arXiv subjects

Can Wang

Publications and source records attributed to Can Wang.

At least 163 records · Page 9Linked to original sources

arXiv2023

Mechanical motion tuned carrier transport characteristic of dynamic diode

Since the invention of dynamic diode, its physical properties and potential applications have attracted wide attentions. A lot of attempts have been made to harvest the rebounding current and voltage of dynamic diode. However, the underlying physical mechanism of its carrier transport characteristic was rarely explored carefully. Here, the electrical transport properties of the dynamic diode are systematically investigated with a mechanical motion tuned method, where the dynamic current-voltage curve shows a gentler growth trend compared to the static curve. The rebounding current increases with motion velocity and contact force, resulting in a reduced current with the same bias voltage and an oscillation current with a changing velocity and force. Besides, we propose a circuit model with an accurate mathematical formula expression to describe the oscillation current, where an imaginary parameter n0 is creatively added to the exponential growth term. This work shows a physical picture of adjust microscopic carrier motion with macroscopic mechanical motion, which provides strong theoretical support for designing dynamic diode devices with better performance in the future.

physics.app-ph↗

arXiv2023

Accelerating Diffusion Sampling with Classifier-based Feature Distillation

Although diffusion model has shown great potential for generating higher quality images than GANs, slow sampling speed hinders its wide application in practice. Progressive distillation is thus proposed for fast sampling by progressively aligning output images of $N$-step teacher sampler with $N/2$-step student sampler. In this paper, we argue that this distillation-based accelerating method can be further improved, especially for few-step samplers, with our proposed \textbf{C}lassifier-based \textbf{F}eature \textbf{D}istillation (CFD). Instead of aligning output images, we distill teacher's sharpened feature distribution into the student with a dataset-independent classifier, making the student focus on those important features to improve performance. We also introduce a dataset-oriented loss to further optimize the model. Experiments on CIFAR-10 show the superiority of our method in achieving high quality and fast sampling. Code is provided at \url{https://github.com/zju-SWJ/RCFD}.

cs.CV↗

arXiv2023

Interplanetary Coronal Mass Ejections and Stream Interaction Regions observed by Tianwen-1 and Maven at Mars

Tianwen-1 spacecraft (Wan et al. 2020) is China's first Mars exploration mission. The Mars Orbiter Magnetometer (MOMAG) is a scientific instrument aboard the Tianwen-1 mission that is designed to study magnetic fields at Mars, including the solar wind to the magnetosheath and the ionosphere. Using the first Tianwen-1/MOMAG data that is publicly available, we present interplanetary coronal mass ejection (ICME) and stream interaction region (SIR) catalogues based on in-situ observations at Mars between November 16, 2021, and December 31, 2021. We compared the magnetic field intensity and vector magnetic field measurements from Tianwen-1/MOMAG and Mars Atmospheric Volatile EvolutioN (MAVEN)/MAG during the ICME and SIR interval and found a generally good consistency between them. Due to MAVEN's orbital adjustment since 2019, the Tianwen-1/MOMAG instrument is currently the almost unique interplanetary magnetic field monitor at Mars. The observations indicate that the MOMAG instrument on Tianwen-1 is performing well and can provide accurate measurements of the vector magnetic field in the near-Mars solar wind space. The multi-point observations combining MOMAG, MINPA, and MEPA on board Tianwen-1 with MAG, SWIA, and STATIC on board MAVEN will open a window to systematically study the characteristic of ICMEs and SIRs at Mars, and their influences on the Martian atmosphere and ionosphere.

physics.space-ph↗

arXiv2023

Controllable Spin-Resolved Photon Emission Enhanced by Slow-Light Mode in Photonic Crystal Waveguides on Chip

We report the slow-light enhanced spin-resolved in-plane emission from a single quantum dot (QD) in a photonic crystal waveguide (PCW). The slow light dispersions in PCWs are designed to match the emission wavelengths of single QDs. The resonance between two spin states emitted from a single QD and a slow light mode of a waveguide is investigated under a magnetic field with Faraday configuration. Two spin states of a single QD experience different degrees of enhancement as their emission wavelengths are shifted by combining diamagnetic and Zeeman effects with an optical excitation power control. A circular polarization degree up to 0.81 is achieved by changing the off-resonant excitation power. Strongly polarized photon emission enhanced by a slow light mode shows great potential to attain controllable spin-resolved photon sources for integrated optical quantum networks on chip.

cond-mat.mes-hall↗

arXiv2023

Robust Sequence Networked Submodular Maximization

In this paper, we study the \underline{R}obust \underline{o}ptimization for \underline{se}quence \underline{Net}worked \underline{s}ubmodular maximization (RoseNets) problem. We interweave the robust optimization with the sequence networked submodular maximization. The elements are connected by a directed acyclic graph and the objective function is not submodular on the elements but on the edges in the graph. Under such networked submodular scenario, the impact of removing an element from a sequence depends both on its position in the sequence and in the network. This makes the existing robust algorithms inapplicable. In this paper, we take the first step to study the RoseNets problem. We design a robust greedy algorithm, which is robust against the removal of an arbitrary subset of the selected elements. The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology. We further conduct experiments on real applications of recommendation and link prediction. The experimental results demonstrate the effectiveness of the proposed algorithm.

cs.AI↗

arXiv2023

Miniature Magnetic Nano islands in a Morphotropic Cobaltite Matrix

High-density magnetic memories are key components in spintronics, quantum computing, and energy-efficient electronics. Reduced dimensionality and magnetic domain stability at the nanoscale are essential for the miniaturization of magnetic storage units. Yet, inducing magnetic order, and selectively tuning spin-orbital coupling at specific locations have remained challenging. Here we demonstrate the construction of switchable magnetic nano-islands in a nonmagnetic matrix based on cobaltite homo-structures. The magnetic and electronic states are laterally modified by epitaxial strain, which is regionally controlled by freestanding membranes. Atomically sharp grain boundaries isolate the crosstalk between magnetically distinct regions. The minimal size of magnetic nano-islands reaches 35 nm in diameter, enabling an areal density of 400 Gbit per inch square. Besides providing an ideal platform for precisely controlled read and write schemes, this methodology can enable scalable and patterned memories on silicon and flexible substrates for various applications.

cond-mat.mtrl-sci↗

arXiv2022

NeRF-Art: Text-Driven Neural Radiance Fields Stylization

As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially on simulating a text-guided style with both the appearance and the geometry altered simultaneously. In this paper, we present NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a simple text prompt. Unlike previous approaches that either lack sufficient geometry deformations and texture details or require meshes to guide the stylization, our method can shift a 3D scene to the target style characterized by desired geometry and appearance variations without any mesh guidance. This is achieved by introducing a novel global-local contrastive learning strategy, combined with the directional constraint to simultaneously control both the trajectory and the strength of the target style. Moreover, we adopt a weight regularization method to effectively suppress cloudy artifacts and geometry noises which arise easily when the density field is transformed during geometry stylization. Through extensive experiments on various styles, we demonstrate that our method is effective and robust regarding both single-view stylization quality and cross-view consistency. The code and more results can be found in our project page: https://cassiepython.github.io/nerfart/.

cs.CV↗

arXiv2022

Online Cross-Layer Knowledge Distillation on Graph Neural Networks with Deep Supervision

Graph neural networks (GNNs) have become one of the most popular research topics in both academia and industry communities for their strong ability in handling irregular graph data. However, large-scale datasets are posing great challenges for deploying GNNs in edge devices with limited resources and model compression techniques have drawn considerable research attention. Existing model compression techniques such as knowledge distillation (KD) mainly focus on convolutional neural networks (CNNs). Only limited attempts have been made recently for distilling knowledge from GNNs in an offline manner. As the performance of the teacher model does not necessarily improve as the number of layers increases in GNNs, selecting an appropriate teacher model will require substantial efforts. To address these challenges, we propose a novel online knowledge distillation framework called Alignahead++ in this paper. Alignahead++ transfers structure and feature information in a student layer to the previous layer of another simultaneously trained student model in an alternating training procedure. Meanwhile, to avoid over-smoothing problem in GNNs, deep supervision is employed in Alignahead++ by adding an auxiliary classifier in each intermediate layer to prevent the collapse of the node feature embeddings. Experimental results on four datasets including PPI, Cora, PubMed and CiteSeer demonstrate that the student performance is consistently boosted in our collaborative training framework without the supervision of a pre-trained teacher model and its effectiveness can generally be improved by increasing the number of students.

cs.LG↗

arXiv2022

Single charge control of localized excitons in heterostructures with ferroelectric thin films and two-dimensional transition metal dichalcogenides

Single charge control of localized excitons (LXs) in two-dimensional transition metal dichalcogenides (TMDCs) is crucial for potential applications in quantum information processing and storage. However, traditional electrostatic doping method with applying metallic gates onto TMDCs may cause the inhomogeneous charge distribution, optical quench, and energy loss. Here, by locally controlling the ferroelectric polarization of the ferroelectric thin film BiFeO3 (BFO) with a scanning probe, we can deterministically manipulate the doping type of monolayer WSe2 to achieve the p-type and n-type doping. This nonvolatile approach can maintain the doping type and hold the localized excitonic charges for a long time without applied voltage. Our work demonstrated that ferroelectric polarization of BFO can control the charges of LXs effectively. Neutral and charged LXs have been observed in different ferroelectric polarization regions, confirmed by magnetic optical measurement. Highly circular polarization degree about 90 % of the photon emission from these quantum emitters have been achieved in high magnetic fields. Controlling single charge of LXs in a non-volatile way shows a great potential for deterministic photon emission with desired charge states for photonic long-term memory.

cond-mat.mes-hall↗

arXiv2022

FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing

We propose a Few-shot Dynamic Neural Radiance Field (FDNeRF), the first NeRF-based method capable of reconstruction and expression editing of 3D faces based on a small number of dynamic images. Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs. Compared to state-of-the-art few-shot NeRFs designed for modeling static scenes, the proposed FDNeRF accepts view-inconsistent dynamic inputs and supports arbitrary facial expression editing, i.e., producing faces with novel expressions beyond the input ones. To handle the inconsistencies between dynamic inputs, we introduce a well-designed conditional feature warping (CFW) module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. As a result, features of different expressions are transformed into the target ones. We then construct a radiance field based on these view-consistent features and use volumetric rendering to synthesize novel views of the modeled faces. Extensive experiments with quantitative and qualitative evaluation demonstrate that our method outperforms existing dynamic and few-shot NeRFs on both 3D face reconstruction and expression editing tasks. Code is available at https://github.com/FDNeRF/FDNeRF.

cs.CV↗

arXiv2022

Emergent magnetic states and tunable exchange bias at all 3d nitride heterointerfaces

Interfacial magnetism stimulates the discovery of giant magnetoresistance and spin-orbital coupling across the heterointerfaces, facilitating the intimate correlation between spin transport and complex magnetic structures. Over decades, functional heterointerfaces composed of nitrides are seldomly explored due to the difficulty in synthesizing high-quality and correct composition nitride films. Here we report the fabrication of single-crystalline ferromagnetic Fe3N thin films with precisely controlled thickness. As film thickness decreasing, the magnetization deteriorates dramatically, and electronic state transits from metallic to insulating. Strikingly, the high-temperature ferromagnetism maintains in a Fe3N layer with a thickness down to 2 u. c. (~ 8 Å). The magnetoresistance exhibits a strong in-plane anisotropy and meanwhile the anomalous Hall resistance reserves its sign when Fe3N layer thickness exceeds 5 u. c. Furthermore, we observe a sizable exchange bias at the interfaces between a ferromagnetic Fe3N and an antiferromagnetic CrN. The exchange bias field and saturation moment strongly depend on the controllable bending curvature using cylinder diameter engineering (CDE) technique, implying the tunable magnetic states under lattice deformation. This work provides a guideline for exploring functional nitride films and applying their interfacial phenomena for innovative perspectives towards the practical applications.

cond-mat.mtrl-sci↗

arXiv2022

Quantum design for advanced qubits: plasmonium

The increasingly complex quantum electronic circuits with a number of coupled quantum degrees of freedom will become intractable to be simulated on classical computers, and requires quantum computers for an efficient simulation. In turn, it will be a central concept in quantum-aided design for next-generation quantum processors. Here, we demonstrate variational quantum eigensolvers to simulate superconducting quantum circuits with varying parameters covering a plasmon-transition regime, which reveals an advanced post-transmon qubit, "plasmonium". We fabricate this new qubit and demonstrate that it exhibits not only high single- and two-qubit gate fidelities (99.85(1)% and 99.58(3)%, respectively), but also a shrinking (by 60%) physical size and larger (by 50%) anharmonicity than the transmon, which can bring a number of advantages for scaling up multi-qubit devices. Our work opens the way to designing advanced quantum processors using existing quantum computing resources.

quant-ph↗

arXiv2022

Braiding lateral morphotropic grain boundary in homogeneitic oxides

Interfaces formed by correlated oxides offer a critical avenue for discovering emergent phenomena and quantum states. However, the fabrication of oxide interfaces with variable crystallographic orientations and strain states integrated along a film plane is extremely challenge by conventional layer-by-layer stacking or self-assembling. Here, we report the creation of morphotropic grain boundaries (GBs) in laterally interconnected cobaltite homostructures. Single-crystalline substrates and suspended ultrathin freestanding membranes provide independent templates for coherent epitaxy and constraint on the growth orientation, resulting in seamless and atomically sharp GBs. Electronic states and magnetic behavior in hybrid structures are laterally modulated and isolated by GBs, enabling artificially engineered functionalities in the planar matrix. Our work offers a simple and scalable method for fabricating unprecedented innovative interfaces through controlled synthesis routes as well as provides a platform for exploring potential applications in neuromorphics, solid state batteries, and catalysis.

cond-mat.mtrl-sci↗

arXiv2022

Atomically engineered cobaltite layers for robust ferromagnetism

Emergent phenomena at heterointerfaces are directly associated with the bonding geometry of adjacent layers. Effective control of accessible parameters, such as the bond length and bonding angles, offers an elegant method to tailor competing energies of the electronic and magnetic ground states. In this study, we construct unit thick syntactic layers of cobaltites within a strongly tilted octahedral matrix via atomically precise synthesis. The octahedral tilt patterns of adjacent layers propagate into cobaltites, leading to a continuation of octahedral tilting while maintaining significant misfit tensile strain. These effects induce severe rumpling within an atomic plane of neighboring layers triggers the electronic reconstruction between the splitting orbitals. First-principles calculations reveal that the cobalt ions transits to a higher spin state level upon octahedral tilting, resulting in robust ferromagnetism in ultrathin cobaltites. This work demonstrates a design methodology for fine-tuning the lattice and spin degrees of freedom in correlated quantum heterostructures by exploiting epitaxial geometric engineering.

cond-mat.mtrl-sci↗

arXiv2022

Asymmetric Ground States in La$_{0.67}$Sr$_{0.33}$MnO$_3$/BaTiO$_3$ heterostructures Induced by Flexoelectric Bending

Misfit strain delivered from single-crystal substrates typically modifies the ground states of transition metal oxides, generating increasing interests in designing modern transducers and sensors. Here, we demonstrate that magnetotransport properties of La$_{0.67}$Sr$_{0.33}$MnO$_3$ (LSMO) films were continuously tuned by uniaxial strain produced by a home-designed bending jig. The electrical conductivity and Curie temperature of LSMO films are enhanced by bending stresses. The resistivity of a u-shape bended LSMO decays three times faster than that of a n-shape bended LSMO as a response to the same magnitude of strain. The asymmetric magnetic states in uniaxially strained LSMO are attributed to the dual actions of Jahn-Teller distortion and strain gradient mediated flexoelectric fields in an adjacent ferroelectric layer. These findings of multi-field regulation in a single material provide a feasible means for developing flexible electronic and spintronic devices.

cond-mat.mtrl-sci↗

arXiv2022

Self-driven water or polarized liquid based ultraviolet photodetector

Traditionally, photodetector is based on solid materials constructed PN junction, which needs many delicate growth technologies. Herein, we demonstrate the feasibility of polarized liquid triggered photodetector where the liquid is sandwiched between P-type or N-type semiconductor which can be chosen freely according to the requirement of the specific response wavelength. Under the multiple cycles of optical switching, transient photo-polarized current, steady state photo-polarized current and depolarized current are repeatably observed in semiconductor/polar liquids/semiconductor structure. The responsivity and specific detectivity of transient photo-polarized current in N-GaN/water/P-GaN reach values of 104.2 mA/W and 4.4*10^12 Jones at 365 nm, and 52.8 mA/W and 1.9*10^12 Jones at 254 nm illumination under zero voltage bias. We anticipate that our research will have a profound impact on integrating self-powered photodetector with freely selectable wavelength bands.

physics.app-ph↗

arXiv2022

Radiative Magnetohydrodynamic Simulation of the Confined Eruption of a Magnetic Flux Rope: Magnetic Structure and Plasma Thermodynamics

It is widely believed that magnetic flux ropes are the key structure of solar eruptions; however, their observable counterparts are not clear yet. We study a flare associated with flux rope eruption in a comprehensive radiative magnetohydrodynamic simulation of flare-productive active regions, especially focusing on the thermodynamic properties of the plasma involved in the eruption and their relation to the magnetic flux rope. The pre-existing flux rope, which carries cold and dense plasma, rises quasi-statically before the eruption onsets. During this stage, the flux rope does not show obvious signatures in extreme ultraviolet (EUV) emission. After the flare onset, a thin `current shell' is generated around the erupting flux rope. Moreover, a current sheet is formed under the flux rope, where two groups of magnetic arcades reconnect and create a group of post-flare loops. The plasma within the `current shell', current sheet, and post-flare loops are heated to more than 10 MK. The post-flare loops give rise to abundant soft X-ray emission. Meanwhile a majority of the plasma hosted in the flux rope is heated to around 1 MK, and the main body of the flux rope is manifested as a bright arch in cooler EUV passbands such as AIA 171 Å~channel.

astro-ph.SR↗

arXiv2022

Infrared Radiation of Graphene Electrothermal Film Triggered Alpha and Theta Brainwaves

The alpha and theta frequency brainwave activity in Electroencephalogram (EEG) signal has been correlated with attention, inhibitory processes, memory, perceptual abilities, and sleep. The enhanced alpha and theta brainwave activity may bring positive behavioral modifications such as promoting creativity and a quick sleep. Herein, we discover that infrared radiation from multilayer graphene electrothermal film can obviously promote the appearance of alpha and theta brainwave in human mind. In particular, the occurrence frequency of the alpha and theta waves in EEG can be effectively enhanced up to 2.3 and 3.0 times, respectively. And the duration time of the alpha and theta waves in EEG can also be effectively extended. The mechanism may be attributed to the efficient infrared radiation caused by graphene mainly focused on the range from 7 to 14 micron, coinciding with the radiation wavelength of natural human body, which can be effectively absorbed by the human skin and speed up the blood microcirculation and metabolism. The comparative effect of different working temperature and heating materials such as water, Cu and even monolayer graphene are systematically investigated, indicating the infrared radiation from the multilayer graphene electrothermal film at 50 degrees has the largest enhancement effect of alpha and theta brainwaves. The multilayer graphene film electrical heater represents a convenient and surprising way for triggering the alpha and theta brainwaves, which has many potential applications in the area of enlarged health cerements.

physics.app-ph↗