Wenyu Jiang

Queen's University

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Co-Authors

Chongjun Wang

Nanjing University

Bingshu E. Chen

Queen's University

Mingcai Chen

Nanjing University

Hao Cheng

Northwestern Polytechnical University

Claus Bauer

Dolby (Netherlands)

Richard Simon

Lismore Base Hospital

Regunathan Radhakrishnan

Pivotal Labs

Dongsheng Tu

Queen's University

Shuai Feng

Hiroshima University

Yuntao Du

Beijing Academy of Artificial Intelligence

Cooperative Institutions

Sichuan Agricultural University

Ministry of Agriculture and Rural Affairs

Queen's University

Nanjing University

Chinese Academy of Sciences

Huazhong University of Science and Technology

Zhejiang University

Nanjing University of Posts and Telecommunications

Nanjing University of Science and Technology

Dolby (Netherlands)

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Using Programmable Graphics Hardware for Acoustics and Audio Rendering

Journal of the Audio Engineering Society (2009)

Nicolas Tsingos Wenyu Jiang Ian Williams

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Citations (36)

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Analytical Chemistry (2024)

Qingxia Yang Shuman Chen Wenyu Jiang Lan Mi Jiarui Liu

Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein,

Multiclass classification

Identification

10.1021/acs.analchem.3c03212

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Citations (2)

High-performance, very low power content-based search engine

Wenyu Jiang Rongshan Yu

Content-based search, such as audio/video fingerprinting, identifies a piece of query content by matching its perceptual features against those from a database of reference content. Such matching is challenging in both scalability and robustness, even with state-of-art methods like Locality Sensitive Hashing (LSH). Previously, Vote Count, a hardware-assisted algorithm, was proposed to provide such scalability and robustness. We have analyzed this algorithm and found that it would however consume very high power, to the point of even making cooling impractical. In this paper, we propose an alternative hardware-assisted search algorithm that is estimated to use very low power while providing scalability and robustness. It is estimated to be able to provide ≥0.95 recall on a 1-Trillion feature vector (~23M hours of video at 12fps signature rate) database within 700μs at <; 150W, when the hashing bit error rate (BER) would have been 20% even with 1-bit quantization. This amounts to over 1000× power and energy savings compared to highly competitive configurations of LSH, while at lower expected system cost and a saving of millions of dollars per year in electricity cost alone.

Robustness

Locality-sensitive hashing

Memory footprint

10.1109/icmew.2013.6618422

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Citations (8)

Consistent covariances estimation for stratum imbalances under minimization method for covariate‐adaptive randomization

Scandinavian Journal of Statistics (2023)

Zixuan Zhao Yanglei Song Wenyu Jiang Dongsheng Tu

Abstract Pocock and Simon's minimization method is a popular approach for covariate‐adaptive randomization in clinical trials. Valid statistical inference with data collected under the minimization method requires the knowledge of the limiting covariance matrix of within‐stratum imbalances, whose existence is only recently established. In this work, we propose a bootstrap‐based estimator for this limit and establish its consistency, in particular, by Le Cam's third lemma. As an application, we consider in simulation studies adjustments to existing robust tests for treatment effects with survival data by the proposed estimator. It shows that the adjusted tests achieve a size close to the nominal level, and unlike other designs, the robust tests without adjustment may have an asymptotic size inflation issue under the minimization method.

Minification

Lemma (botany)

Statistical Inference

Nominal level

10.1111/sjos.12703

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Citations (1)

A review of video fingerprints invariant to geometric attacks

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2009)

Regunathan Radhakrishnan Wenyu Jiang Claus Bauer

Video fingerprints can help us identify a large amount of video on the Internet and enable interesting services to the end user. One of the main challenges for video fingerprints is for them to be robust against intentional/ unintentional geometric modifications on the content such as scaling, aspect ratio conversion, rotation and cropping. In this paper, we review a number of fingerprinting methods proposed in literature that are particularly designed to be robust against such modifications. We also present two approaches that we adopted. One that is based on estimation of Singular Value Decomposition (SVD) bases from a window of past video frames (Approach 1) and another that is based on extraction of moment invariant features from concentric circular regions and doesn't require any specific transform (Approach 2). While both approaches provide the desired robustness against geometric modifications, Approach 1 is computationally more intensive than Approach 2 as the SVD bases are updated for every input frame at 12fps. It also requires a longer query clip than Approach 2 for reliable identification. We present results comparing the performance of both of these approaches for a 150hr video database.

Robustness

Singular value

10.1117/12.805627

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Citations (6)

Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise

arXiv (Cornell University) (2021)

Mingcai Chen Hao Cheng Yuntao Du Ming Xu Wenyu Jiang

Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pipeline alternates between eliminating possible incorrect labels and semi-supervised training. However, discarding part of noisy labels could result in a loss of information, especially when the corruption has a dependency on data, e.g., class-dependent or instance-dependent. Moreover, from the training dynamics of a representative two-stage method DivideMix, we identify the domination of confirmation bias: pseudo-labels fail to correct a considerable amount of noisy labels, and consequently, the errors accumulate. To sufficiently exploit information from noisy labels and mitigate wrong corrections, we propose Robust Label Refurbishment (Robust LR) a new hybrid method that integrates pseudo-labeling and confidence estimation techniques to refurbish noisy labels. We show that our method successfully alleviates the damage of both label noise and confirmation bias. As a result, it achieves state-of-the-art performance across datasets and noise types, namely CIFAR under different levels of synthetic noise and Mini-WebVision and ANIMAL-10N with real-world noise.

Noisy data

10.48550/arxiv.2112.02960

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Citations (1)

Optimal configuration of hash table based multimedia fingerprint databases using weak bits

Claus Bauer Regunathan Radhakrishnan Wenyu Jiang

The increasingly large amount of digital multimedia content has created a need for technologies to search and identify multimedia files. Multimedia fingerprinting has been widely researched and successfully commercialized as a technology to trace and recognize both audio and video content. Most published research on multimedia fingerprinting focuses on the design of the fingerprinting extraction algorithm. The architecture of the fingerprinting database and the database query search algorithm has only been investigated in very few publications. In particular, no comprehensive mathematical models describing the accuracy of database query algorithms have been developed. Therefore, most performance evaluations of fingerprint system rely on experimental data. In this paper, we develop an analytic model to describe the operation of hash-table based multimedia fingerprinting databases. It allows to predict the accuracy of a fingerprinting query algorithm as a function of the statistical distribution of the fingerprints and the specific design of the fingerprint database.

Table (database)

TRACE (psycholinguistics)

Multimedia database

MD5

10.1109/icme.2010.5583568

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Citations (3)

Bootstrapping U-statistics: applications in least squares and robust regression

Sankhya B (2012)

Wenyu Jiang John D. Kalbfleisch

Resampling

Robust regression

Bootstrapping (finance)

Ordinary least squares

Statistic

Estimating equations

10.1007/s13571-012-0043-2

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Citations (2)

Supplement 1. Description of "Bias Corrected Accelerated Interval Using Bootstrap Case Cross-Validation (BCCV-BCa)"

Wenyu Jiang Sudhir Varma Richard Simon

α θ − using the 1 1 α − empirical quantile of cross-validation estimate � *,b θ , b=1, …, B, calculated through the bootstrap case cross-validation (BCCV). Specifically, we calculate the bias correction term as (Proportion of 1 0 z − =Φ �

Cross-validation

Quantile

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Comparison and optimization of packet loss repair methods on VoIP perceived quality under bursty loss

Wenyu Jiang Henning Schulzrinne

Packet loss degrades the perceived quality of voice over IP (VoIP). In addition, packet loss in the Internet tends to come in bursts, which may further degrade audio quality. Using the Gilbert loss model, we infer that changing the packet interval affects loss burstiness, which in turn influences forward error correction (FEC) performance. Next, we perform subjective listening tests based on Mean Opinion Score (MOS) to evaluate the effect of bursty loss on VoIP perceived quality. Then, we compare the perceived quality achieved by two major loss repair methods: FEC and low bit-rate redundancy (LBR). Our MOS test results show that FEC is much preferred over LBR. In addition, our MOS results reveal that, under bursty loss, FEC quality is much better with a moderately large packet interval. Finally, because FEC introduces an extra delay proportional to the packet interval, we present a method of optimizing the packet interval to maximize FEC MOS by considering the delay impairment in ITU's E-model standard.

Packet loss

Burstiness

Forward error correction

10.1145/507678.507681

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Citations (6)