arXiv subjects

Li Wang

Publications and source records attributed to Li Wang.

At least 523 records · Page 29Linked to original sources

arXiv2018

Direct measurement of neutrons induced in lead by cosmic muons at a shallow underground site

Neutron production in lead by cosmic muons has been studied with a Gadolinium doped liquid scintillator detector. The detector was installed next to the Muon-Induced Neutron Indirect Detection EXperiment (MINIDEX), permanently located in the Tübingen shallow underground laboratory where the mean muon energy is approximately 7 GeV. The MINIDEX plastic scintillators were used to tag muons; the neutrons were detected through neutron capture and neutron-induced nuclear recoil signals in the liquid scintillator detector. Results on the rates of observed neutron captures and nuclear recoils are presented and compared to predictions from GEANT4-9.6 and GEANT4-10.3. The predicted rates are significantly too low for both versions of GEANT4. For neutron capture events, the observation exceeds the predictions by factors of $ 1.65\,\pm\,0.02\,\textrm{(stat.)}\,\pm\,0.07\,\textrm{(syst.)} $ and $ 2.58\,\pm\,0.03\,\textrm{(stat.)}\,\pm\,0.11\,\textrm{(syst.)} $ for GEANT4-9.6 and GEANT4-10.3, respectively. For neutron nuclear recoil events, which require neutron energies above approximately 5 MeV, the factors are even larger, $ 2.22\,\pm\,0.05\,\textrm{(stat.)}\,\pm\,0.25\,\textrm{(syst.)} $ and $ 3.76\,\pm\,0.09\,\textrm{(stat.)}\,\pm\,0.41\,\textrm{(syst.)} $, respectively. Also presented is the first statistically significant measurement of the spectrum of neutrons induced by cosmic muons in lead between 5 and 40 MeV. It was obtained by unfolding the nuclear recoil spectrum. The observed neutron spectrum is harder than predicted by GEANT4. An investigation of the distribution of the time difference between muon tags and nuclear recoil signals confirms the validity of the unfolding procedure and shows that GEANT4 cannot properly describe the time distribution of nuclear recoil events. In general, the description of the data is worse for GEANT4-10.3 than for GEANT4-9.6.

physics.ins-det↗

arXiv2018

Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity

Non-rigid inter-modality registration can facilitate accurate information fusion from different modalities, but it is challenging due to the very different image appearances across modalities. In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images. In particular, the training of our inter-modality registration network is supervised by intra-modality similarity metric based on the available paired data, which is derived from a pre-aligned CT and MR dataset. Specifically, in the training stage, to register the input CT and MR images, their similarity is evaluated on the warped MR image and the MR image that is paired with the input CT. So that, the intra-modality similarity metric can be directly applied to measure whether the input CT and MR images are well registered. Moreover, we use the idea of dual-modality fashion, in which we measure the similarity on both CT modality and MR modality. In this way, the complementary anatomies in both modalities can be jointly considered to more accurately train the inter-modality registration network. In the testing stage, the trained inter-modality registration network can be directly applied to register the new multimodal images without any paired data. Experimental results have shown that, the proposed method can achieve promising accuracy and efficiency for the challenging non-rigid inter-modality registration task and also outperforms the state-of-the-art approaches.

cs.CV↗

arXiv2018

Branching Brownian Motion with spatially-homogeneous and point-catalytic branching

We consider a model of Branching Brownian Motion in which the usual spatially-homogeneous and catalytic branching at a single point are simultaneously present. We establish the almost sure growth rates of population in certain time-dependent regions and as a consequence the first-order asymptotic behaviour of the rightmost particle.

math.PR↗

arXiv2018

Type-II Dirac photons at the metasurfaces

Topological characteristics of energy bands, such as Dirac/Weyl nodes, have attracted substantial interest in condensed matter systems as well as in classical wave systems. Among these energy bands, the type-II Dirac point is a nodal degeneracy with tilted conical dispersion, leading to a peculiar crossing dispersion in the constant energy plane. Such nodal points have recently been found in electronic materials. The analogous topological feature in photonic systems remains a theoretical curiosity, with experimental realization expected to be challenging. Here, we experimentally realize the type-II Dirac point using a planar metasurface architecture, where the band degeneracy point is protected by the underlying mirror symmetry of the metasurface. Gapless edge modes are found and measured at the boundary between the different domains of the symmetry-broken metasurface. Our work shows that metasurfaces are simple and practical platforms for realizing electromagnetic type-II Dirac points, and their planar structure is a distinct advantage that facilitates applications in two-dimensional topological photonics.

physics.app-ph↗

arXiv2018

TextZoo, a New Benchmark for Reconsidering Text Classification

Text representation is a fundamental concern in Natural Language Processing, especially in text classification. Recently, many neural network approaches with delicate representation model (e.g. FASTTEXT, CNN, RNN and many hybrid models with attention mechanisms) claimed that they achieved state-of-art in specific text classification datasets. However, it lacks an unified benchmark to compare these models and reveals the advantage of each sub-components for various settings. We re-implement more than 20 popular text representation models for classification in more than 10 datasets. In this paper, we reconsider the text classification task in the perspective of neural network and get serval effects with analysis of the above results.

cs.CL↗

arXiv2018

Arbitrary-Oriented Scene Text Detection via Rotation Proposals

This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images. We present the Rotation Region Proposal Networks (RRPN), which are designed to generate inclined proposals with text orientation angle information. The angle information is then adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation. The Rotation Region-of-Interest (RRoI) pooling layer is proposed to project arbitrary-oriented proposals to a feature map for a text region classifier. The whole framework is built upon a region-proposal-based architecture, which ensures the computational efficiency of the arbitrary-oriented text detection compared with previous text detection systems. We conduct experiments using the rotation-based framework on three real-world scene text detection datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.

cs.CV↗

arXiv2018

Response of gadolinium doped liquid scintillator to charged particles: measurement based on intrinsic U/Th contamination

A measurement is reported for the response to charged particles of a liquid scintillator named EJ-335 doped with 0.5% gadolinium by weight. This liquid scintillator was used as the detection medium in a neutron detector. The measurement is based on the in-situ $α$-particles from the intrinsic Uranium and Thorium contamination in the scintillator. The $β$-$α$ and the $α$-$α$ cascade decays from the U/Th decay chains were used to select $α$-particles. The contamination levels of U/Th were consequently measured to be $(5.54\pm0.15)\times 10^{-11}$ g/g, $(1.45\pm0.01)\times 10^{-10}$ g/g and $(1.07\pm0.01)\times 10^{-11}$ g/g for $^{232}$Th, $^{238}$U and $^{235}$U, respectively, assuming secular equilibrium. The stopping power of $α$-particles in the liquid scintillator was simulated by the TRIM software. Then the Birks constant, $kB$, of the scintillator for $α$-particles was determined to be $(7.28\pm0.23)$ mg/(cm$^{2}\cdot$MeV) by Birks' formulation. The response for protons is also presented assuming the $kB$ constant is the same as for $α$-particles.

physics.ins-det↗

arXiv2018

Time-sensitive Customer Churn Prediction based on PU Learning

With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the observation period, and start to train model as long as enough positive samples are collected, ignoring the absence of the negative examples. We conduct thoroughly experiments on real industry data from Alipay.com. The experimental results demonstrate that TCCP outperforms the rule-based models and the traditional supervised learning models.

cs.LG↗

arXiv2018

Permissive Barrier Certificates for Safe Stabilization Using Sum-of-squares

Motivated by the need to simultaneously guarantee safety and stability of safety-critical dynamical systems, we construct permissive barrier certificates in this paper that explicitly maximize the region where the system can be stabilized without violating safety constraints. An optimization strategy is developed to search for the maximum volume barrier certified region of safe stabilization. The barrier certified region, which is allowed to take any arbitrary shape, is proved to be strictly larger than safe regions generated with Lyapunov sublevel set based methods. The proposed approach effectively unites a Lyapunov function with multiple barrier functions that might not be compatible with each other. Iterative search algorithms are developed using sum-of-squares to compute the most permissive, that is, the maximum volume, barrier certificates. Simulation results of the iterative search algorithm demonstrate the effectiveness of the proposed method.

math.OC↗

arXiv2018

Accuracy of the Muskingum-Cunge method for constant-parameter diffusion-wave channel routing with lateral inflow

Channel routing is important in flood forecasting and watershed modeling. The general constant-parameter Muskingum-Cunge (CPMC) method is second-order accurate and easy to implement. With specific discretizations such that the temporal and spatial intervals maintain a unique relationship, the CPMC method can be third-order accurate. In this paper, we derive the average lateral inflow term in the second- and third-order accuracy CPMC method, and demonstrate that For spatially and temporally variable lateral inflow, the effect of lateral inflow on simulated discharge varies with spatial and temporal discretizations, the value and spatial and temporal variations of lateral inflow, wave celerity, and diffusion coefficient. Comparison of the CPMC solution with the analytical solution shows that both the second- and third-order accuracy schemes are more accurate than the simplified method by which spatial derivatives of lateral inflow are ignored. For small time steps, the third-order accuracy CPMC method results in higher accuracy than the second-order scheme even when the third-order accuracy criterion is not fully met. For large time steps, the temporal and spatial discretization of the third- and second-order scheme becomes the same, but the third-order scheme yields higher accuracy than the second-order scheme because of the third-order accurate estimation of the lateral inflow term.

physics.flu-dyn↗

arXiv2018

Analysis and computation of some tumor growth models with nutrient: from cell density models to free boundary dynamics

In this paper, we study the tumor growth equation along with various models for the nutrient component, including the \emph{in vitro} model and the \emph{in vivo} model. At the cell density level, the spatial availability of the tumor density $n$ is governed by the Darcy law via the pressure $p(n)=n^γ$. For finite $γ$, we prove some a priori estimates of the tumor growth model, such as boundedness of the nutrient density, and non-negativity and growth estimate of the tumor density. As $γ\rightarrow \infty$, the cell density models formally converge to Hele-Shaw flow models, which determine the free boundary dynamics of the tumor tissue in the incompressible limit. We derive several analytical solutions to the Hele-Shaw flow models, which serve as benchmark solutions to the geometric motion of tumor front propagation. Finally, we apply a conservative and positivity preserving numerical scheme to the cell density models, with numerical results verifying the link between cell density models and the free boundary dynamical models.

math.AP↗

arXiv2018

Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks

Recently, deep learning has achieved very promising results in visual object tracking. Deep neural networks in existing tracking methods require a lot of training data to learn a large number of parameters. However, training data is not sufficient for visual object tracking as annotations of a target object are only available in the first frame of a test sequence. In this paper, we propose to learn hierarchical features for visual object tracking by using tree structure based Recursive Neural Networks (RNN), which have fewer parameters than other deep neural networks, e.g. Convolutional Neural Networks (CNN). First, we learn RNN parameters to discriminate between the target object and background in the first frame of a test sequence. Tree structure over local patches of an exemplar region is randomly generated by using a bottom-up greedy search strategy. Given the learned RNN parameters, we create two dictionaries regarding target regions and corresponding local patches based on the learned hierarchical features from both top and leaf nodes of multiple random trees. In each of the subsequent frames, we conduct sparse dictionary coding on all candidates to select the best candidate as the new target location. In addition, we online update two dictionaries to handle appearance changes of target objects. Experimental results demonstrate that our feature learning algorithm can significantly improve tracking performance on benchmark datasets.

cs.CV↗

arXiv2017

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement. However, computing such segmentations is very challenging, especially for 6-month infant brain, due to the poor image quality, among other difficulties inherent to infant brain MRI, e.g., the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes. This study investigates the problem with an ensemble of semi-dense fully convolutional neural networks (CNNs), which employs T1-weighted and T2-weighted MR images as input. We demonstrate that the ensemble agreement is highly correlated with the segmentation errors. Therefore, our method provides measures that can guide local user corrections. To the best of our knowledge, this work is the first ensemble of 3D CNNs for suggesting annotations within images. Furthermore, inspired by the very recent success of dense networks, we propose a novel architecture, SemiDenseNet, which connects all convolutional layers directly to the end of the network. Our architecture allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net. Another contribution of our work is the study of the impact that early or late fusions of multiple image modalities might have on the performances of deep architectures. We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.

cs.CV↗

arXiv2017

A metasurface with bidirectional hyperbolic surface modes and positionsensing applications

We theoretically and experimentally studied resonance-induced hyperbolic metasurfaces, proving that it is an efficient way to introduce Fano-resonance and decreasethe Q-factor in our system in order to create hyperbolic iso-frequency contours (IFCs) along two orthogonal directions. The metasurface with a continuous topological transition for such IFCs has been designed and experimentally implemented. In particular, two independent selfcollimation frequencies corresponding to the transition frequencies in orthogonal directions. As a consequence, we experimentally demonstrated that the metasurface can function as a position- sensor by utilizing the bidirectional hyperbolic surface waves, opening a new avenue for position-sensing.

physics.app-ph↗

arXiv2017

Elasticutor: Rapid Elasticity for Realtime Stateful Stream Processing

Elasticity is highly desirable for stream processing systems to guarantee low latency against workload dynamics, such as surges in data arrival rate and fluctuations in data distribution. Existing systems achieve elasticity following a resource-centric approach that uses dynamic key partitioning across the parallel instances, i.e. executors, to balance the workload and scale operators. However, such operator-level key repartitioning needs global synchronization and prohibits rapid elasticity. To address this problem, we propose an executor-centric approach, whose core idea is to avoid operator-level key repartitioning while implementing each executor as the building block of elasticity. Following this new approach, we design the Elasticutor framework with two level of optimizations: i) a novel implementation of executors, i.e., elastic executors, that perform elastic multi-core execution via efficient intra-executor load balancing and executor scaling and ii) a global model-based scheduler that dynamically allocates CPU cores to executors based on the instantaneous workloads. We implemented a prototype of Elasticutor and conducted extensive experiments. Our results show that Elasticutor doubles the throughput and achieves an average processing latency up to 2 orders of magnitude lower than previous methods, for a dynamic workload of real-world applications.

cs.DB↗

arXiv2017

Recent Advances in Convolutional Neural Networks

In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

cs.CV↗

arXiv2017

Measurement of the fast neutron background at the China Jinping Underground Laboratory

We report on the measurements of the fluxes and spectra of the environmental fast neutron background at the China Jinping Underground Laboratory (CJPL) with a rock overburden of about 6700 meters water equivalent, using a liquid scintillator detector doped with 0.5% gadolinium. The signature of a prompt nuclear recoil followed by a delayed high energy $γ$-ray cascade is used to identify neutron events. The large energy deposition of the delayed $γ$-rays from the $(n, γ)$ reaction on gadolinium, together with the excellent n-$γ$ discrimination capability provides a powerful background suppression which allows the measurement of a low intensity neutron flux. The neutron flux of $(1.51\pm0.03(stat.)\pm0.10(syst.))\times10^{-7}$ cm$^{-2}$s$^{-1}$ in the energy range of 1 -- 10 MeV in the Hall A of CJPL was measured based on 356 days of data. In the same energy region, measurement with the same detector placed in a one meter thick polyethylene room gives a significantly lower flux of $(4.9\pm0.9(stat.)\pm0.5(syst.))\times10^{-9}$ cm$^{-2}$s$^{-1}$ with 174 days of data. This represents a measurement of the lowest environmental fast neutron background among the underground laboratories in the world, prior to additional experiment-specific attenuation. Additionally, the fast neutron spectra both in the Hall A and the polyethylene room were reconstructed with the help of GEANT4 simulation.

physics.ins-det↗

arXiv2017

Safe Learning of Quadrotor Dynamics Using Barrier Certificates

To effectively control complex dynamical systems, accurate nonlinear models are typically needed. However, these models are not always known. In this paper, we present a data-driven approach based on Gaussian processes that learns models of quadrotors operating in partially unknown environments. What makes this challenging is that if the learning process is not carefully controlled, the system will go unstable, i.e., the quadcopter will crash. To this end, barrier certificates are employed for safe learning. The barrier certificates establish a non-conservative forward invariant safe region, in which high probability safety guarantees are provided based on the statistics of the Gaussian Process. A learning controller is designed to efficiently explore those uncertain states and expand the barrier certified safe region based on an adaptive sampling scheme. In addition, a recursive Gaussian Process prediction method is developed to learn the complex quadrotor dynamics in real-time. Simulation results are provided to demonstrate the effectiveness of the proposed approach.

cs.LG↗