arXiv subjects

Yin Wang

Publications and source records attributed to Yin Wang.

At least 55 records · Page 3Linked to original sources

arXiv2022

Identification of a non-axisymmetric mode in laboratory experiments searching for standard magnetorotational instability

The standard magnetorotational instability (SMRI) is a promising mechanism for turbulence and rapid accretion in astrophysical disks. It is a magnetohydrodynamic (MHD) instability that destabilizes otherwise hydrodynamically stable disk flow. Due to its microscopic nature at astronomical distances and stringent requirements in laboratory experiments, SMRI has remained unconfirmed since its proposal, despite its astrophysical importance. Here we report a nonaxisymmetric MHD instability in a modified Taylor-Couette experiment. To search for SMRI, a uniform magnetic field is imposed along the rotation axis of a swirling liquid-metal flow. The instability initially grows exponentially, becoming prominent only for sufficient flow shear and moderate magnetic field. These conditions for instability are qualitatively consistent with SMRI, but at magnetic Reynolds numbers below the predictions of linear analyses with periodic axial boundaries. Three-dimensional numerical simulations, however, reproduce the observed instability, indicating that it grows linearly from the primary axisymmetric flow modified by the applied magnetic field.

physics.plasm-ph↗

arXiv2022

Spherical Transformer: Adapting Spherical Signal to CNNs

Convolutional neural networks (CNNs) have been widely used in various vision tasks, e.g. image classification, semantic segmentation, etc. Unfortunately, standard 2D CNNs are not well suited for spherical signals such as panorama images or spherical projections, as the sphere is an unstructured grid. In this paper, we present Spherical Transformer which can transform spherical signals into vectors that can be directly processed by standard CNNs such that many well-designed CNNs architectures can be reused across tasks and datasets by pretraining. To this end, the proposed method first uses local structured sampling methods such as HEALPix to construct a transformer grid by using the information of spherical points and its adjacent points, and then transforms the spherical signals to the vectors through the grid. By building the Spherical Transformer module, we can use multiple CNN architectures directly. We evaluate our approach on the tasks of spherical MNIST recognition, 3D object classification and omnidirectional image semantic segmentation. For 3D object classification, we further propose a rendering-based projection method to improve the performance and a rotational-equivariant model to improve the anti-rotation ability. Experimental results on three tasks show that our approach achieves superior performance over state-of-the-art methods.

cs.CV↗

arXiv2022

Training a universal instance segmentation network for live cell images of various cell types and imaging modalities

We share our recent findings in an attempt to train a universal segmentation network for various cell types and imaging modalities. Our method was built on the generalized U-Net architecture, which allows the evaluation of each component individually. We modified the traditional binary training targets to include three classes for direct instance segmentation. Detailed experiments were performed regarding training schemes, training settings, network backbones, and individual modules on the segmentation performance. Our proposed training scheme draws minibatches in turn from each dataset, and the gradients are accumulated before an optimization step. We found that the key to training a universal network is all-time supervision on all datasets, and it is necessary to sample each dataset in an unbiased way. Our experiments also suggest that there might exist common features to define cell boundaries across cell types and imaging modalities, which could allow application of trained models to totally unseen datasets. A few training tricks can further boost the segmentation performance, including uneven class weights in the cross-entropy loss function, well-designed learning rate scheduler, larger image crops for contextual information, and additional loss terms for unbalanced classes. We also found that segmentation performance can benefit from group normalization layer and Atrous Spatial Pyramid Pooling module, thanks to their more reliable statistics estimation and improved semantic understanding, respectively. We participated in the 6th Cell Tracking Challenge (CTC) held at IEEE International Symposium on Biomedical Imaging (ISBI) 2021 using one of the developed variants. Our method was evaluated as the best runner up during the initial submission for the primary track, and also secured the 3rd place in an additional round of competition in preparation for the summary publication.

cs.CV↗

arXiv2022

Spinful topological phases in acoustic crystals with projective PT symmetry

For the classification of topological phases of matter, an important consideration is whether a system is spinless or spinful, as these two classes have distinct symmetry algebra that gives rise to fundamentally different topological phases. However, only recently has it been realized theoretically that in the presence of gauge symmetry, the algebraic structure of symmetries can be projectively represented, which possibly enables the switch between spinless and spinful topological phases. Here, we report the first experimental demonstration of this idea by realizing spinful topological phases in "spinless" acoustic crystals with projective space-time inversion symmetry. In particular, we realize a DIII-class one-dimensional topologically gapped phase characterized by a 2Z winding number, which features Kramers degenerate bands and Kramers pair of topological boundary modes. Our work thus overcomes a fundamental constraint on topological phases by spin classes.

cond-mat.mes-hall↗

arXiv2022

Observation of Dirac hierarchy in three-dimensional acoustic topological insulators

Dirac cones (DCs) play a pivotal role in various unique phenomena ranging from massless electrons in graphene to robust surface states in topological insulators (TIs). Recent studies have theoretically revealed a full Dirac hierarchy comprising an eightfold bulk DC, a fourfold surface DC, and a twofold hinge DC, associated with a hierarchy of topological phases including first-order to third-order three-dimensional (3D) topological insulators, using the same 3D base lattice. Here, we report the first experimental observation of the Dirac hierarchy in 3D acoustic TIs. Using acoustic measurements, we unambiguously reveal that lifting of multifold DCs in each hierarchy can induce two-dimensional (2D) topological surface states with a fourfold DC in a first-order 3D TI, one-dimensional (1D) topological hinge states with a twofold DC in a second-order 3D TI, and zero-dimensional (0D) topological corner states in a third-order 3D TI. Our work not only expands the fundamental research scope of Dirac physics, but also opens up a new route for multidimensional robust wave manipulation.

cond-mat.mes-hall↗

arXiv2021

HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization

Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis is of paramount importance. Human expertise is required to diagnose and correctly classify breast cancer and define appropriate therapy, which depends on the evaluation of the expression of different biomarkers such as the transmembrane protein receptor HER2. This evaluation requires several steps, including special techniques such as immunohistochemistry or in situ hybridization to assess HER2 status. With the goal of reducing the number of steps and human bias in diagnosis, the HEROHE Challenge was organized, as a parallel event of the 16th European Congress on Digital Pathology, aiming to automate the assessment of the HER2 status based only on hematoxylin and eosin stained tissue sample of invasive breast cancer. Methods to assess HER2 status were presented by 21 teams worldwide and the results achieved by some of the proposed methods open potential perspectives to advance the state-of-the-art.

q-bio.QM↗

arXiv2021

Observation of roton-like dispersion relations in acoustic metamaterials

Roton dispersion relations, displaying a pronounced "roton" minimum at finite momentum, were firstly predicted by Landau and have been extensively explored in correlated quantum systems at low temperatures. Recently, the roton-like dispersion relations were theoretically extended to classical acoustics, which, however, have remained elusive in reality. Here, we report the experimental observation of roton-like dispersions in acoustic metamaterials with beyond-nearest-neighbour interactions at ambient temperatures. The resulting metamaterial supports multiple coexisting modes with different wavevectors and group velocities at the same frequency and broadband backward waves, analogous to the "return flow" termed by Feynman in the context of rotons. Moreover, by increasing the order of long-range interaction, we observe multiple rotons on a single dispersion band, which have never appeared in Landau's prediction or any other condensed matter study. The realization of roton-like dispersions in metamaterials could pave the way to explore novel physics and applications on quantum-inspired phenomena in classical systems.

physics.app-ph↗

arXiv2021

A comprehensive first-principle study of borophene-based nano gas sensor with gold electrodes

Using density functional theory combined with nonequilibrium Green's function method, the transport properties of borophene-based nano gas sensors with gold electrodes are calculated, and comprehensive understandings regarding the effects of gas molecules, MoS$_2$ substrate and gold electrodes to the transport properties of borophene are made. Results show that borophene-based sensors can be used to detect and distinguish CO, NO, NO$_2$ and NH$_3$ gas molecules, MoS$_2$ substrate leads to a non-linear behavior on the current-voltage characteristic, and gold electrodes provide charges to borophene and form a potential barrier, which reduced the current values compared to the current of the systems without gold electrodes. Our studies not only provide useful information on the computationally design of borophene-based gas sensors, but also help understand the transport behaviors and underlying physics of 2D metallic materials with metal electrodes.

cond-mat.mes-hall↗

arXiv2021

Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization

Visual pattern recognition over agricultural areas is an important application of aerial image processing. In this paper, we consider the multi-modality nature of agricultural aerial images and show that naively combining different modalities together without taking the feature divergence into account can lead to sub-optimal results. Thus, we apply a Switchable Normalization block to our DeepLabV3 segmentation model to alleviate the feature divergence. Using the popular symmetric Kullback Leibler divergence measure, we show that our model can greatly reduce the divergence between RGB and near-infrared channels. Together with a hybrid loss function, our model achieves nearly 10\% improvements in mean IoU over previously published baseline.

cs.CV↗

arXiv2021

Large-Scale Unsupervised Person Re-Identification with Contrastive Learning

Existing public person Re-Identification~(ReID) datasets are small in modern terms because of labeling difficulty. Although unlabeled surveillance video is abundant and relatively easy to obtain, it is unclear how to leverage these footage to learn meaningful ReID representations. In particular, most existing unsupervised and domain adaptation ReID methods utilize only the public datasets in their experiments, with labels removed. In addition, due to small data sizes, these methods usually rely on fine tuning by the unlabeled training data in the testing domain to achieve good performance. Inspired by the recent progress of large-scale self-supervised image classification using contrastive learning, we propose to learn ReID representation from large-scale unlabeled surveillance video alone. Assisted by off-the-shelf pedestrian detection tools, we apply the contrastive loss at both the image and the tracklet levels. Together with a principal component analysis step using camera labels freely available, our evaluation using a large-scale unlabeled dataset shows far superior performance among unsupervised methods that do not use any training data in the testing domain. Furthermore, the accuracy improves with the data size and therefore our method has great potential with even larger and more diversified datasets.

cs.CV↗

arXiv2020

A Variational Approach for Learning from Positive and Unlabeled Data

Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally. Most recent PU learning methods are developed based on the conventional misclassification risk of the supervised learning type, and they require to solve the intractable risk estimation problem by approximating the negative data distribution or the class prior. In this paper, we introduce a variational principle for PU learning that allows us to quantitatively evaluate the modeling error of the Bayesian classifier directly from given data. This leads to a loss function which can be efficiently calculated without any intermediate step or model, and a variational learning method can then be employed to optimize the classifier under general conditions. In addition, the discriminative performance and numerical stability of the variational PU learning method can be further improved by incorporating a margin maximizing loss function. We illustrate the effectiveness of the proposed variational method on a number of benchmark examples.

cs.LG↗

arXiv2020

Largely enhanced photogalvanic effects in the phosphorene photodetector by strain-increased device asymmetry

Photogalvanic effect (PGE) occurring in noncentrosymmetric materials enables the generation of the open-circuit voltage that is much larger than the bandgap, making it rather attractive in solar cells. However, the magnitude of the PGE photocurrent is usually small, which severely hampers its practical application. Here we propose a mechanism to largely enhance the PGE photocurrent by mechanical strain based on the quantum transport simulations for the two-dimensional nickel-phosphorene-nickel photodetector. Broadband PGE photocurrent governed by the Cs noncentrosymmetry is generated at zero bias under the illumination of linearly polarized light. The photocurrent depends linearly on the device asymmetry, while nonlinearly on the optical absorption. By applying the appropriate mechanical tension stress on the phosphorene, the photocurrent can be substantially enhanced by up to 3 orders of magnitude, which is primarily ascribed to the largely increased device asymmetry. The change in the optical absorption in some cases can also play a critical role in tuning the photocurrent due to the nonlinear dependence. Moreover, the photocurrent can even be further enhanced by the mechanical bending, mainly owing to the considerably enhanced device asymmetry. Our results reveal the dependence of the PGE photocurrent on the device asymmetry and absorption in transport process through a device, and also explore the potentials of the PGE in the self-powered low-dimensional flexible optoelectronics.

physics.app-ph↗

arXiv2020

Key Frame Proposal Network for Efficient Pose Estimation in Videos

Human pose estimation in video relies on local information by either estimating each frame independently or tracking poses across frames. In this paper, we propose a novel method combining local approaches with global context. We introduce a light weighted, unsupervised, key frame proposal network (K-FPN) to select informative frames and a learned dictionary to recover the entire pose sequence from these frames. The K-FPN speeds up the pose estimation and provides robustness to bad frames with occlusion, motion blur, and illumination changes, while the learned dictionary provides global dynamic context. Experiments on Penn Action and sub-JHMDB datasets show that the proposed method achieves state-of-the-art accuracy, with substantial speed-up.

cs.CV↗

arXiv2020

The 1st Agriculture-Vision Challenge: Methods and Results

The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-Vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task; the link can be found here.

cs.CV↗

arXiv2019

High-Performance Logic and Memory Devices Based on a Dual-Gated MoS2 Architecture

In this work, we demonstrate a dual-gated (DG) MoS2 field effect transistors (FETs) in which the degraded switching performance of multilayer MoS2 can be compensated by the DG structure. It produces large current density (>100 μA/μm for a monolayer), steep subthreshold swing (SS) (~100 mV/dec for 5 nm thickness), and high on/off current ratio (greater than 107 for 10 nm thickness). Such DG structure not only improves electrostatic control but also provides an extra degree of freedom for manipulating the threshold voltage (VTH) and SS by separately tuning the top and back gate voltages, which are demonstrated in a logic inverter. Dynamic random access memory (DRAM) has a short retention time because of large OFF-state current in the Si MOSFET. Based on our DG MoS2-FETs, and a DRAM unit cell with a long retention time of 1260 ms are realized. A large-scale isolated MoS2 DG-FETs based on CVD-synthesized continuous films is also demonstrated, which shows potential applications for future wafer-scale digital and low-power electronics.

physics.app-ph↗

arXiv2019

A Dual-gate MoS2 Photodetector Based on Interface Coupling Effect

Two-dimensional (2D) transition metal dichalcogenides (TMDs) based photodetectors have shown great potential for the next generation optoelectronics. However, most of the reported MoS2 photodetectors function under the photogating effect originated from the charge-trap mechanism, which is difficult for quantitative control. Such devices generally suffer from a poor compromise between response speed and responsivity (R) and large dark current. Here, a dual-gated (DG) MoS2 phototransistor operating based on the interface coupling effect (ICE) is demonstrated. By simultaneously applying a negative top-gate voltage (VTG) and positive back-gate voltage (VBG) to the MoS2 channel, the photo-generated holes can be effectively trapped in the depleted region under TG. An ultrahigh R of ~1E5 A/W and detectivity (D*) of ~1E14 Jones have been achieved in several devices with different thickness under Pin of 53 uW/cm2 at VTG=-5 V. Moreover, the response time of the DG phototransistor can also be modulated based on the ICE. Based on these systematic measurements of MoS2 DG phototransistors, the results show that the ICE plays an important role in the modulation of photoelectric performances. Our results also pave the way for the future optoelectrical application of 2D TMDs materials and prompt for further investigation in the DG structured phototransistors.

physics.app-ph↗

arXiv2019

MoS$_2$ Dual-gate Transistors with Electrostatically Doped Contacts

Two-dimensional (2D) transition metal dichalcogenides (TMDs) such as molybdenum disulfide (MoS2) have been intensively investigated because of their exclusive physical properties for advanced electronics and optoelectronics. In the present work, we study the MoS2 transistor based on a novel tri-gate device architecture, with dual-gate (Dual-G) in the channel and the buried side-gate (Side-G) for the source/drain regions. All gates can be independently controlled without interference. For a MoS2 sheet with a thickness of 3.6 nm, the Schottky barrier (SB) and non-overlapped channel region can be effectively tuned by electrostatically doping the source/drain regions with Side-G. Thus, the extrinsic resistance can be effectively lowered, and a boost of the ON-state current can be achieved. Meanwhile, the channel control remains efficient under the Dual-G mode, with an ON-OFF current ratio of 3E7 and subthreshold swing of 83 mV/decade. The corresponding band diagram is also discussed to illustrate the device operation mechanism. This novel device structure opens up a new way toward fabrication of high-performance devices based on 2D-TMDs.

physics.app-ph↗

arXiv2019

Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery

Deep learning is revolutionizing the mapping industry. Under lightweight human curation, computer has generated almost half of the roads in Thailand on OpenStreetMap (OSM) using high-resolution aerial imagery. Bing maps are displaying 125 million computer-generated building polygons in the U.S. While tremendously more efficient than manual mapping, one cannot map out everything from the air. Especially for roads, a small prediction gap by image occlusion renders the entire road useless for routing. Misconnections can be more dangerous. Therefore computer-based mapping often requires local verifications, which is still labor intensive. In this paper, we propose to leverage crowdsourced GPS data to improve and support road extraction from aerial imagery. Through novel data augmentation, GPS rendering, and 1D transpose convolution techniques, we show almost 5% improvements over previous competition winning models, and much better robustness when predicting new areas without any new training data or domain adaptation.

cs.CV↗