arXiv subjects

Xiao Guo

Publications and source records attributed to Xiao Guo.

At least 55 records · Page 3Linked to original sources

arXiv2024

Detection Rate of Galaxy Cluster Lensed Stellar Binary Black Hole Mergers by the Third-generation Gravitational Wave Detectors

Gravitational waves (GWs) from stellar binary black hole (sBBH) mergers can be strongly gravitational lensed by intervening galaxies/galaxy clusters. Only a few works investigated the cluster-lensed sBBH mergers by adopting oversimplified models, while galaxy-lensed ones were intensively studied. In this paper, we estimate the detection rate of cluuster-lensed sBBH mergers with the third-generation GW detectors and its dependence on the lens models. We adopt detailed modeling of galaxy cluster lenses by using the mock clusters in the Synthetic Sky Catalog for Dark Energy Science with LSST (CosmoDC2) and/or approximations of the pseudo-Jaffe profile or an eccentric Navarro-Frenk-White dark matter halo plus a bright central galaxy with singular isothermal sphere profile. Considering the formation of sBBH mergers dominates by the channel of evolution of massive binary stars (EMBS), we find that the detection rate of cluster-lensed sBBHs is $\sim5-84$ yr$^{-1}$, depending on the adopted lens model and uncertainty in the merger rate density, and it is about $\sim{13_{-2.0}^{+28}}$yr$^{-1}$ if adopting relatively more realistic galaxy clusters with central main and member galaxies in the CosmoDC2 catalog, close to the estimated detection rate of sBBH mergers lensed by galaxies. In addition, we also consider the case that the production of sBBH mergers dominated by the dynamical interactions in dense stellar systems. We find that the detection rate of cluster-lensed sBBHs if from the dynamical channel is about $1.5$ times larger than that from the EMBS channel and the redshift distribution of former peaking at a higher redshift ($\sim3$) compared with that from latter ($\sim2$).

astro-ph.HE↗

arXiv2024

Privacy-Preserving Community Detection for Locally Distributed Multiple Networks

Modern multi-layer networks are commonly stored and analyzed in a local and distributed fashion because of the privacy, ownership, and communication costs. The literature on the model-based statistical methods for community detection based on these data is still limited. This paper proposes a new method for consensus community detection and estimation in a multi-layer stochastic block model using locally stored and computed network data with privacy protection. A novel algorithm named privacy-preserving Distributed Spectral Clustering (ppDSC) is developed. To preserve the edges' privacy, we adopt the randomized response (RR) mechanism to perturb the network edges, which satisfies the strong notion of differential privacy. The ppDSC algorithm is performed on the squared RR-perturbed adjacency matrices to prevent possible cancellation of communities among different layers. To remove the bias incurred by RR and the squared network matrices, we develop a two-step bias-adjustment procedure. Then we perform eigen-decomposition on the debiased matrices, aggregation of the local eigenvectors using an orthogonal Procrustes transformation, and k-means clustering. We provide theoretical analysis on the statistical errors of ppDSC in terms of eigen-vector estimation. In addition, the blessings and curses of network heterogeneity are well-explained by our bounds.

cs.SI↗

arXiv2024

Common Sense Reasoning for Deepfake Detection

State-of-the-art deepfake detection approaches rely on image-based features extracted via neural networks. While these approaches trained in a supervised manner extract likely fake features, they may fall short in representing unnatural `non-physical' semantic facial attributes -- blurry hairlines, double eyebrows, rigid eye pupils, or unnatural skin shading. However, such facial attributes are easily perceived by humans and used to discern the authenticity of an image based on human common sense. Furthermore, image-based feature extraction methods that provide visual explanations via saliency maps can be hard to interpret for humans. To address these challenges, we frame deepfake detection as a Deepfake Detection VQA (DD-VQA) task and model human intuition by providing textual explanations that describe common sense reasons for labeling an image as real or fake. We introduce a new annotated dataset and propose a Vision and Language Transformer-based framework for the DD-VQA task. We also incorporate text and image-aware feature alignment formulation to enhance multi-modal representation learning. As a result, we improve upon existing deepfake detection models by integrating our learned vision representations, which reason over common sense knowledge from the DD-VQA task. We provide extensive empirical results demonstrating that our method enhances detection performance, generalization ability, and language-based interpretability in the deepfake detection task.

cs.CV↗

arXiv2024

Limit Results for Estimation of Connectivity Matrix in Multi-layer Stochastic Block Models

Multi-layer networks arise naturally in various domains including biology, finance and sociology, among others. The multi-layer stochastic block model (multi-layer SBM) is commonly used for community detection in the multi-layer networks. Most of current literature focuses on statistical consistency of community detection methods under multi-layer SBMs. However, the asymptotic distributional properties are also indispensable which play an important role in statistical inference. In this work, we aim to study the estimation and asymptotic properties of the layer-wise scaled connectivity matrices in the multi-layer SBMs. We develop a novel and efficient method to estimate the scaled connectivity matrices. Under the multi-layer SBM and its variant multi-layer degree-corrected SBM, we establish the asymptotic normality of the estimated matrices under mild conditions, which can be used for interval estimation and hypothesis testing. Simulations show the superior performance of proposed method over existing methods in two considered statistical inference tasks. We also apply the method to a real dataset and obtain interpretable results.

math.ST↗

arXiv2024

Spectral co-Clustering in Multi-layer Directed Networks

Modern network analysis often involves multi-layer network data in which the nodes are aligned, and the edges on each layer represent one of the multiple relations among the nodes. Current literature on multi-layer network data is mostly limited to undirected relations. However, direct relations are more common and may introduce extra information. This study focuses on community detection (or clustering) in multi-layer directed networks. To take into account the asymmetry, a novel spectral-co-clustering-based algorithm is developed to detect co-clusters, which capture the sending patterns and receiving patterns of nodes, respectively. Specifically, the eigendecomposition of the debiased sum of Gram matrices over the layer-wise adjacency matrices is computed, followed by the k-means, where the sum of Gram matrices is used to avoid possible cancellation of clusters caused by direct summation. Theoretical analysis of the algorithm under the multi-layer stochastic co-block model is provided, where the common assumption that the cluster number is coupled with the rank of the model is relaxed. After a systematic analysis of the eigenvectors of the population version algorithm, the misclassification rates are derived, which show that multi-layers would bring benefits to the clustering performance. The experimental results of simulated data corroborate the theoretical predictions, and the analysis of a real-world trade network dataset provides interpretable results.

math.ST↗

arXiv2024

Testing an exact diffraction formula with gravitational wave source lensed by a supermassive black hole in binary systems

When it comes to long-wavelength gravitational waves (GWs), diffraction effect becomes significant when these waves are lensed by celestial bodies. Typically, the traditional diffraction integral formula neglects large-angle diffraction, which is often adequate for most of cases. Nonetheless, there are specific scenarios, such as when a GW source is lensed by a supermassive black hole in a binary system, where the lens and source are in close proximity, where large-angle diffraction can play a crucial role. In our prior research, we have introduced an exact, general diffraction integral formula that accounts for large-angle diffraction as well. This paper explores the disparities between this exact diffraction formula and the traditional, approximate one under various special conditions. Our findings indicate that, under specific parameters, such as a lens-source distance of $D_{\rm LS}=0.1$\,AU and a lens mass of $M_{\rm L}=4\times10^{6}M_\odot$, the amplification factor for the exact diffraction formula is notably smaller than that of the approximate formula, differing by a factor of approximately $r_F\simeq0.806$. This difference is substantial enough to be detectable. Furthermore, our study reveals that the proportionality factor $r_F$ gradually increases from 0.5 to 1 as $D_{\rm LS}$ increases, and decreases as $M_{\rm L}$ increases. Significant differences between the exact and approximate formulas are observable when $D_{\rm LS}\lesssim0.2$ AU (assuming $M_{\rm L}=4\times10^{6}M_\odot$) or when $M_{\rm L}\gtrsim2\times10^6M_\odot$ (assuming $D_{\rm LS}=0.1$ AU). These findings suggest that there is potential to validate our general diffraction formula through future GW detections.

astro-ph.HE↗

arXiv2023

Terahertz nanospectroscopy of plasmon polaritons for the evaluation of doping in quantum devices

Terahertz (THz) waves are a highly sensitive probe of free carrier concentrations in semiconducting materials. However, most experiments operate in the far-field, which precludes the observation of nanoscale features that affect the material response. Here, we demonstrate the use of nanoscale THz plasmon polaritons as an indicator of surface quality in prototypical quantum devices properties. Using THz near-field hyperspectral measurements, we observe polaritonic features in doped silicon near a metal-semiconductor interface. The presence of the THz surface plasmon polariton indicates the existence of a thin film doped layer on the device. Using a multilayer extraction procedure utilising vector calibration, we quantitatively probe the doped surface layer and determine its thickness and complex permittivity. The recovered multilayer characteristics match the dielectric conditions necessary to support the THz surface plasmon polariton. Applying these findings to superconducting resonators, we show that etching of this doped layer leads to an increase of the quality factor as determined by cryogenic measurements. This study demonstrates that THz scattering-type scanning near-field optical microscopy (s-SNOM) is a promising diagnostic tool for characterization of surface dielectric properties of quantum devices.

physics.optics↗

arXiv2023

On the detection of the electromagnetic counterparts from lensed gravitational wave events by binary neutron star mergers

Future ground-based gravitational wave (GW) detectors, i.e., Einstein telescope (ET) and Cosmic Explorer (CE), are expected to detect a significant number of lensed binary neutron star (BNS) mergers, which may provide a unique tool to probe cosmology. In this paper, we investigate the detectability of the optical/infrared electromagnetic (EM) counterparts (kilonovae/afterglows) from these lensed BNS mergers by future GW detectors and EM telescopes using simple kilonova, afterglow, and lens models. ET and CE are expected to detect $\sim5.32^{+26.1}_{-5.10}$ and $67.3^{+332}_{-64.7}$ lensed BNS mergers per year. We find that the EM counterparts associated with all these mergers will be detectable by an all sky-survey in the H-band with the limiting magnitude $m_{\textrm{lim}}\gtrsim27$, while the detectable fraction is $\lesssim0.4\%$ in the g-/z-band if with $m_{\textrm{lim}}\lesssim24$. Generally it is more efficient to search the lensed EM counterparts by adopting the infrared bands than the optical/UV bands with the same $m_{\textrm{lim}}$. Future telescopes like Vera C. Rubin Observatory, China Space Station Telescope, and Euclid can hardly detect the EM counterparts of even one lensed BNS merger. Roman Space Telescope (RST) and James Webb Space Telescope (JWST) have the capability to detect about a few or more such events per year. Moreover, the time delays and separations between the lensed image pairs are typically in the ranges from minutes to months and from $0.1$ to $1$\,arcsec, suggesting that both the GW and EM images of most lensed BNS mergers can be well resolved by not only CE/ET in the time domain but also RST/JWST spatially.

astro-ph.HE↗

arXiv2023

FedPower: Privacy-Preserving Distributed Eigenspace Estimation

Eigenspace estimation is fundamental in machine learning and statistics, which has found applications in PCA, dimension reduction, and clustering, among others. The modern machine learning community usually assumes that data come from and belong to different organizations. The low communication power and the possible privacy breaches of data make the computation of eigenspace challenging. To address these challenges, we propose a class of algorithms called \textsf{FedPower} within the federated learning (FL) framework. \textsf{FedPower} leverages the well-known power method by alternating multiple local power iterations and a global aggregation step, thus improving communication efficiency. In the aggregation, we propose to weight each local eigenvector matrix with {\it Orthogonal Procrustes Transformation} (OPT) for better alignment. To ensure strong privacy protection, we add Gaussian noise in each iteration by adopting the notion of \emph{differential privacy} (DP). We provide convergence bounds for \textsf{FedPower} that are composed of different interpretable terms corresponding to the effects of Gaussian noise, parallelization, and random sampling of local machines. Additionally, we conduct experiments to demonstrate the effectiveness of our proposed algorithms.

stat.ML↗

arXiv2023

Multi-domain Learning for Updating Face Anti-spoofing Models

In this work, we study multi-domain learning for face anti-spoofing(MD-FAS), where a pre-trained FAS model needs to be updated to perform equally well on both source and target domains while only using target domain data for updating. We present a new model for MD-FAS, which addresses the forgetting issue when learning new domain data, while possessing a high level of adaptability. First, we devise a simple yet effective module, called spoof region estimator(SRE), to identify spoof traces in the spoof image. Such spoof traces reflect the source pre-trained model's responses that help upgraded models combat catastrophic forgetting during updating. Unlike prior works that estimate spoof traces which generate multiple outputs or a low-resolution binary mask, SRE produces one single, detailed pixel-wise estimate in an unsupervised manner. Secondly, we propose a novel framework, named FAS-wrapper, which transfers knowledge from the pre-trained models and seamlessly integrates with different FAS models. Lastly, to help the community further advance MD-FAS, we construct a new benchmark based on SIW, SIW-Mv2 and Oulu-NPU, and introduce four distinct protocols for evaluation, where source and target domains are different in terms of spoof type, age, ethnicity, and illumination. Our proposed method achieves superior performance on the MD-FAS benchmark than previous methods. Our code and newly curated SIW-Mv2 are publicly available.

cs.CV↗

arXiv2023

On the efficacy of higher-order spectral clustering under weighted stochastic block models

Higher-order structures of networks, namely, small subgraphs of networks (also called network motifs), are widely known to be crucial and essential to the organization of networks. There has been a few work studying the community detection problem -- a fundamental problem in network analysis, at the level of motifs. In particular, higher-order spectral clustering has been developed, where the notion of motif adjacency matrix is introduced as the input of the algorithm. However, it remains largely unknown that how higher-order spectral clustering works and when it performs better than its edge-based counterpart. To elucidate these problems, we investigate higher-order spectral clustering from a statistical perspective. In particular, we theoretically study the clustering performance of higher-order spectral clustering under a weighted stochastic block model and compare the resulting bounds with the corresponding results of edge-based spectral clustering. It turns out that when the network is dense with weak signal of weights, higher-order spectral clustering can really lead to the performance gain in clustering. We also use simulations and real data experiments to support the findings.

stat.ME↗

arXiv2023

Hierarchical Fine-Grained Image Forgery Detection and Localization

Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine-grained formulation for IFDL representation learning. Specifically, we first represent forgery attributes of a manipulated image with multiple labels at different levels. Then we perform fine-grained classification at these levels using the hierarchical dependency between them. As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation. Our proposed IFDL framework contains three components: multi-branch feature extractor, localization and classification modules. Each branch of the feature extractor learns to classify forgery attributes at one level, while localization and classification modules segment the pixel-level forgery region and detect image-level forgery, respectively. Lastly, we construct a hierarchical fine-grained dataset to facilitate our study. We demonstrate the effectiveness of our method on $7$ different benchmarks, for both tasks of IFDL and forgery attribute classification. Our source code and dataset can be found: \href{https://github.com/CHELSEA234/HiFi_IFDL}{github.com/CHELSEA234/HiFi-IFDL}.

cs.CV↗

arXiv2023

Near-field localization of the boson peak on tantalum films for superconducting quantum devices

Superconducting circuits are among the most advanced quantum computing technologies, however their performance is currently limited by losses found in surface oxides and disordered materials. Here, we identify and spatially localize a near-field signature of loss centers on tantalum films using terahertz scattering-type scanning near-field optical microscopy (s-SNOM). Making use of terahertz nanospectroscopy, we observe a localized excess vibrational mode around 0.5 THz and identify this resonance as the boson peak, a signature of amorphous materials. Grazing-incidence wide-angle x-ray scattering (GIWAXS) shows that oxides on freshly solvent-cleaned samples are amorphous, whereas crystalline phases emerge after aging in air. By localizing defect centers at the nanoscale, our characterization techniques and results will inform the optimization of fabrication procedures for new low-loss superconducting circuits.

cond-mat.mes-hall↗

arXiv2023

Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks

Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance understanding of the disorder mechanisms and early intervention. However, due to the small sample size and the high dimension of available neuroimaging data, the performance of existing methods is often limited. Multi-site data could enhance the statistical power and sample size, while they are often subject to inter-site heterogeneity and data-sharing policies. In this paper, we propose a federated joint estimator, NOTEARS-PFL, for simultaneous learning of multiple Bayesian networks (BNs) with continuous optimization, to identify disease-induced alterations in MDD patients. We incorporate information shared between sites and site-specific information into the proposed federated learning framework to learn personalized BN structures by introducing the group fused lasso penalty. We develop the alternating direction method of multipliers, where in the local update step, the neuroimaging data is processed at each local site. Then the learned network structures are transmitted to the center for the global update. In particular, we derive a closed-form expression for the local update step and use the iterative proximal projection method to deal with the group fused lasso penalty in the global update step. We evaluate the performance of the proposed method on both synthetic and real-world multi-site rs-fMRI datasets. The results suggest that the proposed NOTEARS-PFL yields superior effectiveness and accuracy than the comparable methods.

cs.LG↗

arXiv2022

On Detecting Nearby Nano-Hertz Gravitational Wave Sources via Pulsar Timing Arrays

Massive binary black holes (MBBHs) in nearby galactic centers, if any, may be nano-Hertz gravitational wave (GW) sources for pulsar timing arrays (PTAs) to detect. Normally the objective GWs for PTA experiments are approximated as plane waves because its sources are presumably located faraway. For nearby GW sources, however, this approximation may be inaccurate due to the curved GW wave front and the GW strength changes along the paths of PTA pulsar pulses. In this paper, we analyze the near-field effect in the PTA detection of nearby sources and find it is important if the source distance is less than a few tens Mpc, and ignoring this effect may lead to a significant signal-to-noise underestimation especially when the source distance is comparable to the pulsar distances. As examples, we assume a nano-Hertz MBBH source located at either the Galactic Center (GC) or the Large Magellanic Cloud (LMC) according to the observational constraints/hints on the MBBH parameter space, and estimate its detectability by current/future PTAs. We find that the GC MBBH may be detectable by the Square Kilometer Array (SKA) PTA. It is challenging for detecting the LMC MBBH; however, if a number ($N\gtrsim10$) of stable millisecond pulsars can be found in the LMC center, the MBBH may be detectable via a PTA formed by these pulsars. We further illustrate the near-field effects on the PTA detection of an isotropic GW background contributed mainly by nearby GW sources, and the resulting angular correlation is similar to the Hellings-Downs curve.

astro-ph.HE↗

arXiv2022

Probing the nature of dark matter via gravitational waves lensed by small dark matter halos

Dark matter (DM) occupies the majority of matter content in the universe and is probably cold (CDM). However, modifications to the standard CDM model may be required by the small-scale observations, and DM may be self-interacting (SIDM) or warm (WDM). Here we show that the diffractive lensing of gravitational waves (GWs) from binary black hole mergers by small halos ($\sim10^3-10^6M_\odot$; mini-halos) may serve as a clean probe to the nature of DM, free from the contamination of baryonic processes in the DM studies based on dwarf/satellite galaxies. The expected lensed GW signals and event rates resulting from CDM, WDM, and SIDM models are significantly different from each other, because of the differences in halo density profiles and abundances predicted by these models. We estimate the detection rates of such lensed GW events for a number of current and future GW detectors, such as the Laser Interferometer Gravitational Observatories (LIGO), the Einstein Telescope (ET), the Cosmic Explorer (CE), Gravitational-wave Lunar Observatory for Cosmology (GLOC), the Deci-Hertz Interferometer Gravitational Wave Observatory (DECIGO), and the Big Bang Observer (BBO). We find that GLOC may detect one such events per year assuming the CDM model, DECIGO (BBO) may detect more than several (hundreds of) such events per year, by assuming the CDM, WDM (with mass $>30$\,keV) or SIDM model, suggesting that the DM nature may be strongly constrained by DECIGO and BBO via the detection of diffractive lensed GW events by mini-halos. Other GW detectors are unlikely to detect a significant number of such events within a limited observational time period. However, if the inner slope of the mini-halo density profile is sufficiently steeper than the Navarro-Frenk-White (NFW) profile, e.g., the pseudo-Jaffe profile, one may be able to detect one to more than hundred such GW events by ET and CE.

astro-ph.CO↗

arXiv2022

Orbital hybridization and electrostatic interaction in a double molecule transistor

Understanding the intermolecular interactions and utilize these interactions to effectively control the transport behavior of single molecule is the key step from single molecule device to molecular circuits1-6. Although many single molecule detection techniques are used to detect the molecular interaction at single-molecule level1,4,5,7,8, probing and tuning the intermolecular interaction all by electrical approaches has not been demonstrated. In this work, we successful assemble a double molecule transistor incorporating two manganese phthalocyanine molecules, on which we probe and tune the interaction in situ by implementing electrical manipulation on molecular orbitals using gate voltage. Orbital levels of the two molecules couple to each other and couple to the universal gate differently. Electrostatic interaction is observed when single electron changing in one molecule alters the transport behavior of the other, providing the information about the dynamic process of electron sequent tunneling through a molecule. Orbital hybridization is found when two orbital levels are put into degeneracy under non-equilibrium condition, making the tunneling electrons no longer localized to a specific molecule but shared by two molecules, offering a new mechanism to control charge transfer between non-covalent molecules. Current work offer a forelook into working principles of functional electrical unit based on single molecules.

cond-mat.mes-hall↗

arXiv2022

Randomized spectral co-clustering for large-scale directed networks

Directed networks are broadly used to represent asymmetric relationships among units. Co-clustering aims to cluster the senders and receivers of directed networks simultaneously. In particular, the well-known spectral clustering algorithm could be modified as the spectral co-clustering to co-cluster directed networks. However, large-scale networks pose great computational challenges to it. In this paper, we leverage sketching techniques and derive two randomized spectral co-clustering algorithms, one \emph{random-projection-based} and the other \emph{random-sampling-based}, to accelerate the co-clustering of large-scale directed networks. We theoretically analyze the resulting algorithms under two generative models -- the stochastic co-block model and the degree-corrected stochastic co-block model, and establish their approximation error rates and misclustering error rates, indicating better bounds than the state-of-the-art results of co-clustering literature. Numerically, we design and conduct simulations to support our theoretical results and test the efficiency of the algorithms on real networks with up to millions of nodes. A publicly available R package \textsf{RandClust} is developed for better usability and reproducibility of the proposed methods.

stat.ML↗