arXiv subjects

Yang Wang

Publications and source records attributed to Yang Wang.

At least 703 records · Page 39Linked to original sources

arXiv2020

Barred galaxies in the Illustris-1 and TNG100 simulations

We carry out a comparison study on the bar structure in the Illustris-1 and TNG100 simulations. At $z=0$, 8.9\% of 1232 disc galaxies with stellar mass $>10^{10.5}M_{\odot}$ in Illustris-1 are barred, while the numbers are 55\% of 1269 in TNG100. The bar fraction as a function of stellar mass in TNG100 agrees well with the survey $S^4G$. The median redshift of bar formation are $\sim 0.4-0.5$ and $\sim 0.25$ in TNG100 and Illustris-1 respectively. Bar fraction generally increases with stellar mass and decreases with gas fraction in both simulations. Barred galaxy had higher gas fraction at high redshift tend to form bar later. When the bars were formed, the disc gas fractions were mostly lower than 0.4. The much higher bar fraction in TNG100 probably have benefit from much lower gas fraction in massive disc galaxies since $z\sim3$, which may result from the combination of more effective stellar and AGN feedback. The latter may be the primary factor at $z<2$. Meanwhile, in both simulations, barred galaxies have higher star formation rate before bar formation, and stronger AGN feedback all the time than unbarred galaxies. The properties of dark matter halos hosting massive disc galaxies are similar between two simulations, and should have minor effect on the different bar frequency. For individual galaxies under similar halo environment cross two simulations, different baryonic physics can lead to striking discrepancy on morphology. The morphology of individual galaxies is subject to combined effects of environment and internal baryonic physics, and is often not predictable.

astro-ph.GA↗

arXiv2020

Satellite Alignment: III. Satellite Galaxies Spatial Distribution and their Dependence on Redshift with A Novel Galaxy Finder

After extensively explored, broad agreement between observations and theories has been reached that satellites are preferentially aligned with major axes of their host centrals. There are still some issues unsolved on this topic. In this paper, we present studies on satellite spatial distribution. To fairly compare with observations, we develop a novel galaxy finder and reconstruction algorithm in hydrodynamical simulation, which is based on the projected mock image, taking into account the full consideration of the point spread function, pixel size, surface brightness limit, resolution and redshift dimming effects. With galaxy samples constructed using such an algorithm, the satellite alignment is examined by comparing with observational results. It is found that the observational alignment can be reproduced for red galaxies, which dominate the sample in this study, but not for blue galaxies. Satellites' radial distribution is also investigated. It exhibits that outer satellites within host halos show stronger alignment signal than satellites in the inner regions, especially for red satellites, which is in contrast with previous studies. The disagreement is mainly due to extra galaxies identified by our new galaxy finder, which are mainly located in the inner region of host halos. Our study illustrates that at lower redshift, the alignment strength becomes stronger, while radial distribution curve becomes flatter. This suggests differences in the evolution of the angular distribution between satellites residing in the inner and outer halos, and implies that the post-infall evolution reduces the original alignment signal, that the impact decreases for satellites with later infall times.

astro-ph.GA↗

arXiv2020

Long distance measurement using single soliton microcomb

Dispersive interferometry (DPI) takes a major interest in optical frequency comb (OFC) based long distance laser-based light detection and ranging (LIDAR) for the merits of strong anti-interference ability and long coherent length. However, the mismatch between the repetition rate of OFC and the resolution of optical spectrum acquisition system induces a large dead-zone which is a major obstacle for practical applications. Here, a new DPI LIDAR on the strength of high-repetition-rate soliton microcomb is demonstrated, which reaches a minimum Allan deviation of 27 nm for an outdoor 1179 m ranging experiment. The proposed scheme approaches a compact, high-accuracy, and none-dead-zone long distance ranging system, opening up new opportunities for emerging applications of frontier scientific researches and advanced manufacturing.

physics.ins-det↗

arXiv2020

Tunable capacitance in all-inkjet-printed nanosheet heterostructures

Heterostructures constructed from two-dimensional building blocks have shown promise for field-effect transistors, memory devices, photosensors and other electronic applications1,2. 2D nanosheet crystals can be constructed into multilayer heterostructures using layer-by-layer methods3, but that method cannot be used to fabricate large-scale and thick heterostructures, due to the time-consuming nature and low efficiency of the process. An alternative approach to deposit different two-dimensional materials is by inkjet printing4-7. Here we show the fabrication of a nanosheet supercapacitor by inkjet printing Ti3C2Tx MXene nanosheets as electrodes, and graphene oxide nanosheets as solid-state electrolyte. The free water molecules trapped between graphene oxide sheets facilitate proton movement through the layered solid electrolyte8. The as-made supercapacitor shows high areal capacitance, good cycling stability and high areal energy and power densities comparable with existing printed supercapacitors. Moreover, the specific capacitance can be increased further by addition of liquid electrolytes.

physics.chem-ph↗

arXiv2020

Convex Shape Representation with Binary Labels for Image Segmentation: Models and Fast Algorithms

We present a novel and effective binary representation for convex shapes. We show the equivalence between the shape convexity and some properties of the associated indicator function. The proposed method has two advantages. Firstly, the representation is based on a simple inequality constraint on the binary function rather than the definition of convex shapes, which allows us to obtain efficient algorithms for various applications with convexity prior. Secondly, this method is independent of the dimension of the concerned shape. In order to show the effectiveness of the proposed representation approach, we incorporate it with a probability based model for object segmentation with convexity prior. Efficient algorithms are given to solve the proposed models using Lagrange multiplier methods and linear approximations. Various experiments are given to show the superiority of the proposed methods.

math.NA↗

arXiv2020

RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.

cs.AI↗

arXiv2020

Study of pseudogap and superconducting quasiparticle dynamics in $\rm{Bi_2Sr_2CaCu_2O_{8+δ}}$ by time-resolved optical reflectivity

The relation between pseudogap (PG) and superconducting (SC) gap, whether PG is a precursor of SC or they coexist or compete, is a long-standing controversy in cuprate high-temperature supercondutors. Here, we report ultrafast time-resolved optical reflectivity investigation of the dynamic densities and relaxations of PG and SC quasiparticles (QPs) in the underdoped $\rm{Bi_2Sr_2CaCu_2O_{8+δ}}$ ($T_c$ = 82 K) single crystals. We find evidence of two distinct PG components in the positive reflectivity changes in the PG state, characterized by relaxation timescales of $τ_{fast}$ $\approx$ 0.2 ps and $τ_{slow}$ $\approx$ 2 ps with abrupt changes in both amplitudes $A_{fast}$ and $A_{slow}$ at the PG-opening temperature $T^*$. The former presents no obvious change at $T_c$ and coexists with the SC QP. The latter's amplitude starts decreasing at the SC phase fluctuation $T_p$ and vanishes at $T_c$ followed by a negative amplitude signifying the emergence of the SC QP, therefore suggesting a competition with superconductivity.

cond-mat.supr-con↗

arXiv2020

Optimal l-one Rank One Matrix Decompositions

In this paper we consider the decomposition of positive semidefinite matrices as a sum of rank one matrices. We introduce and investigate the properties of various measures of optimality of such decompositions. For some classes of positive semidefinite matrices we give explicitly these optimal decompositions. These classes include diagonally dominant matrices and certain of their generalizations, $2\times 2$, and a class of $3\times 3$ matrices.

math.FA↗

arXiv2020

Dual Convolutional LSTM Network for Referring Image Segmentation

We consider referring image segmentation. It is a problem at the intersection of computer vision and natural language understanding. Given an input image and a referring expression in the form of a natural language sentence, the goal is to segment the object of interest in the image referred by the linguistic query. To this end, we propose a dual convolutional LSTM (ConvLSTM) network to tackle this problem. Our model consists of an encoder network and a decoder network, where ConvLSTM is used in both encoder and decoder networks to capture spatial and sequential information. The encoder network extracts visual and linguistic features for each word in the expression sentence, and adopts an attention mechanism to focus on words that are more informative in the multimodal interaction. The decoder network integrates the features generated by the encoder network at multiple levels as its input and produces the final precise segmentation mask. Experimental results on four challenging datasets demonstrate that the proposed network achieves superior segmentation performance compared with other state-of-the-art methods.

cs.CV↗

arXiv2020

Deep Learning-based Image Compression with Trellis Coded Quantization

Recently many works attempt to develop image compression models based on deep learning architectures, where the uniform scalar quantizer (SQ) is commonly applied to the feature maps between the encoder and decoder. In this paper, we propose to incorporate trellis coded quantizer (TCQ) into a deep learning based image compression framework. A soft-to-hard strategy is applied to allow for back propagation during training. We develop a simple image compression model that consists of three subnetworks (encoder, decoder and entropy estimation), and optimize all of the components in an end-to-end manner. We experiment on two high resolution image datasets and both show that our model can achieve superior performance at low bit rates. We also show the comparisons between TCQ and SQ based on our proposed baseline model and demonstrate the advantage of TCQ.

eess.IV↗

arXiv2020

Unsupervised Learning of Camera Pose with Compositional Re-estimation

We consider the problem of unsupervised camera pose estimation. Given an input video sequence, our goal is to estimate the camera pose (i.e. the camera motion) between consecutive frames. Traditionally, this problem is tackled by placing strict constraints on the transformation vector or by incorporating optical flow through a complex pipeline. We propose an alternative approach that utilizes a compositional re-estimation process for camera pose estimation. Given an input, we first estimate a depth map. Our method then iteratively estimates the camera motion based on the estimated depth map. Our approach significantly improves the predicted camera motion both quantitatively and visually. Furthermore, the re-estimation resolves the problem of out-of-boundaries pixels in a novel and simple way. Another advantage of our approach is that it is adaptable to other camera pose estimation approaches. Experimental analysis on KITTI benchmark dataset demonstrates that our method outperforms existing state-of-the-art approaches in unsupervised camera ego-motion estimation.

cs.CV↗

arXiv2020

Direct Measurement of the Electronic Structure and band gap nature of atomic-layer-thick 2H-MoTe2

The millimeter sized monolayer and bilayer 2H-MoTe2 single crystal samples are prepared by a new mechanical exfoliation method. Based on such high-quality samples, we report the first direct electronic structure study on them, using standard high resolution angle-resolved photoemission spectroscopy (ARPES). A direct band gap of 0.924eV is found at K in the rubidium-doped monolayer MoTe2. Similar valence band alignment is also observed in bilayer MoTe2,supporting an assumption of a analogous direct gap semiconductor on it. Our measurements indicate a rather large band splitting of 212meV at the valence band maximum (VBM) in monolayer MoTe2, and the splitting is systematically enlarged with layer stacking, from monolayer to bilayer and to bulk. Meanwhile, our PBE band calculation on these materials show excellent agreement with ARPES results. Some fundamental electronic parameters are derived from the experimental and calculated electronic structures. Our findings lay a foundation for further application-related study on monolayer and bilayer MoTe2.

cond-mat.mtrl-sci↗

arXiv2020

Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized. In this work, we address these problems by developing a novel deep network model called Fully-Convolutional Intensive Feature Flow Neural Network (IntensiveNet). Specifically, we design a further dense block called intensive block to extract the feature information, where the original inputs and two dense blocks are connected tightly. To encode data appropriately, we present the concepts of dense fusion block and further dense fusion opera-tions for our new intensive block. By adding short connections to different layers, the feature flow and coupling between layers are enhanced. We also replace the traditional convolution by depthwise separable convolution to make the operation efficient. To prevent important feature information being lost to a certain extent, we use a convolution operation with stride 2 to replace the original pooling operation in the customary transition layers. The recognition results on large-scale Chinese string and MNIST datasets show that our IntensiveNet can deliver enhanced recog-nition results, compared with other related deep models.

cs.CV↗

arXiv2020

Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery

For subspace recovery, most existing low-rank representation (LRR) models performs in the original space in single-layer mode. As such, the deep hierarchical information cannot be learned, which may result in inaccurate recoveries for complex real data. In this paper, we explore the deep multi-subspace recovery problem by designing a multilayer architecture for latent LRR. Technically, we propose a new Multilayer Collabora-tive Low-Rank Representation Network model termed DeepLRR to discover deep features and deep subspaces. In each layer (>2), DeepLRR bilinearly reconstructs the data matrix by the collabo-rative representation with low-rank coefficients and projection matrices in the previous layer. The bilinear low-rank reconstruc-tion of previous layer is directly fed into the next layer as the input and low-rank dictionary for representation learning, and is further decomposed into a deep principal feature part, a deep salient feature part and a deep sparse error. As such, the coher-ence issue can be also resolved due to the low-rank dictionary, and the robustness against noise can also be enhanced in the feature subspace. To recover the sparse errors in layers accurately, a dynamic growing strategy is used, as the noise level will be-come smaller for the increase of layers. Besides, a neighborhood reconstruction error is also included to encode the locality of deep salient features by deep coefficients adaptively in each layer. Extensive results on public databases show that our DeepLRR outperforms other related models for subspace discovery and clustering.

cs.CV↗

arXiv2020

Convolutional Dictionary Pair Learning Network for Image Representation Learning

Both the Dictionary Learning (DL) and Convolutional Neural Networks (CNN) are powerful image representation learning systems based on different mechanisms and principles, however whether we can seamlessly integrate them to improve the per-formance is noteworthy exploring. To address this issue, we propose a novel generalized end-to-end representation learning architecture, dubbed Convolutional Dictionary Pair Learning Network (CDPL-Net) in this paper, which integrates the learning schemes of the CNN and dictionary pair learning into a unified framework. Generally, the architecture of CDPL-Net includes two convolutional/pooling layers and two dictionary pair learn-ing (DPL) layers in the representation learning module. Besides, it uses two fully-connected layers as the multi-layer perception layer in the nonlinear classification module. In particular, the DPL layer can jointly formulate the discriminative synthesis and analysis representations driven by minimizing the batch based reconstruction error over the flatted feature maps from the convolution/pooling layer. Moreover, DPL layer uses l1-norm on the analysis dictionary so that sparse representation can be delivered, and the embedding process will also be robust to noise. To speed up the training process of DPL layer, the efficient stochastic gradient descent is used. Extensive simulations on real databases show that our CDPL-Net can deliver enhanced performance over other state-of-the-art methods.

cs.CV↗

arXiv2019

Machine Learning the Effective Hamiltonian in High Entropy Alloys

The development of machine learning sheds new light on the problem of statistical thermodynamics in multicomponent alloys. However, a data-driven approach to construct the effective Hamiltonian requires sufficiently large data sets, which is expensive to calculate with conventional density functional theory (DFT). To solve this problem, we propose to use the atomic local energy as the target variable, and harness the power of the linear-scaling DFT to accelerate the data generating process. Using the large amounts of DFT data sets, various complex models are devised and applied to learn the effective Hamiltonians of a range of refractory high entropy alloys (HEAs). The testing $R^2$ scores of the effective pair interaction model are higher than 0.99, demonstrating that the pair interactions within the 6-th coordination shell provide an excellent description of the atomic local energies for all the four HEAs. This model is further improved by including nonlinear and multi-site interactions. In particular, the deep neural networks (DNNs) built directly in the local configuration space (therefore no hand-crafted features) are employed to model the effective Hamiltonian. The results demonstrate that neural networks are promising for the modeling of effective Hamiltonian due to its excellent representation power.

cond-mat.mtrl-sci↗

arXiv2019

Edge-Epitaxial Growth of InSe Nanowires toward High-Performance Photodetectors

Semiconducting nanowires offer many opportunities for electronic and optoelectronic device applications due to their special geometries and unique physical properties. However, it has been challenging to synthesize semiconducting nanowires directly on SiO2/Si substrate due to lattice mismatch. Here, we developed a catalysis-free approach to achieve direct synthesis of long and straight InSe nanowires on SiO2/Si substrate through edge-homoepitaxial growth. We further achieved parallel InSe nanowires on SiO2/Si substrate through controlling growth conditions. We attributed the underlying growth mechanism to selenium self-driven vapor-liquid-solid process, which is distinct from conventional metal-catalytic vapor-liquid-solid method widely used for growing Si and III-V nanowires. Furthermore, we demonstrated that the as-grown InSe nanowire-based visible light photodetector simultaneously possesses an extraordinary photoresponsivity of 271 A/W, ultrahigh detectivity of 1.57*10^14 Jones and a fast response speed of microsecond scale. The excellent performance of the photodetector indicates that as-grown InSe nanowires are promising in future optoelectronic applications. More importantly, the proposed edge-homoepitaxial approach may open up a novel avenue for direct synthesis of semiconducting nanowire arrays on SiO2/Si substrate.

physics.app-ph↗

arXiv2019

Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space

In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. A Joint Robust Factorization and Projective Dictionary Learning (J-RFDL) model is presented. The setting of J-RFDL aims at improving the data representations by enhancing the robustness to outliers and noise in data, encoding the reconstruction error more accurately and obtaining hybrid salient coefficients with accurate reconstruction ability. Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient. To make the encoding process robust to noise in data, J-RFDL clearly uses sparse L2, 1-norm that can potentially minimize the factorization and reconstruction errors jointly by forcing rows of the reconstruction errors to be zeros. To deliver salient coefficients with good structures to reconstruct given data well, J-RFDL imposes the joint low-rank and sparse constraints on the embedded coefficients with a synthesis dictionary. Based on the hybrid salient coefficients, we also extend J-RFDL for the joint classification and propose a discriminative J-RFDL model, which can improve the discriminating abilities of learnt coeffi-cients by minimizing the classification error jointly. Extensive experiments on public datasets demonstrate that our formulations can deliver superior performance over other state-of-the-art methods.

cs.LG↗