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4,295 results on '"Wang, Xiaoliang"'

1. SynthDoc: Bilingual Documents Synthesis for Visual Document Understanding

Author

Ding, Chuanghao, Liu, Xuejing, Tang, Wei, Li, Juan, Wang, Xiaoliang, Zhao, Rui, Nguyen, Cam-Tu, and Tan, Fei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

This paper introduces SynthDoc, a novel synthetic document generation pipeline designed to enhance Visual Document Understanding (VDU) by generating high-quality, diverse datasets that include text, images, tables, and charts. Addressing the challenges of data acquisition and the limitations of existing datasets, SynthDoc leverages publicly available corpora and advanced rendering tools to create a comprehensive and versatile dataset. Our experiments, conducted using the Donut model, demonstrate that models trained with SynthDoc's data achieve superior performance in pre-training read tasks and maintain robustness in downstream tasks, despite language inconsistencies. The release of a benchmark dataset comprising 5,000 image-text pairs not only showcases the pipeline's capabilities but also provides a valuable resource for the VDU community to advance research and development in document image recognition. This work significantly contributes to the field by offering a scalable solution to data scarcity and by validating the efficacy of end-to-end models in parsing complex, real-world documents.

Published
2024

6. HardTaint: Production-Run Dynamic Taint Analysis via Selective Hardware Tracing

Author

Zhang, Yiyu, Liu, Tianyi, Wang, Yueyang, Qi, Yun, Ji, Kai, Tang, Jian, Wang, Xiaoliang, Li, Xuandong, and Zuo, Zhiqiang

Subjects
Computer Science - Cryptography and Security, Computer Science - Programming Languages, Computer Science - Software Engineering
Abstract

Dynamic taint analysis (DTA), as a fundamental analysis technique, is widely used in security, privacy, and diagnosis, etc. As DTA demands to collect and analyze massive taint data online, it suffers extremely high runtime overhead. Over the past decades, numerous attempts have been made to lower the overhead of DTA. Unfortunately, the reductions they achieved are marginal, causing DTA only applicable to the debugging/testing scenarios. In this paper, we propose and implement HardTaint, a system that can realize production-run dynamic taint tracking. HardTaint adopts a hybrid and systematic design which combines static analysis, selective hardware tracing and parallel graph processing techniques. The comprehensive evaluations demonstrate that HardTaint introduces only around 9% runtime overhead which is an order of magnitude lower than the state-of-the-arts, while without sacrificing any taint detection capability.

Published
2024

11. Secure Inter-domain Routing and Forwarding via Verifiable Forwarding Commitments

Author

Wang, Xiaoliang, Liu, Zhuotao, Li, Qi, Guo, Yangfei, Ling, Sitong, Zhan, Jiangou, Xu, Yi, Xu, Ke, and Wu, Jianping

Subjects
Computer Science - Networking and Internet Architecture
Abstract

The Internet inter-domain routing system is vulnerable. On the control plane, the de facto Border Gateway Protocol (BGP) does not have built-in mechanisms to authenticate routing announcements, so an adversary can announce virtually arbitrary paths to hijack network traffic; on the data plane, it is difficult to ensure that actual forwarding path complies with the control plane decisions. The community has proposed significant research to secure the routing system. Yet, existing secure BGP protocols (e.g., BGPsec) are not incrementally deployable, and existing path authorization protocols are not compatible with the current Internet routing infrastructure. In this paper, we propose FC-BGP, the first secure Internet inter-domain routing system that can simultaneously authenticate BGP announcements and validate data plane forwarding in an efficient and incrementally-deployable manner. FC-BGP is built upon a novel primitive, name Forwarding Commitment, to certify an AS's routing intent on its directly connected hops. We analyze the security benefits of FC-BGP in the Internet at different deployment rates. Further, we implement a prototype of FC-BGP and extensively evaluate it over a large-scale overlay network with 100 virtual machines deployed globally. The results demonstrate that FC-BGP saves roughly 55% of the overhead required to validate BGP announcements compared with BGPsec, and meanwhile FC-BGP introduces a small overhead for building a globally-consistent view on the desirable forwarding paths., Comment: 16 pages, 17 figures

Published
2023

13. Genetic Susceptibility to Nonalcoholic Fatty Liver Disease and Risk for Pancreatic Cancer: Mendelian Randomization.

Author

Canzian, Federico, Du, Mengmeng, Gallinger, Steven, Giles, Graham, Goodman, Phyllis, Haiman, Christopher, Kogevinas, Manolis, Kooperberg, Charles, LeMarchand, Loic, Neale, Rachel, Visvanathan, Kala, White, Emily, Albanes, Demetrius, Andreotti, Gabriella, Babic, Ana, Berndt, Sonja, Brais, Lauren, Brennan, Paul, Buring, Julie, Rabe, Kari, Bamlet, William, Chanock, Stephen, Fuchs, Charles, Gaziano, J, Giovannucci, Edward, Hackert, Thilo, Hassan, Manal, Katzke, Verena, Kurtz, Robert, Lee, I-Min, Malats, Núria, Murphy, Neil, Oberg, Ann, Orlow, Irene, Porta, Miquel, Real, Francisco, Rothman, Nathaniel, Sesso, Howard, Silverman, Debra, Thompson, Ian, Wactawski-Wende, Jean, Wang, Xiaoliang, Wentzensen, Nicolas, Yu, Herbert, Zeleniuch-Jacquotte, Anne, Yu, Kai, Wolpin, Brian, Duell, Eric, Li, Donghui, Hung, Rayjean, Perdomo, Sandra, McCullough, Marjorie, Freedman, Neal, Patel, Alpa, Peters, Ulrike, Riboli, Elio, Sund, Malin, Tjønneland, Anne, Zhong, Jun, Van Den Eeden, Stephen, Kraft, Peter, Risch, Harvey, Amundadottir, Laufey, Klein, Alison, Stolzenberg-Solomon, Rachael, Antwi, Samuel, King, Sontoria, Veliginti, Swathi, Brouwers, Martijn, Ren, Zhewen, Zheng, Wei, Setiawan, Veronica, Wilkens, Lynne, Shu, Xiao-Ou, Arslan, Alan, Beane Freeman, Laura, and Bracci, Paige

Subjects
Humans, Non-alcoholic Fatty Liver Disease, Genetic Predisposition to Disease, Genome-Wide Association Study, Mendelian Randomization Analysis, Pancreatic Neoplasms, Obesity, Polymorphism, Single Nucleotide
Abstract

BACKGROUND: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. METHODS: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. RESULTS: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88-1.22; MR-Egger OR, 0.89; 95% CI, 0.65-1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90-1.27; MR-Egger OR, 0.93; 95% CI, 0.67-1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. CONCLUSIONS: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. IMPACT: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.

Published
2023

24. Voltage-gated potassium channels (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

Author

Attali, Bernard, Chandy, K George, Giese, M Hunter, Grissmer, Stephan, Gutman, George A, Jan, Lily Y, Lazdunski, Michel, Mckinnon, David, Nerbonne, Jeanne, Pardo, Luis A, Robertson, Gail A, Rudy, Bernardo, Sanguinetti, Michael C, Stühmer, Walter, Trimmer, James S, and Wang, Xiaoliang

Abstract

The 6TM family of K channels comprises the voltage-gated KV subfamilies, the EAG subfamily (which includes hERG channels), the Ca2+-activated Slo subfamily (actually with 7TM, termed BK) and the Ca2+-activated SK subfamily. These channels possess a pore-forming α subunit that comprise tetramers of identical subunits (homomeric) or of different subunits (heteromeric). Heteromeric channels can only be formed within subfamilies (e.g. Kv1.1 with Kv1.2; Kv7.2 with Kv7.3). The pharmacology largely reflects the subunit composition of the functional channel.

Published
2023

25. Voltage-gated potassium channels (Kv) in GtoPdb v.2021.3

Author

Attali, Bernard, Chandy, K George, Giese, M Hunter, Grissmer, Stephan, Gutman, George A, Jan, Lily Y, Lazdunski, Michel, Mckinnon, David, Nerbonne, Jeanne, Pardo, Luis A, Robertson, Gail A, Rudy, Bernardo, Sanguinetti, Michael C, Stühmer, Walter, Trimmer, James S, and Wang, Xiaoliang

Abstract

The 6TM family of K channels comprises the voltage-gated KV subfamilies, the EAG subfamily (which includes hERG channels), the Ca2+-activated Slo subfamily (actually with 7TM, termed BK) and the Ca2+-activated SK subfamily. These channels possess a pore-forming α subunit that comprise tetramers of identical subunits (homomeric) or of different subunits (heteromeric). Heteromeric channels can only be formed within subfamilies (e.g. Kv1.1 with Kv1.2; Kv7.2 with Kv7.3). The pharmacology largely reflects the subunit composition of the functional channel.

Published
2023

26. An Experimental Research of Text-to-SQL for Heterogeneous Data in Large Language Models

Author

Yang, Weiwei, Wang, Xiaoliang, Chen, Bosheng, Liu, Yong, Wang, Bing, Wang, Hui, Wang, Xiaoke, Zhua, Haitao, Wang, Zhehao, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Xiankun, editor, and Zhang, Qinhu, editor

Published
2024
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27. FMUnet: Frequency Feature Enhancement Multi-level U-Net for Low-Dose CT Denoising with a Real Collected LDCT Image Dataset

Author

Zhang, Yu, Yang, Xinqi, Gong, Guoliang, Meng, Xianghong, Wang, Xiaoliang, Zhang, Zhongwei, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Chuanlei, editor, and Zhang, Qinhu, editor

Published
2024
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28. Joint Video Transcoding and Representation Selection for Edge-Assisted Multi-party Video Conferencing

Author

Kong, Fanhao, Cao, Tuo, Qian, Zhuzhong, Wang, Xiaoliang, Zhao, Ming, Wang, Liming, Lin, Zhenjie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tari, Zahir, editor, Li, Keqiu, editor, and Wu, Hongyi, editor

Published
2024
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30. From RDMA to RDCA: Toward High-Speed Last Mile of Data Center Networks Using Remote Direct Cache Access

Author

Li, Qiang, Xiang, Qiao, Liu, Derui, Wang, Yuxin, Qiu, Haonan, Wang, Xiaoliang, Zhang, Jie, Wen, Ridi, Song, Haohao, Tian, Gexiao, Huang, Chenyang, Chen, Lulu, Liu, Shaozong, Wu, Yaohui, Wu, Zhiwu, Luo, Zicheng, Shao, Yuchao, Han, Chao, Wu, Zhongjie, Dong, Jianbo, Cao, Zheng, Wu, Jinbo, Shu, Jiwu, and Wu, Jiesheng

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

In this paper, we conduct systematic measurement studies to show that the high memory bandwidth consumption of modern distributed applications can lead to a significant drop of network throughput and a large increase of tail latency in high-speed RDMA networks.We identify its root cause as the high contention of memory bandwidth between application processes and network processes. This contention leads to frequent packet drops at the NIC of receiving hosts, which triggers the congestion control mechanism of the network and eventually results in network performance degradation. To tackle this problem, we make a key observation that given the distributed storage service, the vast majority of data it receives from the network will be eventually written to high-speed storage media (e.g., SSD) by CPU. As such, we propose to bypass host memory when processing received data to completely circumvent this performance bottleneck. In particular, we design Lamda, a novel receiver cache processing system that consumes a small amount of CPU cache to process received data from the network at line rate. We implement a prototype of Lamda and evaluate its performance extensively in a Clos-based testbed. Results show that for distributed storage applications, Lamda improves network throughput by 4.7% with zero memory bandwidth consumption on storage nodes, and improves network throughput by up 17% and 45% for large block size and small size under the memory bandwidth pressure, respectively. Lamda can also be applied to latency-sensitive HPC applications, which reduces their communication latency by 35.1%.

Published
2022

31. ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations

Author

Xu, Zhiying, Xu, Jiafan, Peng, Hongding, Wang, Wei, Wang, Xiaoliang, Wan, Haoran, Dai, Haipeng, Xu, Yixu, Cheng, Hao, Wang, Kun, and Chen, Guihai

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Deep learning models rely on highly optimized tensor libraries for efficient inference on heterogeneous hardware. Current deep compilers typically predetermine layouts of tensors and then optimize loops of operators. However, such unidirectional and one-off workflow strictly separates graph-level optimization and operator-level optimization into different system layers, missing opportunities for unified tuning. This paper proposes ALT, a compiler that performs joint graph- and operator-level optimizations for deep models. ALT provides a generic transformation module to manipulate layouts and loops with easy-to-use primitive functions. ALT further integrates an auto-tuning module that jointly optimizes graph-level data layouts and operator-level loops while guaranteeing efficiency. Experimental results show that ALT significantly outperforms state-of-the-art compilers (e.g., Ansor) in terms of both single operator performance (e.g., 1.5x speedup on average) and end-to-end inference performance (e.g., 1.4x speedup on average).

Published
2022

33. A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

Author

Middha, Pooja, Wang, Xiaoliang, Behrens, Sabine, Bolla, Manjeet K, Wang, Qin, Dennis, Joe, Michailidou, Kyriaki, Ahearn, Thomas U, Andrulis, Irene L, Anton-Culver, Hoda, Arndt, Volker, Aronson, Kristan J, Auer, Paul L, Augustinsson, Annelie, Baert, Thaïs, Freeman, Laura E Beane, Becher, Heiko, Beckmann, Matthias W, Benitez, Javier, Bojesen, Stig E, Brauch, Hiltrud, Brenner, Hermann, Brooks-Wilson, Angela, Campa, Daniele, Canzian, Federico, Carracedo, Angel, Castelao, Jose E, Chanock, Stephen J, Chenevix-Trench, Georgia, Cordina-Duverger, Emilie, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Dossus, Laure, Dugué, Pierre-Antoine, Eliassen, A Heather, Eriksson, Mikael, Evans, D Gareth, Fasching, Peter A, Figueroa, Jonine D, Fletcher, Olivia, Flyger, Henrik, Gabrielson, Marike, Gago-Dominguez, Manuela, Giles, Graham G, González-Neira, Anna, Grassmann, Felix, Grundy, Anne, Guénel, Pascal, Haiman, Christopher A, Håkansson, Niclas, Hall, Per, Hamann, Ute, Hankinson, Susan E, Harkness, Elaine F, Holleczek, Bernd, Hoppe, Reiner, Hopper, John L, Houlston, Richard S, Howell, Anthony, Hunter, David J, Ingvar, Christian, Isaksson, Karolin, Jernström, Helena, John, Esther M, Jones, Michael E, Kaaks, Rudolf, Keeman, Renske, Kitahara, Cari M, Ko, Yon-Dschun, Koutros, Stella, Kurian, Allison W, Lacey, James V, Lambrechts, Diether, Larson, Nicole L, Larsson, Susanna, Le Marchand, Loic, Lejbkowicz, Flavio, Li, Shuai, Linet, Martha, Lissowska, Jolanta, Martinez, Maria Elena, Maurer, Tabea, Mulligan, Anna Marie, Mulot, Claire, Murphy, Rachel A, Newman, William G, Nielsen, Sune F, Nordestgaard, Børge G, Norman, Aaron, O’Brien, Katie M, Olson, Janet E, Patel, Alpa V, Prentice, Ross, Rees-Punia, Erika, Rennert, Gad, Rhenius, Valerie, Ruddy, Kathryn J, and Sandler, Dale P

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer Genomics, Human Genome, Estrogen, Cancer, Women's Health, Genetics, Prevention, Aging, Breast Cancer, 2.1 Biological and endogenous factors, Adult, Female, Humans, Gene-Environment Interaction, Genetic Predisposition to Disease, Breast Neoplasms, Bayes Theorem, Genome-Wide Association Study, Risk Factors, Polymorphism, Single Nucleotide, Case-Control Studies, Breast cancer, Gene-environment interactions, Genetic epidemiology, European ancestry, CTS Consortium, ABCTB Investigators, kConFab Investigators, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

BackgroundGenome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.MethodsAnalyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.ResultsAssuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability

Published
2023

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