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Zheng Huang

Publications and source records attributed to Zheng Huang.

At least 55 records · Page 3Linked to original sources
arXiv2021

Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US

To mitigate the spread of COVID-19 pandemic, decision-makers and public authorities have announced various non-pharmaceutical policies. Analyzing the causal impact of these policies in reducing the spread of COVID-19 is important for future policy-making. The main challenge here is the existence of unobserved confounders (e.g., vigilance of residents). Besides, as the confounders may be time-varying during COVID-19 (e.g., vigilance of residents changes in the course of the pandemic), it is even more difficult to capture them. In this paper, we study the problem of assessing the causal effects of different COVID-19 related policies on the outbreak dynamics in different counties at any given time period. To this end, we integrate data about different COVID-19 related policies (treatment) and outbreak dynamics (outcome) for different United States counties over time and analyze them with respect to variables that can infer the confounders, including the covariates of different counties, their relational information and historical information. Based on these data, we develop a neural network based causal effect estimation framework which leverages above information in observational data and learns the representations of time-varying (unobserved) confounders. In this way, it enables us to quantify the causal impact of policies at different granularities, ranging from a category of policies with a certain goal to a specific policy type in this category. Besides, experimental results also indicate the effectiveness of our proposed framework in capturing the confounders for quantifying the causal impact of different policies. More specifically, compared with several baseline methods, our framework captures the outbreak dynamics more accurately, and our assessment of policies is more consistent with existing epidemiological studies of COVID-19.

cs.LG↗
arXiv2021

Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning

Cluster assignment of large and complex images is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep feature representation learning loss function and embedded fuzzy clustering with the weighted adaptive entropy is implemented. We joint fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments and jointly optimize for the deep representation learning and clustering. Also, the joint model evaluates current clustering performance by inspecting whether the re-sampled data from estimated bottleneck space have consistent clustering properties to progressively improve the deep clustering model. Comprehensive experiments on a variety of datasets show that the proposed method obtains a substantially better performance for both reconstruction and clustering quality when compared to the other state-of-the-art deep clustering methods, as demonstrated with the in-depth analysis in the extensive experiments.

cs.CV↗
arXiv2021

ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction

Scanned receipts OCR and key information extraction (SROIE) represent the processeses of recognizing text from scanned receipts and extracting key texts from them and save the extracted tests to structured documents. SROIE plays critical roles for many document analysis applications and holds great commercial potentials, but very little research works and advances have been published in this area. In recognition of the technical challenges, importance and huge commercial potentials of SROIE, we organized the ICDAR 2019 competition on SROIE. In this competition, we set up three tasks, namely, Scanned Receipt Text Localisation (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3). A new dataset with 1000 whole scanned receipt images and annotations is created for the competition. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, submission statistics, performance of submitted methods and results analysis.

cs.AI↗
arXiv2020

Bifurcation for Minimal Surface Equation in Hyperbolic $3$-Manifolds

Initiated by the work of Uhlenbeck in late 1970s, we study questions about the existence, multiplicity and asymptotic behavior for minimal immersions of closed surface in some hyperbolic three-manifold, with prescribed conformal structure on the surface and second fundamental form of the immersion. We prove several results in these directions. In particular, we determine when exactly the solution is unique and when multiple solutions appear. Moreover, we analyze in detail the asymptotic behavior of the solutions when (and how) blowing up might occur. Interestingly the blow-up analysis exhibit different behaviors when the surface is of genus two or greater. Furthermore, we extend this program to consider similar problems where the total extrinsic curvature is prescribed and we prove an existence result.

math.DG↗
arXiv2020

GFTE: Graph-based Financial Table Extraction

Tabular data is a crucial form of information expression, which can organize data in a standard structure for easy information retrieval and comparison. However, in financial industry and many other fields tables are often disclosed in unstructured digital files, e.g. Portable Document Format (PDF) and images, which are difficult to be extracted directly. In this paper, to facilitate deep learning based table extraction from unstructured digital files, we publish a standard Chinese dataset named FinTab, which contains more than 1,600 financial tables of diverse kinds and their corresponding structure representation in JSON. In addition, we propose a novel graph-based convolutional neural network model named GFTE as a baseline for future comparison. GFTE integrates image feature, position feature and textual feature together for precise edge prediction and reaches overall good results.

cs.CV↗
arXiv2020

Varied fusion reaction probability induced by ion stopping modification in laser-driven plasma with different temperature

The dynamics of nuclear reaction in plasma is a fundamental issue in many high energy density researches, such as the astrophysical reactions and the inertial confinement fusion. The effective reaction cross-sections and ion stopping power in plasma need to be taken into account to analyze the reactivity. In this research, we have experimentally investigated the from D-D reactions from interactions between deuteron beams and deuterated polystyrene (CD) plasma, driven by two laser pulses respectively. The neutron yields, plasma density and deuteron energy loss in plasma have been measured, and the plasma temperature and deuteron stopping power have been analyzed from simulations. It is shown that, compared with a cold target, the reaction probability in plasma conditions can be enhanced or suppressed, which is ascribed to the deuteron stopping power modifications in plasma. In hotter CD plasma, the energy loss of moderate energetic deuterons reduces, which leads to higher D-D reaction probability, while the contrary happens in colder plasma. This work provides new understanding of fusion reactions in plasma environment.

physics.plasm-ph↗
arXiv2019

Double Supervised Network with Attention Mechanism for Scene Text Recognition

In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition. It incorporates one text attention module during feature extraction which enforces the model to focus on text regions and the whole framework is supervised by two branches. One supervision branch comes from context-level modelling and another comes from one extra supervision enhancement branch which aims at tackling inexplicit semantic information at character level. These two supervisions can benefit each other and yield better performance. The proposed approach can recognize text in arbitrary length and does not need any predefined lexicon. Our method outperforms the current state-of-the-art methods on three text recognition benchmarks: IIIT5K, ICDAR2013 and SVT reaching accuracy 88.6%, 92.3% and 84.1% respectively which suggests the effectiveness of the proposed method.

cs.CV↗
arXiv2019

EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing

With the development of Internet of Things (IoT), data is increasingly appearing on the edge of the network. Processing tasks on the edge of the network can effectively solve the problems of personal privacy leaks and server overload. As a result, it has attracted a great deal of attention and made substantial progress. This progress includes efficient convolutional neural network (CNN) models such as MobileNet and ShuffleNet. However, all of these networks appear as a common network model and they usually need to identify multiple targets when applied. So the size of the input is very large. In some specific cases, only the target needs to be classified. Therefore, a small input network can be designed to reduce computation. In addition, other efficient neural network models are primarily designed for mobile phones. Mobile phones have faster memory access, which allows them to use group convolution. In particular, this paper finds that the recently widely used group convolution is not suitable for devices with very slow memory access. Therefore, the EdgeCNN of this paper is designed for edge computing devices with low memory access speed and low computing resources. EdgeCNN has been run successfully on the Raspberry Pi 3B+ at a speed of 1.37 frames per second. The accuracy of facial expression classification for the FER-2013 and RAF-DB datasets outperforms other proposed networks that are compatible with the Raspberry Pi 3B+. The implementation of EdgeCNN is available at https://github.com/yangshunzhi1994/EdgeCNN

cs.CV↗
arXiv2019

Transmutation elements Re/Ta effect on vacancy formation and dissociation behavior in W

Transmutation elements are the essential products in plasma facing materials tungsten, and have further effects on point defects evolution resulted by radiation. Here, transmutation elements Re and Ta atoms have been selected to assess the effects on property of vacancy and vacancy cluster in W material via first-principles calculations. The formation energy indicates mono-vacancy is more likely to form in W-Re system than pure W and W-Ta system. Both Re and Ta have reduced the diffusion barrier energy in the mono-vacancy migration. The calculation presents that vacancy cluster prefers to grow up by combining another vacancy cluster relative to a single mono-vacancy. Re is favorable to the nucleation and growth of vacancies clusters, while Ta has a suppressive effect on the aggregation of small vacancy cluster. The emphasis analysis is obtained according the volumetric dependent strain. Vacancy dissociation calculations show that the dissociation of vacancy clusters is easier to begin with a single vacancy dissociation process.

cond-mat.mtrl-sci↗
arXiv2019

Effect of tungsten on vacancy behaviors in Ta-W alloys from first-principles

Alloying elements play an important role in the design of plasma facing materials with good comprehensive properties. Based on first-principles calculations, the stability of alloying element W and its interaction with vacancy defects in Ta-W alloys are studied. The results show that W tends to distribute dispersedly in Ta lattice, and is not likely to form precipitation even with the coexistence of vacancy. The aggregation behaviors of W and vacancy can be affected by their concentration competition. The increase of W atoms has a negative effect on the vacancy clustering, as well as delays the vacancy nucleation process, which is favorable to the recovery of point defects. Our results are in consistent with the defect evolution observed in irradiation experiments in Ta-W alloys. Our calculations suggest that Ta is a potential repairing element that can be doped into Ta-based materials to improve their radiation resistance.

cond-mat.mtrl-sci↗
arXiv2019

Mean curvature flow in Fuchsian manifolds

Motivated by questions in detecting minimal surfaces in hyperbolic manifolds, we study the behavior of geometric flows in complete hyperbolic three-manifolds. In most cases the flows develop singularities in finite time. In this paper, we investigate the mean curvature flow in a class of complete hyperbolic 3-manifolds (Fuchsian manifolds) which are warped products of a closed surface of genus at least two and the real line. In particular, we prove that there exists a large class of closed initial surfaces, as geodesic graphs over the totally geodesic surface $Σ$, such that the mean curvature flow exists for all time and converges to $Σ$. This is among the first examples of converging mean curvature flows of compact hypersurfaces in Riemannian manifolds. We also provide some useful calculations for the general warped product setting.

math.DG↗
arXiv2019

Mean Curvature Flows of Closed Hypersurfaces in Warped Product Manifolds

We investigate the mean curvature flows in a class of warped product manifolds with closed hypersurfaces fibering over $\mathbb{R}$. In particular, we prove that under natural conditions on the warping function and Ricci curvature bound for the ambient space, there exists a large class of closed initial hypersurfaces, as geodesic graphs over the totally geodesic hypersurface $Σ$, such that the mean curvature flow starting from $S_0$ exists for all time and converges to $Σ$.

math.DG↗
arXiv2018

Complex length of short curves and Minimal Fibration in hyperbolic $3$-Manifolds fibering over the circle

We investigate the maximal solid tubes around short simple geodesics in hyperbolic three-manifolds and how complex length of curves relate to closed, incompressible, least area minimal surfaces. As applications, we prove, there are some closed hyperbolic three-manifolds fibering over the circle which are not foliated by closed incompressible minimal surfaces diffeomorphic to the fiber. We also show, the existence of quasi-Fuchsian manifolds containing arbitrarily many embedded closed incompressible minimal surfaces. Our strategy is to prove main theorems under natural geometric conditions on the complex length of closed curves on a fibered hyperbolic three-manifold, then we find explicit examples where these conditions are satisfied via computer programs.

math.DG↗
arXiv2018

The Weil-Petersson curvature operator on the universal Teichmüller space

The universal Teichmüller space is an infinitely dimensional generalization of the classical Teichmüller space of Riemann surfaces. It carries a natural Hilbert structure, on which one can define a natural Riemannian metric, the Weil-Petersson metric. In this paper we investigate the Weil-Petersson Riemannian curvature operator $\tilde{Q}$ of the universal Teichmüller space with the Hilbert structure, and prove the following: (i) $\tilde{Q}$ is non-positive definite. (ii) $\tilde{Q}$ is a bounded operator. (iii) $\tilde{Q}$ is not compact; the set of the spectra of $\tilde{Q}$ is not discrete. As an application, we show that neither the Quaternionic hyperbolic space nor the Cayley plane can be totally geodesically immersed in the universal Teichmüller space endowed with the Weil-Petersson metric.

math.DG↗
arXiv2018

Fused Text Segmentation Networks for Multi-oriented Scene Text Detection

In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during the feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Not involving any extra pipelines, our approach surpasses the current state of the art on multi-oriented scene text detection benchmarks: ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever, we report a baseline on total-text containing curved text which suggests effectiveness of the proposed approach.

cs.CV↗
arXiv2016

Closed Minimal Surfaces in Cusped Hyperbolic Three-manifolds

Motivated by classical theorems on minimal surface theory in compact hyperbolic three-manifolds, we investigate the questions of existence and deformations for least area minimal surfaces in complete noncompact hyperbolic three-manifold of finite volume. We prove any closed immersed incompressible surface can be deformed to a closed immersed least area surface within its homotopy class in any cusped hyperbolic three-manifold. Our techniques highlight how special structures of these cusped hyperbolic three-manifolds prevent any least area minimal surface going too deep into the cusped region.

math.DG↗
arXiv2016

Audio Recording Device Identification Based on Deep Learning

In this paper we present a research on identification of audio recording devices from background noise, thus providing a method for forensics. The audio signal is the sum of speech signal and noise signal. Usually, people pay more attention to speech signal, because it carries the information to deliver. So a great amount of researches have been dedicated to getting higher Signal-Noise-Ratio (SNR). There are many speech enhancement algorithms to improve the quality of the speech, which can be seen as reducing the noise. However, noises can be regarded as the intrinsic fingerprint traces of an audio recording device. These digital traces can be characterized and identified by new machine learning techniques. Therefore, in our research, we use the noise as the intrinsic features. As for the identification, multiple classifiers of deep learning methods are used and compared. The identification result shows that the method of getting feature vector from the noise of each device and identifying them with deep learning techniques is viable, and well-preformed.

cs.SD↗