arXiv subjects

Yao Wang

Publications and source records attributed to Yao Wang.

At least 217 records · Page 12Linked to original sources

arXiv2023

Witnessing Light-Driven Entanglement using Time-Resolved Resonant Inelastic X-Ray Scattering

Characterizing and controlling entanglement in quantum materials is crucial for the development of next-generation quantum technologies. However, defining a quantifiable figure of merit for entanglement in macroscopic solids is theoretically and experimentally challenging. At equilibrium the presence of entanglement can be diagnosed by extracting entanglement witnesses from spectroscopic observables and a nonequilibrium extension of this method could lead to the discovery of novel dynamical phenomena. Here, we propose a systematic approach to quantify the time-dependent quantum Fisher information and entanglement depth of transient states of quantum materials with time-resolved resonant inelastic x-ray scattering. Using a quarter-filled extended Hubbard model as an example, we benchmark the efficiency of this approach and predict a light-enhanced many-body entanglement due to the proximity to a phase boundary. Our work sets the stage for experimentally witnessing and controlling entanglement in light-driven quantum materials via ultrafast spectroscopic measurements.

cond-mat.str-el↗

arXiv2023

Electronic ratchet effect in a moiré system: signatures of excitonic ferroelectricity

Electronic ferroelectricity represents a new paradigm where spontaneous symmetry breaking driven by electronic correlations, in contrast to traditional lattice-driven ferroelectricity, leads to the formation of electric dipoles. Despite the potential application advantages arising from its electronic nature, switchable electronic ferroelectricity remains exceedingly rare. Here, we report the discovery of an electronic ratchet effect that manifests itself as switchable electronic ferroelectricity in a layer-contrasting graphene-boron nitride moiré heterostructure. Our engineered layer-asymmetric moiré potential landscapes result in layer-polarized localized and itinerant electronic subsystems. At particular fillings of the localized subsystem, we find a ratcheting injection of itinerant carriers in a non-volatile manner, leading to a highly unusual ferroelectric response. Strikingly, the remnant polarization can be stabilized at multiple (quasi-continuous) states with behavior markedly distinct from known ferroelectrics. Our experimental observations, simulations, and theoretical analysis suggest that dipolar excitons are the driving force and elementary ferroelectric units in our system. This signifies a new type of electronic ferroelectricity where the formation of dipolar excitons with aligned moments generates a macroscopic polarization and leads to an electronically-driven ferroelectric response, which we term excitonic ferroelectricity. Such new ferroelectrics, driven by quantum objects like dipolar excitons, could pave the way to innovative quantum analog memory and synaptic devices.

cond-mat.mes-hall↗

arXiv2023

Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning

Nonlocal self-similarity within natural images has become an increasingly popular prior in deep-learning models. Despite their successful image restoration performance, such models remain largely uninterpretable due to their black-box construction. Our previous studies have shown that interpretable construction of a fully convolutional denoiser (CDLNet), with performance on par with state-of-the-art black-box counterparts, is achievable by unrolling a dictionary learning algorithm. In this manuscript, we seek an interpretable construction of a convolutional network with a nonlocal self-similarity prior that performs on par with black-box nonlocal models. We show that such an architecture can be effectively achieved by upgrading the $\ell 1$ sparsity prior of CDLNet to a weighted group-sparsity prior. From this formulation, we propose a novel sliding-window nonlocal operation, enabled by sparse array arithmetic. In addition to competitive performance with black-box nonlocal DNNs, we demonstrate the proposed sliding-window sparse attention enables inference speeds greater than an order of magnitude faster than its competitors.

eess.IV↗

arXiv2023

Efficient Fraud Detection Using Deep Boosting Decision Trees

Fraud detection is to identify, monitor, and prevent potentially fraudulent activities from complex data. The recent development and success in AI, especially machine learning, provides a new data-driven way to deal with fraud. From a methodological point of view, machine learning based fraud detection can be divided into two categories, i.e., conventional methods (decision tree, boosting...) and deep learning, both of which have significant limitations in terms of the lack of representation learning ability for the former and interpretability for the latter. Furthermore, due to the rarity of detected fraud cases, the associated data is usually imbalanced, which seriously degrades the performance of classification algorithms. In this paper, we propose deep boosting decision trees (DBDT), a novel approach for fraud detection based on gradient boosting and neural networks. In order to combine the advantages of both conventional methods and deep learning, we first construct soft decision tree (SDT), a decision tree structured model with neural networks as its nodes, and then ensemble SDTs using the idea of gradient boosting. In this way we embed neural networks into gradient boosting to improve its representation learning capability and meanwhile maintain the interpretability. Furthermore, aiming at the rarity of detected fraud cases, in the model training phase we propose a compositional AUC maximization approach to deal with data imbalances at algorithm level. Extensive experiments on several real-life fraud detection datasets show that DBDT can significantly improve the performance and meanwhile maintain good interpretability. Our code is available at https://github.com/freshmanXB/DBDT.

stat.ML↗

arXiv2023

A Lightweight Authentication Protocol against Modeling Attacks based on a Novel LFSR-APUF

Simple authentication protocols based on conventional physical unclonable function (PUF) are vulnerable to modeling attacks and other security threats. This paper proposes an arbiter PUF based on a linear feedback shift register (LFSR-APUF). Different from the previously reported linear feedback shift register for challenge extension, the proposed scheme feeds the external random challenges into the LFSR module to obfuscate the linear mapping relationship between the challenge and response. It can prevent attackers from obtaining valid challenge-response pairs (CRPs), increasing its resistance to modeling attacks significantly. A 64-stage LFSR-APUF has been implemented on a field programmable gate array (FPGA) board. The experimental results reveal that the proposed design can effectively resist various modeling attacks such as logistic regression (LR), evolutionary strategy (ES), Artificial Neuro Network (ANN), and support vector machine (SVM) with a prediction rate of 51.79% and a slight effect on the randomness, reliability, and uniqueness. Further, a lightweight authentication protocol is established based on the proposed LFSR-APUF. The protocol incorporates a low-overhead, ultra-lightweight, novel private bit conversion Cover function that is uniquely bound to each device in the authentication network. A dynamic and timevariant obfuscation scheme in combination with the proposed LFSR-APUF is implemented in the protocol. The proposed authentication protocol not only resists spoofing attacks, physical attacks, and modeling attacks effectively, but also ensures the security of the entire authentication network by transferring important information in encrypted form from the server to the database even when the attacker completely controls the server.

cs.CR↗

arXiv2023

Role of electron-phonon coupling in excitonic insulator candidate Ta2NiSe5

Electron-hole bound pairs, or excitons, are common excitations in semiconductors. They can spontaneously form and ``condense'' into a new insulating ground state -- the so-called excitonic insulator -- when the energy of electron-hole Coulomb attraction exceeds the band gap. In the presence of electron-phonon coupling, a periodic lattice distortion often concomitantly occurs with this exciton condensation. However, similar structural transition can also be induced by electron-phonon coupling itself, therefore hindering the clean identification of bulk excitonic insulators based on reductionistic reasoning (e.g. which instability is the ``driving force'' of the phase transition). Using high-resolution synchrotron x-ray diffraction and angle-resolved photoemission spectroscopy techniques, we identify key electron-phonon coupling effects in a leading excitonic insulator candidate Ta2NiSe5. These include an extensive unidirectional lattice fluctuation and an electronic pseudogap in the normal state, as well as a negative electronic compressibility in the charge-doped broken-symmetry state. In combination with first principles and model calculations, we determine a minimal lattice model and the corresponding interaction parameters that capture the experimental observations. More importantly, we show how the Coulomb and electron-phonon coupling effects can be separated on the level of lattice model, and demonstrate a general framework beyond the reductionist approach in the investigation of correlated systems with intertwined orders.

cond-mat.str-el↗

arXiv2023

Lightweight Estimation of Hand Mesh and Biomechanically Feasible Kinematic Parameters

3D hand pose estimation is a long-standing challenge in both robotics and computer vision communities due to its implicit depth ambiguity and often strong self-occlusion. Recently, in addition to the hand skeleton, jointly estimating hand pose and shape has gained more attraction. State-of-the-art methods adopt a model-free approach, estimating the vertices of the hand mesh directly and providing superior accuracy compared to traditional model-based methods directly regressing the parameters of the parametric hand mesh. However, with the large number of mesh vertices to estimate, these methods are often slow in inference. We propose an efficient variation of the previously proposed image-to-lixel approach to efficiently estimate hand meshes from the images. Leveraging recent developments in efficient neural architectures, we significantly reduce the computation complexity without sacrificing the estimation accuracy. Furthermore, we introduce an inverted kinematic(IK) network to translate the estimated hand mesh to a biomechanically feasible set of joint rotation parameters, which is necessary for applications that leverage pose estimation for controlling robotic hands. Finally, an optional post-processing module is proposed to refine the rotation and shape parameters to compensate for the error introduced by the IK net. Our Lite I2L Mesh Net achieves state-of-the-art joint and mesh estimation accuracy with less than $13\%$ of the total computational complexity of the original I2L hand mesh estimator. Adding the IK net and post-optimization modules can improve the accuracy slightly at a small computation cost, but more importantly, provide the kinematic parameters required for robotic applications.

cs.CV↗

arXiv2023

Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets

In this paper, we study the generalization performance of global minima for implementing empirical risk minimization (ERM) on over-parameterized deep ReLU nets. Using a novel deepening scheme for deep ReLU nets, we rigorously prove that there exist perfect global minima achieving almost optimal generalization error bounds for numerous types of data under mild conditions. Since over-parameterization is crucial to guarantee that the global minima of ERM on deep ReLU nets can be realized by the widely used stochastic gradient descent (SGD) algorithm, our results indeed fill a gap between optimization and generalization.

cs.LG↗

arXiv2023

Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks

Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. However, the key evidence from neuroimaging data for pathological commonness remains unrevealed. To explore this hypothesis, we build a deep learning model, using multi-site functional magnetic resonance imaging data (N=4,410, 6 sites), for classifying 5 different brain disorders from healthy controls, with a set of common features. Our model achieves 62.6(1.9)% overall classification accuracy on data from the 6 investigated sites and detects a set of commonly affected functional subnetworks at different spatial scales, including default mode, executive control, visual, and limbic networks. In the deep-layer feature representation for individual data, we observe young and aging patients with disorders are continuously distributed, which is in line with the clinical concept of the "spectrum of disorders". The revealed spectrum underlying early- and late-life brain disorders promotes the understanding of disorder comorbidities in the lifespan.

cs.AI↗

arXiv2023

Dual-mode adaptive-SVD ghost imaging

In this paper, we present a dual-mode adaptive singular value decomposition ghost imaging (A-SVD GI), which can be easily switched between the modes of imaging and edge detection. It can adaptively localize the foreground pixels via a threshold selection method. Then only the foreground region is illuminated by the singular value decomposition (SVD) - based patterns, consequently retrieving high-quality images with fewer sampling ratios. By changing the selecting range of foreground pixels, the A-SVD GI can be switched to the mode of edge detection to directly reveal the edge of objects, without needing the original image. We investigate the performance of these two modes through both numerical simulations and experiments. We also develop a single-round scheme to halve measurement numbers in experiments, instead of separately illuminating positive and negative patterns in traditional methods. The binarized SVD patterns, generated by the spatial dithering method, are modulated by a digital micromirror device (DMD) to speed up the data acquisition. This dual-mode A-SVD GI can be applied in various applications, such as remote sensing or target recognition, and could be further extended for multi-modality functional imaging/detection.

eess.IV↗

arXiv2023

Scanpath Prediction on Information Visualisations

We propose Unified Model of Saliency and Scanpaths (UMSS) -- a model that learns to predict visual saliency and scanpaths (i.e. sequences of eye fixations) on information visualisations. Although scanpaths provide rich information about the importance of different visualisation elements during the visual exploration process, prior work has been limited to predicting aggregated attention statistics, such as visual saliency. We present in-depth analyses of gaze behaviour for different information visualisation elements (e.g. Title, Label, Data) on the popular MASSVIS dataset. We show that while, overall, gaze patterns are surprisingly consistent across visualisations and viewers, there are also structural differences in gaze dynamics for different elements. Informed by our analyses, UMSS first predicts multi-duration element-level saliency maps, then probabilistically samples scanpaths from them. Extensive experiments on MASSVIS show that our method consistently outperforms state-of-the-art methods with respect to several, widely used scanpath and saliency evaluation metrics. Our method achieves a relative improvement in sequence score of 11.5% for scanpath prediction, and a relative improvement in Pearson correlation coefficient of up to 23.6% for saliency prediction. These results are auspicious and point towards richer user models and simulations of visual attention on visualisations without the need for any eye tracking equipment.

cs.CV↗

arXiv2023

Open quantum systems with nonlinear environmental backactions: Extended dissipaton theory versus core-system hierarchy construction

In this paper, we present a comprehensive account of quantum dissipation theories with the quadratic environment couplings. The theoretical development includes the Brownian solvation mode embedded hierarchical quantum master equations, a core-system hierarchy construction that verifies the extended dissipaton equation of motion (DEOM) formalism [R. X. Xu et al., J. Chem. Phys. 148, 114103 (2018)]. Developed are also the quadratic imaginary-time DEOM for equilibrium and the λ (t)-DEOM for nonequilibrium thermodynamics problems. Both the celebrated Jarzynski equality and Crooks relation are accurately reproduced, which in turn confirms the rigorousness of the extended DEOM theories. While the extended DEOM is more numerically efficient, the core-system hierarchy quantum master equation is favorable for ''visualizing'' the correlated solvation dynamics.

quant-ph↗

arXiv2023

Spin relaxation dynamics with a continuous spin environment: the dissipaton equation of motion approach

We present the quantum dynamics of a spin coupling to a bath of independent spins via the dissipaton equation of motion (DEOM) approach. The bath, characterized by a continuous spectral density function, is composed of spins that are independent level systems described by the su(2) Lie algebra. This represents an extreme class of anharmonic environment. Based on the conclusion drawn by Suarez and Silbey [J. Chem. Phys. 95, 9115 (1991)] and Makri [J. Chem. Phys. 111, 6164 (1999)] that the spin bath can be mapped to a Gaussian environment under its linear response limit, we derive the fluctuation-dissipation theorem (FDT) of the spin bath from a microscopic perspective, and generalize the discussion to the case of arbitrary bath spin quantum number S. Next, the time-domain Prony fitting decomposition scheme is applied to the bare-bath time correlation function (TCF) given by FDT to generate the exponential decay basis (or pseudo modes) for DEOM construction. The accuracy and efficiency of this strategy has been justified by a variety of numerical results. We envision this work provides new insights to extend the hierarchical equations of motion (HEOM) and DEOM approach to certain types of anharmonic enviroments with arbitrary TCF or spectral density

quant-ph↗

arXiv2022

Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices

It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local smoothness (\textbf{LSS}) is a vitally essential prior for many real-world matrix data such as hyperspectral images and surveillance videos, which makes such matrices have low-rankness and local smoothness properties at the same time. This poses an interesting question: Can we make a matrix decomposition in terms of \textbf{L\&LSS +S } form exactly? To address this issue, we propose in this paper a new RPCA model based on three-dimensional correlated total variation regularization (3DCTV-RPCA for short) by fully exploiting and encoding the prior expression underlying such joint low-rank and local smoothness matrices. Specifically, using a modification of Golfing scheme, we prove that under some mild assumptions, the proposed 3DCTV-RPCA model can decompose both components exactly, which should be the first theoretical guarantee among all such related methods combining low rankness and local smoothness. In addition, by utilizing Fast Fourier Transform (FFT), we propose an efficient ADMM algorithm with a solid convergence guarantee for solving the resulting optimization problem. Finally, a series of experiments on both simulations and real applications are carried out to demonstrate the general validity of the proposed 3DCTV-RPCA model.

cs.CV↗

arXiv2022

Learning Neural Volumetric Field for Point Cloud Geometry Compression

Due to the diverse sparsity, high dimensionality, and large temporal variation of dynamic point clouds, it remains a challenge to design an efficient point cloud compression method. We propose to code the geometry of a given point cloud by learning a neural volumetric field. Instead of representing the entire point cloud using a single overfit network, we divide the entire space into small cubes and represent each non-empty cube by a neural network and an input latent code. The network is shared among all the cubes in a single frame or multiple frames, to exploit the spatial and temporal redundancy. The neural field representation of the point cloud includes the network parameters and all the latent codes, which are generated by using back-propagation over the network parameters and its input. By considering the entropy of the network parameters and the latent codes as well as the distortion between the original and reconstructed cubes in the loss function, we derive a rate-distortion (R-D) optimal representation. Experimental results show that the proposed coding scheme achieves superior R-D performances compared to the octree-based G-PCC, especially when applied to multiple frames of a point cloud video. The code is available at https://github.com/huzi96/NVFPCC/.

cs.CV↗

arXiv2022

The Radial Profile of Dust Grain Size in the Protoplanetary Disk of DS Tau

How do dust grains in protoplanetary disks overcome rapid radial drift and grow from micron size particles to planets is not well understood. The key is to search for evidence of dust accumulation and growth as a function of radius in the disk. We investigate the radial profile of grain size in the DS Tau disk by fitting multi-band ALMA observations with self-consistent radiative transfer models. The best-fit grain sizes range from centimeters in the inner disk down to 30 micron in the outer regions. Such an inside-out decreasing tendency is consistent with theories of dust evolution. Based on the best-fit model, we find that dust of 2 Jupiter masses has been depleted within the gap. By taking the gas-to-dust mass ratio into account, the lost mass is enough to form the 3.5 Jupiter mass planet inferred by literature hydrodynamic simulations. Moreover, our modeling also indicates that at the interface region between the gap and the ring, the grain size profile shows a discontinuity, with its amplitude dependent on the dust model adopted in the radiative transfer analysis. Future multi-wavelength observations at higher angular resolutions are required to better constrain the grain size and its variation in the vicinity of disk substructures.

astro-ph.EP↗

arXiv2022

A Chemical Study of Nine Star-forming Regions with Evidence of Infall Motion

The study of the physical and chemical properties of gas infall motion in the molecular clumps helps us understand the initial stages of star formation. We used the FTS wide-sideband mode of the IRAM 30-m telescope to observe nine infall sources with significant double peaked blue line profile. The observation frequency range are 83.7 - 91.5 GHz and 99.4 - 107.2 GHz. We have obtained numbers of molecular line data. Using XCLASS, a total of 7 to 27 different molecules and isotopic transition lines have been identified in these nine sources, including carbon chain molecules such as CCH, c-C3H2 and HC3N. According to the radiation transfer model, we estimated the rotation temperatures and column densities of these sources. Chemical simulations adopting a physical model of HMSFRs are used to fit the observed molecular abundances. The comparison shows that most sources are in the early HMPO stage, with the inner temperature around several ten K.

astro-ph.GA↗

arXiv2022

Quantum mechanics of open systems: Dissipaton theories

This Perspective presents a comprehensive account of the dissipaton theories developed in our group since 2014, including the physical picture of dissipatons and the phase-space dissipaton algebra. The dissipaton-equation-of-motion-space (DEOM-space) formulations cover the Schrödinger picture, the Heisenberg picture, and further the imaginary-time DEOM. Recently developed are also the dissipaton theories for studying equilibrium and nonequilibrium thermodynamic mixing processes. The Jarzynski equality and Crooks relation are accurately reproduced numerically. It is anticipated that dissipaton theories would remain essential towards a maturation of quantum mechanics of open systems.

quant-ph↗