arXiv subjects

Yan Zhou

Publications and source records attributed to Yan Zhou.

At least 127 records · Page 7Linked to original sources

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

Tuning the Interlayer Microstructure and Residual Stress of Buffer-Free Direct Bonding GaN/Si Heterostructures

The direct integration of GaN with Si can boost great potential for low-cost, large-scale, and high-power device applications. However, it is still challengeable to directly grow GaN on Si without using thick strain relief buffer layers due to their large lattice and thermal-expansion-coefficient mismatches. In this work, a GaN/Si heterointerface without any buffer layer is successfully fabricated at room temperature via surface activated bonding (SAB). The residual stress states and interfacial microstructures of GaN/Si heterostructures were systematically investigated through micro-Raman spectroscopy and transmission electron microscopy. Compared to the large compressive stress that existed in GaN layers grown-on-Si by MOCVD, a significantly relaxed and uniform small tensile stress was observed in GaN layers bonded-to-Si by SAB; this is mainly ascribed to the amorphous layer formed at the bonding interface. In addition, the interfacial microstructure and stress states of bonded GaN/Si heterointerfaces was found can be significantly tuned by appropriate thermal annealing. This work moves an important step forward directly integrating GaN to the present Si CMOS technology with high quality thin interfaces, and brings great promises for wafer-scale low-cost fabrication of GaN electronics.

cond-mat.mtrl-sci↗

arXiv2023

Elongated Skyrmion as Spin Torque Nano-Oscillator and Magnonic Waveguide

Spin torque nano-oscillator has been extensively studied both theoretically and experimentally in recent decades due to its potential applications in future microwave communication technology and neuromorphic computing. In this work, we present a skyrmion-based spin torque nano-oscillator driven by a spatially uniform direct current, where the skyrmion is confined by two pinning sites. Different from other skyrmion-based oscillators that arise from the circular motion or the breathing mode of a skyrmion, the steady-state oscillatory motions are produced by the periodic deformation of an elongated skyrmion. Through micromagnetic simulations, we find that the oscillation frequency depends on the driving current, the damping constant as well as the characteristics of pinning sites. This nonlinear response to direct current turns out to be universal and can also appear in the case of antiskyrmions, skyrmioniums and domain walls. Furthermore, the elongated skyrmion possesses a rectangle-like domain wall, which could also serve as a magnonic waveguide. Utilizing the propagation of spin waves in this waveguide, we propose a device design of logic gate and demonstrate its performance.

cond-mat.mes-hall↗

arXiv2023

Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning

This paper models stochastic process of price time series of CSI 300 index in Chinese financial market, analyzes volatility characteristics of intraday high-frequency price data. In the new generalized Barndorff-Nielsen and Shephard model, the lag caused by asynchrony of market information is considered, and the problem of lack of long-term dependence is solved. To speed up the valuation process, several machine learning and deep learning algorithms are used to estimate parameter and evaluate forecast results. Tracking historical jumps of different magnitudes offers promising avenues for simulating dynamic price processes and predicting future jumps. Numerical results show that the deterministic component of stochastic volatility processes would always be captured over short and longer-term windows. Research finding could be suitable for influence investors and regulators interested in predicting market dynamics based on realized volatility.

q-fin.ST↗

arXiv2022

HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval

Image-text retrieval (ITR) is a challenging task in the field of multimodal information processing due to the semantic gap between different modalities. In recent years, researchers have made great progress in exploring the accurate alignment between image and text. However, existing works mainly focus on the fine-grained alignment between image regions and sentence fragments, which ignores the guiding significance of context background information. Actually, integrating the local fine-grained information and global context background information can provide more semantic clues for retrieval. In this paper, we propose a novel Hierarchical Graph Alignment Network (HGAN) for image-text retrieval. First, to capture the comprehensive multimodal features, we construct the feature graphs for the image and text modality respectively. Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module, which enhances the semantic corresponding relations between the local and global information, and obtains more accurate feature representations for the image and text modalities. Finally, the ultimate image and text features are further refined through three-level similarity functions to achieve the hierarchical alignment. To justify the proposed model, we perform extensive experiments on MS-COCO and Flickr30K datasets. Experimental results show that the proposed HGAN outperforms the state-of-the-art methods on both datasets, which demonstrates the effectiveness and superiority of our model.

cs.CV↗

arXiv2022

An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation

High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In MRI, restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3D HR image acquisition typically requests long scan time and, results in small spatial coverage and low SNR. Recent studies showed that, with deep convolutional neural networks, isotropic HR MR images could be recovered from low-resolution (LR) input via single image super-resolution (SISR) algorithms. However, most existing SISR methods tend to approach a scale-specific projection between LR and HR images, thus these methods can only deal with a fixed up-sampling rate. For achieving different up-sampling rates, multiple SR networks have to be built up respectively, which is very time-consuming and resource-intensive. In this paper, we propose ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D HR MR images. In the ArSSR model, the reconstruction of HR images with different up-scaling rates is defined as learning a continuous implicit voxel function from the observed LR images. Then the SR task is converted to represent the implicit voxel function via deep neural networks from a set of paired HR-LR training examples. The ArSSR model consists of an encoder network and a decoder network. Specifically, the convolutional encoder network is to extract feature maps from the LR input images and the fully-connected decoder network is to approximate the implicit voxel function. Due to the continuity of the learned function, a single ArSSR model can achieve arbitrary up-sampling rate reconstruction of HR images from any input LR image after training. Experimental results on three datasets show that the ArSSR model can achieve state-of-the-art SR performance for 3D HR MR image reconstruction while using a single trained model to achieve arbitrary up-sampling scales.

eess.IV↗

arXiv2022

Reversible Transformation between Isolated Skyrmions and Bimerons

Skyrmions and bimerons are versatile topological spin textures that can be used as information bits for both classical and quantum computing. The transformation between isolated skyrmions and bimerons is an essential operation for computing architecture based on multiple different topological bits. Here we report the creation of isolated skyrmions and their subsequent transformation to bimerons by harnessing the electric current-induced Oersted field and temperature-induced perpendicular magnetic anisotropy variation. The transformation between skyrmions and bimerons is reversible, which is controlled by the current amplitude and scanning direction. Both skyrmions and bimerons can be created in the same system through the skyrmion-bimeron transformation and magnetization switching. Deformed skyrmion bubbles and chiral labyrinth domains are found as nontrivial intermediate transition states. Our results may provide a unique way for building advanced information-processing devices using different types of topological spin textures in the same system.

cond-mat.mes-hall↗

arXiv2022

Modeling dynamic volatility under uncertain environment with fuzziness and randomness

The problem related to predicting dynamic volatility in financial market plays a crucial role in many contexts. We build a new generalized Barndorff-Nielsen and Shephard (BN-S) model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Through the experiment of Dow Jones futures price, we find that compared with the classical model, this method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility has more ideal performance.

q-fin.MF↗

arXiv2022

Dynamical mass generation for ferromagnetic skyrmions in two dimensions

Magnetic skyrmions are topological magnetization textures that are characterized by the homotopy group of two dimensional spheres. Despite years of intensive research on skyrmions, the fundamental problem of the inertia of a skyrmion in driven motion remains unresolved. By properly taking into account a direct coupling between skyrmion motion and the correspondingly excited magnons, we identify a dynamical mass for the skyrmion in motion. The direct coupling between skyrmion motion and magnons can be employed to engineer skyrmion dynamics with magnons through ingenious material and geometry design.

cond-mat.mes-hall↗

arXiv2022

Deep Multi-Scale Representation Learning with Attention for Automatic Modulation Classification

Currently, deep learning methods with stacking small size convolutional filters are widely used for automatic modulation classification (AMC). In this report, we find some experienced improvements by using large kernel size for convolutional deep convolution neural network based AMC, which is more efficient in extracting multi-scale features of the raw signal I/Q sequence data. Also, Squeeze-and-Excitation (SE) mechanisms can significantly help AMC networks to focus on the more important features of the signal. As a result, we propose a multi-scale feature network with large kernel size and SE mechanism (SE-MSFN) in this paper. SE-MSFN achieves state-of-the-art classification performance on the public well-known RADIOML 2018.01A dataset, with average classification accuracy of 64.50%, surpassing CLDNN by 1.42%, maximum classification accuracy of 98.5%, and an average classification accuracy of 85.53% in the lower SNR range 0dB to 10dB, surpassing CLDNN by 2.85%. In addition, we also verified that ensemble learning can help further improve classification performance. We hope this report can provide some references for developers and researchers in practical scenes.

eess.SP↗

arXiv2022

Bifurcation of a Topological Skyrmion String

Manipulation of three-dimensional (3D) topological objects is of both fundamental interest and practical importance in many branches of physics. Here, we show by spin dynamics simulations that the bifurcation of a 3D skyrmion string in a layered frustrated system could be induced by the dampinglike spin-orbit torque. The bifurcation of a skyrmion string happens when the skyrmion string carries a minimal topological charge of $Q=2$. We demonstrate that three types of bifurcations could be realized by applying different current injection geometries, which lead to the transformation from I-shaped skyrmion strings to Y-, X-, and O-shaped ones. Besides, different branches of a bifurcated skyrmion string may merge into an isolated skyrmion string spontaneously. The mechanism of bifurcation should be universal to any skyrmion strings with $Q\geq 2$ in the layered frustrated system and could offer a general approach to manipulate 3D stringlike topological objects for spintronic functions.

cond-mat.mes-hall↗

arXiv2022

Reliable and Broad-range Layer Identification of Au-assisted Exfoliated Large Area MoS$_2$ and WS$_2$ Using Reflection Spectroscopic Fingerprints

The emerging Au-assisted exfoliation technique provides a wealth of large-area and high-quality ultrathin two-dimensional (2D) materials compared with traditional tape-based exfoliation. Fast, damage-free, and reliable determination of the layer number of such 2D films is essential to study layer-dependent physics and promote device applications. Here, an optical method has been developed for simple, high throughput, and accurate determination of the layer number for Au-assisted exfoliated MoS$_2$ and WS$_2$ films in a broad thickness range. The method is based on quantitative analysis of layer-dependent white light reflection spectra, revealing that the reflection peak intensity can be used as a clear indicator for determining the layer number. The simple yet robust method will facilitate the fundamental study on layer-dependent optical, electrical, and thermal properties and device applications of 2D materials. The technique can also be readily combined with photoluminescence and Raman spectroscopies to study other layer-dependent physical properties of 2D materials.

cond-mat.mtrl-sci↗

arXiv2022

Qubits based on merons in magnetic nanodisks

Merons and skyrmions are classical topological solitons. However, they will become quantum mechanical objects when their sizes are of the order of nanometers. Recently, quantum computation based on nanoscale skyrmions was proposed. Here, we propose to use a nanoscale meron in a magnetic nanodisk as a qubit, where the up and down directions of the core spin are assigned to be the qubit states $|0\rangle$ and $|1\rangle$. First, we show numerically that a meron with the radius containing only $7$ spins can be stabilized in a ferromagnetic nanodisk classically. Then, we show theoretically that universal quantum computation is possible based on merons by explicitly constructing the arbitrary phase-shift gate, the Hadamard gate, and the CNOT gate. They are executed by magnetic field or electric current. It would serve as a qubit with long coherence time as a remnant of topological stability from its classical counterpart.

cond-mat.mes-hall↗

arXiv2022

Investigation of Deformation and Fracture Mechanisms in Two-dimensional Gallium Telluride Multilayers Using Nanoindentation

Two-dimensional (2D) materials possess great potential for flexible devices, ascribing to their outstanding electrical, optical, and mechanical properties. However, their mechanical deformation property and fracture mechanism, which are inescapable in many applications like flexible optoelectronics, are still unclear or not thoroughly investigated due methodology limitations. In light of this, such mechanical properties and mechanisms are explored on example of gallium telluride (GaTe), a promising optoelectronic candidate with an ultrahigh photo-responsibility and a high plasticity within 2D family. Considering the driving force insufficient in atomic force microscopy (AFM)-based nanoindentation method, here the mechanical properties of both substrate-supported and suspended GaTe multilayers were systematically investigated through full-scale Berkovich-tip nanoindentation, micro-Raman spectroscopy, AFM, and scanning electron microscopy. An unusual concurrence of multiple pop-in and load-drop events in loading curve was observed. By further correlating to molecular dynamics calculations, this concurrence was unveiled originating from the interlayer sliding mediated layers-by-layers fracture mechanism within GaTe multilayers. The van der Waals force between GaTe multilayers and substrates was revealed much stronger than that between GaTe interlayers, resulting in the easy sliding and fracture of multilayers within GaTe. This work provides new insights into the deformation and fracture mechanisms of GaTe and other similar 2D multilayers in flexible applications.

cond-mat.mtrl-sci↗

arXiv2022

Stabilization and application of asymmetric Néel skyrmions in hybrid nanostructures

Increasing amounts of information force the continuous improvement of information storage and processing technologies, further device miniaturization, and their efficiency increase. Magnetic skyrmions, topological quasiparticles, and the smallest stable magnetic textures possess intriguing properties and potential for data storage applications. Hybrid nanostructures with elements of different magnetization orientations can offer additional advantages for developing skyrmion-based spintronic and magnonic devices. We show that an Néel-type skyrmion confined within a nanodot placed on top of a ferromagnetic stripe produces a unique and compelling platform for exploring mutual coupling between magnetization textures. The skyrmion induces an imprint upon the stripe, which, in turn, asymmetrically squeezes the skyrmion in the dot, increasing their size and the range of skyrmion stability for small values of DMI, as well as introducing skyrmion bi-stability. At the end, we present a proof-of-concept technique for unconstrained transport of a skyrmion along a racetrack based on proposed hybrid systems. Our results demonstrate a hybrid structure that is promising for applications in magnonics and spintronics.

cond-mat.mes-hall↗

arXiv2022

Fast optimal structures generator for parameterized quantum circuits

Current structure optimization algorithms optimize the structure of quantum circuit from scratch for each new task of variational quantum algorithms (VQAs) without using any prior experience, which is inefficient and time-consuming. Besides, the number of quantum gates is a hyper-parameter of these algorithms, which is difficult and time-consuming to determine. In this paper, we propose a rapid structure optimization algorithm for VQAs which automatically determines the number of quantum gates and directly generates the optimal structures for new tasks with the meta-trained graph variational autoencoder (VAE) on a number of training tasks. We also develop a meta-trained predictor to filter out circuits with poor performances to further accelerate the algorithm. Simulation results show that our method output structures with lower loss and it is 70 times faster in running time compared to a state-of-the-art algorithm, namely DQAS.

quant-ph↗

arXiv2022

Universal quantum computation based on nanoscale skyrmion helicity qubits in frustrated magnets

Skyrmions in frustrated magnets have the helicity degree of freedom, where two different configurations of Bloch-type skyrmions are energetically favored by the magnetic dipole-dipole interaction and characterized by opposite helicities. A skyrmion must become a quantum-mechanical object when its radius is of the order of nanometer. We construct a qubit based on the two-fold degeneracy of the Bloch-type nanoscale skyrmions in frustrated magnets. It is shown that the universal quantum computation is possible based on nanoscale skyrmions in a multilayered system. We explicitly show how to construct the $π/4$ phase-shift gate, the Hadamard gate, and the CNOT gate. The one-qubit quantum gates are materialized by temporally controlling the electric field and the spin current. The two-qubit gate is materialized with the use of the Ising-type exchange coupling by controlling the distance between the skyrmion centers in two adjacent layers. The merit of the present mechanism is that an external magnetic field is not necessary. Our results may open a possible way toward universal quantum computations based on nanoscale topological spin textures.

cond-mat.mes-hall↗

arXiv2022

Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging

Objective: Estimation of the global optima of multiple model parameters is valuable in imaging to form a reliable diagnostic image. Given non convexity of the objective function, it is challenging to avoid from different local minima. Methods: We first formulate the global searching of multiply parameters to be a k-D move in the parametric space, and convert parameters updating to be state-action decision-making problem. We proposed a novel Deep Q-learning of Model Parameters (DQMP) method for global optimization of model parameters by updating the parameter configurations through actions that maximize a Q-value, which employs a Deep Reward Network designed to learn global reward values from both visible curve fitting errors and hidden parameter errors. Results: The DQMP method was evaluated by viscoelastic imaging on soft matter by Kelvin-Voigt fractional derivative (KVFD) modeling. In comparison to other methods, imaging of parameters by DQMP yielded the smallest errors (< 2%) to the ground truth images. DQMP was applied to viscoelastic imaging on biological tissues, which indicated a great potential of imaging on physical parameters in diagnostic applications. Conclusions: DQMP method is able to achieve global optima, yielding accurate model parameter estimates in viscoelastic imaging. Assessment of DQMP by simulation imaging and ultrasound breast imaging demonstrated the consistency, reliability of the imaged parameters, and powerful global searching ability of DQMP. Significance: DQMP method is promising for imaging of multiple parameters, and can be generalized to global optimization for many other complex nonconvex functions and imaging of physical parameters.

cs.LG↗