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Jiang Liu

Publications and source records attributed to Jiang Liu.

At least 217 records · Page 12Linked to original sources
arXiv2018

Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT

A major cause of irreversible visual impairment is angle-closure glaucoma, which can be screened through imagery from Anterior Segment Optical Coherence Tomography (AS-OCT). Previous computational diagnostic techniques address this screening problem by extracting specific clinical measurements or handcrafted visual features from the images for classification. In this paper, we instead propose to learn from training data a discriminative representation that may capture subtle visual cues not modeled by predefined features. Based on clinical priors, we formulate this learning with a presented Multi-Context Deep Network (MCDN) architecture, in which parallel Convolutional Neural Networks are applied to particular image regions and at corresponding scales known to be informative for clinically diagnosing angle-closure glaucoma. The output feature maps of the parallel streams are merged into a classification layer to produce the deep screening result. Moreover, we incorporate estimated clinical parameters to further enhance performance. On a clinical AS-OCT dataset, our system is validated through comparisons to previous screening methods.

cs.CV↗
arXiv2018

A Linear Algebra Formulation for Boolean Satisfiability Testing

In the article \The State of SAT", the authors asked whether a procedure dramatically different from DPLL can be found for handling unsatisfiable instances. This study proposes a new linear programming approach to address this issue efficiently. Our experiments showed that the new method works for many unsatisfiable instances. However, we must concede that this method should be incomplete; otherwise, it will imply P=co-NP.

cs.CC↗
arXiv2018

Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis

Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration and diabetic retinopathy. With the development of computer science, computer aided diagnosis has been developed to process and analyse the retinal images automatically. One of the challenges in the analysis is that the quality of the retinal image is often degraded. For example, a cataract in human lens will attenuate the retinal image, just as a cloudy camera lens which reduces the quality of a photograph. It often obscures the details in the retinal images and posts challenges in retinal image processing and analysing tasks. In this paper, we approximate the degradation of the retinal images as a combination of human-lens attenuation and scattering. A novel structure-preserving guided retinal image filtering (SGRIF) is then proposed to restore images based on the attenuation and scattering model. The proposed SGRIF consists of a step of global structure transferring and a step of global edge-preserving smoothing. Our results show that the proposed SGRIF method is able to improve the contrast of retinal images, measured by histogram flatness measure, histogram spread and variability of local luminosity. In addition, we further explored the benefits of SGRIF for subsequent retinal image processing and analysing tasks. In the two applications of deep learning based optic cup segmentation and sparse learning based cup-to-disc ratio (CDR) computation, our results show that we are able to achieve more accurate optic cup segmentation and CDR measurements from images processed by SGRIF.

cs.CV↗
arXiv2018

Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image

Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods firstly segment the main structure, and subsequently calculate the clinical measurement for detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy, and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information, and screen glaucoma from the fundus image directly. Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are respectively considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma datasets (SCES and new SINDI datasets) show our method outperforms other state-of-the-art algorithms.

cs.CV↗
arXiv2018

DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.

cs.CV↗
arXiv2018

Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The U-shape convolutional network is employed as the main body network structure to learn the rich hierarchical representation, while the side-output layer acts as an early classifier that produces a companion local prediction map for different scale layers. Finally, a multi-label loss function is proposed to generate the final segmentation map. For improving the segmentation performance further, we also introduce the polar transformation, which provides the representation of the original image in the polar coordinate system. The experiments show that our M-Net system achieves state-of-the-art OD and OC segmentation result on ORIGA dataset. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated CDR value on both ORIGA and SCES datasets.

cs.CV↗
arXiv2016

Integrated Resource Management in Software Defined Networking, Caching and Computing

Recently, there are significant advances in the areas of networking, caching and computing. Nevertheless, these three important areas have traditionally been addressed separately in the existing research. In this paper, we present a novel framework that integrates networking, caching and computing in a systematic way and enables dynamic orchestration of these three resources to improve the end-to-end system performance and meet the requirements of different applications. Then, we consider the bandwidth, caching and computing resource allocation issue and formulate it as a joint caching/computing strategy and servers selection problem to minimize the combination cost of network usage and energy consumption in the framework. To minimize the combination cost of network usage and energy consumption in the framework, we formulate it as a joint caching/computing strategy and servers selection problem. In addition, we solve the joint caching/computing strategy and servers selection problem using an exhaustive-search algorithm. Simulation results show that our proposed framework significantly outperforms the traditional network without in-network caching/computing in terms of network usage and energy consumption.

cs.NI↗
arXiv2016

A novel learning-based frame pooling method for Event Detection

Detecting complex events in a large video collection crawled from video websites is a challenging task. When applying directly good image-based feature representation, e.g., HOG, SIFT, to videos, we have to face the problem of how to pool multiple frame feature representations into one feature representation. In this paper, we propose a novel learning-based frame pooling method. We formulate the pooling weight learning as an optimization problem and thus our method can automatically learn the best pooling weight configuration for each specific event category. Experimental results conducted on TRECVID MED 2011 reveal that our method outperforms the commonly used average pooling and max pooling strategies on both high-level and low-level 2D image features.

cs.CV↗
arXiv2015

Abstraction of Elementary Hybrid Systems by Variable Transformation

Elementary hybrid systems (EHSs) are those hybrid systems (HSs) containing elementary functions such as exp, ln, sin, cos, etc. EHSs are very common in practice, especially in safety-critical domains. Due to the non-polynomial expressions which lead to undecidable arithmetic, verification of EHSs is very hard. Existing approaches based on partition of state space or over-approximation of reachable sets suffer from state explosion or inflation of numerical errors. In this paper, we propose a symbolic abstraction approach that reduces EHSs to polynomial hybrid systems (PHSs), by replacing all non-polynomial terms with newly introduced variables. Thus the verification of EHSs is reduced to the one of PHSs, enabling us to apply all the well-established verification techniques and tools for PHSs to EHSs. In this way, it is possible to avoid the limitations of many existing methods. We illustrate the abstraction approach and its application in safety verification of EHSs by several real world examples.

eess.SY↗
arXiv2011

Computing Semi-algebraic Invariants for Polynomial Dynamical Systems

In this paper, we consider an extended concept of invariant for polynomial dynamical system (PDS) with domain and initial condition, and establish a sound and complete criterion for checking semi-algebraic invariants (SAI) for such PDSs. The main idea is encoding relevant dynamical properties as conditions on the high order Lie derivatives of polynomials occurring in the SAI. A direct consequence of this criterion is a relatively complete method of SAI generation based on template assumption and semi-algebraic constraint solving. Relative completeness means if there is an SAI in the form of a predefined template, then our method can indeed find one using this template.

cs.SC↗
arXiv2011

Automatically Discovering Relaxed Lyapunov Functions for Polynomial Dynamical Systems

The notion of Lyapunov function plays a key role in design and verification of dynamical systems, as well as hybrid and cyber-physical systems. In this paper, to analyze the asymptotic stability of a dynamical system, we generalize standard Lyapunov functions to relaxed Lyapunov functions (RLFs), by considering higher order Lie derivatives of certain functions along the system's vector field. Furthermore, we present a complete method to automatically discovering polynomial RLFs for polynomial dynamical systems (PDSs). Our method is complete in the sense that it is able to discover all polynomial RLFs by enumerating all polynomial templates for any PDS.

math.DS↗
arXiv1993

Unstable particle mixing and CP violation in weak decays

We discuss unstable particle mixing in CP-violating weak decays. It is shown that for a completely degenerate system unstable particle mixing does not introduce a CP-violating partial rate difference, and that when the mixings are small only the off-diagonal mixings are relevant. Also, in the absence of mixing, unstable particle wave function renormalization does not introduce any additional effect. An illustrative example is given to heavy scalar decays with arbitrary mixing.

hep-ph↗
arXiv1993

Testing the handedness of a heavy $W^{\prime}$ at future hadron colliders

We show that the associated production $pp\to W^{\prime}W$, and the rare decay $pp\to W'\to \bar\ell\ell W$ are useful tests of $W'$ couplings to fermions at future hadron colliders. For $M_{W'}\sim (1 - 3)$ TeV they would allow a clean determination on whether the $W'$ couples to $V-A$ or $V+A$ currents. As an illustration a model in which the $W^{\prime\pm}$ couples only to $V-A$ currents is contrasted to the left-right symmetric models which involve $V+A$ currents.

hep-ph↗
arXiv1993

Loop-Induced Majoronic Decay of Neutrinos:

In models where the suppression of the tree level couplings of fermions with Goldstone bosons is not protected by symmetry, the loop-induced couplings can in general dominate over the tree couplings. We demonstrate this by calculating the decay $ν\to ν' + \mbox{Majoron}$ in the simplest singlet Majoron model. It is shown that 1-loop contributions can be orders of magnitude bigger than the tree-level result, and hence allows a wide range of interesting values of neutrino mass and mixing to be compatible with cosmological constraints.

hep-ph↗
arXiv1993

Phenomenological Description of an Unstable Fermion

We present a phenomenological description for an unstable fermion based upon one-loop renormalization of quantum field theory. It is emphasized that wave function renormalization can introduce important $CP$-conserving and $CP$-violating phases. Implications for the study of $CP$ violation are examined. Applications are given to $CP$-violating asymmetries in the $t$ decays: $t\to bW^+, bH^+$, in which we show that a naive calculation following the standard rule either is incomplete or violates $CPT$.

hep-ph↗
arXiv1992

Particle Width Effect on $CP$-Violating Asymmetries

$CP$-violating asymmetries calculated from the Breit-Wigner approximation for unstable particle propagators violate $CPT$. A formalism satisfying $CP$ invariance and unitarity is presented. Applications are given to $t$ decays. For the decay $t\to bν_τ\barτ$ in a class of $CP$-violating models the $CP$-violating asymmetry turns out to be $10^{-4}$ times smaller than the previously reported results.

hep-ph↗