arXiv subjects

Yun Liu

Publications and source records attributed to Yun Liu.

At least 271 records · Page 15Linked to original sources

arXiv2018

Local Elasticity in Nonlinear Rheology of Interacting Colloidal Glasses Revealed by Neutron Scattering and Rheometry

The flow of colloidal suspensions is ubiquitous in nature and industry. Colloidal suspensions exhibit a wide range of rheological behavior, which should be closely related to the microscopic structure of the systems. With in-situ small-angle neutron scattering complemented by rheological measurements, we investigated the deformation behavior of a charge-stabilized colloidal glass at particle level undergoing steady shear. A short-lived, localized elastic response at particle level, termed as transient elasticity zone (TEZ), was identified from the neutron spectra. The existence of the TEZ is a signature of the dynamical heterogeneity: The body of fluids under shear behaves like an elastic solid within the spatial range of TEZ but like fluid outside the TEZ. The size of TEZ shrinks as the shear rate increases in the shear thinning region, which shows that the shear thinning is accompanied by a diminishing dynamical heterogeneity. More interestingly, the TEZ is found to be the structural unit that provides the resistance to the imposed shear, as evidenced by the quantitative agreement between the local elastic stress sustained by TEZ and the macroscopic stress from rheological measurements at low and moderate shear rates. Besides the charged-stabilized colloidal suspension, a hard-sphere colloidal suspension at the same volume fraction and shear rates was also measured. The result highlights the key role of the electrostatic interparticle repulsion in promoting the local elasticity. Our findings provide an understanding on the nonlinear rheology of interacting colloidal glasses from a micro-mechanical view.

cond-mat.soft↗

arXiv2018

Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration

The brightfield microscope is instrumental in the visual examination of both biological and physical samples at sub-millimeter scales. One key clinical application has been in cancer histopathology, where the microscopic assessment of the tissue samples is used for the diagnosis and staging of cancer and thus guides clinical therapy. However, the interpretation of these samples is inherently subjective, resulting in significant diagnostic variability. Moreover, in many regions of the world, access to pathologists is severely limited due to lack of trained personnel. In this regard, Artificial Intelligence (AI) based tools promise to improve the access and quality of healthcare. However, despite significant advances in AI research, integration of these tools into real-world cancer diagnosis workflows remains challenging because of the costs of image digitization and difficulties in deploying AI solutions. Here we propose a cost-effective solution to the integration of AI: the Augmented Reality Microscope (ARM). The ARM overlays AI-based information onto the current view of the sample through the optical pathway in real-time, enabling seamless integration of AI into the regular microscopy workflow. We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows. We anticipate that ARM will remove barriers towards the use of AI in microscopic analysis and thus improve the accuracy and efficiency of cancer diagnosis. This approach is applicable to other microscopy tasks and AI algorithms in the life sciences and beyond.

cs.CV↗

arXiv2018

Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1,226 slides, and evaluated on an independent validation dataset of 331 slides, where the reference standard was established by genitourinary specialist pathologists. On the validation dataset, the mean accuracy among 29 general pathologists was 0.61. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p=0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.

cs.CV↗

arXiv2018

On the Choice of Instruments in Mixed Frequency Specification Tests

Time averaging has been the traditional approach to handle mixed sampling frequencies. However, it ignores information possibly embedded in high frequency. Mixed data sampling (MIDAS) regression models provide a concise way to utilize the additional information in high-frequency variables. In this paper, we propose a specification test to choose between time averaging and MIDAS models, based on a Durbin-Wu-Hausman test. In particular, a set of instrumental variables is proposed and theoretically validated when the frequency ratio is large. As a result, our method tends to be more powerful than existing methods, as reconfirmed through the simulations.

econ.EM↗

arXiv2018

Image Matching: An Application-oriented Benchmark

Image matching approaches have been widely used in computer vision applications in which the image-level matching performance of matchers is critical. However, it has not been well investigated by previous works which place more emphases on evaluating local features. To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods. The proposed metrics are application-oriented as they emphasize application requirements for matchers. The dataset contains two portions for benchmarking video frame matching and unordered image matching separately, where each portion consists of real-world image sequences and each sequence has a specific attribute. Subsequently, we carry out a comprehensive performance evaluation of different state-of-the-art methods and conduct in-depth analyses regarding various aspects such as application requirements, matching types, and data diversity. Moreover, we shed light on how to choose appropriate approaches for different applications based on empirical results and analyses. Conclusions in this benchmark can be used as general guidelines to design practical matching systems and also advocate potential future research directions in this field.

cs.CV↗

arXiv2018

MatchBench: An Evaluation of Feature Matchers

Feature matching is one of the most fundamental and active research areas in computer vision. A comprehensive evaluation of feature matchers is necessary, since it would advance both the development of this field and also high-level applications such as Structure-from-Motion or Visual SLAM. However, to the best of our knowledge, no previous work targets the evaluation of feature matchers while they only focus on evaluating feature detectors and descriptors. This leads to a critical absence in this field that there is no standard datasets and evaluation metrics to evaluate different feature matchers fairly. To this end, we present the first uniform feature matching benchmark to facilitate the evaluation of feature matchers. In the proposed benchmark, matchers are evaluated in different aspects, involving matching ability, correspondence sufficiency, and efficiency. Also, their performances are investigated in different scenes and in different matching types. Subsequently, we carry out an extensive evaluation of different state-of-the-art matchers on the benchmark and make in-depth analyses based on the reported results. This can be used to design practical matching systems in real applications and also advocates the potential future research directions in the field of feature matching.

cs.CV↗

arXiv2018

Learning Pixel-wise Labeling from the Internet without Human Interaction

Deep learning stands at the forefront in many computer vision tasks. However, deep neural networks are usually data-hungry and require a huge amount of well-annotated training samples. Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation. To solve this fundamental issue, we consider a new challenging vision task, Internetly supervised semantic segmentation, which only uses Internet data with noisy image-level supervision of corresponding query keywords for segmentation model training. We address this task by proposing the following solution. A class-specific attention model unifying multiscale forward and backward convolutional features is proposed to provide initial segmentation "ground truth". The model trained with such noisy annotations is then improved by an online fine-tuning procedure. It achieves state-of-the-art performance under the weakly-supervised setting on PASCAL VOC2012 dataset. The proposed framework also paves a new way towards learning from the Internet without human interaction and could serve as a strong baseline therein. Code and data will be released upon the paper acceptance.

cs.CV↗

arXiv2018

High efficiency laser-assisted H- charge exchange for microsecond duration beams

Laser-assisted stripping is a novel approach to H- charge exchange that overcomes long-standing limitations associated with the traditional, foil-based method of producing high-intensity, time-structured beams of protons. This paper reports on the first successful demonstration of the laser stripping technique for microsecond duration beams. The experiment represents a factor of 1000 increase in the stripped pulse duration compared with the previous proof-of-principle demonstration. The central theme of the experiment is the implementation of methods to reduce the required average laser power such that high efficiency stripping can be accomplished for microsecond duration beams using conventional laser technology. The experiment was performed on the Spallation Neutron Source 1 GeV H- beam using a 1 MW peak power UV laser and resulted in ~95% stripping efficiency.

physics.acc-ph↗

arXiv2018

Scaling Behavior of Anisotropy Relaxation in Deformed Polymers

Drawing an analogy to the paradigm of quasi-elastic neutron scattering, we present a general approach for quantitatively investigating the spatiotemporal dependence of structural anisotropy relaxation in deformed polymers by using small-angle neutron scattering. Experiments and non-equilibrium molecular dynamics simulations on polymer melts over a wide range of molecular weights reveal that their conformational relaxation at relatively high momentum transfer $Q$ and short time can be described by a simple scaling law, with the relaxation rate proportional to $Q$. This peculiar scaling behavior, which cannot be derived from the classical Rouse and tube models, is indicative of a surprisingly weak direct influence of entanglement on the microscopic mechanism of single-chain anisotropy relaxation.

cond-mat.soft↗

arXiv2017

Dynamically Correlated Region in Sheared Colloidal Glasses Revealed by Neutron Scattering

The microscopic deformation mechanism of charged colloidal glasses with extended-range interactions under shear is investigated by in-situ small-angle neutron scattering, and a dynamically correlated region (DCR) is identified. This short-lived region provides the resistance to the configurational rearrangement imposed by the external deformation, as evidenced by the evolution of the size of DCR in the shear thinning regime and the quantitative agreement between the local stress sustained by DCR and the macroscopic stress from rheological measurements at low and mediate shear rates. This finding suggests that DCR is an important quantity for microscopically addressing the flow and deformation behavior of strongly interacting colloids.

cond-mat.soft↗

arXiv2017

Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning

Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses. However, observing and quantifying associations in images can be difficult because of the wide variety of features, patterns, colors, values, shapes in real data. In this paper, we use deep learning, a machine learning technique that learns its own features, to discover new knowledge from retinal fundus images. Using models trained on data from 284,335 patients, and validated on two independent datasets of 12,026 and 999 patients, we predict cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as such as age (within 3.26 years), gender (0.97 AUC), smoking status (0.71 AUC), HbA1c (within 1.39%), systolic blood pressure (within 11.23mmHg) as well as major adverse cardiac events (0.70 AUC). We further show that our models used distinct aspects of the anatomy to generate each prediction, such as the optic disc or blood vessels, opening avenues of further research.

cs.CV↗

arXiv2017

Structure-measure: A New Way to Evaluate Foreground Maps

Foreground map evaluation is crucial for gauging the progress of object segmentation algorithms, in particular in the filed of salient object detection where the purpose is to accurately detect and segment the most salient object in a scene. Several widely-used measures such as Area Under the Curve (AUC), Average Precision (AP) and the recently proposed Fbw have been utilized to evaluate the similarity between a non-binary saliency map (SM) and a ground-truth (GT) map. These measures are based on pixel-wise errors and often ignore the structural similarities. Behavioral vision studies, however, have shown that the human visual system is highly sensitive to structures in scenes. Here, we propose a novel, efficient, and easy to calculate measure known an structural similarity measure (Structure-measure) to evaluate non-binary foreground maps. Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map. We demonstrate superiority of our measure over existing ones using 5 meta-measures on 5 benchmark datasets.

cs.CV↗

arXiv2017

Sequential Optimization for Efficient High-Quality Object Proposal Generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5% and 16.7% on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.

cs.CV↗

arXiv2017

Fingerprinting molecular relaxation in deformed polymers

The flow and deformation of macromolecules is ubiquitous in nature and industry, and an understanding of this phenomenon at both macroscopic and microscopic length scales is of fundamental and practical importance. Here we present the formulation of a general mathematical framework, which could be used to extract, from scattering experiments, the molecular relaxation of deformed polymers. By combining and modestly extending several key conceptual ingredients in the literature, we show how the anisotropic single-chain structure factor can be decomposed by spherical harmonics and experimentally reconstructed from its cross sections on the scattering planes. The resulting wavenumber-dependent expansion coefficients constitute a characteristic fingerprint of the macromolecular deformation, permitting detailed examinations of polymer dynamics at the microscopic level. We apply this approach to survey a long-standing problem in polymer physics regarding the molecular relaxation in entangled polymers after a large step deformation. The classical tube theory of Doi and Edwards predicts a fast chain retraction process immediately after the deformation, followed by a slow orientation relaxation through the reptation mechanism. This chain retraction hypothesis, which is the keystone of the tube theory for macromolecular flow and deformation, was critically examined by analyzing the fine features of the two-dimensional anisotropic spectra from small-angle neutron scattering by entangled polystyrenes. It is shown that the unique scattering patterns associated with the chain retraction mechanism were not experimentally observed. This result calls for a fundamental revision of the current theoretical picture for nonlinear rheological behavior of entangled polymeric liquids.

cond-mat.soft↗

arXiv2017

Global existence and decay rate of strong solution to incompressible Oldroyd type model equations

This paper investigates the global existence and the decay rate in time of a solution to the Cauchy problem for an incompressible Oldroyd model with a deformation tensor damping term. There are three major results. The first is the global existence of the solution for small initial data. Second, we derive the sharp time decay of the solution in $L^{2}-$norm. Finally, the sharp time decay of the solution of higher order Sobolev norms is obtained.

math.AP↗

arXiv2017

Performance Optimization of Network Coding Based Communication and Reliable Storage in Internet of Things

Internet or things (IoT) is changing our daily life rapidly. Although new technologies are emerging everyday and expanding their influence in this rapidly growing area, many classic theories can still find their places. In this paper, we study the important applications of the classic network coding theory in two important components of Internet of things, including the IoT core network, where data is sensed and transmitted, and the distributed cloud storage, where the data generated by the IoT core network is stored. First we propose an adaptive network coding (ANC) scheme in the IoT core network to improve the transmission efficiency. We demonstrate the efficacy of the scheme and the performance advantage over existing schemes through simulations. %Next we study the application of network coding in the distributed cloud storage. Next we introduce the optimal storage allocation problem in the network coding based distributed cloud storage, which aims at searching for the most reliable allocation that distributes the $n$ data components into $N$ data centers, given the failure probability $p$ of each data center. Then we propose a polynomial-time optimal storage allocation (OSA) scheme to solve the problem. Both the theoretical analysis and the simulation results show that the storage reliability could be greatly improved by the OSA scheme.

cs.NI↗

arXiv2017

Detecting Cancer Metastases on Gigapixel Pathology Images

Each year, the treatment decisions for more than 230,000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x 100,000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task. At 8 false positives per image, we detect 92.4% of the tumors, relative to 82.7% by the previous best automated approach. For comparison, a human pathologist attempting exhaustive search achieved 73.2% sensitivity. We achieve image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides. In addition, we discover that two slides in the Camelyon16 training set were erroneously labeled normal. Our approach could considerably reduce false negative rates in metastasis detection.

cs.CV↗

arXiv2016

Soft Phonon Modes and Diffuse Scattering in Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 Relaxor Ferroelectrics

Single crystals of a ternary relaxor ferroelectric system, 0.29Pb(In1/2Nb1/2)O3-0.45Pb(Mg1/3Nb2/3)O3-0.26PbTiO3, have been studied using triple-axis based elastic and inelastic neutron scattering. Elastic diffuse scattering confirms the presence of polar nano-regions (PNRs) in this system. The PNRs emerge at the Burns temperature, TB = 630 K and then grow continuously in population and correlation size as the crystal cools down to 100 K. At 300 K, characteristic 'butterfly' and ellipsoid shaped diffuse scattering patterns are observed on the HK0 reciprocal space plane. Electrical poling along the [110] direction produces a marked asymmetry in the diffuse scattering patterns, with the parallel-to-the-field components enhanced while the perpendicular-to-the-field components suppressed. Several low energy phonon branches along the [001] and [111] directions were studied. Most significantly, the PNR-acoustic phonon coupling is confirmed for the [110] transverse acoustic (TA) phonons polarized along the [1-10] real space direction and the [100] TA phonons. This coupling appears to be anisotropic and correlated with the distribution of PNRs, and also affected by the relative length scales of the PNRs and phonon wave vectors. The well-known 'waterfall' phenomenon is observed on the [001] and [110] transverse optical (TO) branches, near the zone center. The optical phonon measurements also reveal a lowest-energy, zone center soft TO mode, whose squared phonon energy increase linearly with decreasing temperature below the TB.

cond-mat.mtrl-sci↗