arXiv subjects

Wei Zhao

Publications and source records attributed to Wei Zhao.

At least 361 records · Page 20Linked to original sources

arXiv2019

Hardy inequalities with best constants on Finsler metric measure manifolds

The paper is devoted to weighted $L^p$-Hardy inequalities with best constants on Finsler metric measure manifolds. There are two major ingredients. The first, which is the main part of this paper, is the Hardy inequalities concerned with distance functions in the Finsler setting. In this case, we find that besides the flag curvature, the Ricci curvature together with two non-Riemannian quantities, i.e., reversibility and S-curvature, also play an important role. And we establish the optimal Hardy inequalities not only on noncompact manifolds, but also on closed manifolds. The second ingredient is the Hardy inequalities for Finsler $p$-sub/superharmonic functions, in which we also investigate the existence of extremals and the Brezis-Vázquez improvement.

math.DG↗

arXiv2019

Dimension-like functions and spectrums of Finsler manifolds

In this paper, we study the spectral problem on a compact Finsler manifold with or without boundary. More precisely, given a certain collection of sets in Sobolev space $H^{1,2}(M)$ and a dimension-like function, we can define a corresponding spectrum. Such a spectrum satisfies nice properties. In particular, the eigenfunction corresponding to each eigenvalue always exists. And a Cheng type upper bound estimate for eigenvalues is obtained. Moreover, some interesting examples are constructed and investigated in this paper.

math.DG↗

arXiv2019

Lower bounds for eigenvalues of Finsler manifolds

In this paper, we study the spectrums of faithful dimension pairs on a closed Finsler manifold and obtain a Gromov type and a Buser type lower bounds for eigenvalues. Furthermore, for the Lusternik-Schnirelmann spectrum, we not only obtain a better lower bound, but also estimate the multiplicity of each eigenvalue.

math.DG↗

arXiv2019

Automatic target positioning and tracking for image-guided radiotherapy without implanted fiducials

Current image-guided prostate radiotherapy often relies on the use of implanted fiducials or transducers for target localization. Fiducial or transducer insertion requires an invasive procedure that adds cost and risks for bleeding, infection, and discomfort to some patients. We are developing a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kV X-ray images without the need for daily cone-beam computed tomography (CBCT). A deep learning model was first trained by using several thousand annotated projection X-ray images. The trained model is capable of identifying the location of the prostate target for a given input X-ray projection image. To assess the accuracy of the approach, three patients with prostate cancer received volumetric modulated arc therapy (VMAT) were retrospectively studied. The results obtained by using the deep learning model and the actual position of the prostate were compared quantitatively. The deviations between the target positions obtained by the deep learning model and the corresponding annotations ranged from 1.66 mm to 2.77 mm for anterior-posterior (AP) direction, and from 1.15 mm to 2.88 mm for lateral direction. Target position provided by deep learning model for the kV images acquired using OBI is found to be consistent that derived from the fiducials. This study demonstrates, for the first time, that highly accurate markerless prostate localization based on deep learning is achievable. The strategy provides a clinically valuable solution to daily patient positioning and real-time target tracking for image-guided radiotherapy (IGRT) and interventions.

physics.med-ph↗

arXiv2019

Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to the target sentence. Since the GEC suffers from not having enough labeled training data to achieve high accuracy. We pre-train the copy-augmented architecture with a denoising auto-encoder using the unlabeled One Billion Benchmark and make comparisons between the fully pre-trained model and a partially pre-trained model. It is the first time copying words from the source context and fully pre-training a sequence to sequence model are experimented on the GEC task. Moreover, We add token-level and sentence-level multi-task learning for the GEC task. The evaluation results on the CoNLL-2014 test set show that our approach outperforms all recently published state-of-the-art results by a large margin. The code and pre-trained models are released at https://github.com/zhawe01/fairseq-gec.

cs.CL↗

arXiv2019

HTDet: A Clustering Method using Information Entropy for Hardware Trojan Detection

Hardware Trojans (HTs) have drawn more and more attention in both academia and industry because of its significant potential threat. In this paper, we proposed a novel HT detection method using information entropy based clustering, named HTDet. The key insight of HTDet is that the Trojan usually be inserted in the regions with low controllability and low observability in order to maintain high concealment, which will result in that Trojan logics appear extremely low transitions during the simulation. This means that the logical regions with the low transitions will provide us with much more abundant and more important information for HT detection. Therefore, HTDet applies information theory technology and a typical density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect all suspicious Trojan logics in circuit under detection (CUD). DBSCAN is an unsupervised learning algorithm, which can improve the applicability of HTDet. Besides, we develop a heuristic test patterns generation method using mutual information to increase the transitions of suspicious Trojan logics. Experimental evaluation with benchmarks demenstrates the effectiveness of HTDet.

cs.IT↗

arXiv2019

Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes. In this paper, we introduce: 1) an agreement score to evaluate the performance of routing processes at instance level; 2) an adaptive optimizer to enhance the reliability of routing; 3) capsule compression and partial routing to improve the scalability of capsule networks. We validate our approach on two NLP tasks, namely: multi-label text classification and question answering. Experimental results show that our approach considerably improves over strong competitors on both tasks. In addition, we gain the best results in low-resource settings with few training instances.

cs.CL↗

arXiv2019

Breather induced quantized superfluid vortex filaments and their characterization

We study and characterize the breather-induced quantized superfluid vortex filaments which correspond to the Kuznetsov-Ma breather and super-regular breather excitations developing from localised perturbations. Such vortex filaments, emerging from an otherwise perturbed helical vortex, exhibit intriguing loop structures corresponding to the large amplitude of breathers due to the dual action of bending and twisting of the vortex. The loop induced by Kuznetsov-Ma breather emerges periodically as time increases, while the loop structure triggered by super-regular breather---the loop pair---exhibits striking symmetry breaking due to the broken reflection symmetry of the group velocities of super-regular breather. In particular, we identify explicitly the generation conditions of these loop excitations by introducing a physical quantity---the integral of the relative quadratic curvature---which corresponds to the effective energy of breathers. Although the nature of nonlinearity, it is demonstrated that this physical quantity shows a linear correlation with the loop size. These results will deepen our understanding of breather-induced vortex filaments and be helpful for controllable ring-like excitations on the vortices.

nlin.PS↗

arXiv2019

A practicable guide to the quantum computation architectures

The primordial model of quantum computation was introduced over thirty years ago and the first quantum algorithms have appeared for over twenty years. Yet the exact architectures for quantum computer seem foreign to an undergraduate student major in computer science or engineering, even though the mass media has helped popularize the terminologies in the past decade. Despite being a cutting-edge technology from both the theoretical and the experimental perspectives, quantum computation is indeed imminent and it would be helpful to give the undergraduate students at least a skeleton understanding of what a quantum computer stands for. Since instruction-set architectures originated from classical computing models are familiar, we propose analogously a set of quantum instructions, which can be composed to implement renowned quantum algorithms. Albeit the similarity one can draw between classical and quantum computer architectures, current quantum instructions are fundamentally incommensurable from their classical counterparts because they lack the innate capability to implement logical deductions and recursions. We discuss this trait in length and illustrate why it is held responsible that current quantum computers not be considered general computers.

quant-ph↗

arXiv2019

Time-sync Video Tag Extraction Using Semantic Association Graph

Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding semantic association graph (SAG) using semantic similarities and timestamps of the time-sync comments. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.

cs.IR↗

arXiv2019

Vector and Line Quantization for Billion-scale Similarity Search on GPUs

Billion-scale high-dimensional approximate nearest neighbour (ANN) search has become an important problem for searching similar objects among the vast amount of images and videos available online. The existing ANN methods are usually characterized by their specific indexing structures, including the inverted index and the inverted multi-index structure. The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs. However, the inverted index requires high memory overhead to index the dataset effectively. The inverted multi-index structure is difficult to implement for GPUs, and also ineffective in dealing with database with different data distributions. In this paper we propose a novel hierarchical inverted index structure generated by vector and line quantization methods. Our quantization method improves both search efficiency and accuracy, while maintaining comparable memory consumption. This is achieved by reducing search space and increasing the number of indexed regions. We introduce a new ANN search system, VLQ-ADC, that is based on the proposed inverted index, and perform extensive evaluation on two public billion-scale benchmark datasets SIFT1B and DEEP1B. Our evaluation shows that VLQ-ADC significantly outperforms the state-of-the-art GPU- and CPU-based systems in terms of both accuracy and search speed. The source code of VLQ-ADC is available at https://github.com/zjuchenwei/vector-line-quantization.

cs.CV↗

arXiv2019

Second order hierarchical partial least squares regression-polynomial chaos expansion for global sensitivity and reliability analyses of high-dimensional models

To tackle the curse of dimensionality and multicollinearity problems of polynomial chaos expansion for analyzing global sensitivity and reliability of models with high stochastic dimensions, this paper proposes a novel non-intrusive algorithm called second order hierarchical partial least squares regression-polynomial chaos expansion. The first step of the innovative algorithm is to divide the polynomials into several groups according to their interaction degrees and nonlinearity degrees, which avoids large data sets and reflects the relationship between polynomial chaos expansion and high dimensional model representation. Then a hierarchical regression algorithm based on partial least squares regression is devised for extracting latent variables from each group at different variable levels. The optimal interaction degree and the corresponding nonlinearity degrees are automatically estimated with an improved cross validation scheme. Based on the relationship between variables at two adjacent levels, Sobol' sensitivity indices can be obtained by a simple post-processing of expansion coefficients. Thus, the expansion is greatly simplified through retaining the important inputs, leading to accurate reliability analysis without requirements of additional model evaluations. Finally, finite element models with three different types of structures verified that the proposed method can greatly improve the computational efficiency compared with the ordinary least squares regression-based method.

math.NA↗

arXiv2019

Compact optical phased array using a serial grating antenna design

We propose an on-chip Si photonics optical phased array based on a serial grating design which eliminates the use of directional couplers in previous designs. It significantly reduces overall phased array size especially when the number of the antenna is small which is often demanded in practice. The simulation results show our design reduces overall phased array size, increases optical power utilization while maintains comparable far field performance.

physics.app-ph↗

arXiv2019

Robust Contrast Enhancement Forensics Using Pixel and Histogram Domain CNNs

Contrast enhancement (CE) forensics has always been ofconcern to image forensics community. It can provide aneffective tool for recovering image history and identifyingtampered images. Although several CE forensic algorithmshave been proposed, their robustness against some processingis still unsatisfactory, such as JPEG compression and anti-forensic attacks. In order to attenuate such deficiency, inthis paper we first present a discriminability analysis of CEforensics in pixel and gray level histogram domains. Then, insuch two domains, two end-to-end methods based on convo-lutional neural networks (P-CNN, H-CNN) are proposed toachieve robust CE forensics against pre-JPEG compressionand anti-forensics attacks. Experimental results show that theproposed methods achieve much better performance than thestate-of-the-art schemes for CE detection in the case of noother operation and comparable performance when pre-JPEGcompression and anti-foresics attacks is used.

cs.MM↗

arXiv2018

Multi-materials beam hardening artifacts correction for computed tomography (CT) based on X-ray spectrum estimation

Due to the energy-dependent nature of the attenuation coefficient and the polychromaticity of the X-ray source, beam hardening effect occurs when X-ray photons penetrate through an object, causing a nonlinear projection data. When a linear reconstruction algorithm, such as filtered backprojection, is applied to reconstruct the projection data, beam hardening artifacts which show as cupping and streaks are present in the CT image. The aim of this study was to develop a fast and accurate beam hardening correction method which can deal with beam hardening artifacts induced by multi-materials objects. Based on spectrum estimation, the nonlinear attenuation process of the X-ray projection was modeled by reprojecting a template image with the estimated polychromatic spectrum. The template images were obtained by segmenting the uncorrected into different components using a simple segmentation algorithm. Numerical simulations, experimental phantom data and animal data which were acquired on a modern diagnostic CT scanner (Discovery CT750 HD, GE Healthcare, WI, USA) and a modern C-Arm CT scanner (Artis Zee, Siemens Healthcare, Forchheim, Germany), respectively, were used to evaluate the proposed method. The results show the proposed method significantly reduced both cupping and streak artifacts, and successfully recovered the Hounsfield Units (HU) accuracy.

physics.med-ph↗

arXiv2018

Coexistence of Intrinsic Superconductivity and Topological Insulator State in Monoclinic Phase WS2

Recently, intriguing phenomena of superconductivity, type-II Weyl semimetal or quantum spin Hall states were discovered in metastable 1T'-type VIB-group transition metal dichalcogenides (TMDs). Here, we report that monoclinic phase WS2 was discovered and synthesized in our experiments. The intrinsic superconducting transition was observed in monoclinic WS2 with a transition temperature Tc of 8.8 K which is the highest among previously reported TMDs without any fine-tuning process. Intersteing, topological insulator state, defined by topological invariant Z2, was also discovered with a single Dirac cone on the surface, which is different from all topological states reported in TMDs. Further, the electronic structure was found to have a strong anisotropy by Shubnikov-de Haas oscillations and first-principles calculations. Our findings reveal that monoclinic WS2 might be a new topological superconductivity candidate with a strong anisotropy.

cond-mat.supr-con↗

arXiv2018

Deterministic generation and switching of dissipative Kerr soliton in a thermally controlled micro-resonator

Dissipative Kerr solitons (DKSs) in the high-Q micro-resonator corresponds to self-organized short pulse in time domain via double balance between dispersion and Kerr nonlinearity, as well as cavity loss and parametric gain. In this paper, we first experimentally demonstrate deterministic generation and switching of DKSs in a thermally controlled micro-ring resonator based on high-index doped silica glass platform. In our scheme, an auxiliary laser is introduced to timely balance the intra-cavity heat fluctuation. By decreasing the operating temperature through a thermo-electric cooler, soliton crystal is firstly generated, then increasing the temperature, DKSs switching and single soliton are robustly accessed, which is independent of the tuning speed. During the switching process, varieties of DKSs are identified by tens of featured discrete soliton-steps, which is favorable for study of on-chip soliton interactions and nonlinear applications.

physics.optics↗

arXiv2018

Investigating Capsule Networks with Dynamic Routing for Text Classification

In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain "background" information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over strong baseline methods. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.

cs.CL↗