arXiv subjects

Jia Liu

Publications and source records attributed to Jia Liu.

At least 361 records · Page 20Linked to original sources

arXiv2019

Phase evolution and thermal stability of high Curie temperature BiScO$_3$-PbTiO$_3$-Pb(Cd${1/3}$Nb$_{2/3}$)O$_3$ ceramics near MPB

Piezoelectric and ferroelectric ceramics with a high Curie temperature (Tc) have attracted a growing attention owning to their applications under severe environments. In this work, phase structure, dielectric, ferroelectric and piezoelectric properties of (0.975-x)BiScO3-xPbTiO3-0.025Pb(Cd1/3Nb2/3)O3 ceramics (x = 0.58-0.64) were studied. A composition-induced structural transformation occurs from rhombohedral phase to tetragonal phase through an intermediate monoclinic phase with the increasing PT concentration. The relationship between structure and electrical properties of the system were discussed. The BS-xPT-PCN system near the morphotropic phase boundary (x = 0.62) exhibits excellent piezoelectric and ferroelectric performances with d33 = 508 pC/N, kp = 56%, and Pr = 40 uC/cm2. The high-temperature piezoelectricity of the sample with MPB (x = 0.62) was characterized by an in situ XRD. The excellent thermal stability of the crystal structure and the piezoelectric property indicate that the BS-xPT-PCN system is a promising candidate for high temperature piezoelectric applications.

cond-mat.mtrl-sci↗

arXiv2019

Urban flows prediction from spatial-temporal data using machine learning: A survey

Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems.

cs.LG↗

arXiv2019

New magicity $N=32$ and $34$ triggered by strong couplings between Dirac inversion partners

Inspired by recent experiments, the successive new magicity $N = 32$ and $34$ in Ca isotopes are studied within the relativistic density functional theory. It is illustrated that the strong couplings between the $s_{1/2}$ and neutron ($ν$) $\nu2p_{1/2}$ orbits, here referred as "Dirac inversion partners" (DIPs), play a key role in opening both subshells $N = 32$ and $34$. Such strong couplings originate from the inversion similarity between the DIPs, that the upper component of the Dirac spinor of one partner shares the same orbital angular momentum as the lower component of the other, and vice versa. Following the revealed mechanism, it is predicted that the magicity $N = 32$ is reserved until $^{48}$S, but vanishes in $^{46}$Si.

nucl-th↗

arXiv2019

Generative Reversible Data Hiding by Image to Image Translation via GANs

The traditional reversible data hiding technique is based on cover image modification which inevitably leaves some traces of rewriting that can be more easily analyzed and attacked by the warder. Inspired by the cover synthesis steganography based generative adversarial networks, in this paper, a novel generative reversible data hiding scheme (GRDH) by image translation is proposed. First, an image generator is used to obtain a realistic image, which is used as an input to the image-to-image translation model with CycleGAN. After image translation, a stego image with different semantic information will be obtained. The secret message and the original input image can be recovered separately by a well-trained message extractor and the inverse transform of the image translation. Experimental results have verified the effectiveness of the scheme.

eess.IV↗

arXiv2019

Seeking for sterile neutrinos with displaced leptons at the LHC

We study the signal of long-lived sterile neutrino at the LHC produced through the decay of the $W$ boson. It decays into charged lepton and jets. The characteristic signature is a hard prompt lepton and a lepton from the displaced decay of the sterile neutrino, which leads to a bundle of displaced tracks with large transverse impact parameter. Different from other studies, we neither reconstruct the displaced vertex nor place requirement on its invariant mass to maintain sensitivity for low sterile neutrino masses. Instead, we focus on the displaced track from the lepton. A difficulty for low mass sterile neutrino study is that the displaced lepton is usually \textit{non-isolated}. Therefore, leptons from heavy flavor quark is the major source of background. We closely follow a search for displaced electron plus muon search at CMS and study their control regions, which is related to our signal regions, in great detail to develop a robust estimation of the background for our signals. After further optimization on the signal limiting the number of jets, low $H_T$ and large lepton displacement $d_0$ to suppress SM background, we reach an exclusion sensitivity of about $10^{-8}$ ($10^{-5}$) for the mixing angle square at 10 (2) GeV sterile neutrino mass respectively. The strategy we propose can cover the light sterile masses complimentary to beam dump and forward detector experiments.

hep-ph↗

arXiv2019

Generative Steganography by Sampling

In this paper, a novel data-driven information hiding scheme called generative steganography by sampling (GSS) is proposed. Unlike in traditional modification-based steganography, in our method the stego image is directly sampled by a powerful generator: no explicit cover is used. Both parties share a secret key used for message embedding and extraction. The Jensen-Shannon divergence is introduced as a new criterion for evaluating the security of generative steganography. Based on these principles, we propose a simple practical generative steganography method that uses semantic image inpainting. The message is written in advance to an uncorrupted region that needs to be retained in the corrupted image. Then, the corrupted image with the secret message is fed into a Generator trained by a generative adversarial network (GAN) for semantic completion. Message loss and prior loss terms are proposed for penalizing message extraction error and unrealistic stego image. In our design, we first train a generator whose training target is the generation of new data samples from the same distribution as that of existing training data. Next, for the trained generator, backpropagation to the message and prior loss are introduced to optimize the coding of the input noise data for the generator. The presented experiments demonstrate the potential of the proposed framework based on both qualitative and quantitative evaluations of the generated stego images.

cs.MM↗

arXiv2019

Massive Neutrinos Leave Fingerprints on Cosmic Voids

Do void statistics contain information beyond the tracer 2-point correlation function? Yes! As we vary the sum of the neutrino masses, we find void statistics contain information absent when using just tracer 2-point statistics. Massive neutrinos uniquely affect cosmic voids. We explore their impact on void clustering using both the DEMNUni and MassiveNuS simulations. For voids, neutrino effects depend on the observed void tracers. As the neutrino mass increases, the number of small voids traced by cold dark matter particles increases and the number of large voids decreases. Surprisingly, when massive, highly biased, halos are used as tracers, we find the opposite effect. The scale at which voids cluster, as well as the void correlation, is similarly sensitive to the sum of neutrino masses and the tracers. This scale dependent trend is not due to simulation volume or halo density. The interplay of these signatures in the void abundance and clustering leaves a distinct fingerprint that could be detected with observations and potentially help break degeneracies between different cosmological parameters. This paper paves the way to exploit cosmic voids in future surveys to constrain the mass of neutrinos.

astro-ph.CO↗

arXiv2019

Impeded Dark Matter

We consider dark matter models in which the mass splitting between the dark matter particles and their annihilation products is tiny. Compared to the previously proposed Forbidden Dark Matter scenario, the mass splittings we consider are much smaller, and are allowed to be either positive or negative. To emphasize this modification, we dub our scenario "Impeded Dark Matter". We demonstrate that Impeded Dark Matter can be easily realized without requiring tuning of model parameters. For negative mass splitting, we demonstrate that the annihilation cross-section for Impeded Dark Matter depends linearly on the dark matter velocity or may even be kinematically forbidden, making this scenario almost insensitive to constraints from the cosmic microwave background and from observations of dwarf galaxies. Accordingly, it may be possible for Impeded Dark Matter to yield observable signals in clusters or the Galactic center, with no corresponding signal in dwarfs. For positive mass splitting, we show that the annihilation cross-section is suppressed by the small mass splitting, which helps light dark matter to survive increasingly stringent constraints from indirect searches. As specific realizations for Impeded Dark Matter, we introduce a model of vector dark matter from a hidden $SU(2)$ sector, and a composite dark matter scenario based on a QCD-like dark sector.

hep-ph↗

arXiv2019

Enabling security and High Energy Efficiency in the Internet of Things with Massive MIMO Hybrid Precoding

Recently, the security of Internet of Things (IoT) has been an issue of great concern. Physical layer security methods can help IoT networks achieve information-theoretical secrecy. Nevertheless, utilizing physical security methods, such as artificial noise (AN) may cost extra power, which leads to low secure energy efficiency. In this paper, the hybrid precoding technique is employed to improve the secure energy efficiency of the IoT network. A secure energy efficiency optimization problem is formulated for the IoT network. Due to the non-convexity of the problem and the feasible domain, the problem is firstly transformed into a tractable suboptimal form. Then a secure hybrid precoding energy efficient (SEEHP) algorithm is proposed to tackle the problem. Numerical results indicate that the proposed SEEHP algorithm achieves higher secure energy efficiency compared with three existing physical layer security algorithms, especially when the number of transmit antennas is large.

cs.NI↗

arXiv2019

Recent Advances of Image Steganography with Generative Adversarial Networks

In the past few years, the Generative Adversarial Network (GAN) which proposed in 2014 has achieved great success. GAN has achieved many research results in the field of computer vision and natural language processing. Image steganography is dedicated to hiding secret messages in digital images, and has achieved the purpose of covert communication. Recently, research on image steganography has demonstrated great potential for using GAN and neural networks. In this paper we review different strategies for steganography such as cover modification, cover selection and cover synthesis by GANs, and discuss the characteristics of these methods as well as evaluation metrics and provide some possible future research directions in image steganography.

cs.CR↗

arXiv2019

NeuralVis: Visualizing and Interpreting Deep Learning Models

Deep Neural Network(DNN) techniques have been prevalent in software engineering. They are employed to faciliatate various software engineering tasks and embedded into many software applications. However, analyzing and understanding their behaviors is a difficult task for software engineers. In this paper, to support software engineers in visualizing and interpreting deep learning models, we present NeuralVis, an instance-based visualization tool for DNN. NeuralVis is designed for: 1). visualizing the structure of DNN models, i.e., components, layers, as well as connections; 2). visualizing the data transformation process; 3). integrating existing adversarial attack algorithms for test input generation; 4). comparing intermediate outputs of different instances to guide the test input generation; To demonstrate the effectiveness of NeuralVis, we conduct an user study involving ten participants on two classic DNN models, i.e., LeNet and VGG-12. The result shows NeuralVis can assist developers in identifying the critical features that determines the prediction results. Video: https://youtu.be/hRxCovrOZFI

cs.SE↗

arXiv2019

Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach

In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the network. A tree-based $l_1$ penalty is proposed to save the computation and communication cost. We design a decentralized generalized alternating direction method of multiplier algorithm for solving the objective function in parallel. The theoretical properties are derived to guarantee both the model consistency and the algorithm convergence. Thorough numerical experiments are also conducted to back up our theory, which also show that our approach outperforms in the aspects of the estimation accuracy, computation speed and communication cost.

stat.ML↗

arXiv2019

Nucleus Neural Network: A Data-driven Self-organized Architecture

Artificial neural networks which are inspired from the learning mechanism of brain have achieved great successes in many problems, especially those with deep layers. In this paper, we propose a nucleus neural network (NNN) and corresponding connecting architecture learning method. In a nucleus, there are no regular layers, i.e., a neuron may connect to all the neurons in the nucleus. This type of architecture gets rid of layer limitation and may lead to more powerful learning capability. It is crucial to determine the connections between them given numerous neurons. Based on the principle that more relevant input and output neuron pair deserves higher connecting density, we propose an efficient architecture learning model for the nucleus. Moreover, we improve the learning method for connecting weights and biases given the optimized architecture. We find that this novel architecture is robust to irrelevant components in test data. So we reconstruct a new dataset based on the MNIST dataset where the types of digital backgrounds in training and test sets are different. Experiments demonstrate that the proposed learner achieves significant improvement over traditional learners on the reconstructed data set.

cs.CV↗

arXiv2019

LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation

The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks. In this paper, we present a lightweight network to address this problem,namely LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation.More specifically, the encoder adopts a ResNet as backbone network, where two new operations, channel split and shuffle, are utilized in each residual block to greatly reduce computation cost while maintaining higher segmentation accuracy. On the other hand, an attention pyramid network (APN) is employed in the decoder to further lighten the entire network complexity. Our model has less than 1M parameters,and is able to run at over 71 FPS in a single GTX 1080Ti GPU. The comprehensive experiments demonstrate that our approach achieves state-of-the-art results in terms of speed and accuracy trade-off on CityScapes dataset.

cs.CV↗

arXiv2019

High-Efficiency Second Harmonic Generation of Low-Temporal-Coherent Light Pulse

The nonlinear frequency conversion of low-temporal-coherent light holds a variety of applications and has attracted considerable interest. However, its physical mechanism remains relatively unexplored, and the conversion efficiency and bandwidth are extremely insufficient. Here, considering the instantaneous broadband characteristic, we establish a model of second harmonic generation (SHG) of low-temporal-coherent pulse, and reveal its differences from the coherent conditions. It is found that the second harmonic (SH) of low-temporal-coherent light is produced by not only the degenerate SH processes but also crossed sum-frequency processes. On the basis of this, we propose a method for realizing low-temporal-coherent SHG with high efficiency and broad bandwidth, and experimentally demonstrate a conversion efficiency up to 70% with a bandwidth of 3.1 THz (2.9 nm centered at 528 nm). To the best of our knowledge, this is the highest efficiency and broadest bandwidth of low-temporal-coherent SHG, and its efficiency is almost the same with that of the narrowband coherent condition. Furthermore, the spectral evolution characteristics of the broadband low-temporal-coherent pulse in SHG process are revealed in experiments, that the SH power spectral density (PSD) is proportional to the self-convolution of the fundamental wave PSD, which is greatly different from that of the coherent process. Our research opens a door for the study of the low-coherent nonlinear optical processes.

physics.optics↗

arXiv2019

Synthetic spin-orbit coupling and topological polaritons in Janeys-Cummings lattices

The interaction between a photon and a qubit in the Janeys-Cummings (JC) model generates a kind of quasiparticle called polariton. While they are widely used in quantum optics, difficulties in engineering controllable coupling of them severely limit their applications to simulate spinful quantum systems. Here we show that, in the superconducting quantum circuit context, polariton states in the single-excitation manifold of a JC lattice can be used to simulate a spin-1/2 system, based on which tunable synthetic spin-orbit coupling and novel topological polaritons can be generated and explored. The lattice is formed by a sequence of coupled transmission line resonators, each of which is connected to a transmon qubit. Synthetic spin-orbit coupling and effective Zeeman field of the polariton can both be tuned by modulating the coupling strength between neighbouring resonators, allowing for the realization of a large variety of polaritonic topological semimetal bands. Methods for detecting the polaritonic topological edge states and topological invariants are also proposed. Therefore, our work suggests that the JC lattice is a versatile platform for exploring spinful topological states of matter, which may inspire developments of topologically protected quantum optical and information processing devices.

quant-ph↗

arXiv2019

Fully Homomorphic Encryption Encapsulated Difference Expansion for Reversible Data hiding in Encrypted Domain

This paper proposes a fully homomorphic encryption encapsulated difference expansion (FHEE-DE) scheme for reversible data hiding in encrypted domain (RDH-ED). In the proposed scheme, we use key-switching and bootstrapping techniques to control the ciphertext extension and decryption failure. To realize the data extraction directly from the encrypted domain without the private key, a key-switching based least-significant-bit (KS-LSB) data hiding method has been designed. In application, the user first encrypts the plaintext and uploads ciphertext to the server. Then the server performs data hiding by FHEE-DE and KS-LSB to obtain the marked ciphertext. Additional data can be extracted directly from the marked ciphertext by the server without the private key. The user can decrypt the marked ciphertext to obtain the marked plaintext. Then additional data or plaintext can be obtained from the marked plaintext by using the standard DE extraction or recovery. A fidelity constraint of DE is introduced to reduce the distortion of the marked plaintext. FHEE-DE enables the server to implement FHEE-DE recovery or extraction on the marked ciphertext, which returns the ciphertext of original plaintext or additional data to the user. In addition, we simplified the homomorphic operations of the proposed universal FHEE-DE to obtain an efficient version. The Experimental results demonstrate that the embedding capacity, fidelity, and reversibility of the proposed scheme are superior to existing RDH-ED methods, and fully separability is achieved without reducing the security of encryption.

cs.CR↗

arXiv2019

Self-Organizing Quantum Networks

As the inevitable development trend of quantum key distribution, quantum networks have attracted extensive attention, and many prototypes have been deployed over recent years. Existing quantum networks based on optical fibers or quantum satellites can realize the metro and even global communications. However, for some application scenarios that require emergency or temporary communications, such networks cannot meet the requirements of rapid deployment, low cost, and high mobility. To solve this problem, we introduce an important concept of classical networks, i.e., self-organization, to quantum networks, and give two simple network prototypes based on acquisition, tracking, and pointing systems. In these networks, the users only need to deploy the network nodes, which will rapidly, automatically, and adaptively organize and mange a quantum network. Our method expands the application scope of quantum networks, and gives a new approach for the design and implementation of quantum networks. It also provides users with a low-cost access network solution, and can be used to fundamentally solve the security problem of classical self-organizing networks.

quant-ph↗