Welcome to the Faculty of Mathematics community.

This community and it’s collections are organized using the University of Waterloo's Faculties and Academics structure. In this structure:

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Research outputs are organized by type (eg. Master Thesis, Article, Conference Paper).

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Collections in this community

Recent deposits

  • Constrained-CNN losses for weakly supervised segmentation 

    Kervadec, Hoel; Dolz, Jose; Tang, Meng; Granger, Eric; Boykov, Yuri; Ben Ayed, Ismail (Elsevier, 2019-05)
    Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need for full and laborious pixel/voxel annotations. ...
  • An Efficient Geometric Multigrid Solver for Viscous Liquids 

    Aanjaneya, Mridul; Han, Chengguizi; Goldade, Ryan; Batty, Christopher (ACM, 2019-07)
    We present an efficient geometric Multigrid solver for simulating viscous liquids based on the variational approach of Batty and Bridson [2008]. Although the governing equations for viscosity are elliptic, the strong ...
  • An adaptive variational finite difference framework for efficient symmetric octree viscosity 

    Goldade, Ryan; Wang, Yipeng; Aanjaneya, Mridul; Batty, Christopher (ACM, 2019-07)
    While pressure forces are often the bottleneck in (near-)inviscid fluid simulations, viscosity can impose orders of magnitude greater computational costs at lower Reynolds numbers. We propose an implicit octree finite ...
  • A Unified Simplicial Model for Mixed-Dimensional and Non-Manifold Deformable Elastic Objects 

    Chang, Jumyung; Da, Fang; Grinspun, Eitan; Batty, Christopher (ACM, 2019-07)
    We present a unified method to simulate deformable elastic bodies consisting of mixed-dimensional components represented with potentially non-manifold simplicial meshes. Building on well-known simplicial rod, shell, and ...
  • A multi-scale model for simulating liquid-fabric interactions 

    Fei, Yun (Raymond); Batty, Christopher; Grinspun, Eitan; Zheng, Changxi (ACM, 2018-07)
    We propose a method for simulating the complex dynamics of partially and fully saturated woven and knit fabrics interacting with liquid, including the effects of buoyancy, nonlinear drag, pore (capillary) pressure, dripping, ...
  • An Application of Out-of-Distribution Detection for Two-Stage Object Detection Networks 

    Denouden, Taylor (University of Waterloo, 2020-02-14)
    Recently, much research has been published for detecting when a classification neural network is presented with data that does not fit into one of the class labels the network learned at train time. These so-called ...
  • The effect of omitted covariates in marginal and partially conditional recurrent event analyses 

    Zhong, Yujie; Cook, Richard J. (Springer, 2019-04-01)
    There have been many advances in statistical methodology for the analysis of recurrent event data in recent years. Multiplicative semiparametric rate-based models are widely used in clinical trials, as are more general ...
  • A multi-scale model for coupling strands with shear-dependent liquid 

    Fei, Yun (Raymond); Batty, Christopher; Grinspun, Eitan; Zheng, Changxi (ACM, 2019-11)
    We propose a framework for simulating the complex dynamics of strands interacting with compressible, shear-dependent liquids, such as oil paint, mud, cream, melted chocolate, and pasta sauce. Our framework contains three ...
  • Mandoline: robust cut-cell generation for arbitrary triangle meshes 

    Tao, Michael; Batty, Christopher; Fiume, Eugene; Levin, David I.W. (ACM, 2019-11)
    Although geometry arising "in the wild" most often comes in the form of a surface representation, a plethora of geometrical and physical applications require the construction of volumetric embeddings either of the geometry ...
  • Multi-scale entropy analysis and Hurst exponent 

    Mollaei, Saeid; Darooneh, Amir Hossein; Karimi, Somaye (Elsevier, 2019-08-15)
    Several methods exist for measuring the complexity in a system through analysis of its associated time series. Multi-scale entropy appears as a successful method on this matter. It has been applied in many disciplines with ...
  • Feature identification in time series data sets 

    Shaw, Justin; Stastna, Marek; Coutino, Aaron; Walter, Ryan K.; Reinhardt, Eduard (Elsevier, 2019-05)
    We present a computationally inexpensive, flexible feature identification method which uses a comparison of time series to identify a rank-ordered set of features in geophysically-sourced data sets. Many physical phenomena ...
  • Learning Filters for the 2D Wavelet Transform 

    Recoskie, Daniel; Mann, Richard (IEEE, 2018)
    We propose a new method for learning filters for the 2D discrete wavelet transform. We extend our previous work on the 1D wavelet transform in order to process images. We show that the 2D wavelet transform can be represented ...
  • Maximum likelihood estimation of first-passage structural credit risk models correcting for the survivorship bias 

    Amaya, Diego; Boudreault, Mathieu; McLeish, Don L. (Elsevier, 2019-03)
    The survivorship bias in credit risk modeling is the bias that results in parameter estimates when the survival of a company is ignored. We study the statistical properties of the maximum likelihood estimator (MLE) accounting ...
  • A 2-class maintenance model with dynamic server behavior 

    Granville, Kevin; Drekic, Steve (Springer, 2019-04-29)
    We analyze a 2-class maintenance system within a single-server polling model framework. There are C+f machines in the system, where C is the cap on the number of machines that can be turned on simultaneously (and hence, ...
  • Error Bounds and Singularity Degree in Semidefinite Programming 

    Sremac, Stefan (University of Waterloo, 2020-01-24)
    An important process in optimization is to determine the quality of a proposed solution. This usually entails calculation of the distance of a proposed solution to the optimal set and is referred to as forward error. ...
  • Training Reject-Classifiers for Out-of-distribution Detection via Explicit Boundary Sample Generation 

    Vernekar, Sachin (University of Waterloo, 2020-01-24)
    Discriminatively trained neural classifiers can be trusted only when the input data comes from the training distribution (in-distribution). Therefore, detecting out-of-distribution (OOD) samples is very important to avoid ...
  • Optimizing MPI Collective Operations for Cloud Deployments 

    AlSader, Zuhair (University of Waterloo, 2020-01-24)
    Cloud infrastructures are increasingly being adopted as a platform for high performance computing (HPC) science and engineering applications. For HPC applications, the Message-Passing Interface (MPI) is widely-used. Among ...
  • Safety-Oriented Stability Biases for Continual Learning 

    Gaurav, Ashish (University of Waterloo, 2020-01-24)
    Continual learning is often confounded by “catastrophic forgetting” that prevents neural networks from learning tasks sequentially. In the case of real world classification systems that are safety-validated prior to ...
  • Real equiangular lines and related codes 

    Winnick, Samuel (University of Waterloo, 2020-01-24)
    We consider real equiangular lines and related codes. The driving question is to find the maximum number of equiangular lines in a given dimension. In the real case, this is controlled by combinatorial phenomena, and until ...
  • Closing the Modelling Gap: Transfer Learning from a Low-Fidelity Simulator for Autonomous Driving 

    Balakrishnan, Aravind (University of Waterloo, 2020-01-24)
    The behaviour planning subsystem, which is responsible for high-level decision making and planning, is an important aspect of an autonomous driving system. There are advantages to using a learned behaviour planning system ...

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