arXiv subjects

Jing Wu

Publications and source records attributed to Jing Wu.

At least 199 records · Page 11Linked to original sources

arXiv2020

Defect-Correlated Skyrmions and Controllable Generation in Perpendicularly Magnetized CoFeB Ultrathin Films

Skyrmions have attracted significant interest due to their topological spin structures and fascinating physical features. The skyrmion phase arises in materials with Dzyaloshinskii-Moriya (DM) interaction at interfaces or in volume of non-centrosymmetric materials. However, although skyrmions were generated experimentally, one critical intrinsic relationship between fabrication, microstructures, magnetization and the existence of skyrmions remains to be established. Here, two series of CoFeB ultrathin films with controlled atomic scale structures are employed to reveal this relationship. By inverting the growth order, the amount of defects can be artificially tuned, and skyrmions are shown to be preferentially formed at defect sites. The stable region and the density of the skyrmions can be efficiently controlled in the return magnetization loops by utilizing first-order reversal curves to reach various metastable states. These findings establish the general and intrinsic relationship from sample preparation to skyrmion generation, offering an universal method to control skyrmion density.

cond-mat.mes-hall↗

arXiv2020

ProbaNet: Proposal-balanced Network for Object Detection

Candidate object proposals generated by object detectors based on convolutional neural network (CNN) encounter easy-hard samples imbalance problem, which can affect overall performance. In this study, we propose a Proposal-balanced Network (ProbaNet) for alleviating the imbalance problem. Firstly, ProbaNet increases the probability of choosing hard samples for training by discarding easy samples through threshold truncation. Secondly, ProbaNet emphasizes foreground proposals by increasing their weights. To evaluate the effectiveness of ProbaNet, we train models based on different benchmarks. Mean Average Precision (mAP) of the model using ProbaNet achieves 1.2$\%$ higher than the baseline on PASCAL VOC 2007. Furthermore, it is compatible with existing two-stage detectors and offers a very small amount of additional computational cost.

cs.CV↗

arXiv2020

Observation of topological polaritons and photonic magic angles in twisted van der Waals bi-layers

Twisted two-dimensional bi-layers offer exquisite control on the electronic bandstructure through the interlayer rotation and coupling, enabling magic-angle flat-band superconductivity and moiré excitons. Here, we demonstrate how analogous principles, combined with large anisotropy, enable extreme control and manipulation of the photonic dispersion of phonon polaritons (PhPs) in van der Waals (vdW) bi-layers. We experimentally observe tunable topological transitions from open (hyperbolic) to closed (elliptic) dispersion contours in twisted bi-layered α-MoO3 at photonic magic angles, induced by polariton hybridization and robustly controlled by a topological quantity. At these transitions the bilayer dispersion flattens, exhibiting low-loss tunable polariton canalization and diffractionless propagation with resolution below λ0/40. Our findings extend twistronics and moiré physics to nanophotonics and polaritonics, with great potential for nano-imaging, nanoscale light propagation, energy transfer and quantum applications.

physics.optics↗

arXiv2020

MLCVNet: Multi-Level Context VoteNet for 3D Object Detection

In this paper, we address the 3D object detection task by capturing multi-level contextual information with the self-attention mechanism and multi-scale feature fusion. Most existing 3D object detection methods recognize objects individually, without giving any consideration on contextual information between these objects. Comparatively, we propose Multi-Level Context VoteNet (MLCVNet) to recognize 3D objects correlatively, building on the state-of-the-art VoteNet. We introduce three context modules into the voting and classifying stages of VoteNet to encode contextual information at different levels. Specifically, a Patch-to-Patch Context (PPC) module is employed to capture contextual information between the point patches, before voting for their corresponding object centroid points. Subsequently, an Object-to-Object Context (OOC) module is incorporated before the proposal and classification stage, to capture the contextual information between object candidates. Finally, a Global Scene Context (GSC) module is designed to learn the global scene context. We demonstrate these by capturing contextual information at patch, object and scene levels. Our method is an effective way to promote detection accuracy, achieving new state-of-the-art detection performance on challenging 3D object detection datasets, i.e., SUN RGBD and ScanNet. We also release our code at https://github.com/NUAAXQ/MLCVNet.

cs.CV↗

arXiv2020

DaST: Data-free Substitute Training for Adversarial Attacks

Machine learning models are vulnerable to adversarial examples. For the black-box setting, current substitute attacks need pre-trained models to generate adversarial examples. However, pre-trained models are hard to obtain in real-world tasks. In this paper, we propose a data-free substitute training method (DaST) to obtain substitute models for adversarial black-box attacks without the requirement of any real data. To achieve this, DaST utilizes specially designed generative adversarial networks (GANs) to train the substitute models. In particular, we design a multi-branch architecture and label-control loss for the generative model to deal with the uneven distribution of synthetic samples. The substitute model is then trained by the synthetic samples generated by the generative model, which are labeled by the attacked model subsequently. The experiments demonstrate the substitute models produced by DaST can achieve competitive performance compared with the baseline models which are trained by the same train set with attacked models. Additionally, to evaluate the practicability of the proposed method on the real-world task, we attack an online machine learning model on the Microsoft Azure platform. The remote model misclassifies 98.35% of the adversarial examples crafted by our method. To the best of our knowledge, we are the first to train a substitute model for adversarial attacks without any real data.

cs.CR↗

arXiv2020

Adversarial Imitation Attack

Deep learning models are known to be vulnerable to adversarial examples. A practical adversarial attack should require as little as possible knowledge of attacked models. Current substitute attacks need pre-trained models to generate adversarial examples and their attack success rates heavily rely on the transferability of adversarial examples. Current score-based and decision-based attacks require lots of queries for the attacked models. In this study, we propose a novel adversarial imitation attack. First, it produces a replica of the attacked model by a two-player game like the generative adversarial networks (GANs). The objective of the generative model is to generate examples that lead the imitation model returning different outputs with the attacked model. The objective of the imitation model is to output the same labels with the attacked model under the same inputs. Then, the adversarial examples generated by the imitation model are utilized to fool the attacked model. Compared with the current substitute attacks, imitation attacks can use less training data to produce a replica of the attacked model and improve the transferability of adversarial examples. Experiments demonstrate that our imitation attack requires less training data than the black-box substitute attacks, but achieves an attack success rate close to the white-box attack on unseen data with no query.

cs.CR↗

arXiv2020

Open Set Modulation Recognition Based on Dual-Channel LSTM Model

Deep neural networks have achieved great success in computer vision, speech recognition and many other areas. The potential of recurrent neural networks especially the Long Short-Term Memory (LSTM) for open set communication signal modulation recognition is investigated in this letter. Time-domain sampled signals are first converted to two normalized matrices which will be fed into a four layer Dual-Channel LSTM network tailored for open set modulation recognition. With two cascaded Dual-Channel LSTM layers, the designed network can automatically learn sequence-correlated features from the raw data. With center loss and weibull distribution, proposed algorithm can recognize partial open set modulations. Experiments on the public RadioML dataset indicates that different analog and digital modulations can be effectively classified by the proposed model, while partial open set modulations can be recognized. Quantitative analysis on the dataset shows that the proposed method can achieve an average accuracy of 90.2% at varying SNR ranging from 0dB to 18dB in classifying the considered 11 classes, while accuracy of open set experiment dramatically improved by 14.2%.

eess.SP↗

arXiv2020

The Simulation, Fabrication Technology and Characteristic Research of Micro-Pressure Sensor with Isosceles Trapezoidal Beam-Membrane

A micro-pressure sensor with an isosceles trapezoidal beam-membrane (ITBM) is proposed in this paper, consisting of a square silicon membrane, four isosceles trapezoidal beams and four piezoresistors.To investigate how the elastic silicon membrane affects pressure sensitive characteristics, a simulation models based on ANSYS 15.0 software were used to analyze the effect of structural dimension on characteristics of pressure sensor. According to that, the chips of micro-pressure sensors were fabricated by micro-electro-mechanical system (MEMS) technology on a silicon wafer with <100> orientation.The experimental results show that the proposed sensor achieves a better sensitivity of 9.64 mV/kPa and an excellent linearity of 0.09%F.S. in the range of 0~3.0 kPa at room temperature and a supply voltage of 5.0 V,with a super temperature coefficient of sensitivity(TCS) about - 684 ppm/K from 235.15 K to 360.15 K and low pressure measurement less than 3.0 kPa.

physics.app-ph↗

arXiv2020

Analyzing the Noise Robustness of Deep Neural Networks

Adversarial examples, generated by adding small but intentionally imperceptible perturbations to normal examples, can mislead deep neural networks (DNNs) to make incorrect predictions. Although much work has been done on both adversarial attack and defense, a fine-grained understanding of adversarial examples is still lacking. To address this issue, we present a visual analysis method to explain why adversarial examples are misclassified. The key is to compare and analyze the datapaths of both the adversarial and normal examples. A datapath is a group of critical neurons along with their connections. We formulate the datapath extraction as a subset selection problem and solve it by constructing and training a neural network. A multi-level visualization consisting of a network-level visualization of data flows, a layer-level visualization of feature maps, and a neuron-level visualization of learned features, has been designed to help investigate how datapaths of adversarial and normal examples diverge and merge in the prediction process. A quantitative evaluation and a case study were conducted to demonstrate the promise of our method to explain the misclassification of adversarial examples.

cs.HC↗

arXiv2020

Diagnostics and Visualization of Point Process Models for Event Times on a Social Network

Point process models have been used to analyze interaction event times on a social network, in the hope to provides valuable insights for social science research. However, the diagnostics and visualization of the modeling results from such an analysis have received limited discussion in the literature. In this paper, we develop a systematic set of diagnostic tools and visualizations for point process models fitted to data from a network setting. We analyze the residual process and Pearson residual on the network by inspecting their structure and clustering structure. Equipped with these tools, we can validate whether a model adequately captures the temporal and/or network structures in the observed data. The utility of our approach is demonstrated using simulation studies and point process models applied to a study of animal social interactions.

stat.AP↗

arXiv2019

Local electric field induced spin photocurrent in ReS2

Circular polarized photocurrent is observed near the electrodes on a few-layer ReS2sample at room temperature. For both electrodes, the spatial distribution of the circular polarized photocurrent shows a feature of two wings, with one positive and the other negative. We suggest that this phenomenon arises from the inverse spin Hall effect due to local electric field near the electrode. Bias voltage that modulates this field further controls the sign and magnitude of the inverse spin Hall effect photocurrent. Our research shows that electric field near electrodes has a significant impact on spin transmission operation, hence it could be taken into account for manufacturing spintronic devices in future.

physics.app-ph↗

arXiv2019

Wafer-Scale and Deterministic Patterned Growth of Monolayer MoS2 via Vapor-Liquid-Solid Method

Vapor transportation is the core process in growing transition-metal dichalcogenides (TMDCs) by chemical vapor deposition (CVD). One inevitable problem is the spatial inhomogeneity of the vapors. The non-stoichiometric supply of transition-metal precursors and chalcogen leads to poor control in products' location, morphology, crystallinity, uniformity and batch to batch reproducibility. While vapor-liquid-solid (VLS) growth involves molten precursors at the growth temperatures higher than their melting points. The liquid sodium molybdate can precipitate solid MoS2 monolayers when saturated with sulfur vapor. Taking advantage of the VLS growth, we achieved three kinds of important achievements: (a) 4-inch-wafer-scale uniform growth of MoS2 flakes on SiO2/Si substrates, (b) 2-inch-wafer-scale growth of continuous MoS2 film with a grain size exceeding 100 um on sapphire substrates, and (c) pattern (site-controlled) growth of MoS2 flakes and film. We clarified that the VLS growth thus pave the new way for the high-efficient, scalable synthesis of two-dimensional TMDC monolayers.

cond-mat.mtrl-sci↗

arXiv2019

Epoxy-inspired Nonlinear Interface Integrating Monolayer Transition-Metal Dichalcogenides with Linear Plasmonic Nanosieves

Although TMDC monolayers offer giant optical nonlinearity within few-angstrom thickness, it is still elusive to modulate and engineer the wavefront of nonlinear emissions. The grain size of high-quality monolayers also restricts possibilities of imparting inhomogeneous or gradient profiles of phases and amplitudes by classical light sources. Plasmonic nanosieves can support huge field enhancement and precise nonlinear phase control with hundred-nanometer pixel-level resolution, however they suffer from intrinsically weak nonlinear susceptibility. In nature, epoxy represents a strong glue whose magic adhesion comes from the bonding of two intrinsic loose and nonsticky resins. Inspired by the nature of epoxy, we report a multifunctional and powerful nonlinear interfaces via integrating transition-metal dichalcogenide (TMDC) monolayers with linear plasmonic nanosieves, which cannot be accessed by either constituents. We experimentally demonstrate orbital angular momentum (OAM) generation, beam steering, versatile polarization control and holograms and of SH emission, with the proposed nonlinear interfaces. In addition, an effective second-harmonic (SH) nonlinearity \c{hi}(2) of ~25 nm/V is obtained for such systems. This designer platform synergizes the complementary advantages of TMDC monolayer, plasmonic nanosieves, geometric phases, and field enhancement, paving a distinct avenue toward designer, multi-functional, and ultra-compact nonlinear optical devices.

physics.optics↗

arXiv2019

Enhancement of intrinsic magnetic damping in defect-free epitaxial Fe3O4 thin films

We have investigated the magnetic damping of precessional spin dynamics in defect-controlled epitaxial grown Fe$_3$O$_4$(111)/Yttria-stabilized Zirconia (YSZ) nanoscale films by all-optical pump-probe measurements. The intrinsic damping constant of the defect-free Fe$_3$O$_4$ film is found to be strikingly larger than that of the as-grown Fe$_3$O$_4$ film with structural defects. We demonstrate that the population of the first-order perpendicular standing spin wave (PSSW) mode, which is exclusively observed in the defect-free film under sufficiently high external magnetic fields, leads to the enhancement of the magnetic damping of the uniform precession (Kittel) mode. We propose a physical picture in which the PSSW mode acts as an additional channel for the extra energy dissipation of the Kittel mode. The energy transfer from Kittel mode to PSSW mode increases as in-plane magnetization precession becomes more uniform, resulting in the unique intrinsic magnetic damping enhancement in the defect-free Fe$_3$O$_4$ film.

cond-mat.mtrl-sci↗

arXiv2019

Contracting axially symmetric hypersurfaces by powers of the $σ_k$-curvature

In this paper, we investigate the contracting curvature flow of closed, strictly convex axially symmetric hypersurfaces in $\mathbb{R}^{n+1}$ and $\mathbb{S}^{n+1}$ by $σ_k^α$, where $σ_k$ is the $k$-th elementary symmetric function of the principal curvatures and $α\ge 1/k$. We prove that for any $n\geq3$ and any fixed $k$ with $1\leq k\leq n$, there exists a constant $c(n,k)>1/k$ such that that if $α$ lies in the interval $[1/k,c(n,k)]$, then we have a nice curvature pinching estimate involving the ratio of the biggest principal curvature to the smallest principal curvature of the flow hypersurface, and we prove that the properly rescaled hypersurfaces converge exponentially to the unit sphere. In the case $1<k\le n \le k^2$, we can choose $c(n,k)=\frac{1}{k-1}$. Our results provide an evidence for the general convergence result without initial curvature pinching conditions.

math.DG↗

arXiv2019

Pentaquark states with the $QQQq\bar{q}$ configuration in a simple model

We discuss the mass splittings for the $S$-wave triply heavy pentaquark states with the $QQQq\bar{q}$ $(Q=b,c;q=u,d,s)$ configuration which is a mirror structure of $Q\bar{Q}qqq$. The latter configuration is related with the nature of $P_c(4380)$ observed by the LHCb Collaboration. The considered pentaquark masses are roughly estimated with a simple method. One finds that such states are probably not narrow even if they do exist. This leaves room for molecule interpretation for a state around the low-lying threshold of a doubly heavy baryon and a heavy-light meson, e.g. $Ξ_{cc}D$, if it were observed. As a by product, we conjecture that upper limits for the masses of the conventional triply heavy baryons can be determined by the masses of the conventional doubly heavy baryons.

hep-ph↗

arXiv2019

Systematic studies of charmonium-, bottomonium-, and $B_c$-like tetraquark states

We study the mass splittings of $Q_1q_2\bar{Q}_3\bar{q}_4$ ($Q=c,b$, $q=u,d,s$) tetraquark states with chromomagnetic interactions between their quark components. Assuming that $X(4140)$ is the lowest $J^{PC}=1^{++}$ $cs\bar{c}\bar{s}$ tetraquark, we estimate the masses of the other tetraquark states. From the obtained masses and defined measure reflecting effective quark interactions, we find the following assignments for several exotic states: (1) both $X(3860)$ and the newly observed $Z_c(4100)$ seem to be $0^{++}$ $cn\bar{c}\bar{n}$ tetraquarks; (2) $Z_c(4200)$ is probably a $1^{+-}$ $cn\bar{c}\bar{n}$ tetraquark; (3) $Z_c(3900)$, $X(3940)$, and $X(4160)$ are unlikely compact tetraquarks; (4) $Z_c(4020)$ is unlikely a compact tetraquark, but seems the hidden-charm correspondence of $Z_b(10650)$ with $J^{PC}=1^{+-}$; and (5) $Z_c(4250)$ can be a tetraquark candidate but the quantum numbers cannot be assigned at present. We hope further studies may check the predictions and assignments given here.

hep-ph↗

arXiv2019

Kondo Impurities in Two Dimensional MoS2 for Achieving Ultrahigh Thermoelectric Powerfactor

Local magnetic impurities arising from atomic vacancies in two-dimensional (2D) nanosheets are predicted to have a profound effect on charge transport due to resonant scattering, and provide a handle for enhancing thermoelectric properties through the Kondo effect. However, the effects of these impurities are often masked by external fluctuations and turbostratic interfaces, therefore, it is highly challenging to probe the correlation between magnetic impurities and thermoelectric parameters experimentally. In this work, we demonstrate that by placing Molybdenum Disulfide on a hexagonal Boron Nitride substrate, a colossal spin splitting of the conduction sub-band up to ~50.0 meV is observed at the sulfur vacancies, suggesting that these are local magnetic states. Transport measurements reveal a large anomalous positive Seebeck coefficient in highly conducting n type MoS2, originating from quasiparticle resonance near the Fermi level described by the Kondo effect. Furthermore, by tuning the chemical potential, a record power factor of 50mW/mK2 in low-dimensional materials was achieved. Our work shows that defect engineering of 2D materials affords a strategy for controlling Kondo impurities and tuning thermoelectric transport.

cond-mat.mes-hall↗