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Jie Liu

Publications and source records attributed to Jie Liu.

At least 559 records · Page 31Linked to original sources
arXiv2020

Non-Abelian statistics of Majorana zero modes in the presence of an Andreev bound state

Braiding Majorana zero modes (MZMs) is the key procedure toward topological quantum computation. We show such braiding can be well performed in a parallel semiconductor-superconductor nanowire structure. Considering the fact that the low-energy Andreev bound states (ABSs) usually mix with the MZMs in the present set-up, we further investigate the braiding properties of MZMs when an ABS is presented. Our numerical simulation suggests that ABS can be regarded as a pair of weakly coupled MZMs. The dynamical hybridization of MZMs plus the non-Abelian braiding of MZMs would induce an arbitrary rotation on the Bloch sphere of a single qubit. Remarkably, such rotation is manipulable since the rotation parameters could be individually modulated. Thus, the dynamic evolution can be eliminated and the non-Abelian braiding statistics, independent of the braiding time, retrieves.

cond-mat.mes-hall↗
arXiv2020

XLBoost-Geo: An IP Geolocation System Based on Extreme Landmark Boosting

IP geolocation aims at locating the geographical position of Internet devices, which plays an essential role in many Internet applications. In this field, a long-standing challenge is how to find a large number of highly-reliable landmarks, which is the key to improve the precision of IP geolocation. To this end, many efforts have been made, while many IP geolocation methods still suffer from unacceptable error distance because of the lack of landmarks. In this paper, we propose a novel IP geolocation system, named XLBoost-Geo, which focuses on enhancing the number and the density of highly reliable landmarks. The main idea is to extract location-indicating clues from web pages and locating the web servers based on the clues. Based on the landmarks, XLBoost-Geo is able to geolocate arbitrary IPs with little error distance. Specifically, we first design an entity extracting method based on a bidirectional LSTM neural network with a self-adaptive loss function (LSTM-Ada) to extract the location-indicating clues on web pages and then generate landmarks based on the clues. Then, by measurements on network latency and topology, we estimate the closest landmark and associate the coordinate of the landmark with the location of the target IP. The results of our experiments clearly validate the effectiveness and efficiency of the extracting method, the precision, number, coverage of the landmarks, and the precision of the IP geolocation. On RIPE Atlas nodes, XLBoost-Geo achieves 2,561m median error distance, which outperforms SLG and IPIP.

cs.NI↗
arXiv2020

Adaptive Gradient Method with Resilience and Momentum

Several variants of stochastic gradient descent (SGD) have been proposed to improve the learning effectiveness and efficiency when training deep neural networks, among which some recent influential attempts would like to adaptively control the parameter-wise learning rate (e.g., Adam and RMSProp). Although they show a large improvement in convergence speed, most adaptive learning rate methods suffer from compromised generalization compared with SGD. In this paper, we proposed an Adaptive Gradient Method with Resilience and Momentum (AdaRem), motivated by the observation that the oscillations of network parameters slow the training, and give a theoretical proof of convergence. For each parameter, AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient, and thus encourages long-term consistent parameter updating with much fewer oscillations. Comprehensive experiments have been conducted to verify the effectiveness of AdaRem when training various models on a large-scale image recognition dataset, e.g., ImageNet, which also demonstrate that our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error, respectively.

cs.LG↗
arXiv2020

Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things

Nowadays, Deep Learning as a service can be deployed in Internet of Things (IoT) to provide smart services and sensor data processing. However, recent research has revealed that some Deep Neural Networks (DNN) can be easily misled by adding relatively small but adversarial perturbations to the input (e.g., pixel mutation in input images). One challenge in defending DNN against these attacks is to efficiently identifying and filtering out the adversarial pixels. The state-of-the-art defense strategies with good robustness often require additional model training for specific attacks. To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erroneous outputs, without a-priori knowledge about the attack type. We evaluated our progressive defense strategy against various attack methods on two well-known datasets. The result shows it outperforms the state-of-the-art while reducing the cost of model training by 50% on average.

cs.CR↗
arXiv2020

Super-exponential diffusion in nonlinear non-Hermitian systems

We investigate the quantum diffusion of a periodically kicked particle subjecting to both nonlinearity induced self-interactions and $\mathcal{PT}$-symmetric potentials. We find that, due to the interplay between the nonlinearity and non-Hermiticity, the expectation value of mean square of momentum scales with time in a super-exponential form $\langle p^2(t)\rangle\propto\exp[β\exp(αt)]$, which is faster than any known rates of quantum diffusion. In the $\mathcal{PT}$-symmetry-breaking phase, the intensity of a state increases exponentially with time, leading to the exponential growth of the interaction strength. The feedback of the intensity-dependent nonlinearity further turns the interaction energy into the kinetic energy, resulting in a super-exponential growth of the mean energy. These theoretical predictions are in good agreement with numerical simulations in a $\cal{PT}$-symmetric nonlinear kicked particle. Our discovery establishes a new mechanism of diffusion in interacting and dissipative quantum systems. Important implications and possible experimental observations are discussed.

nlin.CD↗
arXiv2020

Spin-orbit coupling and linear crossings of dipolar magnons in van der Waals antiferromagnets

A magnon spin-orbit coupling, induced by the dipole-dipole interaction, is derived in monoclinic-stacked bilayer honeycomb spin lattice with perpendicular magnetic anisotropy and antiferromagnetic interlayer coupling. Linear crossings are predicted in the magnon spectrum around the band minimum in G valley, as well as in the high frequency range around the zone boundary. The linear crossings in K and K' valleys, which connect the acoustic and optical bands, can be gapped when the intralayer dipole-dipole or Kitaev interactions exceed the interlayer dipole-dipole interaction, resulting in a phase transition from semimetal to insulator. Our results are useful for analyzing the magnon spin dynamics and transport properties in van der Waals antiferromagnet.

cond-mat.mes-hall↗
arXiv2020

Residual Feature Distillation Network for Lightweight Image Super-Resolution

Recent advances in single image super-resolution (SISR) explored the power of convolutional neural network (CNN) to achieve a better performance. Despite the great success of CNN-based methods, it is not easy to apply these methods to edge devices due to the requirement of heavy computation. To solve this problem, various fast and lightweight CNN models have been proposed. The information distillation network is one of the state-of-the-art methods, which adopts the channel splitting operation to extract distilled features. However, it is not clear enough how this operation helps in the design of efficient SISR models. In this paper, we propose the feature distillation connection (FDC) that is functionally equivalent to the channel splitting operation while being more lightweight and flexible. Thanks to FDC, we can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distillation network (RFDN). RFDN uses multiple feature distillation connections to learn more discriminative feature representations. We also propose a shallow residual block (SRB) as the main building block of RFDN so that the network can benefit most from residual learning while still being lightweight enough. Extensive experimental results show that the proposed RFDN achieve a better trade-off against the state-of-the-art methods in terms of performance and model complexity. Moreover, we propose an enhanced RFDN (E-RFDN) and won the first place in the AIM 2020 efficient super-resolution challenge. Code will be available at https://github.com/njulj/RFDN.

eess.IV↗
arXiv2020

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

eess.IV↗
arXiv2020

Supporting the Problem-Solving Loop: Designing Highly Interactive Optimisation Systems

Efficient optimisation algorithms have become important tools for finding high-quality solutions to hard, real-world problems such as production scheduling, timetabling, or vehicle routing. These algorithms are typically "black boxes" that work on mathematical models of the problem to solve. However, many problems are difficult to fully specify, and require a "human in the loop" who collaborates with the algorithm by refining the model and guiding the search to produce acceptable solutions. Recently, the Problem-Solving Loop was introduced as a high-level model of such interactive optimisation. Here, we present and evaluate nine recommendations for the design of interactive visualisation tools supporting the Problem-Solving Loop. They range from the choice of visual representation for solutions and constraints to the use of a solution gallery to support exploration of alternate solutions. We first examined the applicability of the recommendations by investigating how well they had been supported in previous interactive optimisation tools. We then evaluated the recommendations in the context of the vehicle routing problem with time windows (VRPTW). To do so we built a sophisticated interactive visual system for solving VRPTW that was informed by the recommendations. Ten participants then used this system to solve a variety of routing problems. We report on participant comments and interaction patterns with the tool. These showed the tool was regarded as highly usable and the results generally supported the usefulness of the underlying recommendations.

cs.HC↗
arXiv2020

Noise signatures for determining chiral Majorana fermion modes

The conductance measurement of a half quantized plateau in a quantum anomalous Hall insulator-superconductor structure is reported by a recent experiment [Q. L. He \textit{et al.}, Science 357, 294-299 (2017)], which suggests the existence of the chiral Majorana fermion modes. However, such half quantized conductance plateau may also originates from a disorder-induced metallic phase. To identify the exact mechanism, we study the transport properties of such a system in the presence of strong disorders. Our results show that the local current density distributions of these two mechanisms are different. In particular, the current noises measurement can be used to distinguish them without any further fabrication of current experimental setup.

cond-mat.mes-hall↗
arXiv2020

Adaptive Neural Network-Based Approximation to Accelerate Eulerian Fluid Simulation

The Eulerian fluid simulation is an important HPC application. The neural network has been applied to accelerate it. The current methods that accelerate the fluid simulation with neural networks lack flexibility and generalization. In this paper, we tackle the above limitation and aim to enhance the applicability of neural networks in the Eulerian fluid simulation. We introduce Smartfluidnet, a framework that automates model generation and application. Given an existing neural network as input, Smartfluidnet generates multiple neural networks before the simulation to meet the execution time and simulation quality requirement. During the simulation, Smartfluidnet dynamically switches the neural networks to make the best efforts to reach the user requirement on simulation quality. Evaluating with 20,480 input problems, we show that Smartfluidnet achieves 1.46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model.

cs.LG↗
arXiv2020

Poet: Product-oriented Video Captioner for E-commerce

In e-commerce, a growing number of user-generated videos are used for product promotion. How to generate video descriptions that narrate the user-preferred product characteristics depicted in the video is vital for successful promoting. Traditional video captioning methods, which focus on routinely describing what exists and happens in a video, are not amenable for product-oriented video captioning. To address this problem, we propose a product-oriented video captioner framework, abbreviated as Poet. Poet firstly represents the videos as product-oriented spatial-temporal graphs. Then, based on the aspects of the video-associated product, we perform knowledge-enhanced spatial-temporal inference on those graphs for capturing the dynamic change of fine-grained product-part characteristics. The knowledge leveraging module in Poet differs from the traditional design by performing knowledge filtering and dynamic memory modeling. We show that Poet achieves consistent performance improvement over previous methods concerning generation quality, product aspects capturing, and lexical diversity. Experiments are performed on two product-oriented video captioning datasets, buyer-generated fashion video dataset (BFVD) and fan-generated fashion video dataset (FFVD), collected from Mobile Taobao. We will release the desensitized datasets to promote further investigations on both video captioning and general video analysis problems.

cs.CV↗
arXiv2020

Simulating periodic systems on quantum computer

The variational quantum eigensolver (VQE) is one of the most appealing quantum algorithms to simulate electronic structure properties of molecules on near-term noisy intermediate-scale quantum devices. In this work, we generalize the VQE algorithm for simulating extended systems. However, the numerical study of an one-dimensional (1D) infinite hydrogen chain using existing VQE algorithms shows a remarkable deviation of the ground state energy with respect to the exact full configuration interaction (FCI) result. Here, we present two schemes to improve the accuracy of quantum simulations for extended systems. The first one is a modified VQE algorithm, which introduces an unitary transformation of Hartree-Fock orbitals to avoid the complex Hamiltonian. The second one is a Post-VQE approach combining VQE with the quantum subspace expansion approach (VQE/QSE). Numerical benchmark calculations demonstrate that both of two schemes provide an accurate enough description of the potential energy curve of the 1D hydrogen chain. In addition, excited states computed with the VQE/QSE approach also agree very well with FCI results.

quant-ph↗
arXiv2020

Three Numerical Eigensolvers for 3-D Cavity Resonators Filled With Anisotropic and Nonconductive Media

This paper mainly investigates the classic resonant cavity problem with anisotropic and nonconductive media, which is a linear vector Maxwell's eigenvalue problem. The finite element method based on edge element of the lowest-order and standard linear element is used to solve this type of 3-D closed cavity problem. In order to eliminate spurious zero modes in the numerical simulation, the divergence-free condition supported by Gauss' law is enforced in a weak sense. After the finite element discretization, the generalized eigenvalue problem with a linear constraint condition needs to be solved. Penalty method, augmented method and projection method are applied to solve this difficult problem in numerical linear algebra. The advantages and disadvantages of these three computational methods are also given in this paper. Furthermore, we prove that the augmented method is free of spurious modes as long as the anisotropic material is not magnetic lossy. The projection method based on singular value decomposition technique can be used to solve the resonant cavity problem. Moreover, the projection method {cannot} introduce any spurious modes. At last, several numerical experiments are carried out to verify our theoretical results.

math.NA↗
arXiv2020

Disorder effects in the two-dimensional Lieb lattice and its extensions

We study the localization properties of the two-dimensional Lieb lattice and its extensions in the presence of disorder using transfer matrix method and finite-size scaling. We find that all states in the Lieb lattice and its extensions are localized for $W \geq 1$. Clear differences in the localization properties between disordered flat band and disordered dispersive bands are identified. Our results complement previous experimental studies of clean photonic Lieb lattices and provide information about their stability with respect to disorder.

cond-mat.dis-nn↗
arXiv2020

An efficient and high-resolution topology optimization method based on convolutional neural networks

2. In Section 3, we used some vague statements to affirm the training process of the neural network, which cannot support others to reproduce the results of the paper. In addition, this section does not show the difference between this paper and other work, nor does it reflect innovation. 3. In Section 5, the numerical examples are not compared with other multi-resolution methods, which is not enough to explain the superiority of the method proposed in this paper. Figure 10 also fails to show that this method is significantly improved compared to the traditional method. Thanks for your understanding. Topology optimization is a pioneering design method that can provide various candidates with high mechanical properties. However, the high-resolution for the optimum structures is highly desired, normally in turn leading to computationally intractable puzzle, especially for the famous Solid Isotropic Material with Penalization (SIMP) method. In this paper, we introduce the Super-Resolution Convolutional Neural Network (SRCNN) technique into topology optimization framework to improve the resolution of topology solutions with extremely high computational efficiency. Additionally, a pooling strategy is established to balance the number of finite element analysis (FEA) and the output mesh in optimization process. Considering the high training cost of 3D neural networks, several 2D neural networks are combined to deal with 3D topology optimization design problems. The combined treatment method used in 3D topology optimization design eliminates the expense of retraining 3D convolutional neural network and guarantees the quality of 3D design. Some typical examples justify that the high-resolution topology optimization method adopting SRCNN has excellent applicability and high efficiency.

math.OC↗
arXiv2020

Classification of Dark Solitons via Topological Vector Potentials

Dark soliton is one of most interesting nonlinear excitations in physical systems, manifesting a spatially localized density "dip" on a uniform background accompanied with a phase jump across the dip. However, the topological properties of the dark solitons are far from fully understood. Our investigation for the first time uncover a vector potential underlying the nonlinear excitation whose line integral gives the striking phase jump. More importantly, we find that the vector potential field has a topological configuration in analogous to the Wess-Zumino term in a Lagrangian representation. It can induce some point-like magnetic fields scattered periodically on a complex plane, each of them has a quantized magnetic flux of elementary $π$. We then calculate the Euler characteristic of the topological manifold of the vector potential field and classify all known dark solitions according to the index.

nlin.PS↗