arXiv subjects

Ying Guo

Publications and source records attributed to Ying Guo.

At least 55 records · Page 3Linked to original sources

arXiv2021

Effective and Interpretable fMRI Analysis via Functional Brain Network Generation

Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions. However, existing functional brain networks are noisy and unaware of downstream prediction tasks, while also incompatible with recent powerful machine learning models of GNNs. In this work, we develop an end-to-end trainable pipeline to extract prominent fMRI features, generate brain networks, and make predictions with GNNs, all under the guidance of downstream prediction tasks. Preliminary experiments on the PNC fMRI data show the superior effectiveness and unique interpretability of our framework.

cs.LG↗

arXiv2021

Joint Learning of Multiple Differential Networks with fMRI data for Brain Connectivity Alteration Detection

In this study we focus on the problem of joint learning of multiple differential networks with function Magnetic Resonance Imaging (fMRI) data sets from multiple research centers. As the research centers may use different scanners and imaging parameters, joint learning of differential networks with fMRI data from different centers may reflect the underlying mechanism of neurological diseases from different perspectives while capturing the common structures. We transform the task as a penalized logistic regression problem, and exploit sparse group Minimax Concave Penalty (gMCP) to induce common structures among multiple differential networks and the sparse structures of each differential network. To further enhance the empirical performance, we develop an ensemble-learning procedure. We conduct thorough simulation study to assess the finite-sample performance of the proposed method and compare with state-of-the-art alternatives. We apply the proposed method to analyze fMRI datasets related with Attention Deficit Hyperactivity Disorder from various research centers. The identified common hub nodes and differential interaction patterns coincides with the existing experimental studies.

stat.ME↗

arXiv2020

Enhancing discrete-modulated continuous-variable measurement-device-independent quantum key distribution via quantum catalysis

The discrete modulation can make up for the shortage of transmission distance in measurement-device-independent continuous-variable quantum key distribution (MDI-CVQKD) that has an unique advantage against all side-channel attacks but also challenging for the further performance improvement. Here we suggest a quantum catalysis (QC) approach for enhancing the performance of the discrete-modulated (DM) MDI-CVQKD in terms of the achievable secret key rate and lengthening the maximal transmission distance. The numerical simulation results show that the QC-based MDI-CVQKD with discrete modulation that involves a zero-photon catalysis (ZPC) operation can not only obtain a higher secret key rate than the original DM protocol, but also contributes to the reasonable increase of the corresponding optimal variance. As for the extreme asymmetric and symmetric cases, the secret key rate and maximal transmission distance of the ZPC-involved DM MDI-CVQKD system can be further improved under the same parameters. This approach enables the system to tolerate lower reconciliation efficiency, which will promote the practical implementations with state-of-art technology.

quant-ph↗

arXiv2020

HINT: A Hierarchical Independent Component Analysis Toolbox for Investigating Brain Functional Networks using Neuroimaging Data

Independent component analysis (ICA) is a popular tool for investigating brain organization in neuroscience research. In fMRI studies, an important goal is to study how brain networks are modulated by subjects' clinical and demographic variables. Existing ICA methods and toolboxes don't incorporate subjects' covariates effects in ICA estimation of brain networks, which potentially leads to loss in accuracy and statistical power in detecting brain network differences between subjects' groups. We introduce a Matlab toolbox, HINT (Hierarchical INdependent component analysis Toolbox), that provides a hierarchical covariate-adjusted ICA (hc-ICA) for modeling and testing covariate effects and generates model-based estimates of brain networks on both the population- and individual-level. HINT provides a user-friendly Matlab GUI that allows users to easily load images, specify covariate effects, monitor model estimation via an EM algorithm, specify hypothesis tests, and visualize results. HINT also has a command line interface which allows users to conveniently run and reproduce the analysis with a script. HINT implements a new multi-level probabilistic ICA model for group ICA. It provides a statistically principled ICA modeling framework for investigating covariate effects on brain networks. HINT can also generate and visualize model-based network estimates for user-specified subject groups, which greatly facilitates group comparisons.

stat.CO↗

arXiv2020

Linear-Time Parameterized Algorithms with Limited Local Resources

We propose a new (theoretical) computational model for the study of massive data processing with limited computational resources. Our model measures the complexity of reading the very large data sets in terms of the data size N and analyzes the computational cost in terms of a parameter k that characterizes the computational power provided by limited local computing resources. We develop new algorithmic techniques that implement algorithms for solving well-known computational problems on the proposed model. In particular, we present an algorithm that finds a k-matching in a general unweighted graph in time O(N + k^{2.5}) and an algorithm that constructs a maximum weighted k-matching in a general weighted graph in time O(N + k^3 log k). Both algorithms have their space complexity bounded by O(k^2).

cs.DS↗

arXiv2020

Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution

Discretely-modulated continuous-variable quantum key distribution (CVQKD) is more suitable for long-distance transmission compared with its Gaussian-modulated CVQKD counterpart. However, its security can only be guaranteed when modulation variance is very small, which limits its further development. To solve this problem, in this work, we propose a novel scheme for discretely-modulated CVQKD using multi-label learning technology, called multi-label learning-based CVQKD (ML-CVQKD). In particular, the proposed scheme divides the whole quantum system into state learning and state prediction. The former is used for training and estimating quantum classifier, and the latter is used for generating final secret key. A quantum multi-label classification (QMLC) algorithm is also designed as an embedded classifier for distinguishing coherent state. Feature extraction for coherent state and related machine learning-based metrics for the quantum classifier are successively suggested. Security analysis shows that QMLC-embedded ML-CVQKD is able to immune intercept-resend attack so that small modulation variance is no longer compulsively required, thereby improving the performance of discretely-modulated CVQKD system.

quant-ph↗

arXiv2019

Continuous-variable measurement-device-independent quantum key distribution via quantum catalysis

The continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) is a promising candidate for the immunity to side-channel attacks, but unfortunately seems to face the limitation of transmission distance in contrast to discrete-variable (DV) counterpart. In this paper, we suggest a method of improving the performance of CV-MDI-QKD involving the achievable secret key rate and transmission distance by using zero-photon catalysis (ZPC), which is indeed a noiseless attenuation process. The numerical stimulation results show that the transmission distance of ZPC-based CV-MDI-QKD under the extreme asymmetric case is better than that of the original protocol. Attractively, in contrast to the previous single-photon subtraction (SPS)-based CV-MDI-QKD, the proposed scheme enables a higher secret key rate and a longer transmission distance. In particular, the ZPC-based CV-MDI-QKD can tolerate more imperfections of detectors than both the original protocol and the SPS-based CV-MDI-QKD.

quant-ph↗

arXiv2019

Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals---including known population brain networks and subject-specific signals---are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability.

stat.AP↗

arXiv2019

Bayesian Joint Modeling of Multiple Brain Functional Networks

Brain function is organized in coordinated modes of spatio-temporal activity (functional networks) exhibiting an intrinsic baseline structure with variations under different experimental conditions. Existing approaches for uncovering such network structures typically do not explicitly model shared and differential patterns across networks, thus potentially reducing the detection power. We develop an integrative modeling approach for jointly modeling multiple brain networks across experimental conditions. The proposed Bayesian Joint Network Learning approach develops flexible priors on the edge probabilities involving a common intrinsic baseline structure and differential effects specific to individual networks. Conditional on these edge probabilities, connection strengths are modeled under a Bayesian spike and slab prior on the off-diagonal elements of the inverse covariance matrix. The model is fit under a posterior computation scheme based on Markov chain Monte Carlo. Numerical simulations illustrate that the proposed joint modeling approach has increased power to detect true differential edges while providing adequate control on false positives and achieving greater accuracy in the estimation of edge strengths compared to existing methods. An application of the method to fMRI Stroop task data provides unique insights into brain network alterations between cognitive conditions which existing graphical modeling techniques failed to reveal.

stat.ME↗

arXiv2018

Long-distance continuous-variable quantum key distribution using separable Gaussian states

Continuous-variable quantum key distribution (CVQKD) is considered to be an alternative to classical cryptography for secure communication. However, its transmission distance is restricted to metropolitan areas, given that it is affected by the channel excess noise and losses. In this paper, we present a scheme for implementing long-distance CVQKD using separable Gaussian states. This tunable QKD protocol requires separable Gaussian states, which are squeezed and displaced, along with the assistance of classical communication and available linear optics compoments. This protocol originates from the entanglement of one mode and the auxiliary mode used for distribution, which is first destroyed by local correlated noises and restored subsequently by the interference of the auxillary mode with the second distant separable correlated mode. The displacement matrix is organized by two six-dimensional vectors and is finally fixed by the separability of the tripartite system. The separability between the ancilla and Alice and Bob's system mitigates the enemy's eavesdropping, leading to tolerating higher excess noise and achieving longer transmission distance.

quant-ph↗

arXiv2018

Continuous-variable quantum key distribution with non-Gaussian quantum catalysis

The non-Gaussian operation can be used not only to enhance and distill the entanglement between Gaussian entangled states, but also to improve quantum communications. In this paper, we propose an non-Gaussian continuous-variable quantum key distribution (CVQKD) by using quantum catalysis (QC), which is an intriguing non-Gaussian operation in essence that can be implemented with current technologies. We perform quantum catalysis on both ends of the Einstein-Podolsky-Rosen (EPR) pair prepared by a sender, Alice, and find that for the single-photon QC-CVQKD, the bilateral symmetrical quantum catalysis (BSQC) performs better than the single-side quantum catalysis (SSQC). Attributing to characteristics of integral within an ordered product (IWOP) of operators, we find that the quantum catalysis operation can improve the entanglement property of Gaussian entangled states by enhancing the success probability of non-Gaussian operation, leading to the improvement of the QC-CVQKD system. As a comparison, the QC-CVQKD system involving zero-photon and single-photon quantum catalysis outperforms the previous non-Gaussian CVQKD scheme via photon subtraction in terms of secret key rate, maximal transmission distance and tolerable excess noise.

quant-ph↗

arXiv2018

A Differential Degree Test for Comparing Brain Networks

Recently, graph theory has become a popular method for characterizing brain functional organization. One important goal in graph theoretical analysis of brain networks is to identify network differences across disease types or conditions. Typical approaches include massive univariate testing of each edge or comparisons of local and/or global network metrics to identify deviations in topological organization. Some limitations of these methods include low statistical power due to the large number of comparisons and difficulty attributing overall differences in networks to local variations in brain function. We propose a novel differential degree test (DDT) to identify brain regions incident to a large number of differentially weighted edges across two populations. The proposed test could help detect key brain locations involved in diseases by demonstrating significantly altered neural connections. We achieve this by generating an appropriate set of null networks which are matched on the first and second moments of the observed difference network using the Hirschberger-Qi-Steuer (HQS) algorithm. This formulation permits separation of the network's true topology from the nuisance topology which is induced by the correlation measure and may drive inter-regional connectivity in ways unrelated to the brain function. Simulations indicate that the proposed approach routinely outperforms competing methods in detecting differentially connected regions of interest. Furthermore, we propose a data-adaptive threshold selection procedure which is able to detect differentially weighted edges and is shown to outperform competing methods that perform edge-wise comparisons controlling for the error rate. An application of our method to a major depressive disorder dataset leads to the identification of brain regions in the default mode network commonly implicated in this ruminative disorder.

stat.AP↗

arXiv2018

Statistical methods for characterizing transfusion-related changes in regional oxygenation using Near-infrared spectroscopy (NIRS) in preterm infants

Near infrared spectroscopy (NIRS) is an imaging-based diagnostic tool that provides non-invasive and continuous evaluation of regional tissue oxygenation in real-time. In recent years, NIRS has show promise as a useful monitoring technology to help detect relative tissue ischemia that could lead to significant morbidity and mortality in preterm infants. However, some issues inherent in NIRS technology use on neonates, such as wide fluctuation in signals, signal dropout and low limit of detection of the device, pose challenges that may obscure reliable interpretation of the NIRS measurements using current methods of analysis. In this paper, we propose new statistical methods to analyse mesenteric rSO2 (regional oxygenation) produced by NIRS to evaluate oxygenation in intestinal tissues and investigate oxygenation response to red blood cell transfusion (RBC) in preterm infants. We present a mean area under the curve (MAUC) measure and a slope measure to capture the mean rSO2 level and temporal trajectory of rSO2, respectively. Estimation methods are developed for these measures and nonparametric testing procedures are proposed to detect RBC-related changes in mesenteric oxygenation in preterm infants. Through simulation studies, we show that the proposed methods demonstrate improved accuracy in characterizing the mean level and changing pattern of mesenteric rSO2 and also increased statistical power in detecting RBC-related changes, as compared with standard approaches. We apply our methods to a NIRS study in preterm infants receiving RBC transfusion from Emory Univerity to evaluate the pre- and post-transfusion mesenteric oxygenation in preterm infants.

stat.AP↗

arXiv2018

A hierarchical independent component analysis model for longitudinal Neuroimaging studies

In recent years, longitudinal neuroimaging study has become increasingly popular in neuroscience research to investigate disease-related changes in brain functions. In current neuroscience literature, one of the most commonly used tools to extract and characterize brain functional networks is independent component analysis (ICA). However, existing ICA methods are not suited for modelling repeatedly measured imaging data. In this paper, we propose a novel longitudinal independent component model (L-ICA) which provides a formal modeling framework for extending ICA to longitudinal studies. By incorporating subject-specific random effects and visit-specific covariate effects, L-ICA is able to provide more accurate estimates of changes in brain functional networks on both the population- and individual-level, borrow information across repeated scans within the same subject to increase statistical power in detecting covariate effects on the networks, and allow for model-based prediction for brain networks changes caused by disease progression, treatment or neurodevelopment. We develop a fully traceable exact EM algorithm to obtain maximum likelihood estimates of L-ICA. We further develop a subspace-based approximate EM algorithm which greatly reduce the computation time while still retaining high accuracy. Moreover, we present a statistical testing procedure for examining covariate effects on brain network changes. Simulation results demonstrate the advantages of our proposed methods. We apply L-ICA to ADNI2 study to investigate changes in brain functional networks in Alzheimer disease. Results from the L-ICA provide biologically insightful findings which are not revealed using existing methods.

stat.ME↗

arXiv2018

Dual-phase-modulated plug-and-play measurement-device-independent continuous-variable quantum key distribution

We suggest an improved plug-and-play measurement-device-independent (MDI) continuous-variable quantum key distribution (CVQKD) via the dual-phase modulation (DPM), aiming to solve an implementation problem with no extra performance penalty. The synchronous loophole of different lasers from Alice and Bob can be elegantly eliminated in the plug-and-play configuration, which gives birth to the convenient implementation when comparing to the Gaussian-modulated coherent-state protocol. While the local oscillator (LO) can be locally generated by the trusted part Charlie, the LO-aimed attacks can be accurately detected in the data post-processing. We derive the security bounds of the DPM-based MDI-CVQKD against optimal Gaussian collective attacks. Taking the finite-size effect into account, the secret key rate can be increased due to the fact that almost all raw keys of the MDI-CVQKD system can be fully exploited for the final secret key generation without sacrificing raw keys in parameter estimation. Moreover, we give an experimental concept of the proposed scheme which can be deemed guideline for final implementation.

quant-ph↗

arXiv2018

Integrative Bayesian Analysis of Brain Functional Networks Incorporating Anatomical Knowledge

Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization. Specifically, accounting for knowledge of anatomical pathways connecting brain regions should lead to desirable outcomes such as increased accuracy in functional brain network estimates and greater reproducibility of topological features across scanning sessions. Despite the clear merits, major challenges persist in integrative analyses including an incomplete understanding of the structure-function relationship and inaccuracies in mapping anatomical structures due to deficiencies in existing imaging technology. Clearly advanced network modeling tools are needed to appropriately incorporate anatomical structure in constructing brain functional networks. We propose a hierarchical Bayesian Gaussian graphical modeling approach that estimates the functional networks via sparse precision matrices whose degree of edge-specific shrinkage is informed by anatomical structure and an independent baseline component. The approach flexibly identifies functional connections supported by structural connectivity knowledge. This enables robust brain network estimation even in the presence of mis-specified anatomical knowledge, while accommodating heterogeneity in the structure-function relationship. We implement the approach via an efficient optimization algorithm yielding maximum a posteriori estimates. Extensive numerical studies reveal the clear advantages of our approach over competing methods in accurately estimating brain functional connectivity, even when the anatomical knowledge is mis-specified. An application of the approach to the Philadelphia Neurodevelopmental Cohort (PNC) study reveals gender based connectivity differences across multiple age groups, and higher reproducibility in the estimation of network metrics compared to alternative methods.

stat.AP↗

arXiv2017

Security of Continuous-Variable Quantum Key Distribution with Discrete Modulation against General Attacks

We provide a security analysis of continuous-variable quantum key distribution (CVQKD) with discrete modulation against general attacks in a realistic finite-size regime. To realize this goal, we first prove security of the continuous-variable quantum key distribution protocol with discrete modulation against collective attacks by using the reliable tomography of the covariance matrix, leading to the reliable and tight error bounds in the derived confidence regions. Combining the proof with de Finetti reduction, the discrete-modulation-based continuous-variable quantum key distribution (DM-CVQKD) is proved to be secure even exposing to general attacks. Specially, we use an energy test to truncate the Hilbert space globally to provide security.

quant-ph↗

arXiv2017

A Bayesian Mixture Model for Clustering on the Stiefel Manifold

Analysis of a Bayesian mixture model for the Matrix Langevin distribution on the Stiefel manifold is presented. The model exploits a particular parametrization of the Matrix Langevin distribution, various aspects of which are elaborated on. A general, and novel, family of conjugate priors, and an efficient Markov chain Monte Carlo (MCMC) sampling scheme for the corresponding posteriors is then developed for the mixture model. Theoretical properties of the prior and posterior distributions, including posterior consistency, are explored in detail. Extensive simulation experiments are presented to validate the efficacy of the framework. Real-world examples, including a large scale neuroimaging dataset, are analyzed to demonstrate the computational tractability of the approach.

stat.ME↗