arXiv subjects

Qian Wang

Publications and source records attributed to Qian Wang.

At least 541 records · Page 30Linked to original sources

arXiv2018

Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction

Spectral computed tomography (CT) reconstructs material-dependent attenuation images with the projections of multiple narrow energy windows, it is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection dataset always contains strong complicated noise and result in the projections has a lower signal-noise-ratio (SNR). Very recently, the spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectrum similarities for spectral CT. The method constructs such a group by clustering up a series of non-local spatial-spectrum cubes. The small size of spatial patch for such a group make SSCMF fails to encode the sparsity and low-rank properties. In addition, the hard-thresholding and collaboration filtering operation in the SSCMF are also rough to recover the image features and spatial edges. While for all steps are operated on 4-D group, we may not afford such huge computational and memory load in practical. To avoid the above limitation and further improve image quality, we first formulate a non-local cube-based tensor instead of the group to encode the sparsity and low-rank properties. Then, as a new regularizer, Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the ability of extracting image features and protecting spatial edges, generating the non-local low-rank cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman strategy is adopted to solve the NLCTF model. Both numerical simulations and realistic preclinical mouse studies are performed to validate and assess the NLCTF algorithm. The results show that the NLCTF method outperforms the other competitors.

cs.CV↗

arXiv2018

Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes

Cultural heritage is the asset of all the peoples of the world. The preservation and inheritance of cultural heritage is conducive to the progress of human civilization. In northwestern China, there is a world heritage site -- Mogao Grottoes -- that has a plenty of mural paintings showing the historical cultures of ancient China. To study these historical cultures, one critical procedure is to date the mural paintings, i.e., determining the era when they were created. Until now, most mural paintings at Mogao Grottoes have been dated by directly referring to the mural texts or historical documents. However, some are still left with creation-era undetermined due to the lack of reference materials. Considering that the drawing style of mural paintings was changing along the history and the drawing style can be learned and quantified through painting data, we formulate the problem of mural-painting dating into a problem of drawing-style classification. In fact, drawing styles can be expressed not only in color or curvature, but also in some unknown forms -- the forms that have not been observed. To this end, besides sophisticated color and shape descriptors, a deep convolution neural network is designed to encode the implicit drawing styles. 3860 mural paintings collected from 194 different grottoes with determined creation-era labels are used to train the classification model and build the dating method. In experiments, the proposed dating method is applied to seven mural paintings which were previously dated with controversies, and the exciting new dating results are approved by the Dunhuang expert.

cs.CV↗

arXiv2018

Probing Attacks on Physical Layer Key Agreement for Automotive Controller Area Networks (Extended Version)

Efficient key management for automotive networks (CAN) is a critical element, governing the adoption of security in the next generation of vehicles. A recent promising approach for dynamic key agreement between groups of nodes, Plug-and-Secure for CAN, has been demonstrated to be information theoretically secure based on the physical properties of the CAN bus. In this paper, we illustrate side-channel attacks, leading to nearly-complete leakage of the secret key bits, by an adversary that is capable of probing the CAN bus. We identify the fundamental characteristics that lead to such attacks and propose techniques to minimize the information leakage at the hardware, controller and system levels.

cs.CR↗

arXiv2018

PatternListener: Cracking Android Pattern Lock Using Acoustic Signals

Pattern lock has been widely used for authentication to protect user privacy on mobile devices (e.g., smartphones and tablets). Given its pervasive usage, the compromise of pattern lock could lead to serious consequences. Several attacks have been constructed to crack the lock. However, these approaches require the attackers to either be physically close to the target device or be able to manipulate the network facilities (e.g., WiFi hotspots) used by the victims. Therefore, the effectiveness of the attacks is significantly impacted by the environment of mobile devices. Also, these attacks are not scalable since they cannot easily infer unlock patterns of a large number of devices. Motivated by an observation that fingertip motions on the screen of a mobile device can be captured by analyzing surrounding acoustic signals on it, we propose PatternListener, a novel acoustic attack that cracks pattern lock by analyzing imperceptible acoustic signals reflected by the fingertip. It leverages speakers and microphones of the victim's device to play imperceptible audio and record the acoustic signals reflected by the fingertip. In particular, it infers each unlock pattern by analyzing individual lines that compose the pattern and are the trajectories of the fingertip. We propose several algorithms to construct signal segments according to the captured signals for each line and infer possible candidates of each individual line according to the signal segments. Finally, we map all line candidates into grid patterns and thereby obtain the candidates of the entire unlock pattern. We implement a PatternListener prototype by using off-the-shelf smartphones and thoroughly evaluate it using 130 unique patterns. The real experimental results demonstrate that PatternListener can successfully exploit over 90% patterns within five attempts.

eess.SP↗

arXiv2018

A Deep X-Ray Look At Abell 2142 --- Viscosity Constraints From Kelvin-Helmholtz Eddies, A Displaced Cool Peak That Makes A Warm Core, And A Possible Plasma Depletion Layer

We analyzed 200 ks of Chandra ACIS observations of the merging galaxy cluster A2142 to examine its prominent cold fronts in detail. We find that the southern cold front exhibits well-developed Kelvin-Helmholtz (KH) eddies seen in the sky plane. Comparing their wavelength and amplitude with those in hydrodynamic simulations of cold fronts in viscous gas, and estimating the gas tangential velocity from centripetal acceleration, we constrain the effective viscosity to be at most 1/5 of Spitzer isotropic viscosity, but consistent with full Braginskii anisotropic viscosity for magnetized plasma. While the northwestern front does not show obvious eddies, its shape and the structure of its brightness profile suggest KH eddies seen in projection. The southern cold front continues in a spiral to the center of the cluster, ending with another cold front only 12 kpc from the gas density peak. The cool peak itself is displaced ~30 kpc from the BCG (the biggest such offset among centrally-peaked clusters), while the X-ray emission on a larger scale is still centered on the BCG, indicating that the BCG is at the center of the gravitational potential and the cool gas is sloshing in it. The specific entropy index of the gas in the peak ($K\approx49$ keV cm$^2$) makes A2142 a rare "warm core"; apparently the large displacement of the cool peak by sloshing is the reason. Finally, we find a subtle narrow, straight channel with a 10% drop in X-ray brightness, aligned with the southern cold front --- possibly a plasma depletion layer in projection.

astro-ph.HE↗

arXiv2018

An Entropy Analysis based Intrusion Detection System for Controller Area Network in Vehicles

Dozens of Electronic Control Units (ECUs) can be found on modern vehicles for safety and driving assistance. These ECUs also introduce new security vulnerabilities as recent attacks have been reported by plugging the in-vehicle system or through wireless access. In this paper, we focus on the security of the Controller Area Network (CAN), which is a standard for communication among ECUs. CAN bus by design does not have sufficient security features to protect it from insider or outsider attacks. Intrusion detection system (IDS) is one of the most effective ways to enhance vehicle security on the insecure CAN bus protocol. We propose a new IDS based on the entropy of the identifier bits in CAN messages. The key observation is that all the known CAN message injection attacks need to alter the CAN ID bits and analyzing the entropy of such bits can be an effective way to detect those attacks. We collected real CAN messages from a vehicle (2016 Ford Fusion) and performed simulated message injection attacks. The experimental results showed that our entropy based IDS can successfully detect all the injection attacks without disrupting the communication on CAN.

cs.CR↗

arXiv2018

Effects of the Dzyaloshinsky-Moriya interaction on nonequilibrium thermodynamics in the $XY$ chain in a transverse field

We examine the effects of the Dzyaloshinsky-Moriya (DM) interaction on the nonequilibrium thermodynamics in an anisotropic $XY$ spin chain, which is driven out of equilibrium by a sudden quench of the control parameter of the Hamiltonian. By analytically evaluating the statistical properties of the work distribution and the irreversible entropy production, we investigate the influences of the DM interaction on the nonequilibrium thermodynamics of the system with different parameters at various temperatures. We find that depending on the anisotropy of the system and the temperature, the DM interaction may have different impacts on the nonequilibrium thermodynamics. Interestingly, the critical line induced by the DM interaction can be revealed via the properties of the nonequilibrium thermodynamics. In addition, our results suggest that the strength of the DM interaction can be detected experimentally by studying the nonequilibrium thermodynamics.

quant-ph↗

arXiv2018

On the exterior stability of nonlinear wave equations

We consider a very general class of nonlinear wave equations, which admit trivial solutions and not necessarily verify any form of null conditions. For compactly supported small data, one can only have a semi-global result which states that the solutions are well-posed upto a finite time-span depending on the size of the Cauchy data. For some of the equations of the class, the solutions blow up within a finite time for the compactly supported data of any size. For data prescribed on ${\mathbb R}^3\setminus B_R$ with small weighted energy, without some form of null conditions on the nonlinearity, the exterior stability is not expected to hold in the full domain of dependence. In this paper, we prove that, there exists a constant $R(\ga_0)\ge 2$, depending on the fixed weight exponent $\ga_0>1$ in the weighted energy norm, if the norm of the data are sufficiently small on ${\mathbb R}^3\setminus B_R$ with the fixed number $R\ge R(\ga_0)$, the solution exists and is unique in the entire exterior of a schwarzschild cone initiating from $\{|x|=R\}$ (including the boundary) with small negative mass $-M_0$. $M_0$ is determined according to the size of the initial data. In this exterior region, by constructing the schwarzschild cone foliation, we can improve the linear behavior of wave equations in particular on the transversal derivative $\Lb ϕ$. Such improvement enables us to control the nonlinearity violating the null condition without loss, and thus show the solutions converge to the trivial solution. As an application, we give the exterior stability result for Einstein (massive and massless) scalar fields. We prove the solution converges to a small static solution, stable in the entire exterior of a schwarzschild cone with positive mass, which then is patchable to the interior results.

math.AP↗

arXiv2018

Supervised and Semi-Supervised Deep Neural Networks for CSI-Based Authentication

From the viewpoint of physical-layer authentication, spoofing attacks can be foiled by checking channel state information (CSI). Existing CSI-based authentication algorithms mostly require a deep knowledge of the channel to deliver decent performance. In this paper, we investigate CSI-based authenticators that can spare the effort to predetermine channel properties by utilizing deep neural networks (DNNs). We first propose a convolutional neural network (CNN)-enabled authenticator that is able to extract the local features in CSI. Next, we employ the recurrent neural network (RNN) to capture the dependencies between different frequencies in CSI. In addition, we propose to use the convolutional recurrent neural network (CRNN)---a combination of the CNN and the RNN---to learn local and contextual information in CSI for user authentication. To effectively train these DNNs, one needs a large amount of labeled channel records. However, it is often expensive to label large channel observations in the presence of a spoofer. In view of this, we further study a case in which only a small part of the the channel observations are labeled. To handle it, we extend these DNNs-enabled approaches into semi-supervised ones. This extension is based on a semi-supervised learning technique that employs both the labeled and unlabeled data to train a DNN. To be specific, our semi-supervised method begins by generating pseudo labels for the unlabeled channel samples through implementing the K-means algorithm in a semi-supervised manner. Subsequently, both the labeled and pseudo labeled data are exploited to pre-train a DNN, which is then fine-tuned based on the labeled channel records.

cs.LG↗

arXiv2018

Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary

Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient L0-norm, which is named as L0TDL. The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images. On the other hand, by introducing the L0-norm constraint in gradient image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The alternative direction minimization method (ADMM) is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed L0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.

cs.CV↗

arXiv2018

Joint Power and Trajectory Design for Physical-Layer Secrecy in the UAV-Aided Mobile Relaying System

Mobile relaying is emerged as a promising technique to assist wireless communication, driven by the rapid development of unmanned aerial vehicles (UAVs). In this paper, we study secure transmission in a four-node (source, destination, mobile relay, and eavesdropper) system, wherein we focus on maximizing the secrecy rate via jointly optimizing the relay trajectory and the source/relay transmit power. Nevertheless, due to the coupling of the trajectory designing and the power allocating, the secrecy rate maximization (SRM) problem is intractable to solve. Accordingly, we propose an alternating optimization (AO) approach, wherein the trajectory designing and the power allocating are tackled in an alternating manner. Unfortunately, the trajectory designing is a nonconvex problem, and thus is still hard to solve. To circumvent the nonconvexity, we exploit sequential convex programming (SCP) to derive an iterative algorithm, which is proven to converge to a Karush-Kuhn-Tucker (KKT) point of the trajectory design problem. The simulation results demonstrate the efficacy of the joint power and trajectory design in improving the secrecy throughput.

cs.IT↗

arXiv2018

BARS: a Blockchain-based Anonymous Reputation System for Trust Management in VANETs

The public key infrastructure (PKI) based authentication protocol provides the basic security services for vehicular ad-hoc networks (VANETs). However, trust and privacy are still open issues due to the unique characteristics of vehicles. It is crucial for VANETs to prevent internal vehicles from broadcasting forged messages while simultaneously protecting the privacy of each vehicle against tracking attacks. In this paper, we propose a blockchain-based anonymous reputation system (BARS) to break the linkability between real identities and public keys to preserve privacy. The certificate and revocation transparency is implemented efficiently using two blockchains. We design a trust model to improve the trustworthiness of messages relying on the reputation of the sender based on both direct historical interactions and indirect opinions about the sender. Experiments are conducted to evaluate BARS in terms of security and performance and the results show that BARS is able to establish distributed trust management, while protecting the privacy of vehicles.

cs.CR↗

arXiv2018

Bow shock in merging cluster A520: the edge of the radio halo and the electron-ion equilibration timescale

We studied the prominent bow shock in the merging galaxy cluster A520 using a deep Chandra X-ray observation and archival VLA radio data. This shock is a useful diagnostic tool, owing to its clear geometry and relatively high Mach number. At the "nose" of the shock, we measure a Mach number of $M=2.4_{-0.2}^{+0.4}$. The shock becomes oblique away from the merger axis, with the Mach number falling to $\simeq$1.6 around 30$^{\circ}$ from the nose. The electron temperature immediately behind the shock nose is consistent with that from the Rankine-Hugoniot adiabat, and is higher (at a 95% confidence) than expected for adiabatic compression of electrons followed by Coulomb electron-proton equilibration, indicating the presence of equilibration mechanisms faster than Coulomb collisions. This is similar to an earlier finding for the Bullet cluster. We also combined four archival VLA datasets to obtain a better image of the cluster's giant radio halo at 1.4 GHz. An abrupt edge of the radio halo traces the shock front, and no emission is detected in the pre-shock region. If the radio edge were due only to adiabatic compression of relativistic electrons in pre-shock plasma, we would expect a pre-shock radio emission detectable in this radio dataset; however, an interferometric artifact dominates the uncertainty, so we cannot rule this model out. Other interesting features of the radio halo include a peak at the remnant of the cool core, suggesting that the core used to have a radio minihalo, and a peak marking a possible region of high turbulence.

astro-ph.HE↗

arXiv2018

Electrically Induced Photonic and Acoustic Quantum Effect From Liquid Metal Droplets in Aqueous Solution

So far, several macroscopic quantum phenomena have been discovered in the Josephson junction. Through introducing such a structure with a liquid membrane sandwiched between two liquid metal electrodes, we had ever observed a lighting and sound phenomenon which was explained before as discharge plasma. In fact, such an effect also belongs to a quantum process. It is based on this conceiving, we proposed here that an electrically controllable method can thus be established to generate and manipulate as much photonic quantum as desired. We attributed such electrically induced lighting among liquid metal droplets immersed inside aqueous solution as photonic quantum effect. Our experiments clarified that a small electrical voltage would be strong enough to trigger blue-violet light and sound inside the aqueous solution system. Meanwhile, thermal heat is released, and chemical reaction occurs over the solution. From an alternative way which differs from former effort in interpreting such effect as discharge plasma, we treated this process as a quantum one and derived new conceptual equations to theoretically quantify this phenomenon in light of quantum mechanics principle. It can be anticipated that given specific designing, such spontaneously generated tremendous quantum can be manipulated to entangle together which would possibly help mold functional elements for developing future quantum computing or communication system. With superior adaptability than that of the conventional rigid junction, the present electro-photonic quantum generation system made of liquid metal droplets structure could work in solution, room temperature situation and is easy to be adjusted. It suggests a macroscopic way to innovate the classical strategies and technologies in generating quantum as frequently adopted in classical quantum engineering area.

physics.app-ph↗

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

Global solution for massive Maxwell-Klein-Gordon equations

We derive the asymptotic properties of the mMKG system (Maxwell coupled with a massive Klein-Gordon scalar field), in the exterior of the domain of influence of a compact set. This complements the previous well known results, restricted to compactly supported initial conditions, based on the so called hyperboloidal method. That method takes advantage of the commutation properties of the Maxwell and Klein Gordon with the generators of the Poincaré group to resolve the difficulties caused by the fact that they have, separately, different asymptotic properties. Though the hyperboloidal method is very robust and applies well to other related systems it has the well known drawback that it requires compactly supported data. In this paper we remove this limitation based on a further extension of the vector-field method adapted to the exterior region. Our method applies, in particular, to nontrivial charges. The full problem could then be treated by patching together the new estimates in the exterior with the hyperboloidal ones in the interior. This purely physical space approach introduced here maintains the robust properties of the old method and can thus be applied to other situations such as the coupled Einstein Klein-Gordon equation.

math.AP↗

arXiv2018

Secure Directional Modulation to Enhance Physical Layer Security in IoT Networks

In this work, an adaptive and robust null-space projection (AR-NSP) scheme is proposed for secure transmission with artificial noise (AN)-aided directional modulation (DM) in wireless networks. The proposed scheme is carried out in three steps. Firstly, the directions of arrival (DOAs) of the signals from the desired user and eavesdropper are estimated by the Root Multiple Signal Classificaiton (Root-MUSIC) algorithm and the related signal-to-noise ratios (SNRs) are estimated based on the ratio of the corresponding eigenvalue to the minimum eigenvalue of the covariance matrix of the received signals. In the second step, the value intervals of DOA estimation errors are predicted based on the DOA and SNR estimations. Finally, a robust NSP beamforming DM system is designed according to the afore-obtained estimations and predictions. Our examination shows that the proposed scheme can significantly outperform the conventional non-adaptive robust scheme and non-robust NSP scheme in terms of achieving a much lower bit error rate (BER) at the desired user and a much higher secrecy rate (SR). In addition, the BER and SR performance gains achieved by the proposed scheme relative to other schemes increase with the value range of DOA estimation error.

cs.IT↗

arXiv2017

A Context-Aware User-Item Representation Learning for Item Recommendation

Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That is, a single static feature vector is derived to encode her preference without considering the particular characteristics of each candidate item. We argue that this static encoding scheme is difficult to fully capture the users' preference. In this paper, we propose a novel context-aware user-item representation learning model for rating prediction, named CARL. Namely, CARL derives a joint representation for a given user-item pair based on their individual latent features and latent feature interactions. Then, CARL adopts Factorization Machines to further model higher-order feature interactions on the basis of the user-item pair for rating prediction. Specifically, two separate learning components are devised in CARL to exploit review data and interaction data respectively: review-based feature learning and interaction-based feature learning. In review-based learning component, with convolution operations and attention mechanism, the relevant features for a user-item pair are extracted by jointly considering their corresponding reviews. However, these features are only review-driven and may not be comprehensive. Hence, interaction-based learning component further extracts complementary features from interaction data alone, also on the basis of user-item pairs. The final rating score is then derived with a dynamic linear fusion mechanism. Experiments on five real-world datasets show that CARL achieves significantly better rating prediction accuracy than existing state-of-the-art alternatives. Also, with attention mechanism, we show that the relevant information in reviews can be highlighted to interpret the rating prediction.

cs.IR↗