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Xu Yang

Publications and source records attributed to Xu Yang.

At least 361 records · Page 20Linked to original sources
arXiv2020

SPDEs with non-Lipschitz coefficients and nonhomogeous boundary conditions

In this paper we establish the strong existence, pathwise uniqueness and a comparison theorem to a stochastic partial differential equation driven by Gaussian colored noise with non-Lipschitz drift, Hölder continuous diffusion coefficients and the spatial domain in finite interval, $[0,1]$, and with Dirichlet, Neumann or mixed nonhomogeneous random conditions imposed on the endpoints. The Hölder continuity of the solution both in time and in space variables is also studied.

math.PR↗
arXiv2020

Rapid Evolution of Type II Spicules Observed in Goode Solar Telescope On-Disk H-alpha Images

We analyze ground-based chromospheric data acquired at a high temporal cadence of 2 s in wings of the H$α$ spectral line using Goode Solar Telescope (GST) operating at the Big Bear Solar Observatory. We inspected a 30 minute long H$α$-0.08~nm data set to find that rapid blue-shifted H$α$ excursions (RBEs), which are a cool component of type II spicules, experience very rapid morphological changes on the time scales of the order of 1 second. Unlike typical reconnection jets, RBEs very frequently appear \textit{in situ} without any clear evidence of H$α$ material being injected from below. Their evolution includes inverted "Y", "V", "N", and parallel splitting (doubling) patterns as well as sudden formation of a diffuse region followed by branching. We also find that the same feature may undergo several splitting episodes within about 1 min time interval.

astro-ph.SR↗
arXiv2020

Nonreciprocal directional dichroism induced by a temperature gradient as a probe for mobile spin dynamics in quantum magnets

Novel states of matter in quantum magnets like quantum spin liquids attract considerable interest recently. Despite the existence of a plenty of candidate materials, there is no confirmed quantum spin liquid, largely due to the lack of proper experimental probes. For instance, spectrosocopy experiments like neutron scattering receive contributions from disorder-induced local modes, while thermal transport experiments receive contributions from phonons. Here we propose a thermo-optic experiment which directly probes the mobile magnetic excitations in spatial-inversion symmetric and/or time-reversal symmetric Mott insulators: the temperature-gradient-induced nonreciprocal directional dichroism (TNDD) spectroscopy. Unlike traditional probes, TNDD directly detects mobile magnetic excitations and decouples from phonons and local magnetic modes.

cond-mat.str-el↗
arXiv2020

Detecting chirality in two-terminal electronic devices

Central to spintronics is the interconversion between electronic charge and spin currents, and this can arise from the chirality-induced spin selectivity (CISS) effect. CISS is often studied as magnetoresistance (MR) in two-terminal (2T) electronic devices containing a chiral (molecular) component and a ferromagnet. However, fundamental understanding of when and how this MR can occur is lacking. Here, we uncover an elementary mechanism that generates such a MR for nonlinear response. It requires energy-dependent transport and energy relaxation within the device. The sign of the MR depends on chirality, charge carrier type, and bias direction. Additionally, we reveal how CISS can be detected in the linear response regime in magnet-free 2T devices, either by forming a chirality-based spin-valve using two or more chiral components, or by Hanle spin precession in devices with a single chiral component. Our results provide operation principles and design guidelines for chirality-based spintronic devices and technologies.

cond-mat.mes-hall↗
arXiv2019

TBC-Net: A real-time detector for infrared small target detection using semantic constraint

Infrared small target detection is a key technique in infrared search and tracking (IRST) systems. Although deep learning has been widely used in the vision tasks of visible light images recently, it is rarely used in infrared small target detection due to the difficulty in learning small target features. In this paper, we propose a novel lightweight convolutional neural network TBC-Net for infrared small target detection. The TBCNet consists of a target extraction module (TEM) and a semantic constraint module (SCM), which are used to extract small targets from infrared images and to classify the extracted target images during the training, respectively. Meanwhile, we propose a joint loss function and a training method. The SCM imposes a semantic constraint on TEM by combining the high-level classification task and solve the problem of the difficulty to learn features caused by class imbalance problem. During the training, the targets are extracted from the input image and then be classified by SCM. During the inference, only the TEM is used to detect the small targets. We also propose a data synthesis method to generate training data. The experimental results show that compared with the traditional methods, TBC-Net can better reduce the false alarm caused by complicated background, the proposed network structure and joint loss have a significant improvement on small target feature learning. Besides, TBC-Net can achieve real-time detection on the NVIDIA Jetson AGX Xavier development board, which is suitable for applications such as field research with drones equipped with infrared sensors.

cs.CV↗
arXiv2019

Period doubling of multiple dissipative-soliton-resonance pulses in a fiber laser

We report on the experimental observation of period doubling of multiple dissipative-soliton-resonance (DSR) pulses in an all-normal-dispersion fiber laser based on a nonlinear amplifying loop mirror. By increasing the pump power, in addition to the typically linearly pulse broadening under fixed pulse peak power, the jump from a single DSR pulse to multiple DSR pulses was observed. During this process, the period doubling appears, that is, the DSR pulses can exhibit the characteristics of period doubling in a multi-pulse state. The typical DSR performance of linearly pulse duration change versus pump power varying can be maintained when the period doubling of multiple DSR pulses appears.

nlin.PS↗
arXiv2019

Deep Learning Seismic Substructure Detection using the Frozen Gaussian Approximation

We propose a deep learning algorithm for seismic interface and pocket detection with neural networks trained by synthetic high-frequency displacement data efficiently generated by the frozen Gaussian approximation (FGA). In seismic imaging high-frequency data is advantageous since it can provide high resolution of substructures. However, generation of sufficient synthetic high-frequency data sets for training neural networks is computationally challenging. This bottleneck is overcome by a highly scalable computational platform built upon the FGA, which comes from the semiclassical theory and approximates the wavefields by a sum of fixed-width (frozen) Gaussian wave packets. Data is generated from a forward simulation of the elastic wave equation using the FGA. This data contains accurate traveltime information (from the ray path) but not exact amplitude information (with asymptotic errors not shrinking to zero even at extremely fine numerical resolution). Using this data we build convolutional neural network models using an open source API, GeoSeg, developed using Keras and Tensorflow. On a simple model, networks, despite only being trained on FGA data, can detect an interface with a high success rate from displacement data generated by the spectral element method. Benchmark tests are done for P-waves (acoustic) and P- and S-waves (elastic) generated using the FGA and a spectral element method. Further, results with a high accuracy are shown for more complicated geometries including a three layered model, and a 2D-pocket model where the neural networks trained by both clean and noisy data.

physics.geo-ph↗
arXiv2019

Convergence of the Frozen Gaussian Approximation for High Frequency Elastic Waves

The frozen Gaussian approximation (FGA) is an effective tool for modeling high frequency wave propagation. In previous works, the convergence of the FGA has established for strict hyperbolic systems. In this work, we derive the frozen Gaussian approximation for the elastic wave equation, which can be cast as a hyperbolic system with repeated eigenvalues. In the derivation, the strong form for the evolution equation is introduced. A diabatic coupling is observed for the amplitude of the evolution equations between the SH, SV waves. Using previous results with new energy estimates we establish the convergence for the first order FGA for the elastic wave equation.

math-ph↗
arXiv2019

Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection

Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not strictly aligned, making one object has different positions in different modalities. In deep learning based methods, this problem makes it difficult to fuse the feature maps from both modalities and puzzles the CNN training. In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the weakly aligned multispectral data in an end-to-end way. Firstly, we design a Region Feature Alignment (RFA) module to capture the position shift and adaptively align the region features of the two modalities. Secondly, we present a new multimodal fusion method, which performs feature re-weighting to select more reliable features and suppress the useless ones. Besides, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns of different devices and system settings. Finally, since our method depends on a new kind of labelling: bounding boxes that match each modality, we manually relabel the KAIST dataset by locating bounding boxes in both modalities and building their relationships, providing a new KAIST-Paired Annotation. Extensive experimental validations on existing datasets are performed, demonstrating the effectiveness and robustness of the proposed method. Code and data are available at https://github.com/luzhang16/AR-CNN.

cs.CV↗
arXiv2019

The classification of local m-GCI-group on finite nonabelian simple groups

Li and Praeger classified finite nonabelian simple groups, it has only one or two fusion classes of any certain value. As a by-product, they classified m-CI-groups, which is critical in the research of Cayley graphs. In the paper, we will consider generalized Cayley graphs. This concept is proposed by Marusic et al. In the paper, (local) m- GCI-group is defined, and we get many properties and characterizations based on the generalized Cayley isomorphism, which are the key measures for the classification of (local) m-GCI-group. And above all, we will give a classification of local 2-GCI-groups and 2-GCI-groups for finite nonabelian simple groups.

math.CO↗
arXiv2019

Unpaired Image Captioning via Scene Graph Alignments

Most of current image captioning models heavily rely on paired image-caption datasets. However, getting large scale image-caption paired data is labor-intensive and time-consuming. In this paper, we present a scene graph-based approach for unpaired image captioning. Our framework comprises an image scene graph generator, a sentence scene graph generator, a scene graph encoder, and a sentence decoder. Specifically, we first train the scene graph encoder and the sentence decoder on the text modality. To align the scene graphs between images and sentences, we propose an unsupervised feature alignment method that maps the scene graph features from the image to the sentence modality. Experimental results show that our proposed model can generate quite promising results without using any image-caption training pairs, outperforming existing methods by a wide margin.

cs.CV↗
arXiv2019

Circuit-Model Analysis for Spintronic Devices with Chiral Molecules as Spin Injectors

Recent research discovered that charge transfer processes in chiral molecules can be spin selective and named the effect chiral-induced spin selectivity (CISS). Follow-up work studied hybrid spintronic devices with conventional electronic materials and chiral (bio)molecules. However, a theoretical foundation for the CISS effect is still in development and the spintronic signals were not evaluated quantitatively. We present a circuit-model approach that can provide quantitative evaluations. Our analysis assumes the scheme of a recent experiment that used photosystem~I (PSI) as spin injectors, for which we find that the experimentally observed signals are, under any reasonable assumptions on relevant PSI time scales, too high to be fully due to the CISS effect. We also show that the CISS effect can in principle be detected using the same type of solid-state device, and by replacing silver with graphene, the signals due to spin generation can be enlarged four orders of magnitude. Our approach thus provides a generic framework for analyzing this type of experiments and advancing the understanding of the CISS effect.

cond-mat.mes-hall↗
arXiv2019

mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation

It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problem. The model would collapse into a plain language model that totally ignore the latent variables and can only generate repeating and dull samples. In this paper, we explore the reason behind this issue and propose an effective regularizer based approach to address it. The proposed method directly injects extra constraints on the posteriors of latent variables into the learning process of VRAE, which can flexibly and stably control the trade-off between the KL term and the reconstruction term, making the model learn dense and meaningful latent representations. The experimental results show that the proposed method outperforms several strong baselines and can make the model learn interpretable latent variables and generate diverse meaningful sentences. Furthermore, the proposed method can perform well without using other strategies, such as KL annealing.

cs.CL↗
arXiv2019

Deep Spectral Clustering using Dual Autoencoder Network

The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods. In this paper, we propose a joint learning framework for discriminative embedding and spectral clustering. We first devise a dual autoencoder network, which enforces the reconstruction constraint for the latent representations and their noisy versions, to embed the inputs into a latent space for clustering. As such the learned latent representations can be more robust to noise. Then the mutual information estimation is utilized to provide more discriminative information from the inputs. Furthermore, a deep spectral clustering method is applied to embed the latent representations into the eigenspace and subsequently clusters them, which can fully exploit the relationship between inputs to achieve optimal clustering results. Experimental results on benchmark datasets show that our method can significantly outperform state-of-the-art clustering approaches.

cs.LG↗
arXiv2019

Non-Kitaev spin liquids in Kitaev materials

We point out that the Kitaev materials may not necessarily support Kitaev spin liquid. It is well-known that having a Kitaev term in the spin interaction is not the sufficient condition for the Kitaev spin liquid ground state. Many other spin liquids may be stabilized by the competing spin interactions of the systems. We thus explore the possibilities of non-Kitaev spin liquids in the honeycomb Kitaev materials. We carry out a systematic classification of gapped $\mathbb{Z}_2$ spin liquids using the Schwinger boson representation for the spin variables. The presence of strong spin-orbit coupling in the Kitaev materials brings new ingredients into the projective symmetry group classification of the non-Kitaev spin liquid. We predict the spectroscopic properties of these gapped non-Kitaev spin liquids. Moreover, among the gapped spin liquids that we discover, we identify the spin liquid whose spinon condensation leads to the magnetic Bragg peak structure of the zig-zag magnetic order that was observed in Na$_2$IrO$_3$ and $α$-RuCl$_3$. We further discuss the possibility of gapped $\mathbb{Z}_2$ spin liquid and the deconfined quantum criticality from the zig-zag magnetic order to spin dimerization in pressurized $α$-RuCl$_3$.

cond-mat.str-el↗
arXiv2019

Learning to Collocate Neural Modules for Image Captioning

We do not speak word by word from scratch; our brain quickly structures a pattern like \textsc{sth do sth at someplace} and then fill in the detailed descriptions. To render existing encoder-decoder image captioners such human-like reasoning, we propose a novel framework: learning to Collocate Neural Modules (CNM), to generate the `inner pattern' connecting visual encoder and language decoder. Unlike the widely-used neural module networks in visual Q\&A, where the language (ie, question) is fully observable, CNM for captioning is more challenging as the language is being generated and thus is partially observable. To this end, we make the following technical contributions for CNM training: 1) compact module design --- one for function words and three for visual content words (eg, noun, adjective, and verb), 2) soft module fusion and multi-step module execution, robustifying the visual reasoning in partial observation, 3) a linguistic loss for module controller being faithful to part-of-speech collocations (eg, adjective is before noun). Extensive experiments on the challenging MS-COCO image captioning benchmark validate the effectiveness of our CNM image captioner. In particular, CNM achieves a new state-of-the-art 127.9 CIDEr-D on Karpathy split and a single-model 126.0 c40 on the official server. CNM is also robust to few training samples, eg, by training only one sentence per image, CNM can halve the performance loss compared to a strong baseline.

cs.CV↗
arXiv2019

Magnetic filed dynamics and varying plasma emission in large coronal loops

In this study we report detailed observations of magnetic environment at four footpoints of two warm coronal loops observed on 5 May 2016 in NOAA AR 12542 (Loop I) and 17 Dec 2015 in NOAA AR 12470 (Loop II). These loops were connecting a plage region with sunspot periphery (Loop I) and a sunspot umbra (Loop II). We used Solar Dynamics Observatory (SDO) and Goode Solar Telescope (GST) data to describe the phenomenon and understand its causes. The study indicates loop brightening episodes were associated with magnetic flux emergence and cancellation processes observed in SDO's Helioseismic and Magnetic Imager (HMI) and GST's Near InfraRed Imaging Spectrapolarimeter (NIRIS) data. The observed activity was driven by magnetic reconnection between small-scale emerging dipoles and large-scale pre-existing fields, suggesting that the reconnection occurred in the lower chromosphere at the edge of an extended plage region, where the loops were rooted. We suggest that plasma, evaporated during these reconnection events, gradually filled the loops and as it cooled the visible density front propagated from one footpoint of the loop to another at a rate of 90-110 km s$^{-1}$. This study also indicates that at least some of the bright loops seen in SDO Atmospheric Imaging Assembly images rooted in sunspot umbra may be heated due to magnetic activity taking place at the remote (non-sunspot) footpoint.

astro-ph.SR↗