arXiv subjects

Yi Zhang

Publications and source records attributed to Yi Zhang.

At least 1,099 records · Page 61Linked to original sources

arXiv2019

Non-destructive three-dimensional measurement of hand vein based on self-supervised network

At present, supervised stereo methods based on deep neural network have achieved impressive results. However, in some scenarios, accurate three-dimensional labels are inaccessible for supervised training. In this paper, a self-supervised network is proposed for binocular disparity matching (SDMNet), which computes dense disparity maps from stereo image pairs without disparity labels: In the self-supervised training, we match the stereo images densely to approximate the disparity maps and use them to warp the left and right images to estimate the right and left images; we build the loss function between estimated images and original images for self-supervised training, which adopts perceptual loss to help improve the quality of disparity maps in both detail and structure. Then, we use SDMNet to obtain disparities of hand vein. SDMNet has achieved excellent results on KITTI 2012, KITTI 2015, simulated vein dataset and real vein dataset, outperforming many state-of-the-art supervised matching methods.

cs.CV↗

arXiv2019

Visual Attention Network for Low Dose CT

Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance. Perfect noise removal and image restoration is intractable in the context of LDCT due to the statistical and technical uncertainties. In this paper, we apply the generative adversarial network (GAN) framework with a visual attention mechanism to deal with this problem in a data-driven/machine learning fashion. Our main idea is to inject visual attention knowledge into the learning process of GAN to provide a powerful prior of the noise distribution. By doing this, both the generator and discriminator networks are empowered with visual attention information so they will not only pay special attention to noisy regions and surrounding structures but also explicitly assess the local consistency of the recovered regions. Our experiments qualitatively and quantitatively demonstrate the effectiveness of the proposed method with clinic CT images.

physics.med-ph↗

arXiv2019

First Detection of Photons with Energy Beyond 100 TeV from an Astrophysical Source

We report on the highest energy photons from the Crab Nebula observed by the Tibet air shower array with the underground water-Cherenkov-type muon detector array. Based on the criterion of muon number measured in an air shower, we successfully suppress 99.92% of the cosmic-ray background events with energies $E>100$ TeV. As a result, we observed 24 photon-like events with $E>100$ TeV against 5.5 background events, which corresponds to 5.6$σ$ statistical significance. This is the first detection of photons with $E>100$ TeV from an astrophysical source.

astro-ph.HE↗

arXiv2019

Calibration, Entropy Rates, and Memory in Language Models

Building accurate language models that capture meaningful long-term dependencies is a core challenge in natural language processing. Towards this end, we present a calibration-based approach to measure long-term discrepancies between a generative sequence model and the true distribution, and use these discrepancies to improve the model. Empirically, we show that state-of-the-art language models, including LSTMs and Transformers, are \emph{miscalibrated}: the entropy rates of their generations drift dramatically upward over time. We then provide provable methods to mitigate this phenomenon. Furthermore, we show how this calibration-based approach can also be used to measure the amount of memory that language models use for prediction.

cs.CL↗

arXiv2019

Locally self-consistent embedding approach for disordered electronic systems

We present a new embedding scheme for the locally self-consistent method to study disordered electron systems. We test this method in a tight-binding basis and apply it to the single band Anderson model. The local interaction zone is used to efficiently compute the local Green's function of a supercell embeded into a local typical medium. We find a quick convergence as the size of the local interaction zone which reduces the computational costs as expected. This method captures the Anderson localization transition and accurately predicts the critical disorder strength. The present work opens the path towards the development of a typical medium embedding scheme for the $O(N)$ multiple scattering methods.

cond-mat.dis-nn↗

arXiv2019

Weighted infinitesimal unitary bialgebras on rooted forests and weighted cocycles

In this paper, we define a new coproduct on the space of decorated planar rooted forests to equip it with a weighted infinitesimal unitary bialgebraic structure. We introduce the concept of $Ω$-cocycle infinitesimal bialgebras of weight $λ$ and then prove that the space of decorated planar rooted forests $H_{\mathrm{RT}}(X,Ω)$, together with a set of grafting operations $\{ B^+_ω\mid ω\in Ω\}$, is the free $Ω$-cocycle infinitesimal unitary bialgebra of weight $λ$ on a set $X$, involving a weighted version of a Hochschild 1-cocycle condition. As an application, we equip a free cocycle infinitesimal unitary bialgebraic structure on the undecorated planar rooted forests, which is the object studied in the well-known (noncommutative) Connes-Kreimer Hopf algebra. Finally, we construct a new pre-Lie algebraic structure on decorated planar rooted forests.

math.RA↗

arXiv2019

Weighted infinitesimal unitary bialgebras, pre-Lie, matrix algebras and polynomial algebras

Motivated by the classical comatrix coalgebra, we introduce the concept of a Newtonian comatrix coalgebra. We construct an infinitesimal unitary bialgebra on a matrix algebra and a weighted infinitesimal unitary bialgebra on a non-commutative polynomial algebra, via two constructions of suitable coproducts. As a consequence, a Newtonian comatrix coalgebra is established. Furthermore, an infinitesimal unitary Hopf algebra, under the view of Aguiar, is constructed on a matrix algebra. By investigating the relationship between weighted infinitesimal bialgebras and pre-Lie algebras, we erect respectively a pre-Lie algebraic structure and further a new Lie algebraic structure on matrix algebras. Finally, a pre-Lie algebraic structure and a Lie algebraic structure on non-commutative polynomial algebras are also given.

math.RA↗

arXiv2019

Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network

Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis. Over the past few years, many algorithms with impressive performances have been proposed. In this paper, inspired by the idea of deep learning, we introduce an MRI denoising method based on the residual encoder-decoder Wasserstein generative adversarial network (RED-WGAN). Specifically, to explore the structure similarity between neighboring slices, a 3D configuration is utilized as the basic processing unit. Residual autoencoders combined with deconvolution operations are introduced into the generator network. Furthermore, to alleviate the oversmoothing shortcoming of the traditional mean squared error (MSE) loss function, the perceptual similarity, which is implemented by calculating the distances in the feature space extracted by a pretrained VGG-19 network, is incorporated with the MSE and adversarial losses to form the new loss function. Extensive experiments are implemented to assess the performance of the proposed method. The experimental results show that the proposed RED-WGAN achieves performance superior to several state-of-the-art methods in both simulated and real clinical data. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.

physics.med-ph↗

arXiv2019

LTE Standard-Compliant D2D Communication: Software-defined Radio Implementation and Evaluation

In this paper, we describe the design and implementation of D2D communication functionality for LTE. To our knowledge, this is the first such implementation that is compliant with the LTE Release 12 standard. In this paper, we design and demonstrate an experiment on mode selection between infrastructure mode and D2D communication mode in LTE networks. We implement our system on a software-defined radio SDR testbed, augmenting the open-source LTE eNodeB and UE implementation provided by the srsLTE software suite. Our measurements demonstrate the cell extension capabilities of D2D and quantify the SNR and throughput obtained by a subscriber when directly served by the eNodeB and when provided connectivity through the relay UE. Our implementation of sidelink, relaying, and mode selection functionality enables experimentation and prototyping of D2D communication that can assist in standardization, research, and development.

cs.NI↗

arXiv2019

A conservative hybrid method for Darcy flow

We present a hybrid mimetic spectral element formulation for Darcy flow. The discrete representations for 1) conservation of mass, and 2) inter-element continuity, are topological relations that lead to sparse matrix systems. These constraints are independent of the element size and shape, and thus invariant under mesh transformations. The resultant algebraic system is extremely sparse even for high degree polynomial basis. Furthermore, the system can be efficiently assembled and solved for each element separately.

math.NA↗

arXiv2019

A Right-of-Way Based Strategy to Implement Safe and Efficient Driving at Non-Signalized Intersections for Automated Vehicles

Non-signalized intersection is a typical and common scenario for connected and automated vehicles (CAVs). How to balance safety and efficiency remains difficult for researchers. To improve the original Responsibility Sensitive Safety (RSS) driving strategy on the non-signalized intersection, we propose a new strategy in this paper, based on right-of-way assignment (RWA). The performances of RSS strategy, cooperative driving strategy, and RWA based strategy are tested and compared. Testing results indicate that our strategy yields better traffic efficiency than RSS strategy, but not satisfying as the cooperative driving strategy due to the limited range of communication and the lack of long-term planning. However, our new strategy requires much fewer communication costs among vehicles.

cs.RO↗

arXiv2019

Machine Learning in Electronic Quantum Matter Imaging Experiments

Essentials of the scientific discovery process have remained largely unchanged for centuries: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into established scientific theory. Today, however, we face major challenges because automated instrumentation and large-scale data acquisition are generating data sets of such volume and complexity as to defy human analysis. Radically different scientific approaches are needed, with machine learning (ML) showing great promise, not least for materials science research. Hence, given recent advances in ML analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is for ML to engage equivalently with experimental data. For example, atomic-scale visualization of EQM yields arrays of complex electronic structure images, that frequently elude effective analyses. Here we report development and training of an array of artificial neural networks (ANN) designed to recognize different types of hypothesized order hidden in EQM image-arrays. These ANNs are used to analyze an experimentally-derived EQM image archive from carrier-doped cuprate Mott insulators. Throughout these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs find these phenomena to be unidirectional, revealing a coincident nematic EQM state. Strong-coupling theories of electronic liquid crystals are congruent with all these observations.

cond-mat.dis-nn↗

arXiv2019

Autonomous radiodetection of air showers with the TREND50 antenna array

TREND50 is a radio detection setup of 50 self-triggered antennas working in the 50-100MHz frequency range and deployed in a radio-quiet valley of the Tianshan mountains (China). TREND50 achieved its goal: the autonomous radiodetection and identification of air showers. Thanks to a dedicated offine selection algorithm, 564 air shower candidates were indeed selected out of $7\cdot10^8$ transient radio signals recorded during the 314 live days of data taken during the first two years of operation of this setup (2011 and 2012). This event rate, as well as the distribution of the candidate directions of arrival, is consistent with what is expected from cosmic-ray-induced air showers according to simulations, assuming an additional $\sim$20% contamination of the final sample by background events. This result is obtained at the cost of a reduced air shower detection efficiency, estimated to be $\sim$3%. This low efficiency is mostly due to the large amount of dead time of the setup. This result paves the way for the GRANDProto35 experiment, the first stage of the GRAND project.

astro-ph.HE↗

arXiv2019

Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. Qualitative and quantitative results demonstrate that the proposed methods achieve better performance than several existing state-of-the-art methods.

physics.med-ph↗

arXiv2019

Fully Discrete Mixed Finite Element Methods for the Stochastic Cahn-Hilliard Equation with Gradient-type Multiplicative Noise

This paper develops and analyzes some fully discrete mixed finite element methods for the stochastic Cahn-Hilliard equation with gradient-type multiplicative noise that is white in time and correlated in space. The stochastic Cahn-Hilliard equation is formally derived as a phase field formulation of the stochastically perturbed Hele-Shaw flow. The main result of this paper is to prove strong convergence with optimal rates for the proposed mixed finite element methods. To overcome the difficulty caused by the low regularity in time of the solution to the stochastic Cahn-Hilliard equation, the Hölder continuity in time with respect to various norms for the stochastic PDE solution is established, and it plays a crucial role in the error analysis. Numerical experiments are also provided to validate the theoretical results and to study the impact of noise on the Hele-Shaw flow as well as the interplay of the geometric evolution and gradient-type noise.

math.NA↗

arXiv2019

Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy. The codes, including the extension, are available at https://github.com/lancopku/meSimp

cs.LG↗

arXiv2019

Design of a general scientific CCD simulation and test system

In this paper, we introduce a general scientific CCD simulation and test system which can meet the test requirements of different types of CCD chips from E2V company, such as measuring different signals output from modules of a CCD controller including power supply, fan, temperature control module, crystal oscillator, shutter and clock-bias generator. Furthermore, the video signal of the CCD detector can be simulated and superimposed with random noise to verify the performance of the video sampling circuit of the CCD controller. The simulation and test system was successful used for the CCD controller which was designed for E2V CCD47-20 detector.

physics.ins-det↗

arXiv2019

Interpreting the relation between the gamma-ray and infrared luminosities of star-forming galaxies

It has been found that there is a quasi-linear scaling relationship between the gamma-ray luminosity in GeV energies and the total infrared luminosity of star-forming galaxies, i.e. $L_γ\propto L_{\rm IR}^α$ with $α\simeq 1$. However, the origin of this linear slope is not well understood. Although extreme starburst galaxies can be regarded as calorimeters for hadronic cosmic ray interaction and thus a quasi-linear scaling may hold, it may not be the case for low star-formation-rate (SFR) galaxies, as the majority of cosmic rays in these galaxies are expected to escape. We calculate the gamma-ray production efficiency in star-forming galaxies by considering realistic galaxy properties, such as the gas density and galactic wind velocity in star-forming galaxies. We find that the slope for the relation between gamma-ray luminosity and the infrared luminosity gets steeper for low infrared luminosity galaxies, i.e. $α\rightarrow 1.6$, due to increasingly lower efficiency for the production of gamma-ray emission. We further find that the measured data of the gamma-ray luminosity is compatible with such a steepening. The steepening in the slope suggests that cosmic-ray escape is very important in low-SFR galaxies.

astro-ph.HE↗