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Dive into the research topics where Pin-Yu Chen is active.

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Featured researches published by Pin-Yu Chen.


ieee global conference on signal and information processing | 2013

Node removal vulnerability of the largest component of a network

Pin-Yu Chen; Alfred O. Hero

The connectivity structure of a network can be very sensitive to removal of certain nodes in the network. In this paper, we study the sensitivity of the largest component size to node removals. We prove that minimizing the largest component size is equivalent to solving a matrix one-norm minimization problem whose column vectors are orthogonal and sparse and they form a basis of the null space of the associated graph Laplacian matrix. A greedy node removal algorithm is then proposed based on the matrix one-norm minimization. In comparison with other node centralities such as node degree and betweenness, experimental results on US power grid dataset validate the effectiveness of the proposed approach in terms of reduction of the largest component size with relatively few node removals.


Social Network Analysis and Mining | 2018

Incremental eigenpair computation for graph Laplacian matrices: theory and applications

Pin-Yu Chen; Baichuan Zhang; Mohammad Al Hasan

The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection. However, in real-life applications, the number of clusters or communities (say, K) is generally unknown a priori. Consequently, the majority of the existing methods either choose K heuristically or they repeat the clustering method with different choices of K and accept the best clustering result. The first option, more often, yields suboptimal result, while the second option is computationally expensive. In this work, we propose an incremental method for constructing the eigenspectrum of the graph Laplacian matrix. This method leverages the eigenstructure of graph Laplacian matrix to obtain the Kth smallest eigenpair of the Laplacian matrix given a collection of all previously computed


ieee transactions on signal and information processing over networks | 2017

Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms

Pin-Yu Chen; Alfred O. Hero


iScience | 2018

Genome Architecture Mediates Transcriptional Control of Human Myogenic Reprogramming

Sijia Liu; Haiming Chen; Scott Ronquist; Laura Seaman; Nicholas Ceglia; Walter Meixner; Pin-Yu Chen; Gerald A. Higgins; Pierre Baldi; Steve Smale; Alfred O. Hero; Lindsey A. Muir; Indika Rajapakse

K-1


bioRxiv | 2018

Dynamic Network Analysis of the 4D Nucleome

Sijia Liu; Pin-Yu Chen; Alfred O. Hero; Indika Rajapakse


bioRxiv | 2017

Genome Architecture Leads a Bifurcation in Cell Identity

Sijia Liu; Haiming Chen; Scott Ronquist; Laura Seaman; Nicholas Ceglia; Walter Meixner; Lindsey A. Muir; Pin-Yu Chen; Gerald A. Higgins; Pierre Baldi; Steve Smale; Alfred O. Hero; Indika Rajapakse

K-1 smallest eigenpairs. Our proposed method adapts the Laplacian matrix such that the batch eigenvalue decomposition problem transforms into an efficient sequential leading eigenpair computation problem. As a practical application, we consider user-guided spectral clustering. Specifically, we demonstrate that users can utilize the proposed incremental method for effective eigenpair computation and for determining the desired number of clusters based on multiple clustering metrics.


national conference on artificial intelligence | 2018

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Pin-Yu Chen; Yash Sharma; Huan Zhang; Jinfeng Yi; Cho-Jui Hsieh

Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. Nonstandard multilayer graph clustering methods are needed for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a multilayer spectral graph clustering (SGC) framework that performs convex layer aggregation. Under a multilayer signal-plus-noise model, we provide a phase transition analysis of clustering reliability. Moreover, we use the phase transition criterion to propose a multilayer iterative model order selection algorithm (MIMOSA) for multilayer SGC, which features automated cluster assignment and layer weight adaptation, and provides statistical clustering reliability guarantees. Numerical simulations on synthetic multilayer graphs verify the phase transition analysis, and experiments on real-world multilayer graphs show that MIMOSA is competitive or better than other clustering methods.


IEEE Transactions on Signal Processing | 2018

Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering

Pin-Yu Chen; Alfred O. Hero

Summary Genome architecture has emerged as a critical element of transcriptional regulation, although its role in the control of cell identity is not well understood. Here we use transcription factor (TF)-mediated reprogramming to examine the interplay between genome architecture and transcriptional programs that transition cells into the myogenic identity. We recently developed new methods for evaluating the topological features of genome architecture based on network centrality. Through integrated analysis of these features of genome architecture and transcriptome dynamics during myogenic reprogramming of human fibroblasts we find that significant architectural reorganization precedes activation of a myogenic transcriptional program. This interplay sets the stage for a critical transition observed at several genomic scales reflecting definitive adoption of the myogenic phenotype. Subsequently, TFs within the myogenic transcriptional program participate in entrainment of biological rhythms. These findings reveal a role for topological features of genome architecture in the initiation of transcriptional programs during TF-mediated human cellular reprogramming.


arXiv: Social and Information Networks | 2015

Incremental Method for Spectral Clustering of Increasing Orders.

Pin-Yu Chen; Baichuan Zhang; Mohammad Al Hasan; Alfred O. Hero

Motivation For many biological systems, it is essential to capture simultaneously the function, structure, and dynamics in order to form a comprehensive understanding of underlying phenomena. The dynamical interaction between 3D genome spatial structure and transcriptional activity creates a genomic signature that we refer to as the four-dimensional organization of the nucleus, or 4D Nucleome (4DN). The study of 4DN requires assessment of genome-wide structure and gene expression as well as development of new approaches for data analysis. Results We propose a dynamic multilayer network approach to study the co-evolution of form and function in the 4D Nucleome. We model the dynamic biological system as a temporal network with node dynamics, where the network topology is captured by chromosome conformation (Hi-C), and the function of a node is measured by RNA sequencing (RNA-seq). Network-based approaches such as von Neumann graph entropy, network centrality, and multilayer network theory are applied to reveal universal patterns of the dynamic genome. Our model integrates knowledge of genome structure and gene expression along with temporal evolution and leads to a description of genome behavior on a system wide level. We illustrate the benefits of our model via a real biological dataset on MYOD1-mediated reprogramming of human fibroblasts into the myogenic lineage. We show that our methods enable better predictions on form-function relationships and refine our understanding on how cell dynamics change during cellular reprogramming. Availability: The software is available upon request. Contact [email protected] Supplementary information See Supplementary Material.


meeting of the association for computational linguistics | 2018

Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning

Hongge Chen; Huan Zhang; Pin-Yu Chen; Jinfeng Yi; Cho-Jui Hsieh

Genome architecture is important in transcriptional regulation, but its dynamics and role during reprogramming are not well understood. Over a time course, we captured genomewide architecture and transcription during MYOD1-mediated reprogramming of human fibroblasts into the myogenic lineage. We found that chromatin reorganization occurred prior to significant transcriptional changes marking activation of the myogenic program. A global bifurcation event delineated the transition into a myogenic cell identity 32 hours after exogenous MYOD1 activation, an event also reflected in the local dynamics of endogenous MYOD1 and MYOG. These data support a model in which master regulators induce lineage-specific nuclear architecture prior to fulfilling a transcriptional role. Interestingly, early in reprogramming, circadian genes that are MYOD1 targets synchronized their expression patterns. After the bifurcation, myogenic transcription factors that are MYOG targets synchronized their expression, suggesting a cell-type specific rhythm. These data support roles for MYOD1 and MYOG in entraining biological rhythms.

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Huan Zhang

University of California

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Sijia Liu

University of Michigan

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Cho-Jui Hsieh

University of California

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Hongge Chen

Massachusetts Institute of Technology

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