arXiv subjects

Yao Wang

Publications and source records attributed to Yao Wang.

At least 325 records · Page 18Linked to original sources

arXiv2020

Observing Movement of Dirac Cones from Single-Photon Dynamics

Graphene with honeycomb structure, being critically important in understanding physics of matter, exhibits exceptionally unusual half-integer quantum Hall effect and unconventional electronic spectrum with quantum relativistic phenomena. Particularly, graphene-like structure can be used for realizing topological insulator which inspires an intrinsic topological protection mechanism with strong immunity for maintaining coherence of quantum information. These various peculiar physics arise from the unique properties of Dirac cones which show high hole degeneracy, massless charge carriers and linear intersection of bands. Experimental observation of Dirac cones conventionally focuses on the energy-momentum space with bulk measurement. Recently, the wave function and band structure have been mapped into the real-space in photonic system, and made flexible control possible. Here, we demonstrate a direct observation of the movement of Dirac cones from single-photon dynamics in photonic graphene under different biaxial strains. Sharing the same spirit of wave-particle nature in quantum mechanics, we identify the movement of Dirac cones by dynamically detecting the edge modes and extracting the diffusing distance of the packets with accumulation and statistics on individual single-particle registrations. Our results of observing movement of Dirac cones from single-photon dynamics, together with the method of direct observation in real space by mapping the band structure defined in momentum space, pave the way to understand a variety of artificial structures in quantum regime.

cond-mat.mes-hall↗

arXiv2020

Quantum Slide and NAND Tree on a Photonic Chip

In the age of post-Moore era, the next-generation computing model would be a hybrid architecture consisting of different physical components such as photonic chips. In 2008, it has been proposed that the solving of NAND-tree problem can be sped up by quantum walk. Such scheme is groundbreaking due to the universality of NAND gate. However, experimental demonstration has never been achieved so far, mostly due to the challenge in preparing the propagating initial state. Here we propose an alternative solution by including a structure called "quantum slide", where a propagating Gaussian wave-packet can be generated deterministically along a properly-engineered chain. In this way, the optical computation can be achieved with ordinary laser light instead of single photon, and the output can be obtained by single-shot measurements instead of repeated quantum measurements. In our experimental demonstration, the optical NAND-tree is capable of solving computational problems with a total of four input bits, based on the femtosecond laser 3D direct-writing technique on a photonic chip. These results remove one main roadblock to photonic NAND-tree computation, and the construction of quantum slide may find other interesting applications in quantum information and quantum optics.

quant-ph↗

arXiv2020

Entangled system-and-environment dynamics: Phase-space dissipaton theory

Dissipaton-equation-of-motion (DEOM) theory [Y. J. Yan, J. Chem. Phys. 140, 054105 (2014)] is an exact and nonperturbative many-particle method for open quantum systems. The existing dissipaton algebra treats also the dynamics of hybrid bath solvation coordinates. The dynamics of conjugate momentums remain to be addressed within the DEOM framework. In this work, we establish this missing ingredient, the dissipaton algebra on solvation momentums, with rigorous validations against necessary and sufficient criteria. The resulted phase-space DEOM theory will serve as a solid ground for further developments of various practical methods toward a broad range of applications. We illustrate this novel dissipaton algebra with the phase-space DEOM-evaluation on heat current fluctuation.

physics.chem-ph↗

arXiv2020

System-bath entanglement theorem with Gaussian environments

In this work, we establish a so-called "system-bath entanglement theorem", for arbitrary systems coupled with Gaussian environments. This theorem connects the entangled system-bath response functions in the total composite space to those of local systems, as long as the interacting bath spectral densities are given. We validate the theorem with the direct evaluation via the exact dissipaton-equation-of-motion approach. Therefore, this work enables various quantum dissipation theories, which originally describe only the reduced system dynamics, for their evaluations on the system-bath entanglement properties. Numerical demonstrations are carried out on the Fano interference spectroscopies of spin-boson systems.

cond-mat.stat-mech↗

arXiv2019

Observation of Magic Angle and Wall State in Twisted Bilayer Photonic Graphene

Graphene, a one-layer honeycomb lattice of carbon atoms, exhibits unconventional phenomena and attracts much interest since its discovery. Recently, an unexpected Mott-like insulator state induced by moiré pattern and a superconducting state are observed in magic-angle-twisted bilayer graphene, especially, without correlations between electrons, which gives more hints for the understanding and investigation of strongly correlated phenomena. The photon as boson, behaving differently with fermion, can also retrieve the unconventional phenomena of graphene, such as the bearded edge state which is even never been observed in graphene due to the unstability. Here, we present a direct observation of magic angle and wall state in twisted bilayer photonic graphene. We successfully observe the strong localization and rapid diffusion of photon at the regions with AA and AB stacking order around the magic angle, respectively. Most importantly, we find a wall state showing the photon distribution distinctly separate at the regions with AA and AB/BA stacking order in the lowest-energy band. The mechanism underlying the wall states may help to understand the existence of both Mott-like insulating state and superconducting state in magic-angle twisted bilayer graphene. The accessibility of magic angle in twisted bilayer photonic graphene adds the boson behavior into graphene superlattice and the observation of wall state will also deep the understanding of matter.

cond-mat.mes-hall↗

arXiv2019

Identification of relevant diffusion MRI metrics impacting cognitive functions using a novel feature selection method

Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which diffusion measures are the most informative with regard to cognitive functions in both the healthy state as well as after injury. In this study, we use diffusion MRI to formulate a predictive model for performance on working memory based on the most relevant MRI features. The key challenge is to identify relevant features over a large feature space with high accuracy in an efficient manner. To tackle this challenge, we propose a novel improvement of the best first search approach with crossover operators inspired by genetic algorithm. Compared against other heuristic feature selection algorithms, the proposed method achieves significantly more accurate predictions and yields clinically interpretable selected features.

eess.IV↗

arXiv2019

Deep Mouse: An End-to-end Auto-context Refinement Framework for Brain Ventricle and Body Segmentation in Embryonic Mice Ultrasound Volumes

High-frequency ultrasound (HFU) is well suited for imaging embryonic mice due to its noninvasive and real-time characteristics. However, manual segmentation of the brain ventricles (BVs) and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume around 1000x faster) while slightly improves the segmentation accuracy over the previous methods using slide-window approaches.

eess.IV↗

arXiv2019

Ab Initio Exact Diagonalization Simulation of the Nagaoka Transition in Quantum Dots

Recent progress of quantum simulators provides insight into the fundamental problems of strongly correlated systems. To adequately assess the accuracy of these simulators, the precise modeling of the many-body physics, with accurate model parameters, is crucially important. In this paper, we introduce an \emph{ab intio} exact diagonalization framework to compute the correlated physics of a few electrons in artificial potentials. We apply this approach to a quantum-dot system and study the magnetism of the correlated electrons, obtaining good agreement with recent experimental measurements. Through dot separation, potential detuning and control of single tunneling, we examine the Nagaoka transition and determine the robustness of the ferromagnetic state. While the standard Nagaoka theorem considers only a single-band Hubbard model, in this work we perform extensive $ab$ $intio$ calculations that include realistic multi-orbital conditions in which the level splitting is smaller than the interactions. This simulation complements the experiments and provides insight into the formation of ferromagnetism in correlated systems. More generally, our calculation sets the stage for further theoretical analysis of analog quantum simulators at a quantitative level.

cond-mat.str-el↗

arXiv2019

A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples

We present a preference learning framework for multiple criteria sorting. We consider sorting procedures applying an additive value model with diverse types of marginal value functions (including linear, piecewise-linear, splined, and general monotone ones) under a unified analytical framework. Differently from the existing sorting methods that infer a preference model from crisp decision examples, where each reference alternative is assigned to a unique class, our framework allows to consider valued assignment examples in which a reference alternative can be classified into multiple classes with respective credibility degrees. We propose an optimization model for constructing a preference model from such valued examples by maximizing the credible consistency among reference alternatives. To improve the predictive ability of the constructed model on new instances, we employ the regularization techniques. Moreover, to enhance the capability of addressing large-scale datasets, we introduce a state-of-the-art algorithm that is widely used in the machine learning community to solve the proposed optimization model in a computationally efficient way. Using the constructed additive value model, we determine both crisp and valued assignments for non-reference alternatives. Moreover, we allow the Decision Maker to prioritize importance of classes and give the method a flexibility to adjust classification performance across classes according to the specified priorities. The practical usefulness of the analytical framework is demonstrated on a real-world dataset by comparing it to several existing sorting methods.

cs.LG↗

arXiv2019

Automatic Mouse Embryo Brain Ventricle & Body Segmentation and Mutant Classification From Ultrasound Data Using Deep Learning

High-frequency ultrasound (HFU) is well suited for imaging embryonic mice in vivo because it is non-invasive and real-time. Manual segmentation of the brain ventricles (BVs) and whole body from 3D HFU images is time-consuming and requires specialized training. This paper presents a deep-learning-based segmentation pipeline which automates several time-consuming, repetitive tasks currently performed to study genetic mutations in developing mouse embryos. Namely, the pipeline accurately segments the BV and body regions in 3D HFU images of mouse embryos, despite significant challenges due to position and shape variation of the embryos, as well as imaging artifacts. Based on the BV segmentation, a 3D convolutional neural network (CNN) is further trained to detect embryos with the Engrailed-1 (En1) mutation. The algorithms achieve 0.896 and 0.925 Dice Similarity Coefficient (DSC) for BV and body segmentation, respectively, and 95.8% accuracy on mutant classification. Through gradient based interrogation and visualization of the trained classifier, it is demonstrated that the model focuses on the morphological structures known to be affected by the En1 mutation.

eess.IV↗

arXiv2019

RIP-based performance guarantee for low-tubal-rank tensor recovery

The essential task of tensor data analysis focuses on the tensor decomposition and the corresponding notion of rank. In this paper, by introducing the notion of tensor Singular Value Decomposition (t-SVD), we establish a Regularized Tensor Nuclear Norm Minimization (RTNNM) model for low-tubal-rank tensor recovery. As we know that many variants of the Restricted Isometry Property (RIP) have proven to be crucial analysis tools for sparse recovery. In the t-SVD framework, we initiatively define a novel tensor Restricted Isometry Property (t-RIP). Furthermore, we show that any third-order tensor $\boldsymbol{\mathcal{X}}$ can stably be recovered from few linear noise measurements under some certain t-RIP conditions via the RTNNM model. We note that, as far as the authors are aware, such kind of result has not previously been reported in the literature.

cs.IT↗

arXiv2019

Masked-RPCA: Sparse and Low-rank Decomposition Under Overlaying Model and Application to Moving Object Detection

Foreground detection in a given video sequence is a pivotal step in many computer vision applications such as video surveillance system. Robust Principal Component Analysis (RPCA) performs low-rank and sparse decomposition and accomplishes such a task when the background is stationary and the foreground is dynamic and relatively small. A fundamental issue with RPCA is the assumption that the low-rank and sparse components are added at each element, whereas in reality, the moving foreground is overlaid on the background. We propose the representation via masked decomposition (i.e. an overlaying model) where each element either belongs to the low-rank or the sparse component, decided by a mask. We propose the Masked-RPCA algorithm to recover the mask and the low-rank components simultaneously, utilizing linearizing and alternating direction techniques. We further extend our formulation to be robust to dynamic changes in the background and enforce spatial connectivity in the foreground component. Our study shows significant improvement of the detected mask compared to post-processing on the sparse component obtained by other frameworks.

cs.CV↗

arXiv2019

Higgs-mediated optical amplification in a non-equilibrium superconductor

The quest for new functionalities in quantum materials has recently been extended to non-equilibrium states, which are interesting both because they exhibit new physical phenomena and because of their potential for high-speed device applications. Notable advances have been made in the creation of metastable phases and in Floquet engineering under external periodic driving. In the context of non-equilibrium superconductivity, examples have included the generation of transient superconductivity above the thermodynamic transition temperature, the excitation of coherent Higgs mode oscillations, and the optical control of the interlayer phase in cuprates. Here, we propose theoretically a novel non-equilibrium phenomenon, through which a prompt quench from a metal to a transient superconducting state could induce large oscillations of the order parameter amplitude. We argue that this oscillating mode could act as a source of parametric amplification of the incident radiation. We report experimental results on optically driven K$_3$C$_{60}$ that are consistent with these predictions. The effect is found to disappear when the onset of the excitation becomes slower than the Higgs mode period, consistent with the theory proposed here. These results open new possibilities for the use of collective modes in many-body systems to induce non-linear optical effects.

cond-mat.supr-con↗

arXiv2019

Quantum Fast Hitting on Glued Trees Mapped on a Photonic chip

Hitting the exit node from the entrance node faster on a graph is one of the properties that quantum walk algorithms can take advantage of to outperform classical random walk algorithms. Especially, continuous-time quantum walks on central-random glued binary trees have been investigated in theories extensively for their exponentially faster hitting speed over classical random walks. Here, using heralded single photons to represent quantum walkers and waveguide arrays written by femtosecond laser to simulate the theoretical graph, we are able to demonstrate the hitting efficiency of quantum walks with tree depth as high as 16 layers for the first time. Furthermore, we expand the graph's branching rate from 2 to 5, revealing that quantum walks exhibit more superiority over classical random walks as branching rate increases. Our results may shed light on the physical implementation of quantum walk algorithms as well as quantum computation and quantum simulation.

quant-ph↗

arXiv2019

Optimizing CNN Model Inference on CPUs

The popularity of Convolutional Neural Network (CNN) models and the ubiquity of CPUs imply that better performance of CNN model inference on CPUs can deliver significant gain to a large number of users. To improve the performance of CNN inference on CPUs, current approaches like MXNet and Intel OpenVINO usually treat the model as a graph and use the high-performance libraries such as Intel MKL-DNN to implement the operations of the graph. While achieving reasonable performance on individual operations from the off-the-shelf libraries, this solution makes it inflexible to conduct optimizations at the graph level, as the local operation-level optimizations are predefined. Therefore, it is restrictive and misses the opportunity to optimize the end-to-end inference pipeline as a whole. This paper presents \emph{NeoCPU}, a comprehensive approach of CNN model inference on CPUs that employs a full-stack and systematic scheme of optimizations. \emph{NeoCPU} optimizes the operations as templates without relying on third-parties libraries, which enables further improvement of the performance via operation- and graph-level joint optimization. Experiments show that \emph{NeoCPU} achieves up to 3.45$\times$ lower latency for CNN model inference than the current state-of-the-art implementations on various kinds of popular CPUs.

cs.DC↗

arXiv2019

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

Modern deep learning applications urge to push the model inference taking place at the edge devices for multiple reasons such as achieving shorter latency, relieving the burden of the network connecting to the cloud, and protecting user privacy. The Convolutional Neural Network (\emph{CNN}) is one of the most widely used model family in the applications. Given the high computational complexity of the CNN models, it is favorable to execute them on the integrated GPUs at the edge devices, which are ubiquitous and have more power and better energy efficiency than the accompanying CPUs. However, programming on integrated GPUs efficiently is challenging due to the variety of their architectures and programming interfaces. This paper proposes an end-to-end solution to execute CNN model inference on the integrated GPUs at the edge, which uses a unified IR to represent and optimize vision-specific operators on integrated GPUs from multiple vendors, as well as leverages machine learning-based scheduling search schemes to optimize computationally-intensive operators like convolution. Our solution even provides a fallback mechanism for operators not suitable or convenient to run on GPUs. The evaluation results suggest that compared to state-of-the-art solutions backed up by the vendor-provided high-performance libraries on Intel Graphics, ARM Mali GPU, and Nvidia integrated Maxwell GPU, our solution achieves similar, or even better (up to 1.62$\times$), performance on a number of popular image classification and object detection models. In addition, our solution has a wider model coverage and is more flexible to embrace new models. Our solution has been adopted in production services in AWS and is open-sourced.

cs.DC↗

arXiv2019

Numerical Investigation of Spin Excitations in a Doped Spin Chain

We study the doping evolution of spin excitations in a 1D Hubbard model and its downfolded spin Hamiltonians, by using exact diagonalization combined with cluster perturbation theory. In all models, we observe hardening (softening) of spin excitations upon electron (hole) doping, which are reminiscent of recent experiments on 2D cuprate materials. We also find that the 3-site and even higher-order terms are crucial for the low-energy effective spin models to reproduce the magnetic spectra of doped Hubbard systems at a quantitative level. To interpret the numerical results, we further employ a strong coupling slave-boson mean-field theory. The mean-field theory provides an intuitive understanding of the overall compact support of dynamic spin structure factors, including the shift of zero-energy modes and change of spin excitation bandwidth with doping. Our results can serve as predictive benchmarks for future inelastic x-ray or neutron scattering experiments on doped 1D antiferromagnetic Mott insulators.

cond-mat.str-el↗

arXiv2019

Frustrated spin order and stripe fluctuations in FeSe

The charge and spin dynamics of the structurally simplest iron-based superconductor, FeSe, may hold the key to understanding the physics of high temperature superconductors in general. Unlike the iron pnictides, FeSe lacks long range magnetic order in spite of a similar structural transition around 90\,K. Here, we report results of Raman scattering experiments as a function of temperature and polarization and simulations based on exact diagonalization of a frustrated spin model. Both experiment and theory find a persistent low energy peak close to 500cm$^{-1}$ in $B_{1g}$ symmetry, which softens slightly around 100\,K, that we assign to spin excitations. By comparing with results from neutron scattering, this study provides evidence for nearly frustrated stripe order in FeSe.

cond-mat.str-el↗