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Xiao Guo

Publications and source records attributed to Xiao Guo.

66 records · Page 4Linked to original sources
arXiv2022

Randomized Spectral Clustering in Large-Scale Stochastic Block Models

Spectral clustering has been one of the widely used methods for community detection in networks. However, large-scale networks bring computational challenges to the eigenvalue decomposition therein. In this paper, we study the spectral clustering using randomized sketching algorithms from a statistical perspective, where we typically assume the network data are generated from a stochastic block model that is not necessarily of full rank. To do this, we first use the recently developed sketching algorithms to obtain two randomized spectral clustering algorithms, namely, the random projection-based and the random sampling-based spectral clustering. Then we study the theoretical bounds of the resulting algorithms in terms of the approximation error for the population adjacency matrix, the misclassification error, and the estimation error for the link probability matrix. It turns out that, under mild conditions, the randomized spectral clustering algorithms lead to the same theoretical bounds as those of the original spectral clustering algorithm. We also extend the results to degree-corrected stochastic block models. Numerical experiments support our theoretical findings and show the efficiency of randomized methods. A new R package called Rclust is developed and made available to the public.

cs.SI↗
arXiv2021

Discourse-level Relation Extraction via Graph Pooling

The ability to capture complex linguistic structures and long-term dependencies among words in the passage is essential for discourse-level relation extraction (DRE) tasks. Graph neural networks (GNNs), one of the methods to encode dependency graphs, have been shown effective in prior works for DRE. However, relatively little attention has been paid to receptive fields of GNNs, which can be crucial for cases with extremely long text that requires discourse understanding. In this work, we leverage the idea of graph pooling and propose to use pooling-unpooling framework on DRE tasks. The pooling branch reduces the graph size and enables the GNNs to obtain larger receptive fields within fewer layers; the unpooling branch restores the pooled graph to its original resolution so that representations for entity mention can be extracted. We propose Clause Matching (CM), a novel linguistically inspired graph pooling method for NLP tasks. Experiments on two DRE datasets demonstrate that our models significantly improve over baselines when modeling long-term dependencies is required, which shows the effectiveness of the pooling-unpooling framework and our CM pooling method.

cs.CL↗
arXiv2021

Near-Field Terahertz Nanoscopy of Coplanar Microwave Resonators

Superconducting quantum circuits are one of the leading quantum computing platforms. To advance superconducting quantum computing to a point of practical importance, it is critical to identify and address material imperfections that lead to decoherence. Here, we use terahertz Scanning Near-field Optical Microscopy (SNOM) to probe the local dielectric properties and carrier concentrations of wet-etched aluminum resonators on silicon, one of the most characteristic components of the superconducting quantum processors. Using a recently developed vector calibration technique, we extract the THz permittivity from spectroscopy in proximity to the microwave feedline. Fitting the extracted permittivity to the Drude model, we find that silicon in the etched channel has a carrier concentration greater than buffer oxide etched silicon and we explore post-processing methods to reduce the carrier concentrations. Our results show that near-field THz investigations can be applied to quantitatively evaluate and identify potential loss channels in quantum devices.

cond-mat.mes-hall↗
arXiv2021

Evolution and universality of Two-stage Kondo effect in single manganese phthalocyanine molecule transistors

Kondo effect offers an important paradigm to understand strong correlated many-body physics. Although under intensive study, some of important properties of Kondo effect, in systems where both itinerant coupling and localized coupling play significant roles, are still elusive. Here we report evolution and universality of two stage Kondo effect, the simplest form where both couplings are important using single molecule transistor devices incorporating Manganese phthalocyanine molecules. Kondo temperature T* of two-stage Kondo effect evolves linearly against effective interaction of involved two spins. Observed Kondo resonance shows universal quadratic dependence with all adjustable parameters: temperature, magnetic field and biased voltages. The difference in nonequilibrium conductance of two stage Kondo effect to spin 1/2 Kondo effect is also identified. Messages learned in this study fill in directive experimental evidence of evolution of two-stage Kondo resonance near quantum phase transition point, and help in understanding sophisticated molecular electron spectroscopy in strong correlation regime.

cond-mat.mes-hall↗
arXiv2020

Convergence and Efficiency of Different Methods to Compute the Diffraction Integral for Gravitational Lensing of Gravitational Waves

Wave optics may need to be considered when studying the lensed waveforms of gravitational waves (GWs). However, the computation of the diffraction integral (amplification factor) in wave optics is challenging and time-consuming. It is vital to develop an accurate and efficient method to calculate the amplification factor for detecting lensed GW systems. In this paper, we investigate the convergence of the diffraction integral for gravitational lensing of GWs and analyze the accuracy and efficiency of a number of numerical methods that can be used to calculate this integral, including the integral mean method, asymptotic expansion method, Levin's method, zero points integral method, etc. We further introduce a new method by combining the zero points integral and the asymptotic expansion methods to calculate the diffraction integral, which provides an efficient and accurate way to calculate the lensed waveform of GWs.

gr-qc↗
arXiv2020

CORD19STS: COVID-19 Semantic Textual Similarity Dataset

In order to combat the COVID-19 pandemic, society can benefit from various natural language processing applications, such as dialog medical diagnosis systems and information retrieval engines calibrated specifically for COVID-19. These applications rely on the ability to measure semantic textual similarity (STS), making STS a fundamental task that can benefit several downstream applications. However, existing STS datasets and models fail to translate their performance to a domain-specific environment such as COVID-19. To overcome this gap, we introduce CORD19STS dataset which includes 13,710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge. To be specific, we generated one million sentence pairs using different sampling strategies. We then used a finetuned BERT-like language model, which we call Sen-SCI-CORD19-BERT, to calculate the similarity scores between sentence pairs to provide a balanced dataset with respect to the different semantic similarity levels, which gives us a total of 32K sentence pairs. Each sentence pair was annotated by five Amazon Mechanical Turk (AMT) crowd workers, where the labels represent different semantic similarity levels between the sentence pairs (i.e. related, somewhat-related, and not-related). After employing a rigorous qualification tasks to verify collected annotations, our final CORD19STS dataset includes 13,710 sentence pairs.

cs.CL↗
arXiv2020

Moderate-Dimensional Inferences on Quadratic Functionals in Ordinary Least Squares

Statistical inferences for quadratic functionals of linear regression parameter have found wide applications including signal detection, global testing, inferences of error variance and fraction of variance explained. Classical theory based on ordinary least squares estimator works perfectly in the low-dimensional regime, but fails when the parameter dimension $p_n$ grows proportionally to the sample size $n$. In some cases, its performance is not satisfactory even when $n\ge 5p_n$. The main contribution of this paper is to develop {\em dimension-adaptive} inferences for quadratic functionals when $\lim_{n\to \infty} p_n/n=τ\in[0,1)$. We propose a bias-and-variance-corrected test statistic and demonstrate that its theoretical validity (such as consistency and asymptotic normality) is adaptive to both low dimension with $τ= 0$ and moderate dimension with $τ\in(0, 1)$. Our general theory holds, in particular, without Gaussian design/error or structural parameter assumption, and applies to a broad class of quadratic functionals covering all aforementioned applications. As a by-product, we find that the classical fixed-dimensional results continue to hold {\em if and only if} the signal-to-noise ratio is large enough, say when $p_n$ diverges but slower than $n$. Extensive numerical results demonstrate the satisfactory performance of the proposed methodology even when $p_n\ge 0.9n$ in some extreme cases. The mathematical arguments are based on the random matrix theory and leave-one-observation-out method.

math.ST↗
arXiv2020

Autonomous quadrotor obstacle avoidance based on dueling double deep recurrent Q-learning with monocular vision

The rapid development of unmanned aerial vehicles (UAV) puts forward a higher requirement for autonomous obstacle avoidance. Due to the limited payload and power supply, small UAVs such as quadrotors usually carry simple sensors and computation units, which makes traditional methods more challenging to implement. In this paper, a novel framework is demonstrated to control a quadrotor flying through crowded environments autonomously with monocular vision. The framework adopts a two-stage architecture, consisting of a sensing module and a decision module. The sensing module is based on an unsupervised deep learning method. And the decision module uses dueling double deep recurrent Q-learning to eliminate the adverse effects of limited observation capacity of an on-board monocular camera. The framework enables the quadrotor to realize autonomous obstacle avoidance without any prior environment information or labeled datasets for training. The trained model shows a high success rate in the simulation and a good generalization ability for transformed scenarios.

eess.SY↗
arXiv2019

Differentially Private Precision Matrix Estimation

In this paper, we study the problem of precision matrix estimation when the dataset contains sensitive information. In the differential privacy framework, we develop a differentially private ridge estimator by perturbing the sample covariance matrix. Then we develop a differentially private graphical lasso estimator by using the alternating direction method of multipliers (ADMM) algorithm. The theoretical results and empirical results that show the utility of the proposed methods are also provided.

stat.ML↗
arXiv2019

Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies

Human motion prediction from motion capture data is a classical problem in the computer vision, and conventional methods take the holistic human body as input. These methods ignore the fact that, in various human activities, different body components (limbs and the torso) have distinctive characteristics in terms of the moving pattern. In this paper, we argue local representations on different body components should be learned separately and, based on such idea, propose a network, Skeleton Network (SkelNet), for long-term human motion prediction. Specifically, at each time-step, local structure representations of input (human body) are obtained via SkelNet's branches of component-specific layers, then the shared layer uses local spatial representations to predict the future human pose. Our SkelNet is the first to use local structure representations for predicting the human motion. Then, for short-term human motion prediction, we propose the second network, named as Skeleton Temporal Network (Skel-TNet). Skel-TNet consists of three components: SkelNet and a Recurrent Neural Network, they have advantages in learning spatial and temporal dependencies for predicting human motion, respectively; a feed-forward network that outputs the final estimation. Our methods achieve promising results on the Human3.6M dataset and the CMU motion capture dataset.

cs.CV↗