arXiv subjects

Qian Wang

Publications and source records attributed to Qian Wang.

At least 415 records · Page 23Linked to original sources

arXiv2022

BcMON: Blockchain Middleware for Offline Networks

Blockchain is becoming a new generation of information infrastructures. However, the current blockchain solutions rely on a continuous connectivity network to query and modify the state of the blockchain. The emerging satellite technology seems to be a good catalyst to forward offline transactions to the blockchain. However, this approach suffers expensive costs, difficult interoperability, and limited computation problems. Therefore, we propose BcMON, the first blockchain middleware for offline networks. BcMON incorporates three innovative designs: 1) it reduces the costs of offline transactions accessing the blockchain through Short Message Service (SMS), 2) it validates the authenticity of offline cross-chain transactions by two-phase consensus, 3) it supports offline clients to perform complex queries and computations on the blockchains. The prototype of BcMON has been implemented to evaluate the performance of the proposed middleware, which can show its stability, efficiency, and scalability.

cs.CR↗

arXiv2022

Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network

In clinical practice, multi-modal magnetic resonance imaging (MRI) with different contrasts is usually acquired in a single study to assess different properties of the same region of interest in the human body. The whole acquisition process can be accelerated by having one or more modalities under-sampled in the $k$-space. Recent research has shown that, considering the redundancy between different modalities, a target MRI modality under-sampled in the $k$-space can be more efficiently reconstructed with a fully-sampled reference MRI modality. However, we find that the performance of the aforementioned multi-modal reconstruction can be negatively affected by subtle spatial misalignment between different modalities, which is actually common in clinical practice. In this paper, we improve the quality of multi-modal reconstruction by compensating for such spatial misalignment with a spatial alignment network. First, our spatial alignment network estimates the displacement between the fully-sampled reference and the under-sampled target images, and warps the reference image accordingly. Then, the aligned fully-sampled reference image joins the multi-modal reconstruction of the under-sampled target image. Also, considering the contrast difference between the target and reference images, we have designed a cross-modality-synthesis-based registration loss in combination with the reconstruction loss, to jointly train the spatial alignment network and the reconstruction network. The experiments on both clinical MRI and multi-coil $k$-space raw data demonstrate the superiority and robustness of the multi-modal MRI reconstruction empowered with our spatial alignment network. Our code is publicly available at \url{https://github.com/woxuankai/SpatialAlignmentNetwork}.

eess.IV↗

arXiv2022

Automatic Facial Skin Feature Detection for Everyone

Automatic assessment and understanding of facial skin condition have several applications, including the early detection of underlying health problems, lifestyle and dietary treatment, skin-care product recommendation, etc. Selfies in the wild serve as an excellent data resource to democratize skin quality assessment, but suffer from several data collection challenges.The key to guaranteeing an accurate assessment is accurate detection of different skin features. We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild. To be specific, we annotate the locations of acne, pigmentation, and wrinkle for selfie images with different skin tone colors, severity levels, and lighting conditions. The annotation is conducted in a two-phase scheme with the help of a dermatologist to train volunteers for annotation. We employ Unet++ as the network architecture for feature detection. This work shows that the two-phase annotation scheme can robustly detect the accurate locations of acne, pigmentation, and wrinkle for selfie images with different ethnicities, skin tone colors, severity levels, age groups, and lighting conditions.

cs.CV↗

arXiv2022

WaveFuzz: A Clean-Label Poisoning Attack to Protect Your Voice

People are not always receptive to their voice data being collected and misused. Training the audio intelligence systems needs these data to build useful features, but the cost for getting permissions or purchasing data is very high, which inevitably encourages hackers to collect these voice data without people's awareness. To discourage the hackers from proactively collecting people's voice data, we are the first to propose a clean-label poisoning attack, called WaveFuzz, which can prevent intelligence audio models from building useful features from protected (poisoned) voice data but still preserve the semantic information to the humans. Specifically, WaveFuzz perturbs the voice data to cause Mel Frequency Cepstral Coefficients (MFCC) (typical representations of audio signals) to generate the poisoned frequency features. These poisoned features are then fed to audio prediction models, which degrades the performance of audio intelligence systems. Empirically, we show the efficacy of WaveFuzz by attacking two representative types of intelligent audio systems, i.e., speaker recognition system (SR) and speech command recognition system (SCR). For example, the accuracies of models are declined by $19.78\%$ when only $10\%$ of the poisoned voice data is to fine-tune models, and the accuracies of models declined by $6.07\%$ when only $10\%$ of the training voice data is poisoned. Consequently, WaveFuzz is an effective technique that enables people to fight back to protect their own voice data, which sheds new light on ameliorating privacy issues.

cs.SD↗

arXiv2022

Localized Interlayer Excitons in MoSe2-WSe2 Heterostructures without a Moiré Potential

Trapped interlayer excitons (IXs) in MoSe2-WSe2 heterobilayers have generated interest for use as single quantum emitter arrays and as an opportunity to study moiré physics in transition metal dichalcogenide (TMD) heterostructures. IXs are spatially indirectly excitons comprised of an electron in the MoSe2 layer bound to a hole in the WSe2 layer. Previous reports of spectrally narrow (<1 meV) photoluminescence (PL) emission lines at low temperature have been attributed to IXs localized by the moiré potential between the TMD layers. Here, we show that spectrally narrow IX PL lines are present even when the moiré potential is suppressed by inserting a bilayer hexagonal boron nitride (hBN) spacer between the TMD layers. We directly compare the doping, electric field, magnetic field, and temperature dependence of IXs in a directly contacted MoSe2-WSe2 region to those in a region separated by bilayer hBN. Our results show that the localization potential resulting in the narrow PL lines is independent of the moiré potential, and instead likely due to extrinsic effects such as nanobubbles or defects. We show that while the doping, electric field, and temperature dependence of the narrow IX lines is similar for both regions, their excitonic g-factors have opposite signs, indicating that the IXs in the directly contacted region are trapped by both moiré and extrinsic localization potentials.

cond-mat.other↗

arXiv2022

Effective range expansion for narrow near-threshold resonances

We discuss some general features of the effective range expansion, the content of its parameters with respect to the nature of the pertinent near-threshold states and the necessary modifications in the presence of coupled channels, isospin violations and unstable constituents. As illustrative examples, we analyse the properties of the $χ_{c1}(3872)$ and $T_{cc}^+$ states supporting the claim that these exotic states have a predominantly molecular nature.

hep-ph↗

arXiv2022

The lineshape of the compact fully heavy tetraquark

Hadrons and their distributions are the most direct observables in experiment, which would shed light on the non-perturbative mystery of quantum chromodynamics (QCD). As the result, any new hadron will challenge our current knowlege on the one hand, and provide additional inputs on the other hand. The fully heavy $cc\bar{c}\bar{c}$ system observed by LHCb recently opens a new era for hadron physics. We first extract the internal structure of the fully heavy tetraquarks directly from the experimental data, within the compact tetraquark picture. By fitting to the di-$J/ψ$ lineshape, we find that the $X(6900)$ is only cusp effect from the $J/ψψ(3770)$ channel. In addition, there is also a cusp slightly below $6.8~\mathrm{GeV}$ stemming from the $J/ψψ^\prime$ channel. The two $0^{++}$ tetraquarks behave as two resonances above the di-$η_c$ and di-$J/ψ$ threshold, respectively. The $2^{++}$ state is a bound state below the di-$J/ψ$ threshold. Furthermore, we find that the $X_{0^{++}}(6035)$ shows a significant structure in the di-$η_c$ lineshape even after the coupled channel effect. This is an unique feature which can distinguish compact $cc\bar{c}\bar{c}$ tetraquark from the loosely hadronic molecules.

hep-ph↗

arXiv2022

Modularity and Mutual Information in Networks: Two Sides of the Same Coin

Modularity, first proposed by [Newman and Girvan, 2004], is one of the most popular ways to quantify the significance of community structure in complex networks. It can serve as both a standard benchmark to compare different community detection algorithms, and an optimization objective to detect communities itself. Previous work on modularity has developed many efficient algorithms for modularity maximization. However, few of researchers considered the interpretation of the modularity function itself. In this paper, we study modularity from an information-theoretical perspective and show that modularity and mutual information in networks are essentially the same. The main contribution is that we develop a family of generalized modularity measures, f-modularity based on f-mutual information. f-Modularity has an information-theoretical interpretation, enjoys the desired properties of mutual information measure, and provides an approach to estimate the mutual information between discrete random variables. At a high level, we show the significance of community structure is equivalent to the amount of information contained in the network. The connection of f-modularity and f-mutual information bridges two important fields, complex network and information theory and also sheds light on the design of measures on community structure in future.

cs.SI↗

arXiv2022

AI-Aided Mapping of the Structure-Composition-Conductivity Relationships of Glass-Ceramic Lithium Thiophosphate Electrolytes

Lithium thiophosphates (LPS) with the composition (Li$_2$S)$_x$(P$_2$S$_5$)$_{1-x}$ are among the most promising prospective electrolyte materials for solid-state batteries (SSBs), owing to their superionic conductivity at room temperature ($>10^{-3}$ S cm$^{-1}$), soft mechanical properties, and low grain boundary resistance. Several glass-ceramic (gc) LPS with different compositions and good Li conductivity have been previously reported, but the relationship between composition, atomic structure, stability, and Li conductivity remains unclear due to the challenges in characterizing non-crystalline phases in experiments or simulations. Here, we mapped the LPS phase diagram by combining first principles and artificial intelligence (AI) methods, integrating density functional theory, artificial neural network potentials, genetic-algorithm sampling, and ab initio molecular dynamics simulations. By means of an unsupervised structure-similarity analysis, the glassy/ceramic phases were correlated with the local structural motifs in the known LPS crystal structures, showing that the energetically most favorable Li environment varies with the composition. Based on the discovered trends in the LPS phase diagram, we propose a candidate solid-state electrolyte composition, (Li$_{2}$S)$_{x}$(P$_{2}$S$_{5}$)$_{1-x}$ ($x\sim{}0.725$), that exhibits high ionic conductivity ($>10^{-2}$ S cm$^{-1}$) in our simulations, thereby demonstrating a general design strategy for amorphous or glassy/ceramic solid electrolytes with enhanced conductivity and stability.

cond-mat.mtrl-sci↗

arXiv2022

Coupled-channel approach to $T_{cc}^+$ including three-body effects

A coupled-channel approach is applied to the charged tetraquark state $T_{cc}^+$ recently discovered by the LHCb Collaboration. The parameters of the interaction are fixed by a fit to the observed line shape in the three-body $D^0D^0π^+$ channel. Special attention is paid to the three-body dynamics in the $T_{cc}^+$ due to the finite life time of the $D^*$. An approach to the $T_{cc}^+$ is argued to be self-consistent only if both manifestations of the three-body dynamics, the pion exchange between the $D$ and $D^*$ mesons and the finite $D^*$ width, are taken into account simultaneously to ensure that three-body unitarity is preserved. This is especially important to precisely extract the pole position in the complex energy plane whose imaginary part is very sensitive to the details of the coupled-channel scheme employed. The $D^0D^0$ and $D^0D^+$ invariant mass distributions, predicted based on this analysis, are in good agreement with the LHCb data. The low-energy expansion of the $D^*D$ scattering amplitude is performed and the low-energy constants (the scattering length and effective range) are extracted. The compositeness parameter of the $T_{cc}^+$ is found to be close to unity, which implies that the $T_{cc}^+$ is a hadronic molecule generated by the interactions in the $D^{*+}D^0$ and $D^{*0}D^+$ channels. Employing heavy-quark spin symmetry, an isoscalar $D^*D^*$ molecular partner of the $T_{cc}^+$ with $J^P=1^+$ is predicted under the assumption that the $ DD^*$-$D^*D^*$ coupled-channel effects can be neglected.

hep-ph↗

arXiv2022

Recent Advances in Tunable Metasurfaces: Materials, Design and Applications

Metasurfaces, a two-dimensional (2D) form of metamaterials constituted by planar meta-atoms, exhibit exotic abilities to freely tailor electromagnetic (EM) waves. Over the past decade, tunable metasurfaces have come to the frontier in the field of nanophotonics, with tremendous effort focused on developing and integrating various active materials into metasurfaces. As a result, tunable/reconfigurable metasurfaces with multi-functionalities triggered by various external stimuli have been successfully demonstrated, openings a new avenue to dynamically manipulate and control EM waves for photonic applications in demand. In this review, we first brief the progress of tunable metasurfaces development in the last decade and highlight representative works from the perspectives of active materials development, design methodologies and application-driven exploration. Then, we elaborate on the active tuning mechanisms and relevant active materials. Next, we discuss recent achievements in theory as well as machine learning (ML) assisted design methodologies to sustain the development of this field. After that, we summarize and describe typical application areas of the tunable metasurfaces. We conclude this review by analyzing existing challenges and presenting our perspectives on future directions and opportunities in this vibrant and fast-developing field.

physics.optics↗

arXiv2022

Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution

Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible by magnetic resonance imaging (MRI). Thus early detection and assessment for knee cartilage defects are important for protecting patients from knee OA. In this way, many attempts have been made on knee cartilage defect assessment by applying convolutional neural networks (CNNs) to knee MRI. However, the physiologic characteristics of the cartilage may hinder such efforts: the cartilage is a thin curved layer, implying that only a small portion of voxels in knee MRI can contribute to the cartilage defect assessment; heterogeneous scanning protocols further challenge the feasibility of the CNNs in clinical practice; the CNN-based knee cartilage evaluation results lack interpretability. To address these challenges, we model the cartilages structure and appearance from knee MRI into a graph representation, which is capable of handling highly diverse clinical data. Then, guided by the cartilage graph representation, we design a non-Euclidean deep learning network with the self-attention mechanism, to extract cartilage features in the local and global, and to derive the final assessment with a visualized result. Our comprehensive experiments show that the proposed method yields superior performance in knee cartilage defect assessment, plus its convenient 3D visualization for interpretability.

eess.IV↗

arXiv2021

Parameter Differentiation based Multilingual Neural Machine Translation

Multilingual neural machine translation (MNMT) aims to translate multiple languages with a single model and has been proved successful thanks to effective knowledge transfer among different languages with shared parameters. However, it is still an open question which parameters should be shared and which ones need to be task-specific. Currently, the common practice is to heuristically design or search language-specific modules, which is difficult to find the optimal configuration. In this paper, we propose a novel parameter differentiation based method that allows the model to determine which parameters should be language-specific during training. Inspired by cellular differentiation, each shared parameter in our method can dynamically differentiate into more specialized types. We further define the differentiation criterion as inter-task gradient similarity. Therefore, parameters with conflicting inter-task gradients are more likely to be language-specific. Extensive experiments on multilingual datasets have demonstrated that our method significantly outperforms various strong baselines with different parameter sharing configurations. Further analyses reveal that the parameter sharing configuration obtained by our method correlates well with the linguistic proximities.

cs.CL↗

arXiv2021

Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis

We consider the problem of abnormality localization for clinical applications. While deep learning has driven much recent progress in medical imaging, many clinical challenges are not fully addressed, limiting its broader usage. While recent methods report high diagnostic accuracies, physicians have concerns trusting these algorithm results for diagnostic decision-making purposes because of a general lack of algorithm decision reasoning and interpretability. One potential way to address this problem is to further train these models to localize abnormalities in addition to just classifying them. However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications. In this work, we take a step towards addressing these issues by means of a new attention-driven weakly supervised algorithm comprising a hierarchical attention mining framework that unifies activation- and gradient-based visual attention in a holistic manner. Our key algorithmic innovations include the design of explicit ordinal attention constraints, enabling principled model training in a weakly-supervised fashion, while also facilitating the generation of visual-attention-driven model explanations by means of localization cues. On two large-scale chest X-ray datasets (NIH ChestX-ray14 and CheXpert), we demonstrate significant localization performance improvements over the current state of the art while also achieving competitive classification performance. Our code is available on https://github.com/oyxhust/HAM.

cs.CV↗

arXiv2021

Charge resolution in the isochronous mass spectrometry and the mass of $^{51}$Co

Isochronous mass spectrometry (IMS) of heavyion storage rings is a powerful tool for the mass measurements of short-lived nuclei. In IMS experiments, masses are determined through precision measurements of the revolution times of the ions stored in the ring. However, the revolution times cannot be resolved for particles with nearly the same mass-to-charge (m/q) ratios. To overcome this limitation and to extract the accurate revolution times for such pairs of ion species with very close m/q ratios, in our early work on particle identification, we analyzed the amplitudes of the timing signals from the detector based on the emission of secondary electrons. Here, the previous data analysis method is further improved by considering the signal amplitudes, detection efficiencies, and number of stored ions in the ring. A sensitive Z-dependent parameter is introduced in the data analysis, leading to a better resolution of $^{34}$Ar$^{18+}$ and $^{51}$Co$^{27+}$ with A/Z=17/9. The mean revolution times of $^{34}$Ar$^{18+}$ and $^{51}$Co$^{27+}$ are deduced, although their time difference is merely 1.8 ps. The uncorrected, overlapped peak of these ions has a full width at half maximum of 7.7 ps. The mass excess of $^{51}$Co was determined to be -27332(41) keV, which is in agreement with the previous value of -27342(48) keV.

nucl-ex↗

arXiv2021

Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information

Adversarial attacks against commercial black-box speech platforms, including cloud speech APIs and voice control devices, have received little attention until recent years. The current "black-box" attacks all heavily rely on the knowledge of prediction/confidence scores to craft effective adversarial examples, which can be intuitively defended by service providers without returning these messages. In this paper, we propose two novel adversarial attacks in more practical and rigorous scenarios. For commercial cloud speech APIs, we propose Occam, a decision-only black-box adversarial attack, where only final decisions are available to the adversary. In Occam, we formulate the decision-only AE generation as a discontinuous large-scale global optimization problem, and solve it by adaptively decomposing this complicated problem into a set of sub-problems and cooperatively optimizing each one. Our Occam is a one-size-fits-all approach, which achieves 100% success rates of attacks with an average SNR of 14.23dB, on a wide range of popular speech and speaker recognition APIs, including Google, Alibaba, Microsoft, Tencent, iFlytek, and Jingdong, outperforming the state-of-the-art black-box attacks. For commercial voice control devices, we propose NI-Occam, the first non-interactive physical adversarial attack, where the adversary does not need to query the oracle and has no access to its internal information and training data. We combine adversarial attacks with model inversion attacks, and thus generate the physically-effective audio AEs with high transferability without any interaction with target devices. Our experimental results show that NI-Occam can successfully fool Apple Siri, Microsoft Cortana, Google Assistant, iFlytek and Amazon Echo with an average SRoA of 52% and SNR of 9.65dB, shedding light on non-interactive physical attacks against voice control devices.

cs.CR↗

arXiv2021

Study of heavy quark conserving weak decays in the quark model

Within the framework of a constituent quark model, we investigate the heavy quark conserving weak decays of $Ξ_c$ and $Ξ_b$. This process involves elementary transitions of $us\to du$ or $s\to ud \bar u$ with the heavy quark as a spectator, while for the charmed baryon in the initial state it can also occur via $cs\to dc$ to conserve the heavy quark flavor in the weak decays. It shows that the heavy quark symmetry (HQS) plays an essential role in the processes where the heavy quark acts as a spectator. For the $Ξ_c$ decays, the dominance of the pole terms in the parity-conserving transitions is evident. This can naturally explain the sizeable branching ratio for $Ξ_c^0\toΛ_cπ^-$ as recently reported by LHCb. The same mechanism predicts a large branching ratio for $Ξ_c^+\toΛ_cπ^0$. In contrast, the decays of $Ξ_b$ will only go through the parity-violating processes because of the HQS. In addition, the branching ratios of the $Ξ_c^+\to Λ_c π^0$ and $Ξ_b^0\to Λ_b π^0$ processes are also predicted for the further experimental measurement.

hep-ph↗

arXiv2021

On Novel Peer Review System for Academic Journals: Experimental Study Based on Social Computing

For improving the performance and effectiveness of peer review, a novel review system is proposed, based on analysis of peer review process for academic journals under a parallel model built via Monte Carlo method. The model can simulate the review, application and acceptance activities of the review systems, in a distributed manner. According to simulation experiments on two distinct review systems respectively, significant advantages manifest for the novel one.

cs.DL↗