellar flares and X-ray binary jets in our observations. Data from the MeerLICHT optical telescope, along with estimates of long time-scale variability induced by scintillation, imply that the majority of the new variables are active galactic nuclei. This tells us that citizen scientists can discover phenomena varying on time-scales from weeks to several years. The success both in terms of volunteer engagement and scientific merit warrants the continued development of the project, whilst we use the classifications from volunteers to develop machine learning techniques for finding transients.

astro-ph.HE↗

arXiv2023

Electric field tunable multi-state tunnel magnetoresistances in 2D van der Waals magnetic heterojunctions

Magnetic tunnel junction (MTJ) based on van der Waals (vdW) magnetic layers has been found to present excellent tunneling magnetoresistance (TMR) property, which has great potential applications in field sensing, non-volatile magnetic random access memories, and spin logics. Although MTJs composed of multilayer vdW magnetic homojunction have been extensively investigated, the ones composed of vdW magnetic heterojunction is still to be explored. Here we use first-principles approaches to reveal that the magnetic heterojunction MTJs have much more distinguishable TMR values than the homojunction ones. In the MTJ composed of bilayer CrI3/bilayer Cr2Ge2Te6 heterojunction, we find there are eight stable magnetic states, leading to six distinguishable electronic resistances. As a result, five sizable TMRs larger than 300% can be obtained (the maximum TMR is up to 620,000%). Six distinguishable memories are obtained which is two times larger than that of a four-layered homojunction MTJ. The underlying relationships among magnetic state, spin-polarized band structures, and transmission spectrums are further revealed to explain the multiple TMR values. We also find that the magnetic states and thus TMRs can be efficiently modulated by an external electric field. This study opens an avenue to the design of high-performance MTJ devices based on vdW heterojunctions.

cond-mat.mes-hall↗

arXiv2023

Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has long been suffering from fragmentary object regions led by Class Activation Map (CAM), which is incapable of generating fine-grained masks for semantic segmentation. To guide CAM to find more non-discriminating object patterns, this paper turns to an interesting working mechanism in agent learning named Complementary Learning System (CLS). CLS holds that the neocortex builds a sensation of general knowledge, while the hippocampus specially learns specific details, completing the learned patterns. Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask. Specifically, GSLM develops a General Learning Module (GLM) and a Specific Learning Module (SLM). The GLM is trained with image-level supervision to extract coarse and general localization representations from CAM. Based on the general knowledge in the GLM, the SLM progressively exploits the specific spatial knowledge from the localization representations, expanding the CAM in an explicit way. To this end, we propose the Seed Reactivation to help SLM reactivate non-discriminating regions by setting a boundary for activation values, which successively identifies more regions of CAM. Without extra refinement processes, our method is able to achieve breakthrough improvements for CAM of over 20.0% mIoU on PASCAL VOC 2012 and 10.0% mIoU on MS COCO 2014 datasets, representing a new state-of-the-art among existing WSSS methods.

cs.CV↗

arXiv2022

Radio observations of the tidal disruption event AT2020opy: a luminous non-relativistic outflow encountering a dense circumnuclear medium

Tidal disruption events (TDEs) occur when a star passes too close to a supermassive black hole and is destroyed by tidal gravitational forces. Radio observations of TDEs trace synchrotron emission from outflowing material that may be ejected from the inner regions of the accretion flow around the SMBH or by the tidal debris stream. Radio detections of tidal disruption events are rare, but provide crucial information about the launching of jets and outflows from supermassive black holes and the circumnuclear environment in galaxies. Here we present the radio detection of the TDE AT2020opy, including three epochs of radio observations taken with the Karl G. Jansky's Very Large Array (VLA), MeerKAT, and upgraded Giant Metrewave Radio telescope. AT2020opy is the most distant thermal TDE with radio emission reported to date, and from modelling the evolving synchrotron spectra we deduce that the host galaxy has a more dense circumnuclear medium than other thermal TDEs detected in the radio band. Based on an equipartition analysis of the synchrotron spectral properties of the event, we conclude that the radio-emitting outflow was likely launched approximately at the time of, or just after, the initial optical flare. We find no evidence for relativistic motion of the outflow. The high luminosity of this event supports that a dense circumnuclear medium of the host galaxy produces brighter radio emission that rises to a peak more quickly than in galaxies with lower central densities.

astro-ph.HE↗

arXiv2022

EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-fingered Robot Hands

Autonomous grasping of novel objects that are previously unseen to a robot is an ongoing challenge in robotic manipulation. In the last decades, many approaches have been presented to address this problem for specific robot hands. The UniGrasp framework, introduced recently, has the ability to generalize to different types of robotic grippers; however, this method does not work on grippers with closed-loop constraints and is data-inefficient when applied to robot hands with multigrasp configurations. In this paper, we present EfficientGrasp, a generalized grasp synthesis and gripper control method that is independent of gripper model specifications. EfficientGrasp utilizes a gripper workspace feature rather than UniGrasp's gripper attribute inputs. This reduces memory use by 81.7% during training and makes it possible to generalize to more types of grippers, such as grippers with closed-loop constraints. The effectiveness of EfficientGrasp is evaluated by conducting object grasping experiments both in simulation and real-world; results show that the proposed method also outperforms UniGrasp when considering only grippers without closed-loop constraints. In these cases, EfficientGrasp shows 9.85% higher accuracy in generating contact points and 3.10% higher grasping success rate in simulation. The real-world experiments are conducted with a gripper with closed-loop constraints, which UniGrasp fails to handle while EfficientGrasp achieves a success rate of 83.3%. The main causes of grasping failures of the proposed method are analyzed, highlighting ways of enhancing grasp performance.

cs.RO↗

arXiv2022

Identification and classification of exfoliated graphene flakes from microscopy images using a hierarchical deep convolutional neural network

Identification of the mechanically exfoliated graphene flakes and classification of the thickness is important in the nanomanufacturing of next-generation materials and devices that overcome the bottleneck of Moore's Law. Currently, identification and classification of exfoliated graphene flakes are conducted by human via inspecting the optical microscope images. The existing state-of-the-art automatic identification by machine learning is not able to accommodate images with different backgrounds while different backgrounds are unavoidable in experiments. This paper presents a deep learning method to automatically identify and classify the thickness of exfoliated graphene flakes on Si/SiO2 substrates from optical microscope images with various settings and background colors. The presented method uses a hierarchical deep convolutional neural network that is capable of learning new images while preserving the knowledge from previous images. The deep learning model was trained and used to classify exfoliated graphene flakes into monolayer (1L), bi-layer (2L), tri-layer (3L), four-to-six-layer (4-6L), seven-to-ten-layer (7-10L), and bulk categories. Compared with existing machine learning methods, the presented method possesses high accuracy and efficiency as well as robustness to the backgrounds and resolutions of images. The results indicated that our deep learning model has accuracy as high as 99% in identifying and classifying exfoliated graphene flakes. This research will shed light on scaled-up manufacturing and characterization of graphene for advanced materials and devices.

cs.CV↗

arXiv2022

Decentralized Verifiable Mail-in Ballot Counting for Postal Voting

As computer vision is prevalently used for mail-in ballot processing and counting, it becomes a point of centralized trust in postal voting. We propose DVote, a prototype system of postal voting that provides decentralized trust in computer vision. With blockchain and layer-2 technologies, DVote decentralizes the computation and model training of computer vision to a group of scrutineers that hold the AnyTrust assumption, i.e., at least one member is honest. Consequently, the computational integrity is anchored to the trustworthiness of a large public blockchain such as Ethereum.

cs.CR↗

arXiv2021

Face Deblurring Based on Separable Normalization and Adaptive Denormalization

Face deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details. However, most conventional image and face deblurring methods focus on the whole generated image resolution without consideration of special face part texture and generally produce unsufficient details. Considering that faces and backgrounds have different distribution information, in this study, we designed an effective face deblurring network based on separable normalization and adaptive denormalization (SNADNet). First, We fine-tuned the face parsing network to obtain an accurate face structure. Then, we divided the face parsing feature into face foreground and background. Moreover, we constructed a new feature adaptive denormalization to regularize fafcial structures as a condition of the auxiliary to generate more harmonious and undistorted face structure. In addition, we proposed a texture extractor and multi-patch discriminator to enhance the generated facial texture information. Experimental results on both CelebA and CelebA-HQ datasets demonstrate that the proposed face deblurring network restores face structure with more facial details and performs favorably against state-of-the-art methods in terms of structured similarity indexing method (SSIM), peak signal-to-noise ratio (PSNR), Frechet inception distance (FID) and L1, and qualitative comparisons.

cs.CV↗

arXiv2021

On the Role of Crystal Defects on the Lattice Thermal Conductivity of Monolayer WSe2 (P63/mmc) Thermoelectric Materials by DFT Calculation

As the energy problem becomes more prominent, researches on thermoelectric (TE) materials have deepened over the past few decades. Low thermal conductivity enables thermoelectric materials better thermal conversion performance. In this study, based on the first principles and phonon Boltzmann transport equation, we studied the thermal conductivities of single-layer WSe2 under several defect conditions using density functional theory (DFT) as implemented in the Vienna Ab-initio Simulation Package (VASP). The lattice thermal conductivities of WSe2 under six kinds of defect states, i.e., PS, SS-c, DS-s, SW-c, SS-e, and DS-d, are 66.1, 41.2, 39.4, 8.8, 42.1, and 38.4 W/(m2K), respectively at 300 K. Defect structures can reduce thermal conductivity up to 86.7% (SW-c) compared with perfect structure. The influences of defect content, type, location factors on thermal properties have been discussed in this research. By introducing atom defects, we can reduce and regulate the thermal property of WSe2, which should provide an interesting idea for other thermoelectric materials to gain a lower thermal conductivity.

cond-mat.mtrl-sci↗

arXiv2021

A circular Airy vortex beam carrying two point vortices symmetrically on the x-axis with the same and opposite signs

We study circular Airy vortex beams (CAVBs) with two same or opposite point vortices symmetrically loaded on x-axis, inside, onside and outside of the main ring (The absolute value of topological charge is one). It is found that the loading position (denoted as dv) can significantly tune the auto-focusing behaviour. For two vortices with same signs, when dv is near zero, the double vortices approximately merge at the focal plane. But as dv increases, the merged vortices will gradually separate along the y-axis. When dv is large, the focal spot becomes Bessel-shaped pattern. For opposite vortices, when dv is small, the focusing pattern is Bessel-shaped (no vortex exists in the centre) but not strictly in the centre. With the increase of dv, the original spot gradually moves up along the y-axis and transfers its energy to the newborn spot below x-axis, at the same time, the newborn spot moves towards the centre. When dv is large, the focus returns to a Bessel-like pattern. Good agreements have been achieved between simulations and experiments. The tailoring effect of dv of the two vortices on the focal pattern may have potential applications in micromanipulation, communication and other fields.

physics.optics↗

arXiv2021

Argus: A Fully Transparent Incentive System for Anti-Piracy Campaigns (Extended Version)

Anti-piracy is fundamentally a procedure that relies on collecting data from the open anonymous population, so how to incentivize credible reporting is a question at the center of the problem. Industrial alliances and companies are running anti-piracy incentive campaigns, but their effectiveness is publicly questioned due to the lack of transparency. We believe that full transparency of a campaign is necessary to truly incentivize people. It means that every role, e.g., content owner, licensee of the content, or every person in the open population, can understand the mechanism and be assured about its execution without trusting any single role. We see this as a distributed system problem. In this paper, we present Argus, a fully transparent incentive system for anti-piracy campaigns. The groundwork of Argus is to formulate the objectives for fully transparent incentive mechanisms, which securely and comprehensively consolidate the different interests of all roles. These objectives form the core of the Argus design, highlighted by our innovations about a Sybil-proof incentive function, a commit-and-reveal scheme, and an oblivious transfer scheme. In the implementation, we overcome a set of unavoidable obstacles to ensure security despite full transparency. Moreover, we effectively optimize several cryptographic operations so that the cost for a piracy reporting is reduced to an equivalent cost of sending about 14 ETH-transfer transactions to run on the public Ethereum network, which would otherwise correspond to thousands of transactions. With the security and practicality of Argus, we hope real-world anti-piracy campaigns will be truly effective by shifting to a fully transparent incentive mechanism.

cs.CR↗

arXiv2021

Agatha: Smart Contract for DNN Computation

Smart contract is one of the core features of Ethereum and has inspired many blockchain descendants. Since its advent, the verification paradigm of smart contract has been improving toward high scalability. It shifts from the expensive on-chain verification to the orchestration of off-chain VM (virtual machine) execution and on-chain arbitration with the pinpoint protocol. The representative projects are TrueBit, Arbitrum, YODA, ACE, and Optimism. Inspired by visionaries in academia and industry, we consider the DNN computation to be promising but on the next level of complexity for the verification paradigm of smart contract. Unfortunately, even for the state-of-the-art verification paradigm, off-chain VM execution of DNN computation has an orders-of-magnitude slowdown compared to the native off-chain execution. To enable the native off-chain execution of verifiable DNN computation, we present Agatha system, which solves the significant challenges of misalignment and inconsistency: (1) Native DNN computation has a graph-based computation paradigm misaligned with previous VM-based execution and arbitration; (2) Native DNN computation may be inconsistent cross platforms which invalidates the verification paradigm. In response, we propose the graph-based pinpoint protocol (GPP) which enables the pinpoint protocol on computational graphs, and bridges the native off-chain execution and the contract arbitration. We also develop a technique named Cross-evaluator Consistent Execution (XCE), which guarantees cross-platform consistency and forms the correctness foundation of GPP. We showcase Agatha for the DNN computation of popular models (MobileNet, ResNet50 and VGG16) on Ethereum. Agatha achieves a negligible on-chain overhead, and an off-chain execution overhead of 3.0%, which represents an off-chain latency reduction of at least 602x compared to the state-of-the-art verification paradigm.

cs.CR↗

arXiv2021

Thermal Conductivities and Interfacial Thermal Conductance of 1- to 3-Layer WSe$_2$

Atomically thin materials such as graphene and semiconducting transition metal dichalcogenides have attracted extensive interest in recent years, motivating investigation into multiple properties. In this work, we used the opto thermal Raman technique to measure the thermal transport properties of a popular TMDC material WSe$_2$, in single atomic layer, bilayer, and trilayer forms.

cond-mat.mtrl-sci↗

arXiv2021

Very High Interfacial Thermal Conductance in Fully hBN-Encapsulated MoS2 van der Waals Heterostructure

We report experimental and computational studies of thermal transport properties in hexagonal boron nitride (hBN) encapsulated molybdenum disulfide (MoS2) structure using refined optothermal Raman techniques, and reveal very high interfacial thermal conductance between hBN and MoS2. By studying the Raman shift of hBN and MoS2 in suspended and substrate-supported thin films under varying laser power and temperature, we calibrate lateral (in-plane) thermal conductivity of hBN and MoS2 and the vertical interfacial thermal conductance in the hBN/MoS2/hBN heterostructure as well as the interfaces between heterostructure and substrate. Crucially, we have found that interfacial thermal conductance between hBN and encapsulated MoS2 is 74MW/m2K and 72MW/m2K in supported and suspended films, respectively, which are significantly higher than interfacial thermal conductance between MoS2 and other substrates. Molecular dynamics (MD) computations conducted in parallel have shown consistent results. This work provides clear evidence of significantly efficient heat dissipation in hBN/MoS2/hBN heterostructures and sheds light on building novel hBN encapsulated nanoelectronics with efficient thermal management.

cond-mat.mes-hall↗