Search Results for author: Yue Zhao

Found 100 papers, 50 papers with code

NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation

1 code implementation20 Nov 2023 Hao Dong, Gaëtan Frusque, Yue Zhao, Eleni Chatzi, Olga Fink

While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the case for semi-supervised anomaly detection.

Data Augmentation Fault Detection +4

Benchmarking Machine Learning Models for Quantum Error Correction

no code implementations18 Nov 2023 Tim Fu, Yue Zhao

Quantum Error Correction (QEC) is one of the fundamental problems in quantum computer systems, which aims to detect and correct errors in the data qubits within quantum computers.


What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?

no code implementations10 Oct 2023 Siting Li, Chenzhuang Du, Yue Zhao, Yu Huang, Hang Zhao

With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention.

Data Augmentation

Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants

1 code implementation1 Oct 2023 Tianyu Yu, Jinyi Hu, Yuan YAO, Haoye Zhang, Yue Zhao, Chongyi Wang, Shan Wang, Yinxv Pan, Jiao Xue, Dahai Li, Zhiyuan Liu, Hai-Tao Zheng, Maosong Sun

The capabilities of MLLMs depend on two crucial factors: the model architecture to facilitate the feature alignment of visual modules and large language models; the multimodal instruction tuning datasets for human instruction following.

Instruction Following

Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages

1 code implementation23 Aug 2023 Jinyi Hu, Yuan YAO, Chongyi Wang, Shan Wang, Yinxu Pan, Qianyu Chen, Tianyu Yu, Hanghao Wu, Yue Zhao, Haoye Zhang, Xu Han, Yankai Lin, Jiao Xue, Dahai Li, Zhiyuan Liu, Maosong Sun

Building a competitive counterpart in other languages is highly challenging due to the low-resource nature of non-English multimodal data (i. e., lack of large-scale, high-quality image-text data).

Language Modelling Large Language Model

Fast Unsupervised Deep Outlier Model Selection with Hypernetworks

no code implementations20 Jul 2023 Xueying Ding, Yue Zhao, Leman Akoglu

Outlier detection (OD) finds many applications with a rich literature of numerous techniques.

Meta-Learning Model Selection +1

Accurate 3D Prediction of Missing Teeth in Diverse Patterns for Precise Dental Implant Planning

no code implementations16 Jul 2023 Lei Ma, Peng Xue, Yuning Gu, Yue Zhao, Min Zhu, Zhongxiang Ding, Dinggang Shen

This study presents a novel framework for accurate prediction of missing teeth in different patterns, facilitating digital implant planning.

DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection

1 code implementation13 Jul 2023 Jaemin Yoo, Yue Zhao, Lingxiao Zhao, Leman Akoglu

DSV captures the alignment between an augmentation function and the anomaly-generating mechanism with surrogate losses, which approximate the discordance and separability of test data, respectively.

Data Augmentation Model Selection +2

Regularized Multivariate Functional Principal Component Analysis

no code implementations24 Jun 2023 Hossein Haghbin, Yue Zhao, Mehdi Maadooliat

Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data.

Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks

1 code implementation23 May 2023 Peng Xu, Lin Zhang, Xuanzhou Liu, Jiaqi Sun, Yue Zhao, Haiqin Yang, Bei Yu

Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures.

Neural Architecture Search

Construction of unbiased dental template and parametric dental model for precision digital dentistry

no code implementations7 Apr 2023 Lei Ma, Jingyang Zhang, Ke Deng, Peng Xue, Zhiming Cui, Yu Fang, Minhui Tang, Yue Zhao, Min Zhu, Zhongxiang Ding, Dinggang Shen

In this study, we develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation.

Image Cropping Segmentation

Stabilization with Prescribed Instant via Lyapunov Method

no code implementations22 Feb 2023 Jiyuan Kuang, Yabin Gao, Yizhuo Sun, Jiahui Wang, Aohua Liu, Yue Zhao, Jianxing Liu

In anothor word, the settling time under the presented controller is independent of the initial conditions and equals the prescribed time instant.

Weakly Supervised Anomaly Detection: A Survey

2 code implementations9 Feb 2023 Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu, Yue Zhao

Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.

Supervised Anomaly Detection Time Series +2

Online Kernel Sliced Inverse Regression

no code implementations23 Jan 2023 Wenquan Cui, Yue Zhao, Jianjun Xu, Haoyang Cheng

Online dimension reduction is a common method for high-dimensional streaming data processing.

Dimensionality Reduction regression +1

EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation

no code implementations5 Jan 2023 Yue Zhao, Wei zhang, Tiejun Li

We present a novel yet simple deep learning approach, dubbed EPR-Net, for constructing the potential landscape of high-dimensional non-equilibrium steady state (NESS) systems.

Dimensionality Reduction

Offline Supervised Learning V.S. Online Direct Policy Optimization: A Comparative Study and A Unified Training Paradigm for Neural Network-Based Optimal Feedback Control

1 code implementation29 Nov 2022 Yue Zhao, Jiequn Han

We first conduct a comparative study of two mainstream approaches: offline supervised learning and online direct policy optimization.

Toward Unsupervised Outlier Model Selection

1 code implementation3 Nov 2022 Yue Zhao, Sean Zhang, Leman Akoglu

At its core, ELECT is based on meta-learning; transferring prior knowledge (e. g. model performance) on historical datasets that are similar to the new one to facilitate UOMS.

Meta-Learning Model Selection +1

Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks

1 code implementation20 Sep 2022 Haodong Duan, Yue Zhao, Kai Chen, Yuanjun Xiong, Dahua Lin

Deep learning models have achieved excellent recognition results on large-scale video benchmarks.

Action Recognition

Diffusion Models: A Comprehensive Survey of Methods and Applications

2 code implementations2 Sep 2022 Ling Yang, Zhilong Zhang, Yang song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, Ming-Hsuan Yang

This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration.

Image Super-Resolution Video Generation

Hyperparameter Optimization for Unsupervised Outlier Detection

no code implementations24 Aug 2022 Yue Zhao, Leman Akoglu

Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without any labels?

Hyperparameter Optimization Meta-Learning +1

ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels

1 code implementation24 Aug 2022 Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann, Ahmed Awadallah

In this work, we propose a method to leverage weak/noisy labels (e. g., risk scores generated by machine rules for detecting malware) that are cheaper to obtain for anomaly detection.

Anomaly Detection

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

2 code implementations21 Jun 2022 Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu

To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.

Anomaly Detection Benchmarking +2

ADBench: Anomaly Detection Benchmark

4 code implementations19 Jun 2022 Songqiao Han, Xiyang Hu, Hailiang Huang, Mingqi Jiang, Yue Zhao

Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data?

Anomaly Detection Outlier Detection

ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training

no code implementations12 May 2022 Yue Zhao, Yantao Shen, Yuanjun Xiong, Shuo Yang, Wei Xia, Zhuowen Tu, Bernt Schiele, Stefano Soatto

We present a method to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model.

Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis

no code implementations6 May 2022 Yufeng Shi, Shuhuang Chen, Xinge You, Qinmu Peng, Weihua Ou, Yue Zhao

Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i. e., hashing-based cross-modal medical data retrieval), provides a new view to promot computeraided diagnosis.

Cross-Modal Retrieval Retrieval

A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images

no code implementations5 May 2022 Tingting Zheng, Weixing Chen, Shuqin Li, Hao Quan, Qun Bai, Tianhang Nan, Song Zheng, Xinghua Gao, Yue Zhao, Xiaoyu Cui

Inspired by the pathologist's clinical diagnosis process, we propose a weakly supervised deep reinforcement learning framework, which can greatly reduce the time required for network inference.

Knowledge Distillation reinforcement-learning +2

Gaussian Kernel Variance For an Adaptive Learning Method on Signals Over Graphs

no code implementations26 Apr 2022 Yue Zhao, Ender Ayanoglu

To be more specific, we focus on SKG with a Gaussian kernel and specify how to find a suitable variance for the kernel.

Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation

1 code implementation19 Apr 2022 Yue Zhao, Lingming Zhang, Yang Liu, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen

The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i. e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation.

Graph Learning Image Segmentation +3

Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics

no code implementations7 Mar 2022 Jeya Vikranth Jeyakumar, Ludmila Cherkasova, Saina Lajevardi, Moray Allan, Yue Zhao, John Fry, Mani Srivastava

In this work, we design a novel, scalable approach, where a general demand forecasting model is built using the combined data of all the companies with a normalization factor.


Learning Robust Representation through Graph Adversarial Contrastive Learning

no code implementations31 Jan 2022 Jiayan Guo, Shangyang Li, Yue Zhao, Yan Zhang

Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matrix and node features.

Contrastive Learning Graph Representation Learning +2

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

2 code implementations2 Jan 2022 Zheng Li, Yue Zhao, Xiyang Hu, Nicola Botta, Cezar Ionescu, George H. Chen

To address these issues, we present a simple yet effective algorithm called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the "rare events" that appear in the tails of a distribution.

Anomaly Detection Outlier Detection

AI-Lancet: Locating Error-inducing Neurons to Optimize Neural Networks

1 code implementation ACM SIGSAC Conference on Computer and Communications Security 2021 Yue Zhao, Hong Zhu, Kai Chen, Shengzhi Zhang

With the knowledge of error-inducing neurons, we propose two methods to fix the errors: the neuron-flip and the neuron-fine-tuning.

BA-Net: Bridge Attention for Deep Convolutional Neural Networks

1 code implementation8 Dec 2021 Yue Zhao, Junzhou Chen, Zirui Zhang, Ronghui Zhang

The core idea of this design is to bridge the outputs of the previous convolution layers through skip connections for channel weights generation.

Automatic Unsupervised Outlier Model Selection

no code implementations NeurIPS 2021 Yue Zhao, Ryan Rossi, Leman Akoglu

Given an unsupervised outlier detection task on a new dataset, how can we automatically select a good outlier detection algorithm and its hyperparameter(s) (collectively called a model)?

Meta-Learning Model Selection +1

Federated Learning Based on Dynamic Regularization

3 code implementations ICLR 2021 Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama

We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round.

Federated Learning

TOD: GPU-accelerated Outlier Detection via Tensor Operations

2 code implementations26 Oct 2021 Yue Zhao, George H. Chen, Zhihao Jia

Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection.

Fraud Detection Intrusion Detection +2

3D Dental model segmentation with graph attentional convolution network

no code implementations Pattern Recognition Letters 2021 Yue Zhao, Lingming Zhang, Chongshi Yang, Yingyun Tan, Yang Liu, Pengcheng Li, Tianhao Huang, Chenqiang Gao

We have evaluated our network on a real-patient dataset of dental models acquired through 3D intraoral scanners, and experimental results show that our method outperforms state-of-the-art deep learning methods for 3D shape segmentation.


Local Patch Network with Global Attention for Infrared Small Target Detection

1 code implementation13 Aug 2021 Fang Chen, Chenqiang Gao, Fangcen Liu, Yue Zhao, Yuxi Zhou, Deyu Meng, WangMeng Zuo

A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images.

Semantic Segmentation

Intrinsically Motivated Self-supervised Learning in Reinforcement Learning

no code implementations26 Jun 2021 Yue Zhao, Chenzhuang Du, Hang Zhao, Tiejun Li

In vision-based reinforcement learning (RL) tasks, it is prevalent to assign auxiliary tasks with a surrogate self-supervised loss so as to obtain more semantic representations and improve sample efficiency.

Decision Making reinforcement-learning +3

TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation

no code implementations CVPR 2021 Lingming Zhang, Yue Zhao, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen

State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.

Graph Learning

GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising

2 code implementations15 May 2021 Kecheng Chen, Jiayu Sun, Jiang Shen, Jixiang Luo, Xinyu Zhang, Xuelin Pan, Dongsheng Wu, Yue Zhao, Miguel Bento, Yazhou Ren, Xiaorong Pu

To address this issue, we propose a novel graph convolutional network-based LDCT denoising model, namely GCN-MIF, to explicitly perform multi-information fusion for denoising purpose.


Revisiting Skeleton-based Action Recognition

4 code implementations CVPR 2022 Haodong Duan, Yue Zhao, Kai Chen, Dahua Lin, Bo Dai

In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.

 Ranked #1 on Skeleton Based Action Recognition on NTU RGB+D (using extra training data)

Action Recognition Group Activity Recognition +2

A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?

1 code implementation3 Apr 2021 Martin Q. Ma, Yue Zhao, Xiaorong Zhang, Leman Akoglu

These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models.

Model Selection Outlier Detection

PointBA: Towards Backdoor Attacks in 3D Point Cloud

no code implementations ICCV 2021 Xinke Li, Zhirui Chen, Yue Zhao, Zekun Tong, Yabang Zhao, Andrew Lim, Joey Tianyi Zhou

We present the backdoor attacks in 3D point cloud with a unified framework that exploits the unique properties of 3D data and networks.

Backdoor Attack Disentanglement

HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks

no code implementations25 Mar 2021 Peizhuo Lv, Pan Li, Shengzhi Zhang, Kai Chen, Ruigang Liang, Yue Zhao, Yingjiu Li

Most existing solutions embed backdoors in DNN model training such that DNN ownership can be verified by triggering distinguishable model behaviors with a set of secret inputs.

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

2 code implementations18 Feb 2021 Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik

Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.

BIG-bench Machine Learning Drug Discovery

Upper Limits on the Isotropic Gravitational-Wave Background from Advanced LIGO's and Advanced Virgo's Third Observing Run

no code implementations28 Jan 2021 The LIGO Scientific Collaboration, The Virgo Collaboration, the KAGRA Collaboration, R. Abbott, T. D. Abbott, S. Abraham, F. Acernese, K. Ackley, A. Adams, C. Adams, R. X. Adhikari, V. B. Adya, C. Affeldt, D. Agarwal, M. Agathos, K. Agatsuma, N. Aggarwal, O. D. Aguiar, L. Aiello, A. Ain, T. Akutsu, K. M. Aleman, G. Allen, A. Allocca, P. A. Altin, A. Amato, S. Anand, A. Ananyeva, S. B. Anderson, W. G. Anderson, M. Ando, S. V. Angelova, S. Ansoldi, J. M. Antelis, S. Antier, S. Appert, Koya Arai, Koji Arai, Y. Arai, S. Araki, A. Araya, M. C. Araya, J. S. Areeda, M. Arène, N. Aritomi, N. Arnaud, S. M. Aronson, H. Asada, Y. Asali, G. Ashton, Y. Aso, S. M. Aston, P. Astone, F. Aubin, P. Aufmuth, K. AultONeal, C. Austin, S. Babak, F. Badaracco, M. K. M. Bader, S. Bae, Y. Bae, A. M. Baer, S. Bagnasco, Y. Bai, L. Baiotti, J. Baird, R. Bajpai, M. Ball, G. Ballardin, S. W. Ballmer, M. Bals, A. Balsamo, G. Baltus, S. Banagiri, D. Bankar, R. S. Bankar, J. C. Barayoga, C. Barbieri, B. C. Barish, D. Barker, P. Barneo, S. Barnum, F. Barone, B. Barr, L. Barsotti, M. Barsuglia, D. Barta, J. Bartlett, M. A. Barton, I. Bartos, R. Bassiri, A. Basti, M. Bawaj, J. C. Bayley, A. C. Baylor, M. Bazzan, B. Bécsy, V. M. Bedakihale, M. Bejger, I. Belahcene, V. Benedetto, D. Beniwal, M. G. Benjamin, T. F. Bennett, J. D. Bentley, M. BenYaala, F. Bergamin, B. K. Berger, S. Bernuzzi, D. Bersanetti, A. Bertolini, J. Betzwieser, R. Bhandare, A. V. Bhandari, D. Bhattacharjee, S. Bhaumik, J. Bidler, I. A. Bilenko, G. Billingsley, R. Birney, O. Birnholtz, S. Biscans, M. Bischi, S. Biscoveanu, A. Bisht, B. Biswas, M. Bitossi, M. -A. Bizouard, J. K. Blackburn, J. Blackman, C. D. Blair, D. G. Blair, R. M. Blair, F. Bobba, N. Bode, M. Boer, G. Bogaert, M. Boldrini, F. Bondu, E. Bonilla, R. Bonnand, P. Booker, B. A. Boom, R. Bork, V. Boschi, N. Bose, S. Bose, V. Bossilkov, V. Boudart, Y. Bouffanais, A. Bozzi, C. Bradaschia, P. R. Brady, A. Bramley, A. Branch, M. Branchesi, J. E. Brau, M. Breschi, T. Briant, J. H. Briggs, A. Brillet, M. Brinkmann, P. Brockill, A. F. Brooks, J. Brooks, D. D. Brown, S. Brunett, G. Bruno, R. Bruntz, J. Bryant, A. Buikema, T. Bulik, H. J. Bulten, A. Buonanno, R. Buscicchio, D. Buskulic, R. L. Byer, L. Cadonati, M. Caesar, G. Cagnoli, C. Cahillane, H. W. Cain III, J. Calderón Bustillo, J. D. Callaghan, T. A. Callister, E. Calloni, J. B. Camp, M. Canepa, M. Cannavacciuolo, K. C. Cannon, H. Cao, J. Cao, Z. Cao, E. Capocasa, E. Capote, G. Carapella, F. Carbognani, J. B. Carlin, M. F. Carney, M. Carpinelli, G. Carullo, T. L. Carver, J. Casanueva Diaz, C. Casentini, G. Castaldi, S. Caudill, M. Cavaglià, F. Cavalier, R. Cavalieri, G. Cella, P. Cerdá-Durán, E. Cesarini, W. Chaibi, K. Chakravarti, B. Champion, C. -H. Chan, C. Chan, C. L. Chan, M. Chan, K. Chandra, P. Chanial, S. Chao, P. Charlton, E. A. Chase, E. Chassande-Mottin, D. Chatterjee, M. Chaturvedi, A. Chen, C. Chen, H. Y. Chen, J. Chen, K. Chen, X. Chen, Y. -B. Chen, Y. -R. Chen, Z. Chen, H. Cheng, C. K. Cheong, H. Y. Cheung, H. Y. Chia, F. Chiadini, C-Y. Chiang, R. Chierici, A. Chincarini, M. L. Chiofalo, A. Chiummo, G. Cho, H. S. Cho, S. Choate, R. K. Choudhary, S. Choudhary, N. Christensen, H. Chu, Q. Chu, Y-K. Chu, S. Chua, K. W. Chung, G. Ciani, P. Ciecielag, M. Cieślar, M. Cifaldi, A. A. Ciobanu, R. Ciolfi, F. Cipriano, A. Cirone, F. Clara, E. N. Clark, J. A. Clark, L. Clarke, P. Clearwater, S. Clesse, F. Cleva, E. Coccia, P. -F. Cohadon, D. E. Cohen, L. Cohen, M. Colleoni, C. G. Collette, M. Colpi, C. M. Compton, M. Constancio Jr., L. Conti, S. J. Cooper, P. Corban, T. R. Corbitt, I. Cordero-Carrión, S. Corezzi, K. R. Corley, N. Cornish, D. Corre, A. Corsi, S. Cortese, C. A. Costa, R. Cotesta, M. W. Coughlin, S. B. Coughlin, J. -P. Coulon, S. T. Countryman, B. Cousins, P. Couvares, P. B. Covas, D. M. Coward, M. J. Cowart, D. C. Coyne, R. Coyne, J. D. E. Creighton, T. D. Creighton, A. W. Criswell, M. Croquette, S. G. Crowder, J. R. Cudell, T. J. Cullen, A. Cumming, R. Cummings, E. Cuoco, M. Curyło, T. Dal Canton, G. Dálya, A. Dana, L. M. DaneshgaranBajastani, B. D'Angelo, S. L. Danilishin, S. D'Antonio, K. Danzmann, C. Darsow-Fromm, A. Dasgupta, L. E. H. Datrier, V. Dattilo, I. Dave, M. Davier, G. S. Davies, D. Davis, E. J. Daw, R. Dean, D. DeBra, M. Deenadayalan, J. Degallaix, M. De Laurentis, S. Deléglise, V. Del Favero, F. De Lillo, N. De Lillo, W. Del Pozzo, L. M. DeMarchi, F. De Matteis, V. D'Emilio, N. Demos, T. Dent, A. Depasse, R. De Pietri, R. De Rosa, C. De Rossi, R. DeSalvo, R. De Simone, S. Dhurandhar, M. C. Díaz, M. Diaz-Ortiz Jr., N. A. Didio, T. Dietrich, L. Di Fiore, C. Di Fronzo, C. Di Giorgio, F. Di Giovanni, T. Di Girolamo, A. Di Lieto, B. Ding, S. Di Pace, I. Di Palma, F. Di Renzo, A. K. Divakarla, A. Dmitriev, Z. Doctor, L. D'Onofrio, F. Donovan, K. L. Dooley, S. Doravari, I. Dorrington, M. Drago, J. C. Driggers, Y. Drori, Z. Du, J. -G. Ducoin, P. Dupej, O. Durante, D. D'Urso, P. -A. Duverne, I. Dvorkin, S. E. Dwyer, P. J. Easter, M. Ebersold, G. Eddolls, B. Edelman, T. B. Edo, O. Edy, A. Effler, S. Eguchi, J. Eichholz, S. S. Eikenberry, M. Eisenmann, R. A. Eisenstein, A. Ejlli, Y. Enomoto, L. Errico, R. C. Essick, H. Estellés, D. Estevez, Z. Etienne, T. Etzel, M. Evans, T. M. Evans, B. E. Ewing, V. Fafone, H. Fair, S. Fairhurst, X. Fan, A. M. Farah, S. Farinon, B. Farr, W. M. Farr, N. W. Farrow, E. J. Fauchon-Jones, M. Favata, M. Fays, M. Fazio, J. Feicht, M. M. Fejer, F. Feng, E. Fenyvesi, D. L. Ferguson, A. Fernandez-Galiana, I. Ferrante, T. A. Ferreira, F. Fidecaro, P. Figura, I. Fiori, M. Fishbach, R. P. Fisher, J. M. Fishner, R. Fittipaldi, V. Fiumara, R. Flaminio, E. Floden, E. Flynn, H. Fong, J. A. Font, B. Fornal, P. W. F. Forsyth, A. Franke, S. Frasca, F. Frasconi, C. Frederick, Z. Frei, A. Freise, R. Frey, P. Fritschel, V. V. Frolov, G. G. Fronzé, Y. Fujii, Y. Fujikawa, M. Fukunaga, M. Fukushima, P. Fulda, M. Fyffe, H. A. Gabbard, B. U. Gadre, S. M. Gaebel, J. R. Gair, J. Gais, S. Galaudage, R. Gamba, D. Ganapathy, A. Ganguly, D. Gao, S. G. Gaonkar, B. Garaventa, C. García-Núñez, C. García-Quirós, F. Garufi, B. Gateley, S. Gaudio, V. Gayathri, G. Ge, G. Gemme, A. Gennai, J. George, L. Gergely, P. Gewecke, S. Ghonge, Abhirup. Ghosh, Archisman Ghosh, Shaon Ghosh, Shrobana Ghosh, Sourath Ghosh, B. Giacomazzo, L. Giacoppo, J. A. Giaime, K. D. Giardina, D. R. Gibson, C. Gier, M. Giesler, P. Giri, F. Gissi, J. Glanzer, A. E. Gleckl, P. Godwin, E. Goetz, R. Goetz, N. Gohlke, B. Goncharov, G. González, A. Gopakumar, M. Gosselin, R. Gouaty, B. Grace, A. Grado, M. Granata, V. Granata, A. Grant, S. Gras, P. Grassia, C. Gray, R. Gray, G. Greco, A. C. Green, R. Green, A. M. Gretarsson, E. M. Gretarsson, D. Griffith, W. Griffiths, H. L. Griggs, G. Grignani, A. Grimaldi, E. Grimes, S. J. Grimm, H. Grote, S. Grunewald, P. Gruning, J. G. Guerrero, G. M. Guidi, A. R. Guimaraes, G. Guixé, H. K. Gulati, H. -K. Guo, Y. Guo, Anchal Gupta, Anuradha Gupta, P. Gupta, E. K. Gustafson, R. Gustafson, F. Guzman, S. Ha, L. Haegel, A. Hagiwara, S. Haino, O. Halim, E. D. Hall, E. Z. Hamilton, G. Hammond, W. -B. Han, M. Haney, J. Hanks, C. Hanna, M. D. Hannam, O. A. Hannuksela, H. Hansen, T. J. Hansen, J. Hanson, T. Harder, T. Hardwick, K. Haris, J. Harms, G. M. Harry, I. W. Harry, D. Hartwig, K. Hasegawa, B. Haskell, R. K. Hasskew, C. -J. Haster, K. Hattori, K. Haughian, H. Hayakawa, K. Hayama, F. J. Hayes, J. Healy, A. Heidmann, M. C. Heintze, J. Heinze, J. Heinzel, H. Heitmann, F. Hellman, P. Hello, A. F. Helmling-Cornell, G. Hemming, M. Hendry, I. S. Heng, E. Hennes, J. Hennig, M. H. Hennig, F. Hernandez Vivanco, M. Heurs, S. Hild, P. Hill, Y. Himemoto, A. S. Hines, Y. Hiranuma, N. Hirata, E. Hirose, S. Hochheim, D. Hofman, J. N. Hohmann, A. M. Holgado, N. A. Holland, I. J. Hollows, Z. J. Holmes, K. Holt, D. E. Holz, Z. Hong, P. Hopkins, J. Hough, E. J. Howell, C. G. Hoy, D. Hoyland, A. Hreibi, B-H. Hsieh, Y. Hsu, G-Z. Huang, H-Y. Huang, P. Huang, Y-C. Huang, Y. -J. Huang, Y. -W. Huang, M. T. Hübner, A. D. Huddart, E. A. Huerta, B. Hughey, D. C. Y. Hui, V. Hui, S. Husa, S. H. Huttner, R. Huxford, T. Huynh-Dinh, S. Ide, B. Idzkowski, A. Iess, B. Ikenoue, S. Imam, K. Inayoshi, H. Inchauspe, C. Ingram, Y. Inoue, G. Intini, K. Ioka, M. Isi, K. Isleif, K. Ito, Y. Itoh, B. R. Iyer, K. Izumi, V. JaberianHamedan, T. Jacqmin, S. J. Jadhav, S. P. Jadhav, A. L. James, A. Z. Jan, K. Jani, K. Janssens, N. N. Janthalur, P. Jaranowski, D. Jariwala, R. Jaume, A. C. Jenkins, C. Jeon, M. Jeunon, W. Jia, J. Jiang, H. -B. Jin, G. R. Johns, A. W. Jones, D. I. Jones, J. D. Jones, P. Jones, R. Jones, R. J. G. Jonker, L. Ju, K. Jung, P. Jung, J. Junker, K. Kaihotsu, T. Kajita, M. Kakizaki, C. V. Kalaghatgi, V. Kalogera, B. Kamai, M. Kamiizumi, N. Kanda, S. Kandhasamy, G. Kang, J. B. Kanner, Y. Kao, S. J. Kapadia, D. P. Kapasi, C. Karathanasis, S. Karki, R. Kashyap, M. Kasprzack, W. Kastaun, S. Katsanevas, E. Katsavounidis, W. Katzman, T. Kaur, K. Kawabe, K. Kawaguchi, N. Kawai, T. Kawasaki, F. Kéfélian, D. Keitel, J. S. Key, S. Khadka, F. Y. Khalili, I. Khan, S. Khan, E. A. Khazanov, N. Khetan, M. Khursheed, N. Kijbunchoo, C. Kim, J. C. Kim, J. Kim, K. Kim, W. S. Kim, Y. -M. Kim, C. Kimball, N. Kimura, P. J. King, M. Kinley-Hanlon, R. Kirchhoff, J. S. Kissel, N. Kita, H. Kitazawa, L. Kleybolte, S. Klimenko, A. M. Knee, T. D. Knowles, E. Knyazev, P. Koch, G. Koekoek, Y. Kojima, K. Kokeyama, S. Koley, P. Kolitsidou, M. Kolstein, K. Komori, V. Kondrashov, A. K. H. Kong, A. Kontos, N. Koper, M. Korobko, K. Kotake, M. Kovalam, D. B. Kozak, C. Kozakai, R. Kozu, V. Kringel, N. V. Krishnendu, A. Królak, G. Kuehn, F. Kuei, A. Kumar, P. Kumar, Rahul Kumar, Rakesh Kumar, J. Kume, K. Kuns, C. Kuo, H-S. Kuo, Y. Kuromiya, S. Kuroyanagi, K. Kusayanagi, K. Kwak, S. Kwang, D. Laghi, E. Lalande, T. L. Lam, A. Lamberts, M. Landry, B. B. Lane, R. N. Lang, J. Lange, B. Lantz, I. La Rosa, A. Lartaux-Vollard, P. D. Lasky, M. Laxen, A. Lazzarini, C. Lazzaro, P. Leaci, S. Leavey, Y. K. Lecoeuche, H. K. Lee, H. M. Lee, H. W. Lee, J. Lee, K. Lee, R. Lee, J. Lehmann, A. Lemaître, E. Leon, M. Leonardi, N. Leroy, N. Letendre, Y. Levin, J. N. Leviton, A. K. Y. Li, B. Li, J. Li, K. L. Li, T. G. F. Li, X. Li, C-Y. Lin, F-K. Lin, F-L. Lin, H. L. Lin, L. C. -C. Lin, F. Linde, S. D. Linker, J. N. Linley, T. B. Littenberg, G. C. Liu, J. Liu, K. Liu, X. Liu, M. Llorens-Monteagudo, R. K. L. Lo, A. Lockwood, M. L. Lollie, L. T. London, A. Longo, D. Lopez, M. Lorenzini, V. Loriette, M. Lormand, G. Losurdo, J. D. Lough, C. O. Lousto, G. Lovelace, H. Lück, D. Lumaca, A. P. Lundgren, L. -W. Luo, R. Macas, M. MacInnis, D. M. Macleod, I. A. O. MacMillan, A. Macquet, I. Magaña Hernandez, F. Magaña-Sandoval, C. Magazzù, R. M. Magee, R. Maggiore, E. Majorana, I. Maksimovic, S. Maliakal, A. Malik, N. Man, V. Mandic, V. Mangano, J. L. Mango, G. L. Mansell, M. Manske, M. Mantovani, M. Mapelli, F. Marchesoni, M. Marchio, F. Marion, Z. Mark, S. Márka, Z. Márka, C. Markakis, A. S. Markosyan, A. Markowitz, E. Maros, A. Marquina, S. Marsat, F. Martelli, I. W. Martin, R. M. Martin, M. Martinez, V. Martinez, K. Martinovic, D. V. Martynov, E. J. Marx, H. Masalehdan, K. Mason, E. Massera, A. Masserot, T. J. Massinger, M. Masso-Reid, S. Mastrogiovanni, A. Matas, M. Mateu-Lucena, F. Matichard, M. Matiushechkina, N. Mavalvala, J. J. McCann, R. McCarthy, D. E. McClelland, P. McClincy, S. McCormick, L. McCuller, G. I. McGhee, S. C. McGuire, C. McIsaac, J. McIver, D. J. McManus, T. McRae, S. T. McWilliams, D. Meacher, M. Mehmet, A. K. Mehta, A. Melatos, D. A. Melchor, G. Mendell, A. Menendez-Vazquez, C. S. Menoni, R. A. Mercer, L. Mereni, K. Merfeld, E. L. Merilh, J. D. Merritt, M. Merzougui, S. Meshkov, C. Messenger, C. Messick, P. M. Meyers, F. Meylahn, A. Mhaske, A. Miani, H. Miao, I. Michaloliakos, C. Michel, Y. Michimura, H. Middleton, L. Milano, A. L. Miller, M. Millhouse, J. C. Mills, E. Milotti, M. C. Milovich-Goff, O. Minazzoli, Y. Minenkov, N. Mio, Ll. M. Mir, A. Mishkin, C. Mishra, T. Mishra, T. Mistry, S. Mitra, V. P. Mitrofanov, G. Mitselmakher, R. Mittleman, O. Miyakawa, A. Miyamoto, Y. Miyazaki, K. Miyo, S. Miyoki, Geoffrey Mo, K. Mogushi, S. R. P. Mohapatra, S. R. Mohite, I. Molina, M. Molina-Ruiz, M. Mondin, M. Montani, C. J. Moore, D. Moraru, F. Morawski, A. More, C. Moreno, G. Moreno, Y. Mori, S. Morisaki, Y. Moriwaki, B. Mours, C. M. Mow-Lowry, S. Mozzon, F. Muciaccia, Arunava Mukherjee, D. Mukherjee, Soma Mukherjee, Subroto Mukherjee, N. Mukund, A. Mullavey, J. Munch, E. A. Muñiz, P. G. Murray, R. Musenich, S. L. Nadji, K. Nagano, S. Nagano, A. Nagar, K. Nakamura, H. Nakano, M. Nakano, R. Nakashima, Y. Nakayama, I. Nardecchia, T. Narikawa, L. Naticchioni, B. Nayak, R. K. Nayak, R. Negishi, B. F. Neil, J. Neilson, G. Nelemans, T. J. N. Nelson, M. Nery, A. Neunzert, K. Y. Ng, S. W. S. Ng, C. Nguyen, P. Nguyen, T. Nguyen, L. Nguyen Quynh, W. -T. Ni, S. A. Nichols, A. Nishizawa, S. Nissanke, F. Nocera, M. Noh, M. Norman, C. North, S. Nozaki, L. K. Nuttall, J. Oberling, B. D. O'Brien, Y. Obuchi, J. O'Dell, W. Ogaki, G. Oganesyan, J. J. Oh, K. Oh, S. H. Oh, M. Ohashi, N. Ohishi, M. Ohkawa, F. Ohme, H. Ohta, M. A. Okada, Y. Okutani, K. Okutomi, C. Olivetto, K. Oohara, C. Ooi, R. Oram, B. O'Reilly, R. G. Ormiston, N. D. Ormsby, L. F. Ortega, R. O'Shaughnessy, E. O'Shea, S. Oshino, S. Ossokine, C. Osthelder, S. Otabe, D. J. Ottaway, H. Overmier, A. E. Pace, G. Pagano, M. A. Page, G. Pagliaroli, A. Pai, S. A. Pai, J. R. Palamos, O. Palashov, C. Palomba, K. Pan, P. K. Panda, H. Pang, P. T. H. Pang, C. Pankow, F. Pannarale, B. C. Pant, F. Paoletti, A. Paoli, A. Paolone, A. Parisi, J. Park, W. Parker, D. Pascucci, A. Pasqualetti, R. Passaquieti, D. Passuello, M. Patel, B. Patricelli, E. Payne, T. C. Pechsiri, M. Pedraza, M. Pegoraro, A. Pele, F. E. Peña Arellano, S. Penn, A. Perego, A. Pereira, T. Pereira, C. J. Perez, C. Périgois, A. Perreca, S. Perriès, J. Petermann, D. Petterson, H. P. Pfeiffer, K. A. Pham, K. S. Phukon, O. J. Piccinni, M. Pichot, M. Piendibene, F. Piergiovanni, L. Pierini, V. Pierro, G. Pillant, F. Pilo, L. Pinard, I. M. Pinto, B. J. Piotrzkowski, K. Piotrzkowski, M. Pirello, M. Pitkin, E. Placidi, W. Plastino, C. Pluchar, R. Poggiani, E. Polini, D. Y. T. Pong, S. Ponrathnam, P. Popolizio, E. K. Porter, J. Powell, M. Pracchia, T. Pradier, A. K. Prajapati, K. Prasai, R. Prasanna, G. Pratten, T. Prestegard, M. Principe, G. A. Prodi, L. Prokhorov, P. Prosposito, L. Prudenzi, A. Puecher, M. Punturo, F. Puosi, P. Puppo, M. Pürrer, H. Qi, V. Quetschke, P. J. Quinonez, R. Quitzow-James, F. J. Raab, G. Raaijmakers, H. Radkins, N. Radulesco, P. Raffai, S. X. Rail, S. Raja, C. Rajan, K. E. Ramirez, T. D. Ramirez, A. Ramos-Buades, J. Rana, P. Rapagnani, U. D. Rapol, B. Ratto, V. Raymond, N. Raza, M. Razzano, J. Read, L. A. Rees, T. Regimbau, L. Rei, S. Reid, D. H. Reitze, P. Relton, P. Rettegno, F. Ricci, C. J. Richardson, J. W. Richardson, L. Richardson, P. M. Ricker, G. Riemenschneider, K. Riles, M. Rizzo, N. A. Robertson, R. Robie, F. Robinet, A. Rocchi, J. A. Rocha, S. Rodriguez, R. D. Rodriguez-Soto, L. Rolland, J. G. Rollins, V. J. Roma, M. Romanelli, J. D. Romano, R. Romano, C. L. Romel, A. Romero, I. M. Romero-Shaw, J. H. Romie, C. A. Rose, D. Rosińska, S. G. Rosofsky, M. P. Ross, S. Rowan, S. J. Rowlinson, Santosh Roy, Soumen Roy, D. Rozza, P. Ruggi, K. Ryan, S. Sachdev, T. Sadecki, J. Sadiq, N. Sago, S. Saito, Y. Saito, K. Sakai, Y. Sakai, M. Sakellariadou, Y. Sakuno, O. S. Salafia, L. Salconi, M. Saleem, F. Salemi, A. Samajdar, E. J. Sanchez, J. H. Sanchez, L. E. Sanchez, N. Sanchis-Gual, J. R. Sanders, A. Sanuy, T. R. Saravanan, N. Sarin, B. Sassolas, H. Satari, S. Sato, T. Sato, O. Sauter, R. L. Savage, V. Savant, T. Sawada, D. Sawant, H. L. Sawant, S. Sayah, D. Schaetzl, M. Scheel, J. Scheuer, A. Schindler-Tyka, P. Schmidt, R. Schnabel, M. Schneewind, R. M. S. Schofield, A. Schönbeck, B. W. Schulte, B. F. Schutz, E. Schwartz, J. Scott, S. M. Scott, M. Seglar-Arroyo, E. Seidel, T. Sekiguchi, Y. Sekiguchi, D. Sellers, A. Sergeev, A. S. Sengupta, N. Sennett, D. Sentenac, E. G. Seo, V. Sequino, Y. Setyawati, T. Shaffer, M. S. Shahriar, B. Shams, L. Shao, S. Sharifi, A. Sharma, P. Sharma, P. Shawhan, N. S. Shcheblanov, H. Shen, S. Shibagaki, M. Shikauchi, R. Shimizu, T. Shimoda, K. Shimode, R. Shink, H. Shinkai, T. Shishido, A. Shoda, D. H. Shoemaker, D. M. Shoemaker, K. Shukla, S. ShyamSundar, M. Sieniawska, D. Sigg, L. P. Singer, D. Singh, N. Singh, A. Singha, A. M. Sintes, V. Sipala, V. Skliris, B. J. J. Slagmolen, T. J. Slaven-Blair, J. Smetana, J. R. Smith, R. J. E. Smith, S. N. Somala, K. Somiya, E. J. Son, K. Soni, S. Soni, B. Sorazu, V. Sordini, F. Sorrentino, N. Sorrentino, H. Sotani, R. Soulard, T. Souradeep, E. Sowell, V. Spagnuolo, A. P. Spencer, M. Spera, A. K. Srivastava, V. Srivastava, K. Staats, C. Stachie, D. A. Steer, J. Steinlechner, S. Steinlechner, D. J. Stops, M. Stover, K. A. Strain, L. C. Strang, G. Stratta, A. Strunk, R. Sturani, A. L. Stuver, J. Südbeck, S. Sudhagar, V. Sudhir, R. Sugimoto, H. G. Suh, T. Z. Summerscales, H. Sun, L. Sun, S. Sunil, A. Sur, J. Suresh, P. J. Sutton, Takamasa Suzuki, Toshikazu Suzuki, B. L. Swinkels, M. J. Szczepańczyk, P. Szewczyk, M. Tacca, H. Tagoshi, S. C. Tait, H. Takahashi, R. Takahashi, A. Takamori, S. Takano, H. Takeda, M. Takeda, C. Talbot, H. Tanaka, Kazuyuki Tanaka, Kenta Tanaka, Taiki Tanaka, Takahiro Tanaka, A. J. Tanasijczuk, S. Tanioka, D. B. Tanner, D. Tao, A. Tapia, E. N. Tapia San Martin, J. D. Tasson, S. Telada, R. Tenorio, L. Terkowski, M. Test, M. P. Thirugnanasambandam, M. Thomas, P. Thomas, J. E. Thompson, S. R. Thondapu, K. A. Thorne, E. Thrane, Shubhanshu Tiwari, Srishti Tiwari, V. Tiwari, K. Toland, A. E. Tolley, T. Tomaru, Y. Tomigami, T. Tomura, M. Tonelli, A. Torres-Forné, C. I. Torrie, I. Tosta e Melo, D. Töyrä, A. Trapananti, F. Travasso, G. Traylor, M. C. Tringali, A. Tripathee, L. Troiano, A. Trovato, L. Trozzo, R. J. Trudeau, D. S. Tsai, D. Tsai, K. W. Tsang, T. Tsang, J-S. Tsao, M. Tse, R. Tso, K. Tsubono, S. Tsuchida, L. Tsukada, D. Tsuna, T. Tsutsui, T. Tsuzuki, M. Turconi, D. Tuyenbayev, A. S. Ubhi, N. Uchikata, T. Uchiyama, R. P. Udall, A. Ueda, T. Uehara, K. Ueno, G. Ueshima, D. Ugolini, C. S. Unnikrishnan, F. Uraguchi, A. L. Urban, T. Ushiba, S. A. Usman, A. C. Utina, H. Vahlbruch, G. Vajente, A. Vajpeyi, G. Valdes, M. Valentini, V. Valsan, N. van Bakel, M. van Beuzekom, J. F. J. van den Brand, C. Van Den Broeck, N. Van Remortel, D. C. Vander-Hyde, L. van der Schaaf, J. V. van Heijningen, M. H. P. M. van Putten, M. Vardaro, A. F. Vargas, V. Varma, M. Vasúth, A. Vecchio, G. Vedovato, J. Veitch, P. J. Veitch, K. Venkateswara, J. Venneberg, G. Venugopalan, D. Verkindt, Y. Verma, D. Veske, F. Vetrano, A. Viceré, A. D. Viets, V. Villa-Ortega, J. -Y. Vinet, S. Vitale, T. Vo, H. Vocca, E. R. G. von Reis, J. von Wrangel, C. Vorvick, S. P. Vyatchanin, L. E. Wade, M. Wade, K. J. Wagner, R. C. Walet, M. Walker, G. S. Wallace, L. Wallace, S. Walsh, J. Wang, J. Z. Wang, W. H. Wang, R. L. Ward, J. Warner, M. Was, T. Washimi, N. Y. Washington, J. Watchi, B. Weaver, L. Wei, M. Weinert, A. J. Weinstein, R. Weiss, C. M. Weller, F. Wellmann, L. Wen, P. Weßels, J. W. Westhouse, K. Wette, J. T. Whelan, D. D. White, B. F. Whiting, C. Whittle, D. Wilken, D. Williams, M. J. Williams, A. R. Williamson, J. L. Willis, B. Willke, D. J. Wilson, W. Winkler, C. C. Wipf, T. Wlodarczyk, G. Woan, J. Woehler, J. K. Wofford, I. C. F. Wong, C. Wu, D. S. Wu, H. Wu, S. Wu, D. M. Wysocki, L. Xiao, W-R. Xu, T. Yamada, H. Yamamoto, Kazuhiro Yamamoto, Kohei Yamamoto, T. Yamamoto, K. Yamashita, R. Yamazaki, F. W. Yang, L. Yang, Yang Yang, Yi Yang, Z. Yang, M. J. Yap, D. W. Yeeles, A. B. Yelikar, M. Ying, K. Yokogawa, J. Yokoyama, T. Yokozawa, A. Yoon, T. Yoshioka, Hang Yu, Haocun Yu, H. Yuzurihara, A. Zadrożny, M. Zanolin, S. Zeidler, T. Zelenova, J. -P. Zendri, M. Zevin, M. Zhan, H. Zhang, J. Zhang, L. Zhang, R. Zhang, T. Zhang, C. Zhao, G. Zhao, Yue Zhao, Yuhang Zhao, Z. Zhou, X. J. Zhu, Z. -H. Zhu, M. E. Zucker, J. Zweizig

Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB.

General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics

Inorganic component imaging of aggregate glue droplets on spider orb webs by TOF-SIMS

no code implementations27 Jan 2021 Yue Zhao, Masato Morita, Tetsuo Sakamoto

A uniform element distribution is seen for suspended pristine aggregate glue droplets, and a differential spreading of aggregate glue components is seen for attached aggregate glue droplets.

Atomic and Molecular Clusters Applied Physics Instrumentation and Detectors

PyHealth: A Python Library for Health Predictive Models

2 code implementations11 Jan 2021 Yue Zhao, Zhi Qiao, Cao Xiao, Lucas Glass, Jimeng Sun

PyHealth consists of data preprocessing module, predictive modeling module, and evaluation module.

Benchmarking BIG-bench Machine Learning

Cross-Attention Guided Network for Visual Tracking

no code implementations1 Jan 2021 Yue Zhao, Zhibin Yu

Here, the channel attention of target template is introduced to guide the feature learning for search branch, and then the self-spatial attention is used to localize the informative part location after the correlation processing.

Visual Tracking

TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation

no code implementations26 Dec 2020 Lingming Zhang, Yue Zhao, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen

State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.

Graph Learning

AutoAudit: Mining Accounting and Time-Evolving Graphs

1 code implementation1 Nov 2020 Meng-Chieh Lee, Yue Zhao, Aluna Wang, Pierre Jinghong Liang, Leman Akoglu, Vincent S. Tseng, Christos Faloutsos

How can we spot money laundering in large-scale graph-like accounting datasets?

Social and Information Networks

A random batch Ewald method for particle systems with Coulomb interactions

no code implementations4 Oct 2020 Shi Jin, Lei LI, Zhenli Xu, Yue Zhao

We develop a random batch Ewald (RBE) method for molecular dynamics simulations of particle systems with long-range Coulomb interactions, which achieves an $O(N)$ complexity in each step of simulating the $N$-body systems.

Computational Physics 65C35, 82M37, 65T50

Automating Outlier Detection via Meta-Learning

1 code implementation22 Sep 2020 Yue Zhao, Ryan A. Rossi, Leman Akoglu

Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a model)?

Anomaly Detection AutoML +3

COPOD: Copula-Based Outlier Detection

3 code implementations20 Sep 2020 Zheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, Xiyang Hu

In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.

Outlier Detection

SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources

1 code implementation20 Sep 2020 Zheng Li, Yue Zhao, Jialin Fu

A synthetic dataset is a data object that is generated programmatically, and it may be valuable to creating a single dataset from multiple sources when direct collection is difficult or costly.

Feature Engineering Synthetic Data Generation

Intra- and Inter-Action Understanding via Temporal Action Parsing

no code implementations CVPR 2020 Dian Shao, Yue Zhao, Bo Dai, Dahua Lin

Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features.

Action Parsing Action Recognition +1

Omni-sourced Webly-supervised Learning for Video Recognition

3 code implementations ECCV 2020 Haodong Duan, Yue Zhao, Yuanjun Xiong, Wentao Liu, Dahua Lin

Then a joint-training strategy is proposed to deal with the domain gaps between multiple data sources and formats in webly-supervised learning.

Ranked #5 on Action Recognition on UCF101 (using extra training data)

Action Classification Action Recognition +1

A random-batch Monte Carlo method for many-body systems with singular kernels

no code implementations14 Mar 2020 Lei Li, Zhenli Xu, Yue Zhao

The cost of the rejection step is $O(1)$ since the interaction used is of short range.

Computational Physics Numerical Analysis Numerical Analysis 82B80, 60H35, 65C05

SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection

1 code implementation11 Mar 2020 Yue Zhao, Xiyang Hu, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, Yunlong Wang, Zhi Qiao, Jimeng Sun, Leman Akoglu

Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.

Dimensionality Reduction Fraud Detection +2

On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks

1 code implementation CVPR 2020 Yue Zhao, Yuwei Wu, Caihua Chen, Andrew Lim

Armed with the Thompson Sampling, we develop a black-box attack with success rate over 95% on ModelNet40 data set.

Thompson Sampling

SUOD: Toward Scalable Unsupervised Outlier Detection

2 code implementations8 Feb 2020 Yue Zhao, Xueying Ding, Jianing Yang, Haoping Bai

In this study, we propose a three-module acceleration framework called SUOD to expedite the training and prediction with a large number of unsupervised detection models.

Knowledge Distillation Outlier Detection +1

Extracting clinical concepts from user queries

no code implementations12 Dec 2019 Yue Zhao, John Handley

Often trained on annotated clinical notes, clinical NER models tend to struggle with tagging clinical entities in user queries because of the structural differences between clinical notes and user queries.

Clinical Concept Extraction named-entity-recognition +2

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

2 code implementations1 Dec 2019 Yue Zhao, Maciej K. Hryniewicki

A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets.

Anomaly Detection Outlier Detection +1

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

1 code implementation23 Nov 2019 Yue Zhao, Maciej K. Hryniewicki

Selecting and combining the outlier scores of different base detectors used within outlier ensembles can be quite challenging in the absence of ground truth.

outlier ensembles Test

RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems

no code implementations11 Nov 2019 Jianmin Guo, Yue Zhao, Quan Zhang, Yu Jiang

Compared with the neuron coverage, the proposed state coverage metrics as guidance excel with 4. 17% to 97. 22% higher success (or generation) rate.

Language Modelling Test


no code implementations25 Sep 2019 Yue Zhao, Xiangsheng Huang, Ludan Kou

Although adaptive algorithms have achieved significant success in training deep neural networks with faster training speed, they tend to have poor generalization performance compared to SGD with Momentum(SGDM).

Combining Machine Learning Models using combo Library

1 code implementation21 Sep 2019 Yue Zhao, Xuejian Wang, Cheng Cheng, Xueying Ding

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications.

Anomaly Detection BIG-bench Machine Learning +2

Learning to Recover Sparse Signals

no code implementations NeurIPS Workshop Deep_Invers 2019 Sichen Zhong, Yue Zhao, Jianshu Chen

In compressed sensing, a primary problem to solve is to reconstruct a high dimensional sparse signal from a small number of observations.

reinforcement-learning Reinforcement Learning (RL)

SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula

2 code implementations16 Apr 2019 Colin Wan, Zheng Li, Alicia Guo, Yue Zhao

Synthetic population generation is the process of combining multiple socioeconomic and demographic datasets from different sources and/or granularity levels, and downscaling them to an individual level.

Feature Engineering

Music Artist Classification with Convolutional Recurrent Neural Networks

5 code implementations14 Jan 2019 Zain Nasrullah, Yue Zhao

To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to the artist20 music artist identification dataset under a comprehensive set of conditions.

Artist classification Classification +4

PyOD: A Python Toolbox for Scalable Outlier Detection

4 code implementations6 Jan 2019 Yue Zhao, Zain Nasrullah, Zheng Li

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.

Anomaly Detection outlier ensembles

Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors

no code implementations26 Dec 2018 Yue Zhao, Hong Zhu, Ruigang Liang, Qintao Shen, Shengzhi Zhang, Kai Chen

In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors.

Adversarial Attack Autonomous Driving

LSCP: Locally Selective Combination in Parallel Outlier Ensembles

1 code implementation4 Dec 2018 Yue Zhao, Zain Nasrullah, Maciej K. Hryniewicki, Zheng Li

The top-performing base detectors in this local region are selected and combined as the model's final output.

Anomaly Detection outlier ensembles +1

Find and Focus: Retrieve and Localize Video Events with Natural Language Queries

no code implementations ECCV 2018 Dian Shao, Yu Xiong, Yue Zhao, Qingqiu Huang, Yu Qiao, Dahua Lin

The thriving of video sharing services brings new challenges to video retrieval, e. g. the rapid growth in video duration and content diversity.

Natural Language Queries Retrieval +1

DLFuzz: Differential Fuzzing Testing of Deep Learning Systems

1 code implementation28 Aug 2018 Jianmin Guo, Yu Jiang, Yue Zhao, Quan Chen, Jiaguang Sun

Deep learning (DL) systems are increasingly applied to safety-critical domains such as autonomous driving cars.

Software Engineering

Federated Learning with Non-IID Data

1 code implementation2 Jun 2018 Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra

Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.

Federated Learning

Recognize Actions by Disentangling Components of Dynamics

no code implementations CVPR 2018 Yue Zhao, Yuanjun Xiong, Dahua Lin

Despite the remarkable progress in action recognition over the past several years, existing methods remain limited in efficiency and effectiveness.

Action Recognition Optical Flow Estimation +2

CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition

no code implementations24 Jan 2018 Xuejing Yuan, Yuxuan Chen, Yue Zhao, Yunhui Long, Xiaokang Liu, Kai Chen, Shengzhi Zhang, Heqing Huang, Xiao-Feng Wang, Carl A. Gunter

For this purpose, we developed novel techniques that address a key technical challenge: integrating the commands into a song in a way that can be effectively recognized by ASR through the air, in the presence of background noise, while not being detected by a human listener.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification

no code implementations21 Oct 2017 Yue Zhao, Jianshu Chen, H. Vincent Poor

Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem.

Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas

no code implementations28 May 2013 Marten Wegkamp, Yue Zhao

Then we study a factor model of $\Sigma$, for which we propose a refined estimator $\widetilde{\Sigma}$ by fitting a low-rank matrix plus a diagonal matrix to $\hat{\Sigma}$ using least squares with a nuclear norm penalty on the low-rank matrix.

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