arXiv subjects
Publications and source records attributed to Chang Liu.
Both LISA and Taiji, planned space-based gravitational-wave detectors in orbit around the Sun, are expected to launch in 2030-2035. Assuming a one-year overlap, we explore a potential LISA-Taiji network to fast and accurately localize the gravitational-wave sources.
Winograd's minimal filtering algorithm has been widely used in Convolutional Neural Networks (CNNs) to reduce the number of multiplications for faster processing. However, it is only effective on convolutions with kernel size as 3x3 and stride as 1, because it suffers from significantly increased FLOPs and numerical accuracy problem for kernel size larger than 3x3 and fails on convolution with stride larger than 1. In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions. DWM decomposes kernels with large size or large stride to several small kernels with stride as 1 for further applying Winograd method, so that DWM can reduce the number of multiplications while keeping the numerical accuracy. It enables the fast exploring of larger kernel size and larger stride value in CNNs for high performance and accuracy and even the potential for new CNNs. Comparing against the original Winograd, the proposed DWM is able to support all kinds of convolutions with a speedup of ~2, without affecting the numerical accuracy.
The recent discovered intrinsic magnetic topological insulator MnBi2Te4 have been met with unusual success in hosting emergent phenomena such as the quantum anomalous Hall effect and the axion insulator states. However, the surface-bulk correspondence of the Mn-Bi-Te family, composed by the superlattice-like MnBi2Te4/(Bi2Te3)n (n = 0, 1, 2, 3 ...) layered structure, remains intriguing but elusive. Here, by using scanning tunneling microscopy (STM) and angle-resolved photoemission spectroscopy (ARPES) techniques, we unambiguously assign the two distinct surface states of MnBi4Te7 (n = 1) to the quintuple-layer (QL) Bi2Te3 termination and the septuple-layer (SL) MnBi2Te4 termination, respectively. A comparison of the experimental observations with theoretical calculations reveals the diverging topological behaviors, especially the hybridization effect between magnetic and nonmagnetic layers, on the two terminations: a gap on the QL termination originating from the topological surface states of the QL hybridizing with the bands of the beneath SL, and a gapless Dirac-cone band structure on the SL termination with time-reversal symmetry. The quasi-particle interference patterns further confirm the topological nature of the surface states for both terminations, continuing far above the Fermi energy. The QL termination carries a spin-helical Dirac state with hexagonal warping, while at the SL termination, a strongly canted helical state from the surface lies between a pair of Rashba-split states from its neighboring layer. Our work elucidates an unprecedented hybridization effect between the building blocks of the topological surface states, and also reveals the termination-dependent time-reversal symmetry breaking in a magnetic topological insulator, rendering an ideal platform to realize the half-integer quantum Hall effect and relevant quantum phenomena.
The intricate interplay between nontrivial topology and magnetism in two-dimensional (2D) materials has led to the emergence of many novel phenomena and functionalities. An outstanding example is the quantum anomalous Hall (QAH) effect, which was realized in magnetically doped topological insulators (TIs) in the absence of magnetic field. Recently, the layered van der Waals compound MnBi2Te4 has been theoretically predicted and experimentally verified to be a TI with interlayer antiferromagnetic (AFM) order. It is a rare stoichiometric material with coexisting topology and magnetism, thus represents a perfect building block for complex topological-magnetic structures. Here we investigate the quantum transport behaviors of both bulk crystal and exfoliated MnBi2Te4 flakes in a field effect transistor geometry. In the 6 septuple layers (SLs) device tuned into the insulating regime, we observe a large longitudinal resistance and zero Hall plateau, which are characteristic of the axion insulator state. The robust axion insulator state occurs in zero magnetic field, over a wide magnetic field range, and at relatively high temperatures. Moreover, a moderate magnetic field drives a quantum phase transition from the axion insulator phase to a Chern insulator phase with zero longitudinal resistance and quantized Hall resistance h/e2 (h is the Plank constant and e is the elemental charge). These results pave the road for using even-number-SL MnBi2Te4 to realize the quantized topological magnetoelectric effect and axion electrodynamics in condensed matter systems.
The Miura vertex is a versatile origami pattern found in a variety of mechanisms. Previous papers have derived and validated a closed-form solution for the kinematics of a symmetric Miura vertex, but the motion of an asymmetric vertex has only been shown numerically. In this paper, we present the trigonometric derivation of a closed-form solution for the folding of an asymmetric Miura vertex.
Efficient and accurate polarimetric synthetic aperture radar (PolSAR) image classification with a limited number of prior labels is always full of challenges. For general supervised deep learning classification algorithms, the pixel-by-pixel algorithm achieves precise yet inefficient classification with a small number of labeled pixels, whereas the pixel mapping algorithm achieves efficient yet edge-rough classification with more prior labels required. To take efficiency, accuracy and prior labels into account, we propose a novel pixel-refining parallel mapping network in the complex domain named CRPM-Net and the corresponding training algorithm for PolSAR image classification. CRPM-Net consists of two parallel sub-networks: a) A transfer dilated convolution mapping network in the complex domain (C-Dilated CNN) activated by a complex cross-convolution neural network (Cs-CNN), which is aiming at precise localization, high efficiency and the full use of phase information; b) A complex domain encoder-decoder network connected parallelly with C-Dilated CNN, which is to extract more contextual semantic features. Finally, we design a two-step algorithm to train the Cs-CNN and CRPM-Net with a small number of labeled pixels for higher accuracy by refining misclassified labeled pixels. We verify the proposed method on AIRSAR and E-SAR datasets. The experimental results demonstrate that CRPM-Net achieves the best classification results and substantially outperforms some latest state-of-the-art approaches in both efficiency and accuracy for PolSAR image classification. The source code and trained models for CRPM-Net is available at: https://github.com/PROoshio/CRPM-Net.
This paper employs an input-output based approach to analyze convective velocities and the transport of fluctuations in turbulent channel flows. The convective velocity for a fluctuating quantity associated with streamwise-spanwise wavelength pairs at each wall-normal location is obtained through the maximization of the power spectral density associated with the linearized Navier-Stokes equations with a turbulent mean profile and delta-correlated Gaussian forcing. We first demonstrate that the mean convective velocities computed in this manner agree well with those reported previously in the literature. We then exploit the analytical framework to probe the underlying mechanisms contributing to the local convective velocity at different wall-normal locations by isolating the contributions of each spanwise-streamwise wavelength pair (flow scale). The resulting analysis suggests that the behavior of the convective velocity in the near-wall region is influenced by large scale structures further away from the wall. These structures resemble Townsend's attached eddies in the cross-plane, yet show incomplete similarity in the streamwise direction. We then investigate the role of each linear term in the momentum equation to isolate the contribution of the pressure, mean shear, and viscous effects to the deviation of the convective velocity from the mean at each flow scale. Our analysis highlights the role of the viscous effects, particularly in regards to large channel spanning structures whose influence extends to the near-wall region. The results of this work suggest the promise of an input-output approach for analyzing convective velocity across a range of flow scales using only the mean velocity profile.
We propose a novel self-supervised method, referred to as Video Cloze Procedure (VCP), to learn rich spatial-temporal representations. VCP first generates "blanks" by withholding video clips and then creates "options" by applying spatio-temporal operations on the withheld clips. Finally, it fills the blanks with "options" and learns representations by predicting the categories of operations applied on the clips. VCP can act as either a proxy task or a target task in self-supervised learning. As a proxy task, it converts rich self-supervised representations into video clip operations (options), which enhances the flexibility and reduces the complexity of representation learning. As a target task, it can assess learned representation models in a uniform and interpretable manner. With VCP, we train spatial-temporal representation models (3D-CNNs) and apply such models on action recognition and video retrieval tasks. Experiments on commonly used benchmarks show that the trained models outperform the state-of-the-art self-supervised models with significant margins.
The layered MnBi2nTe3n+1 family represents the first intrinsic antiferromagnetic topological insulator (AFM TI, protected by a combination symmetry ) ever discovered, providing an ideal platform to explore novel physics such as quantum anomalous Hall effect at elevated temperature and axion electrodynamics. Recent angle-resolved photoemission spectroscopy (ARPES) experiments on this family have revealed that all terminations exhibit (nearly) gapless topological surface states (TSSs) within the AFM state, violating the definition of the AFM TI, as the surfaces being studied should be -breaking and opening a gap. Here we explain this curious paradox using a surface-bulk band hybridization picture. Combining ARPES and first-principles calculations, we prove that only an apparent gap is opened by hybridization between TSSs and bulk bands. The observed (nearly) gapless features are consistently reproduced by tight-binding simulations where TSSs are coupled to a Rashba-split bulk band. The Dirac-cone-like spectral features are actually of bulk origin, thus not sensitive to the-breaking at the AFM surfaces. This picture explains the (nearly) gapless behaviour found in both Bi2Te3- and MnBi2Te4-terminated surfaces and is applicable to all terminations of MnBi2nTe3n+1 family. Our findings highlight the role of band hybridization, superior to magnetism in this case, in shaping the general surface band structure in magnetic topological materials for the first time.
Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure. Recently, an efficient quantum algorithm was proposed [Lloyd, Garnerone, Zanardi, Nat. Commun. 7, 10138 (2016)] for calculating Betti numbers of data points -- topological features that count the number of topological holes of various dimensions in a scatterplot. Here, we implement a proof-of-principle demonstration of this quantum algorithm by employing a six-photon quantum processor to successfully analyze the topological features of Betti numbers of a network including three data points, providing new insights into data analysis in the era of quantum computing.
Gradient porous structured materials possess significant potential of being applied in many engineering fields. To accelerate the design process of infill graded microstructures, a novel asymptotic homogenisation topology optimisation method was proposed by Zhu et al.(2019), aiming for 1) significantly enriching the pool of representable graded microstructures; 2) deriving an homogenised formulation for stress analysis in consistency with fine-scale results. But the work is severely confined from being widely applied, mainly due to the following two reasons. Firstly, to circumvent the macroscopically pointwise computation for solving various microscopic cell problems, linearisation had to be adopted for its numerical implementation, and this significantly reduces the design freedom. Secondly, lacking of sensitive analysis, genetic algorithm was chosen for optimisation, inevitably decreasing the computational efficiency. To address these bottleneck challenging issues, a zoning scheme empowered by computational parallelism is introduced, and the sensitivity analysis associated with the new asymptotic framework is conducted. Through comparisons with fine-scale simulation results, the proposed algorithm is shown to be an effective tool for evaluating the mechanical behaviour of graded microstructures. As an optimisation tool, the mapping function takes a concise and explicit form. But its parameterisation still needs further investigation, so as to improve the solution optimality of the present approach, especially in comparison with another recently proposed method by Groen et al (2018). The high computational efficiency shown by the proposed scheme is also demonstrated with optimisation results for three-dimensional graded microstructures, which are not frequently discussed in literature, possibly because of the high computational cost generated.
We propose a holographic map between Einstein gravity coupled to matter in a de Sitter background and large N quantum mechanics of a system of spins. Holography maps a spin model with a finite dimensional Hilbert space defined on a version of the stretched horizon into bulk gravitational dynamics. The full Hamiltonian of the spin model contains a non-local piece which generates chaotic dynamics, widely conjectured to be a necessary part of quantum gravity, and a local piece which recovers the perturbative spectrum in the bulk.
In Gaussian graphical models, the zero entries in the precision matrix determine the dependence structure, so estimating that sparse precision matrix and, thereby, learning this underlying structure, is an important and challenging problem. We propose an empirical version of the $G$-Wishart prior for sparse precision matrices, where the prior mode is informed by the data in a suitable way. Paired with a prior on the graph structure, a marginal posterior distribution for the same is obtained that takes the form of a ratio of two $G$-Wishart normalizing constants. We show that this ratio can be easily and accurately computed using a Laplace approximation, which leads to fast and efficient posterior sampling even in high-dimensions. Numerical results demonstrate the proposed method's superior performance, in terms of speed and accuracy, across a variety of settings, and theoretical support is provided in the form of a posterior concentration rate theorem.
Strain is commonly used in metal-oxide-semiconductor technologies to boost on-state performance. This booster has been in production for at least a decade. Despite this, a systematic study of the impact of strain on off-state leakage current has been lacking. In this work we use experimental data and ab-initio calculations to refine existing models to account for the impact of strain on band-to-band tunnelling and trap-assisted tunnelling in silicon. We observe that the strain may dramatically increase the leakage current, depending on the type of tunnelling involved. For band-to-band and trap-assisted tunnelling, low uniaxial strains of 0.1% (or 180 MPa) can increase the leakage current by 60% and 10% compared to the unstrained case, respectively. Using our models, we predict that compressive strain on the order of 1% (or 2 GPa) can increase the leakage current by 150 times. Conversely, tensile strain may diminish or at most double the leakage current in all observed cases. Though detrimental in conventional inversion-mode MOSFETs, these processes may be used to boost the performance of Tunnel Field Effect Transistors, where on-state current is defined by band-to-band tunnelling.
In the present work, a hybrid MMC-MMV approach is developed for designing additive manufacturing-oriented shell-graded-infill structures. The key idea is to describe the geometry of a shell-graded-infill structure explicitly using some geometry parameters. To this end, a set of morphable voids is adopted to describe the boundary of the coating shell, while a set of morphable components combing with a coordinate perturbation technique are introduced to represent the graded infill distribution. Under such treatment, both the crisp boundary of the coating shell and the graded infill can be optimized simultaneously, with a small number of design variables. Numerical examples demonstrate the effectiveness of the proposed approach.
Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner. Our approach, referred to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure. FreeAnchor targets at learning features which best explain a class of objects in terms of both classification and localization. FreeAnchor is implemented by optimizing detection customized likelihood and can be fused with CNN-based detectors in a plug-and-play manner. Experiments on COCO demonstrate that FreeAnchor consistently outperforms their counterparts with significant margins.
An oblivious computation is one that is free of direct and indirect information leaks, e.g., due to observable differences in timing and memory access patterns. This paper presents Lambda Obliv, a core language whose type system enforces obliviousness. Prior work on type-enforced oblivious computation has focused on deterministic programs. Lambda Obliv is new in its consideration of programs that implement probabilistic algorithms, such as those involved in cryptography. Lambda Obliv employs a substructural type system and a novel notion of probability region to ensure that information is not leaked via the observed distribution of visible events. Probability regions support reasoning about probabilistic correlation and independence between values, and our use of probability regions is motivated by a source of unsoundness that we discovered in the type system of ObliVM, a language for implementing state of the art oblivious algorithms. We prove that Lambda Obliv's type system enforces obliviousness and show that it is expressive enough to typecheck advanced tree-based oblivious RAMs.
Fine-grained recognition task deals with sub-category classification problem, which is important for real-world applications. In this work, we are particularly interested in the segmentation task on the \emph{finest-grained} level, which is specifically named "individual segmentation". In other words, the individual-level category has no sub-category under it. Segmentation problem in the individual level reveals some new properties, limited training data for single individual object, unknown background, and difficulty for the use of depth. To address these new problems, we propose a "Context Less-Aware" (CoLA) pipeline, which produces RGB-D object-predominated images that have less background context, and enables a scale-aware training and testing with 3D information. Extensive experiments show that the proposed CoLA strategy largely outperforms baseline methods on YCB-Video dataset and our proposed Supermarket-10K dataset. Code, trained model and new dataset will be published with this paper.