arXiv subjects

Hao Zhang

Publications and source records attributed to Hao Zhang.

At least 1,135 records · Page 63Linked to original sources

arXiv2020

Study of the Modified Gaussian Model on olivine diagnostic spectral features and its applications in space weathering experiments

The absorption features of olivine in visible and near-infrared (VNIR) reflectance spectra are the key spectral parameters in its mineralogical studies. Generally, these spectral parameters can be obtained by exploiting the Modified Gaussian Model (MGM) with a proper continuum removal. However, different continua may change the deconvolution results of these parameters. This paper investigates the diagnostic spectral features of olivine with diverse chemical compositions. Four different continuum removal methods with MGM for getting the deconvolution results are presented and the regression equations for predicting the Mg-number (Fo#) are introduced. The results show that different continua superimposed on the mineral absorption features will make the absorption center shift, as well as the obvious alterations in shape, width, and strength of the absorption band. Additionally, it is also found that the logarithm of a second-order polynomial continuum can match the overall shape of the spectrum in logarithmic space, and the improved regression equations applied to estimate the chemical composition of olivine-dominated spectra also have a better performance. As an application example, the improved approach is applied to pulse laser irradiated olivine grains to simulate and study the space weathering effects on olivine diagnostic spectral features. The experiments confirm that space weathering can make the absorption band center shift toward longer wavelength. Therefore, the Fo# estimated from remote sensing spectra may be less than its actual chemical composition. These results may provide valuable information for revealing the difference between the spectra of olivine grains and olivine-dominated asteroids.

astro-ph.IM↗

arXiv2020

Propagating Asymptotic-Estimated Gradients for Low Bitwidth Quantized Neural Networks

The quantized neural networks (QNNs) can be useful for neural network acceleration and compression, but during the training process they pose a challenge: how to propagate the gradient of loss function through the graph flow with a derivative of 0 almost everywhere. In response to this non-differentiable situation, we propose a novel Asymptotic-Quantized Estimator (AQE) to estimate the gradient. In particular, during back-propagation, the graph that relates inputs to output remains smoothness and differentiability. At the end of training, the weights and activations have been quantized to low-precision because of the asymptotic behaviour of AQE. Meanwhile, we propose a M-bit Inputs and N-bit Weights Network (MINW-Net) trained by AQE, a quantized neural network with 1-3 bits weights and activations. In the inference phase, we can use XNOR or SHIFT operations instead of convolution operations to accelerate the MINW-Net. Our experiments on CIFAR datasets demonstrate that our AQE is well defined, and the QNNs with AQE perform better than that with Straight-Through Estimator (STE). For example, in the case of the same ConvNet that has 1-bit weights and activations, our MINW-Net with AQE can achieve a prediction accuracy 1.5\% higher than the Binarized Neural Network (BNN) with STE. The MINW-Net, which is trained from scratch by AQE, can achieve comparable classification accuracy as 32-bit counterparts on CIFAR test sets. Extensive experimental results on ImageNet dataset show great superiority of the proposed AQE and our MINW-Net achieves comparable results with other state-of-the-art QNNs.

cs.LG↗

arXiv2020

Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects

Background: With the fast development of next generation sequencing technologies, increasing numbers of genomes are being de novo sequenced and assembled. However, most are in fragmental and incomplete draft status, and thus it is often difficult to know the accurate genome size and repeat content. Furthermore, many genomes are highly repetitive or heterozygous, posing problems to current assemblers utilizing short reads. Therefore, it is necessary to develop efficient assembly-independent methods for accurate estimation of these genomic characteristics. Results: Here we present a framework for modeling the distribution of k-mer frequency from sequencing data and estimating the genomic characteristics such as genome size, repeat structure and heterozygous rate. By introducing novel techniques of k-mer individuals, float precision estimation, and proper treatment of sequencing error and coverage bias, the estimation accuracy of our method is significantly improved over existing methods. We also studied how the various genomic and sequencing characteristics affect the estimation accuracy using simulated sequencing data, and discussed the limitations on applying our method to real sequencing data. Conclusion: Based on this research, we show that the k-mer frequency analysis can be used as a general and assembly-independent method for estimating genomic characteristics, which can improve our understanding of a species genome, help design the sequencing strategy of genome projects, and guide the development of assembly algorithms. The programs developed in this research are written using C/C++, and freely accessible at Github URL (https://github.com/fanagislab/GCE) or BGI ftp ( ftp://ftp.genomics.org.cn/pub/gce).

q-bio.GN↗

arXiv2020

Multimode interference induced optical nonreciprocity and routing in an optical microcavity

Optical nonreciprocity and routing using optocal microcavities draw much atttention in recent years. Here, we report the results of the study on the nonreciprocity and routing using optomechanical multimode interference in an optical microcavity. The optomechanical system used here possesses multi-optical modes and a mechanical mode. Optomechanical induced transparency and absorption, appear in the system due to the interference between different paths. The system can present significant nonreciprocity and routing properties when appropriate parameters of the system are set. We design quantum devices, such as diode, circulator and router, which are important applications. Our work shows that optomechanical multimode system can be used as a promising platform for buliding photonic and quantum network.

physics.optics↗

arXiv2020

Graph Ordering: Towards the Optimal by Learning

Graph representation learning has achieved a remarkable success in many graph-based applications, such as node classification, link prediction, and community detection. These models are usually designed to preserve the vertex information at different granularity and reduce the problems in discrete space to some machine learning tasks in continuous space. However, regardless of the fruitful progress, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks. Moreover, these problems are closely related to reformulating a global layout for a specific graph, which is an important NP-hard combinatorial optimization problem: graph ordering. In this paper, we propose to attack the graph ordering problem behind such applications by a novel learning approach. Distinguished from greedy algorithms based on predefined heuristics, we propose a neural network model: Deep Order Network (DON) to capture the hidden locality structure from partial vertex order sets. Supervised by sampled partial order, DON has the ability to infer unseen combinations. Furthermore, to alleviate the combinatorial explosion in the training space of DON and make the efficient partial vertex order sampling , we employ a reinforcement learning model: the Policy Network, to adjust the partial order sampling probabilities during the training phase of DON automatically. To this end, the Policy Network can improve the training efficiency and guide DON to evolve towards a more effective model automatically. Comprehensive experiments on both synthetic and real data validate that DON-RL outperforms the current state-of-the-art heuristic algorithm consistently. Two case studies on graph compression and edge partitioning demonstrate the potential power of DON-RL in real applications.

cs.SI↗

arXiv2020

Interpretable Complex-Valued Neural Networks for Privacy Protection

Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features. We study the possibility of preventing such adversarial inference, yet without too much accuracy degradation. We propose a generic method to revise the neural network to boost the challenge of inferring input attributes from features, while maintaining highly accurate outputs. In particular, the method transforms real-valued features into complex-valued ones, in which the input is hidden in a randomized phase of the transformed features. The knowledge of the phase acts like a key, with which any party can easily recover the output from the processing result, but without which the party can neither recover the output nor distinguish the original input. Preliminary experiments on various datasets and network structures have shown that our method significantly diminishes the adversary's ability in inferring about the input while largely preserves the resulting accuracy.

cs.LG↗

arXiv2020

High-sensitivity bio-sensor based on the real-splitting indirectly coupled Anti-Parity time symmetric WGMs

Detecting the size of single nanoparticle with high precision is crucial to understanding the characteristic of the nanoparticle. In this paper, we research the single particle detection based on the Anti-parity time symmetric (APT) indirectly coupled WGMs. The results show that the Anti-parity time symmetric WGM nanoparticle sensor exhibits giant enhancement in frequency splitting compared with single WGM sensor, when the system operating at exceptional point (EP). With respect to the parity-time symmetric nanoparticle sensor, our research exhibits a real eigenfrequency splitting, which can be directly detected.

physics.optics↗

arXiv2020

Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling

For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework. VHE randomized GAN (VHE-GAN) encodes an image to decode its associated text, and feeds the variational posterior as the source of randomness into the GAN image generator. We plug three off-the-shelf modules, including a deep topic model, a ladder-structured image encoder, and StackGAN++, into VHE-GAN, which already achieves competitive performance. This further motivates the development of VHE-raster-scan-GAN that generates photo-realistic images in not only a multi-scale low-to-high-resolution manner, but also a hierarchical-semantic coarse-to-fine fashion. By capturing and relating hierarchical semantic and visual concepts with end-to-end training, VHE-raster-scan-GAN achieves state-of-the-art performance in a wide variety of image-text multi-modality learning and generation tasks.

cs.CV↗

arXiv2019

RoboCoDraw: Robotic Avatar Drawing with GAN-based Style Transfer and Time-efficient Path Optimization

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the Avatar-GAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.

cs.RO↗

arXiv2019

Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning

Learning-based methods have been used to pro-gram robotic tasks in recent years. However, extensive training is usually required not only for the initial task learning but also for generalizing the learned model to the same task but in different environments. In this paper, we propose a novel Deep Reinforcement Learning algorithm for efficient task generalization and environment adaptation in the robotic task learning problem. The proposed method is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem. The proposed Deep Model Fusion (DMF) method reuses and combines the previously trained model to improve the learning efficiency and results.Besides, we also introduce a Multi-objective Guided Reward(MGR) shaping technique to further improve training efficiency.The proposed method was benchmarked with previous methods in various environments to validate its effectiveness.

cs.RO↗

arXiv2019

Frequency-tuning induced state transfer in optical microcavities

Quantum state transfer in optical microcavities plays an important role in quantum information processing, and is essential in many optical devices, such as optical frequency converter and diode. Existing schemes are effective and realized by tuning the coupling strengths between modes. However, such approaches are severely restricted due to the small amount of strength that can be tuned and the difficulty to perform the tuning in some situations, such as on-chip microcavity system. Here, we propose a novel approach that realizes the state transfer between different modes in optical microcavities by tuning the frequency of an intermediate mode. We showed that for typical functions of frequency-tuning, such as linear and periodic functions, the state transfer can be realized successfully with different features. To optimize the process, we use gradient descent technique to find an optimal tuning function for the fast and perfect state transfer. We also showed that our approach has significant nonreciprocity with appropriate tuning variables, where one can unidirectionally transfer a state from one mode to another, but the inverse direction transfer is forbidden. This work provides an effective method for controlling the multimode interactions in on-chip optical microcavities via simple operations and it has practical applications in all-optical devices.

physics.optics↗

arXiv2019

Towards a Unified Evaluation of Explanation Methods without Ground Truth

This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges. The core challenge is that people usually cannot obtain ground-truth explanations of the neural network. To this end, we design four metrics to evaluate explanation results without ground-truth explanations. Our metrics can be broadly applied to nine benchmark methods of interpreting neural networks, which provides new insights of explanation methods.

cs.LG↗

arXiv2019

Detection of hidden gratings through multilayer nanostructures using light and sound

We report on the detection of diffraction gratings buried below a stack of tens of 18 nm thick $\mathrm{SiO_2}$ and $\mathrm{Si_3N_4}$ layers and an optically opaque metal layer, using laser-induced, extremely-high frequency ultrasound. In our experiments, the shape and amplitude of a buried metal grating is encoded on the spatial phase of the reflected acoustic wave. This grating-shaped acoustic echo from the buried grating is detected by diffraction of a delayed probe pulse. The shape and strength of the time-dependent diffraction signal can be accurately predicted using a 2D numerical model. Surprisingly, our numerical calculations show that the diffracted signal strength is not strongly influenced by the number of dielectric layers through which the acoustic wave has to propagate. Replacing the $\mathrm{SiO_2}$/$\mathrm{Si_3N_4}$ layer stack with a single layer having an equivalent time-averaged sound velocity and average density, has only a small effect on the shape and amplitude of the diffracted signal as a function of time. Our results show that laser-induced ultrasound is a promising technique for sub-surface nano-metrology applications.

physics.app-ph↗

arXiv2019

Next steps of quantum transport in Majorana nanowire devices

Majorana zero modes are localized quasiparticles that obey non-Abelian exchange statistics. Braiding Majorana zero modes forms the basis of topologically protected quantum operations which could in principle significantly reduce qubit decoherence and gate control errors in the device level. Therefore, searching for Majorana zero modes in various solid state systems is a major topic in condensed matter physics and quantum computer science. Since the first experimental signature observed in hybrid superconductor-semiconductor nanowire devices, this field has witnessed a dramatic expansion in material science, transport experiments and theory. While making the first topological qubit based on these Majorana nanowires is currently an on-going effort, several related important transport experiments are still being pursued in the near term. These will not only serve as intermediate steps but also show Majorana physics in a more fundamental aspect. In this perspective, we summarize these key Majorana experiments and the potential challenges.

cond-mat.mes-hall↗

arXiv2019

Reveal the non-local coherent nature from a dissipative Majorana teleportation

The non-local coherent nature of the Majorana devices is one of the key factors for realizing decoherence-free topological qubits. Direct observation of this coherent nature could provide a first-step benchmarking scheme to validate Majorana qubit quality. We propose a simple transport scheme with a Majorana island device along with a dissipative environment in the electrodes. We found that the dissipative environment renormalizes the quantum transport in significant different ways: As reducing temperature, while the conductance for Majorana coherent teleportation increases, all other incoherent signals are strongly suppressed due to dissipation. This special conductance scaling behavior is a clear benchmark to reveal the non-local coherent nature of Majorana devices.

cond-mat.mes-hall↗

arXiv2019

Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning

Deep reinforcement learning has been successfully used in many dynamic decision making domains, especially those with very large state spaces. However, it is also well-known that deep reinforcement learning can be very slow and resource intensive. The resulting system is often brittle and difficult to explain. In this paper, we attempt to address some of these problems by proposing a framework of Rule-interposing Learning (RIL) that embeds high level rules into the deep reinforcement learning. With some good rules, this framework not only can accelerate the learning process, but also keep it away from catastrophic explorations, thus making the system relatively stable even during the very early stage of training. Moreover, given the rules are high level and easy to interpret, they can be easily maintained, updated and shared with other similar tasks.

cs.AI↗

arXiv2019

Solving Fokker-Planck equation using deep learning

The probability density function of stochastic differential equations is governed by the Fokker-Planck (FP) equation. A novel machine learning method is developed to solve the general FP equations based on deep neural networks. The proposed algorithm does not require any interpolation and coordinate transformation, which is different from the traditional numercial methods. The main novelty of this paper is that penalty factors are introduced to overcome the local optimization for the deep learning approach, and the corresponding setting rules are given. Meanwhile, we consider a normalization condition as a supervision condition to effectively avoid that the trial solution is zero. Several numerical examples are presented to illustrate performances of the proposed algorithm, including one- and two-dimensional systems. All the results suggest that the deep learning is quite feasible and effective to calculate the FP equation. Further, influences of the number of hidden layers, the penalty factors, and the optimization algorithm are discussed in detail. These results indicate that the performances of the machine learning technique can be improved through constructing the neural networks appropriately.

physics.comp-ph↗

arXiv2019

Real space Berry curvature of itinerant electron systems with spin-orbit interaction

By considering an extended double-exchange model with spin-orbit coupling (SOC), we derive a general form of the Berry phase $γ$ that electrons pick up when moving around a closed loop. This form generalizes the well-known result valid for SU(2) invariant systems, $γ=Ω/2$, where $Ω$ is the solid angle subtended by the local magnetic moments enclosed by the loop. The general form of $γ$ demonstrates that collinear and coplanar magnetic textures can also induce a Berry phase different from 0 or $π$, smoothly connecting the result for SU(2) invariant systems with the well-known result of Karplus and Luttinger for collinear ferromagnets with finite SOC. By taking the continuum limit of the theory, we also derive the corresponding generalized form of the real space Berry curvature. The new expression is a generalization of the scalar spin chirality, which is presented in an explicitly covariant form. We finally show how these simple concepts can be used to understand the origin of the spontaneous topological Hall effect that has been recently reported in collinear and coplanar antiferromagnetic phases of correlated materials.

cond-mat.str-el↗