arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 757 records · Page 42Linked to original sources

arXiv2023

Universality in relaxation of spin helices under the $XXZ$- spin chain dynamics

We describe dynamics of transverse spin-helix state (SHS) -- a product state with spatially rotating magnetization -- under anisotropic Heisenberg XXZ spin chain evolution. Due to experimental relevance we especially focus on magnetization dynamics. At long times the $U(1)$ symmetry of the Hamiltonian is restored, leading to the decay of transverse magnetization, which can be described as an exponential decay of a spatially harmonic profile. We show that the dependence of the short and intermediate-time decay timescale, which in principle depends on all different parameters, like the wavevector of the initial helix, the anisotropy, etc., can be described well by a single scaling function. We also briefly discuss the evolution of magnetization current.

cond-mat.stat-mech↗

arXiv2023

GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks

Relation extraction (RE) tasks show promising performance in extracting relations from two entities mentioned in sentences, given sufficient annotations available during training. Such annotations would be labor-intensive to obtain in practice. Existing work adopts data augmentation techniques to generate pseudo-annotated sentences beyond limited annotations. These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations. In this work, we propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures. We adopt a generative formulation and design a multi-tasking solution to achieve synergies. Furthermore, GDA adopts entity hints as the prior knowledge of the generative model to augment diverse sentences. Experimental results in three datasets under a low-resource setting showed that GDA could bring {\em 2.0\%} F1 improvements compared with no augmentation technique. Source code and data are available.

cs.CL↗

arXiv2023

Understanding Privacy Over-collection in WeChat Sub-app Ecosystem

Nowadays the app-in-app paradigm is becoming increasingly popular, and sub-apps have become an important form of mobile applications. WeChat, the leading app-in-app platform, provides millions of sub-apps that can be used for online shopping, financing, social networking, etc. However, privacy issues in this new ecosystem have not been well understood. This paper performs the first systematic study of privacy over-collection in sub-apps (denoted as SPO), where sub-apps actually collect more privacy data than they claim in their privacy policies. We propose a taxonomy of privacy for this ecosystem and a framework named SPOChecker to automatically detect SPO in real-world sub-apps. Based on SPOChecker, we collect 5,521 popular and representative WeChat sub-apps and conduct a measurement study to understand SPO from three aspects: its landscape, accountability, and defense methods. The result is worrisome, that more than half of all studied sub-apps do not provide users with privacy policies. Among 2,511 sub-apps that provide privacy policies, 489 (19.47%) of them contain SPO. We look into the detailed characteristics of SPO, figure out possible reasons and the responsibilities of stakeholders in the ecosystem, and rethink current defense methods. The measurement leads to several insightful findings that can help the community to better understand SPO and protect privacy in sub-apps.

cs.CR↗

arXiv2023

Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease

Objective: Bleeding from gastroesophageal varices (GEV) is a medical emergency associated with high mortality. We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography (2D-SWE) of the liver and spleen to precisely assess the risk of GEV and high-risk gastroesophageal varices (HRV). Design: A prospective multicenter study was conducted in patients with compensated advanced chronic liver disease. 305 patients were enrolled from 12 hospitals, and finally 265 patients were included, with 1136 liver stiffness measurement (LSM) images and 1042 spleen stiffness measurement (SSM) images generated by 2D-SWE. We leveraged deep learning methods to uncover associations between image features and patient risk, and thus conducted models to predict GEV and HRV. Results: A multi-modality Deep Learning Risk Prediction model (DLRP) was constructed to assess GEV and HRV, based on LSM and SSM images, and clinical information. Validation analysis revealed that the AUCs of DLRP were 0.91 for GEV (95% CI 0.90 to 0.93, p < 0.05) and 0.88 for HRV (95% CI 0.86 to 0.89, p < 0.01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM. Moreover, DLPR was better than the model using individual parameters, including LSM and SSM images. In HRV prediction, the 2D-SWE images of SSM outperform LSM (p < 0.01). Conclusion: DLRP shows excellent performance in predicting GEV and HRV over canonical risk indicators LSM and SSM. Additionally, the 2D-SWE images of SSM provided more information for better accuracy in predicting HRV than the LSM.

q-bio.TO↗

arXiv2023

Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model

Text-to-image generative models have attracted rising attention for flexible image editing via user-specified descriptions. However, text descriptions alone are not enough to elaborate the details of subjects, often compromising the subjects' identity or requiring additional per-subject fine-tuning. We introduce a new framework called \textit{Paste, Inpaint and Harmonize via Denoising} (PhD), which leverages an exemplar image in addition to text descriptions to specify user intentions. In the pasting step, an off-the-shelf segmentation model is employed to identify a user-specified subject within an exemplar image which is subsequently inserted into a background image to serve as an initialization capturing both scene context and subject identity in one. To guarantee the visual coherence of the generated or edited image, we introduce an inpainting and harmonizing module to guide the pre-trained diffusion model to seamlessly blend the inserted subject into the scene naturally. As we keep the pre-trained diffusion model frozen, we preserve its strong image synthesis ability and text-driven ability, thus achieving high-quality results and flexible editing with diverse texts. In our experiments, we apply PhD to both subject-driven image editing tasks and explore text-driven scene generation given a reference subject. Both quantitative and qualitative comparisons with baseline methods demonstrate that our approach achieves state-of-the-art performance in both tasks. More qualitative results can be found at \url{https://sites.google.com/view/phd-demo-page}.

cs.CV↗

arXiv2023

A path to precision cosmology: synergy between four promising late-universe cosmological probes

In the next decades, it is necessary to forge new late-universe cosmological probes to precisely constrain the Hubble constant and the equation of state of dark energy simultaneously. In this work, we show that the four typical late-universe cosmological probes, the 21 cm intensity mapping (IM), fast radio burst (FRB), gravitational wave (GW) standard siren, and strong gravitational lensing (SGL), are expected to be forged into useful tools in solving the Hubble tension and exploring dark energy. We propose that the synergy of them is rather important in cosmology. We simulate the 21 cm IM, FRB, GW, and SGL data based on the hypothetical observations of the Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX), the Square Kilometre Array (SKA), the Einstein Telescope (ET), and the Large Synoptic Survey Telescope (LSST), respectively. We find that the four probes show obviously different parameter degeneracy orientations in cosmological constraints, so any combination of them can break the parameter degeneracies and thus significantly improve the constraint precision. The joint 21 cm IM+FRB+GW+SGL data can provide the constraint errors of $σ(Ω_{\rm m})=0.0022$ and $σ(H_0)=0.16\ \rm km\ s^{-1}\ Mpc^{-1}$ in the $Λ$CDM model, which meet the standard of precision cosmology, i.e., the constraint precision of parameters is better than 1%. In addition, the joint data give $σ(w)=0.020$ in the $w$CDM model, and $σ(w_0)=0.066$ and $σ(w_a)=0.25$ in the $w_0w_a$CDM model, which are all better than the constraints obtained by the CMB+BAO+SN data. We show that the synergy between the four late-universe cosmological probes has magnificent prospects.

astro-ph.CO↗

arXiv2023

ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models

We study the domain adaptation problem with label shift in this work. Under the label shift context, the marginal distribution of the label varies across the training and testing datasets, while the conditional distribution of features given the label is the same. Traditional label shift adaptation methods either suffer from large estimation errors or require cumbersome post-prediction calibrations. To address these issues, we first propose a moment-matching framework for adapting the label shift based on the geometry of the influence function. Under such a framework, we propose a novel method named \underline{E}fficient \underline{L}abel \underline{S}hift \underline{A}daptation (ELSA), in which the adaptation weights can be estimated by solving linear systems. Theoretically, the ELSA estimator is $\sqrt{n}$-consistent ($n$ is the sample size of the source data) and asymptotically normal. Empirically, we show that ELSA can achieve state-of-the-art estimation performances without post-prediction calibrations, thus, gaining computational efficiency.

stat.ML↗

arXiv2023

Preferential bond formation and interstitial/vacancy annihilation rate drive atomic clustering in gallium ion sputtered compound materials

The investigation of chemical reactions during the ion irradiation is a frontier for the study of the ion-material interaction. In order to derive the contribution of bond formation to chemistry of ion produced nanoclusters, the valence electron energy loss spectroscopy (VEELS) was exploited to investigate the Ga$^+$ ion damage in Al$_2$O$_3$, InP and InGaAs, where each target material has been shown to yield different process for altering the clustering of recoil atoms: metallic Ga, metallic In and InGaP clusters in Al$_2$O$_3$, InP and InGaAs respectively. Supporting simulations based on Monte Carlo and crystal orbital Hamiltonianindicate that the chemical constitution of cascade induced nano-precipitates is a result of a competition between interstitial/vacancy consumption rate and preferential bond formation.

cond-mat.mtrl-sci↗

arXiv2023

SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities

Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT. However, current speech-language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer. In this paper, we propose SpeechGPT, a large language model with intrinsic cross-modal conversational abilities, capable of perceiving and generating multi-model content. With discrete speech representations, we first construct SpeechInstruct, a large-scale cross-modal speech instruction dataset. Additionally, we employ a three-stage training strategy that includes modality-adaptation pre-training, cross-modal instruction fine-tuning, and chain-of-modality instruction fine-tuning. The experimental results demonstrate that SpeechGPT has an impressive capacity to follow multi-modal human instructions and highlight the potential of handling multiple modalities with one model. Demos are shown in https://0nutation.github.io/SpeechGPT.github.io/.

cs.CL↗

arXiv2023

DesignTracking: Track and Replay BIM-based Design Process

Among different phases of the life cycle of a building or facility, design is of the utmost importance to ensure safety, efficiency and sustainability of the building or facility. How to control and improve design quality and efficiency has been explored for years, and more studies emerged with the popularization of Building Information Modelling (BIM). However, most of them focused on the extraction of design behaviors, while paying less attention to how a design is formed. Therefore, this study proposes an approach to tracking and replaying the BIM-based design process by integrating data logging and 4D visualization techniques. First of all, potential design behaviors and procedures are analyzed and extracted by observing how a designer designs a BIM model. Meanwhile, the required data for logging design process is defined and a relevant method to collect these data is developed based on the APIs of BIM software. Then, strategies on how to visualize different design procedures are designed and implemented via 4D visualization. Finally, a prototype system is developed based on Autodesk Revit and validated through a case study. Result shows that the proposed approach enables intuitively and interactively review of the design process, and makes it easier to understand design behaviors and even identify potential pitfalls, thus improving the design efficiency and quality.

cs.HC↗

arXiv2023

Active IRS-Aided MIMO Systems: How Much Gain Can We Get?

Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to improve the efficiency of wireless communication systems. However, passive IRSs suffer from the ``multiplicative fading" effect, because the transmit signal will go through two fading hops. With the ability to amplify and reflect signals, active IRSs offer a potential way to tackle this issue, where the amplification energy only experiences the second hop. However, the fundamental limit and system design for active IRSs have not been fully understood, especially for multiple-input multiple-output (MIMO) systems. In this work, we consider the analysis and design for the large-scale active IRS-aided MIMO system assuming only statistical channel state information (CSI) at the transmitter and the IRS. The evaluation of the fundamental limit, i.e., ergodic rate, turns out to be a very difficult problem. To this end, we leverage random matrix theory (RMT) to derive the deterministic approximation (DA) for the ergodic rate, and then design an algorithm to jointly optimize the transmit covariance matrix at the transmitter and the reflection matrix at the active IRS. Numerical results demonstrate the accuracy of the derived DA and the effectiveness of the proposed optimization algorithm. The results in this work reveal interesting physical insights with respect to the advantage of active IRSs over their passive counterparts.

cs.IT↗

arXiv2023

FAST drift scan survey for HI intensity mapping: I. preliminary data analysis

This work presents the initial results of the drift-scan observation for the neutral hydrogen (HI) intensity mapping survey with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The data analyzed in this work were collected in night observations from 2019 through 2021. The primary findings are based on 28 hours of drift-scan observation carried out over seven nights in 2021, which covers $60\,{\rm deg}^2$ sky area. Our main findings are: (i) Our calibration strategy can successfully correct both the temporal and bandpass gain variation over the $4$-hour drift-scan observation. (ii) The continuum maps of the surveyed region are made with frequency resolution of $28$ kHz and pixel area of $2.95\,{\rm arcmin}^2$. The pixel noise levels of the continuum maps are slightly higher than the forecast assuming $T_{\rm sys}=20\,{\rm K}$, which are $36.0$ mK (for 10.0 s integration time) at the $1050$--$1150$ MHz band, and $25.9$ mK (for 16.7 s integration time) at the $1323$--$1450$ MHz band, respectively. (iii) The flux-weighted differential number count is consistent with the NRAO-VLA Sky Survey (NVSS) catalog down to the confusion limit $\sim7\,{\rm mJy}/{\rm beam}^{-1}$. (iv) The continuum flux measurements of the sources are consistent with that found in the literature. The difference in the flux measurement of $81$ isolated NVSS sources is about $6.3\%$. Our research offers a systematic analysis for the FAST HI intensity mapping drift-scan survey and serves as a helpful resource for further cosmology and associated galaxies sciences with the FAST drift-scan survey.

astro-ph.CO↗

arXiv2023

Atomic-scale Mapping Unravel Anisotropic Dissolution Behaviors of Gibbsite Nanosheets

This study examines the anisotropic dissolution of the basal plane gibbsite (γ-Al(OH)3) nanoplates in sodium hydroxide solution using in situ atomic force microscopy (AFM) and density functional theory (DFT) calculations. In the surface-reaction controlled regime, in situ AFM measurements reveal anisotropic dissolution of hillocks and etch pits on the gibbsite basal plane, with preferred dissolution directions alternating between layers. The mirror-symmetric pattern of dissolution preference between adjacent gibbsite aluminum hydroxide sheet, observed along the crystallographic a-c plane, results from the matching symmetry between the structures of the adjacent (001) and (002) crystal planes. Consequently, the overall dissolution rate of gibbsite nanoplates exhibits crystallographic a-c plane symmetry, as the rate of parallel steps is governed by the slower ones. DFT calculations suggest that the anisotropic dissolution is partially due to the orientation and strength of Al-OH-Al linkages pair within gibbsite surface structure. These findings offer a comprehensive understanding of anisotropic dissolution behavior of gibbsite and illuminate the mechanisms behind preferential dissolution.

cond-mat.mtrl-sci↗

arXiv2023

Prospects for constraining interacting dark energy models from gravitational wave and gamma ray burst joint observation

With the measurement of the electromagnetic (EM) counterpart, a gravitational wave (GW) event could be treated as a standard siren. As a novel cosmological probe, GW standard sirens will bring significant implications for cosmology. In this paper, by considering the coincident detections of GW and associated $γ$ ray burst (GRB), we find that only about 400 GW bright standard sirens from binary neutron star mergers could be detected in a 10-year observation of the Einstein Telescope and the THESEUS satellite mission. Based on this mock sample, we investigate the implications of GW standard sirens on the interaction between dark energy and dark matter. In our analysis, four viable interacting dark energy (IDE) models, with interaction forms $Q=3βH ρ_{\mathrm{de}}$ and $Q=3 βH ρ_{\mathrm{c}}$, are considered. Compared with the traditional EM observational data such as CMB, BAO, and SN Ia, the combination of both GW and EM observations could effectively break the degeneracies between different cosmological parameters and provide more stringent cosmological fits. We find that the GW data could play a more important role for determining the interaction in the models with $Q=3 βH ρ_{\mathrm{c}}$, compared with the models with $Q=3βH ρ_{\mathrm{de}}$. We also show that constraining IDE models with mock GW data based on different fiducial $H_0$ values yield different results, indicating that accurate determination of $H_0$ is significant for exploring the interaction between dark energy and dark matter.

astro-ph.CO↗

arXiv2023

A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method

This study aimed to improve the surface defect detection accuracy of ZrO2 ceramic bearing balls. Combined with the noise damage of the image samples, a surface defect detection method for ZrO2 ceramic bearing balls based on cartoon-texture decomposition model was proposed. Building a ZrO2 ceramic bearing ball surface defect detection system. The ZrO2 ceramic bearing ball surface defect image was decomposed by using the Gaussian curvature model and the decomposed image layer was filtered by using Winner filter and wavelet value domain filter. Then they were fused into a clear and undamaged ZrO2 ceramic bearing ball surface defect image and detected. The experimental results show that the image denoising method of ZrO2 ceramic bearing ball surface defect based on cartoon-texture decomposition model can denoise while retaining the image details. The PSNR of image is 34.1 dB, the SSIM is 0.9476, the detection accuracy is 95.8%, and the detection speed of a single defect image is 191ms / img. This method can effectively improve the efficiency and accuracy of ZrO2 ceramic bearing ball surface defect detection.

physics.ins-det↗

arXiv2023

Slicing-free Inverse Regression in High-dimensional Sufficient Dimension Reduction

Sliced inverse regression (SIR, Li 1991) is a pioneering work and the most recognized method in sufficient dimension reduction. While promising progress has been made in theory and methods of high-dimensional SIR, two remaining challenges are still nagging high-dimensional multivariate applications. First, choosing the number of slices in SIR is a difficult problem, and it depends on the sample size, the distribution of variables, and other practical considerations. Second, the extension of SIR from univariate response to multivariate is not trivial. Targeting at the same dimension reduction subspace as SIR, we propose a new slicing-free method that provides a unified solution to sufficient dimension reduction with high-dimensional covariates and univariate or multivariate response. We achieve this by adopting the recently developed martingale difference divergence matrix (MDDM, Lee & Shao 2018) and penalized eigen-decomposition algorithms. To establish the consistency of our method with a high-dimensional predictor and a multivariate response, we develop a new concentration inequality for sample MDDM around its population counterpart using theories for U-statistics, which may be of independent interest. Simulations and real data analysis demonstrate the favorable finite sample performance of the proposed method.

stat.ME↗

arXiv2023

Chemical tuning of a honeycomb magnet through a critical point

BaCo2(AsO4)2 (BCAO) has seen extensive study since its initial identification as a proximate Kitaev quantum spin liquid candidate. Thought to be described by the highly anisotropic XXZ-J_1-J_3 model, the ease with which magnetic order is suppressed in the system indicates proximity to a spin liquid phase. Upon chemical tuning via partial arsenic substitution with vanadium, we show an initial suppression of long-range incommensurate order in the BCAO system to T = 3.0 K, followed by increased spin freezing at higher substitution levels. Between these two regions, at around 10% substitution, the system is shown to pass through a critical point where the competing J_1/J_3 exchange interactions become more balanced, producing a more complex magnetic ground state, likely stabilized by quantum fluctuations. This state shows how slight compositional change in magnetically-frustrated systems may be leveraged to tune ground state degeneracies and potentially realize a quantum spin liquid state.

cond-mat.str-el↗

arXiv2023

Discrimination of pp solar neutrinos and $^{14}$C double pile-up events in a large-scale LS detector

As a unique probe, precision measurement of \textit{pp} solar neutrinos is important for studying the Sun's energy mechanism, monitoring thermodynamic equilibrium, and studying neutrino oscillation in the vacuum-dominated region. For a large-scale liquid scintillator detector, one bottleneck for \textit{pp} solar neutrino detection comes from pile-up events of intrinsic $^{14}$C decays. This paper presents a few approaches to discriminate \textit{pp} solar neutrinos and $^{14}$C pile-up events by considering the difference in their time and spatial distributions. In this work, a Geant4-based Monte Carlo simulation is constructed. Then multivariate analysis and deep learning technology were adopted respectively to investigate the capability of $^{14}$C pile-up reduction. As a result, the BDTG model and VGG network showed good performance in discriminating \textit{pp} solar neutrinos and $^{14}$C double pile-up events. Their signal significance can achieve 10.3 and 15.6 using only one day of statistics. In this case, the signal efficiency is 51.1\% for discrimination using the BDTG model when rejecting 99.18\% $^{14}$C double pile-up events, and the signal efficiency is 42.7\% for the case using the VGG network when rejecting 99.81\% $^{14}$C double pile-up events.

hep-ex↗