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<meta name="theme-color" content="#b31b1b">  <title>Search | arXiv e-print repository</title> <script defer src="https://static.arxiv.org/static/base/1.0.0a5/fontawesome-free-5.11.2-web/js/all.js"></script> <link rel="stylesheet" href="https://static.arxiv.org/static/base/1.0.0a5/css/arxivstyle.css" /> <script type="text/x-mathjax-config"> MathJax.Hub.Config({ messageStyle: "none", extensions: ["tex2jax.js"], jax: ["input/TeX", "output/HTML-CSS"], tex2jax: { inlineMath: [ ['$','$'], ["\$","\$"] ], displayMath: [ ['$$','$$'], ["\\[","\\]"] ], processEscapes: true, ignoreClass: '.*', processClass: 'mathjax.*' }, TeX: { extensions: ["AMSmath.js", "AMSsymbols.js", "noErrors.js"], noErrors: { inlineDelimiters: ["$","$"], multiLine: false, style: { "font-size": "normal", "border": "" } } }, "HTML-CSS": { availableFonts: ["TeX"] } }); </script> <script src='//static.arxiv.org/MathJax-2.7.3/MathJax.js'></script> <script 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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Wong, H S"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.06436">arXiv:2412.06436</a> <span> [<a href="https://arxiv.org/pdf/2412.06436">pdf</a>, <a href="https://arxiv.org/format/2412.06436">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bogensperger%2C+L">Lea Bogensperger</a>, <a href="/search/cs?searchtype=author&query=Ehrhardt%2C+M+J">Matthias J. Ehrhardt</a>, <a href="/search/cs?searchtype=author&query=Pock%2C+T">Thomas Pock</a>, <a href="/search/cs?searchtype=author&query=Salehi%2C+M+S">Mohammad Sadegh Salehi</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">Hok Shing Wong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.06436v1-abstract-short" style="display: inline;"> We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minimizer of a convex optimization problem (denoted lower-level problem). We utilize an iterative algorithm called `piggyback' to compute the gradient of the loss and minimizer of the lower-level problem. Given that the lower… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06436v1-abstract-full').style.display = 'inline'; document.getElementById('2412.06436v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.06436v1-abstract-full" style="display: none;"> We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minimizer of a convex optimization problem (denoted lower-level problem). We utilize an iterative algorithm called `piggyback' to compute the gradient of the loss and minimizer of the lower-level problem. Given that the lower-level problem is solved numerically, the loss function and thus its gradient can only be computed inexactly. To estimate the accuracy of the computed hypergradient, we derive an a-posteriori error bound, which provides guides for setting the tolerance for the lower-level problem, as well as the piggyback algorithm. To efficiently solve the upper-level optimization, we also propose an adaptive method for choosing a suitable step-size. To illustrate the proposed method, we consider a few learned regularizer problems, such as training an input-convex neural network. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06436v1-abstract-full').style.display = 'none'; document.getElementById('2412.06436v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.12441">arXiv:2410.12441</a> <span> [<a href="https://arxiv.org/pdf/2410.12441">pdf</a>, <a href="https://arxiv.org/format/2410.12441">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> </div> </div> <p class="title is-5 mathjax"> A Primal-dual algorithm for image reconstruction with ICNNs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">Hok Shing Wong</a>, <a href="/search/cs?searchtype=author&query=Ehrhardt%2C+M+J">Matthias J. Ehrhardt</a>, <a href="/search/cs?searchtype=author&query=Mukherjee%2C+S">Subhadip Mukherjee</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.12441v1-abstract-short" style="display: inline;"> We address the optimization problem in a data-driven variational reconstruction framework, where the regularizer is parameterized by an input-convex neural network (ICNN). While gradient-based methods are commonly used to solve such problems, they struggle to effectively handle non-smoothness which often leads to slow convergence. Moreover, the nested structure of the neural network complicates th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.12441v1-abstract-full').style.display = 'inline'; document.getElementById('2410.12441v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.12441v1-abstract-full" style="display: none;"> We address the optimization problem in a data-driven variational reconstruction framework, where the regularizer is parameterized by an input-convex neural network (ICNN). While gradient-based methods are commonly used to solve such problems, they struggle to effectively handle non-smoothness which often leads to slow convergence. Moreover, the nested structure of the neural network complicates the application of standard non-smooth optimization techniques, such as proximal algorithms. To overcome these challenges, we reformulate the problem and eliminate the network's nested structure. By relating this reformulation to epigraphical projections of the activation functions, we transform the problem into a convex optimization problem that can be efficiently solved using a primal-dual algorithm. We also prove that this reformulation is equivalent to the original variational problem. Through experiments on several imaging tasks, we demonstrate that the proposed approach outperforms subgradient methods in terms of both speed and stability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.12441v1-abstract-full').style.display = 'none'; document.getElementById('2410.12441v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 65K10; 90C06; 90C25; 94A08 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.05889">arXiv:2409.05889</a> <span> [<a href="https://arxiv.org/pdf/2409.05889">pdf</a>, <a href="https://arxiv.org/ps/2409.05889">ps</a>, <a href="https://arxiv.org/format/2409.05889">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> Unravelling the interplay between steel rebar corrosion rate and corrosion-induced cracking of reinforced concrete </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Korec%2C+E">E. Korec</a>, <a href="/search/cs?searchtype=author&query=Jirasek%2C+M">M. Jirasek</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">H. S. Wong</a>, <a href="/search/cs?searchtype=author&query=Mart%C3%ADnez-Pa%C3%B1eda%2C+E">E. Mart铆nez-Pa帽eda</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.05889v1-abstract-short" style="display: inline;"> Accelerated impressed current testing is the most common experimental method for assessing the susceptibility to corrosion-induced cracking, the most prominent challenge to the durability of reinforced concrete structures. Although it is well known that accelerated impressed current tests lead to slower propagation of cracks (with respect to corrosion penetration) than in natural conditions, which… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05889v1-abstract-full').style.display = 'inline'; document.getElementById('2409.05889v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05889v1-abstract-full" style="display: none;"> Accelerated impressed current testing is the most common experimental method for assessing the susceptibility to corrosion-induced cracking, the most prominent challenge to the durability of reinforced concrete structures. Although it is well known that accelerated impressed current tests lead to slower propagation of cracks (with respect to corrosion penetration) than in natural conditions, which results in overestimations of the delamination/spalling time, the origins of this phenomenon have puzzled researchers for more than a quarter of a century. In view of recent experimental findings, it is postulated that the phenomenon can be attributed to the variability of rust composition and density, specifically to the variable ratio of the mass fractions of iron oxide and iron hydroxide-oxide, which is affected by the magnitude of the applied corrosion current density. Based on this hypothesis, a corrosion-induced cracking model for virtual impressed-current testing is presented. The simulation results obtained with the proposed model are validated against experimental data, showing good agreement. Importantly, the model can predict corrosion-induced cracking under natural conditions and thus allows for the calculation of a newly proposed crack width slope correction factor, which extrapolates the surface crack width measured during accelerated impressed current tests to corrosion in natural conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05889v1-abstract-full').style.display = 'none'; document.getElementById('2409.05889v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.06209">arXiv:2312.06209</a> <span> [<a href="https://arxiv.org/pdf/2312.06209">pdf</a>, <a href="https://arxiv.org/format/2312.06209">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> Phase-field chemo-mechanical modelling of corrosion-induced cracking in reinforced concrete subjected to non-uniform chloride-induced corrosion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Korec%2C+E">E. Korec</a>, <a href="/search/cs?searchtype=author&query=Jirasek%2C+M">M. Jirasek</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">H. S. Wong</a>, <a href="/search/cs?searchtype=author&query=Mart%C3%ADnez-Pa%C3%B1eda%2C+E">E. Mart铆nez-Pa帽eda</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.06209v1-abstract-short" style="display: inline;"> A model for corrosion-induced cracking of reinforced concrete subjected to non-uniform chloride-induced corrosion is presented. The gradual corrosion initiation of the steel surface is investigated by simulating chloride transport considering binding. The transport of iron from the steel surface, its subsequent precipitation into rust, and the associated precipitation-induced pressure are explicit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.06209v1-abstract-full').style.display = 'inline'; document.getElementById('2312.06209v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.06209v1-abstract-full" style="display: none;"> A model for corrosion-induced cracking of reinforced concrete subjected to non-uniform chloride-induced corrosion is presented. The gradual corrosion initiation of the steel surface is investigated by simulating chloride transport considering binding. The transport of iron from the steel surface, its subsequent precipitation into rust, and the associated precipitation-induced pressure are explicitly modelled. Model results, obtained through finite element simulations, agree very well with experimental data, showing significantly improved accuracy over uniform corrosion modelling. The results obtained from case studies reveal that crack-facilitated transport of chlorides cannot be neglected, that the size of the anodic region must be considered, and that precipitate accumulation in pores can take years. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.06209v1-abstract-full').style.display = 'none'; document.getElementById('2312.06209v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.15294">arXiv:2309.15294</a> <span> [<a href="https://arxiv.org/pdf/2309.15294">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Fluid Dynamics">physics.flu-dyn</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Multiple Case Physics-Informed Neural Network for Biomedical Tube Flows </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">Hong Shen Wong</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+W+X">Wei Xuan Chan</a>, <a href="/search/cs?searchtype=author&query=Li%2C+B+H">Bing Huan Li</a>, <a href="/search/cs?searchtype=author&query=Yap%2C+C+H">Choon Hwai Yap</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.15294v2-abstract-short" style="display: inline;"> Fluid dynamics computations for tube-like geometries are important for biomedical evaluation of vascular and airway fluid dynamics. Physics-Informed Neural Networks (PINNs) have recently emerged as a good alternative to traditional computational fluid dynamics (CFD) methods. The vanilla PINN, however, requires much longer training time than the traditional CFD methods for each specific flow scenar… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15294v2-abstract-full').style.display = 'inline'; document.getElementById('2309.15294v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.15294v2-abstract-full" style="display: none;"> Fluid dynamics computations for tube-like geometries are important for biomedical evaluation of vascular and airway fluid dynamics. Physics-Informed Neural Networks (PINNs) have recently emerged as a good alternative to traditional computational fluid dynamics (CFD) methods. The vanilla PINN, however, requires much longer training time than the traditional CFD methods for each specific flow scenario and thus does not justify its mainstream use. Here, we explore the use of the multi-case PINN approach for calculating biomedical tube flows, where varied geometry cases are parameterized and pre-trained on the PINN, such that results for unseen geometries can be obtained in real time. Our objective is to identify network architecture, tube-specific, and regularization strategies that can optimize this, via experiments on a series of idealized 2D stenotic tube flows. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15294v2-abstract-full').style.display = 'none'; document.getElementById('2309.15294v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages, 8 figures, 5 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.01903">arXiv:2306.01903</a> <span> [<a href="https://arxiv.org/pdf/2306.01903">pdf</a>, <a href="https://arxiv.org/ps/2306.01903">ps</a>, <a href="https://arxiv.org/format/2306.01903">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Other Condensed Matter">cond-mat.other</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> A phase-field chemo-mechanical model for corrosion-induced cracking in reinforced concrete </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Korec%2C+E">E. Korec</a>, <a href="/search/cs?searchtype=author&query=Jirasek%2C+M">M. Jirasek</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">H. S. Wong</a>, <a href="/search/cs?searchtype=author&query=Mart%C3%ADnez-Pa%C3%B1eda%2C+E">E. Mart铆nez-Pa帽eda</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.01903v1-abstract-short" style="display: inline;"> We present a new mechanistic framework for corrosion-induced cracking in reinforced concrete that resolves the underlying chemo-mechanical processes. The framework combines, for the first time, (i) a model for reactive transport and precipitation of dissolved Fe2+ and Fe3+ ions in the concrete pore space, (ii) a precipitation eigenstrain model for the pressure caused by the accumulation of precipi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.01903v1-abstract-full').style.display = 'inline'; document.getElementById('2306.01903v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.01903v1-abstract-full" style="display: none;"> We present a new mechanistic framework for corrosion-induced cracking in reinforced concrete that resolves the underlying chemo-mechanical processes. The framework combines, for the first time, (i) a model for reactive transport and precipitation of dissolved Fe2+ and Fe3+ ions in the concrete pore space, (ii) a precipitation eigenstrain model for the pressure caused by the accumulation of precipitates (rusts) under pore confinement conditions, (iii) a phase-field model calibrated for the quasi-brittle fracture behaviour of concrete, and (iv) a damage-dependent diffusivity tensor. Finite element model predictions show good agreement with experimental data from impressed current tests under natural-like corrosion current densities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.01903v1-abstract-full').style.display = 'none'; document.getElementById('2306.01903v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.00743">arXiv:2210.00743</a> <span> [<a href="https://arxiv.org/pdf/2210.00743">pdf</a>, <a href="https://arxiv.org/format/2210.00743">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tan%2C+Z+Q">Zhi Qin Tan</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">Hao Shan Wong</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+C+S">Chee Seng Chan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.00743v2-abstract-short" style="display: inline;"> Capitalise on deep learning models, offering Natural Language Processing (NLP) solutions as a part of the Machine Learning as a Service (MLaaS) has generated handsome revenues. At the same time, it is known that the creation of these lucrative deep models is non-trivial. Therefore, protecting these inventions intellectual property rights (IPR) from being abused, stolen and plagiarized is vital. Th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.00743v2-abstract-full').style.display = 'inline'; document.getElementById('2210.00743v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.00743v2-abstract-full" style="display: none;"> Capitalise on deep learning models, offering Natural Language Processing (NLP) solutions as a part of the Machine Learning as a Service (MLaaS) has generated handsome revenues. At the same time, it is known that the creation of these lucrative deep models is non-trivial. Therefore, protecting these inventions intellectual property rights (IPR) from being abused, stolen and plagiarized is vital. This paper proposes a practical approach for the IPR protection on recurrent neural networks (RNN) without all the bells and whistles of existing IPR solutions. Particularly, we introduce the Gatekeeper concept that resembles the recurrent nature in RNN architecture to embed keys. Also, we design the model training scheme in a way such that the protected RNN model will retain its original performance iff a genuine key is presented. Extensive experiments showed that our protection scheme is robust and effective against ambiguity and removal attacks in both white-box and black-box protection schemes on different RNN variants. Code is available at https://github.com/zhiqin1998/RecurrentIPR <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.00743v2-abstract-full').style.display = 'none'; document.getElementById('2210.00743v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at AACL-IJCNLP 2022 (Fig. 1 updated)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.09555">arXiv:2207.09555</a> <span> [<a href="https://arxiv.org/pdf/2207.09555">pdf</a>, <a href="https://arxiv.org/format/2207.09555">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Xronos: Predictable Coordination for Safety-Critical Distributed Embedded Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bateni%2C+S">Soroush Bateni</a>, <a href="/search/cs?searchtype=author&query=Lohstroh%2C+M">Marten Lohstroh</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">Hou Seng Wong</a>, <a href="/search/cs?searchtype=author&query=Tabish%2C+R">Rohan Tabish</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H">Hokeun Kim</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+S">Shaokai Lin</a>, <a href="/search/cs?searchtype=author&query=Menard%2C+C">Christian Menard</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+C">Cong Liu</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+E+A">Edward A. Lee</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.09555v1-abstract-short" style="display: inline;"> Asynchronous frameworks for distributed embedded systems, like ROS and MQTT, are increasingly used in safety-critical applications such as autonomous driving, where the cost of unintended behavior is high. The coordination mechanism between the components in these frameworks, however, gives rise to nondeterminism, where factors such as communication timing can lead to arbitrary ordering in the han… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09555v1-abstract-full').style.display = 'inline'; document.getElementById('2207.09555v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.09555v1-abstract-full" style="display: none;"> Asynchronous frameworks for distributed embedded systems, like ROS and MQTT, are increasingly used in safety-critical applications such as autonomous driving, where the cost of unintended behavior is high. The coordination mechanism between the components in these frameworks, however, gives rise to nondeterminism, where factors such as communication timing can lead to arbitrary ordering in the handling of messages. In this paper, we demonstrate the significance of this problem in an open-source full-stack autonomous driving software, Autoware.Auto 1.0, which relies on ROS 2. We give an alternative: Xronos, an open-source framework for distributed embedded systems that has a novel coordination strategy with predictable properties under clearly stated assumptions. If these assumptions are violated, Xronos provides for application-specific fault handlers to be invoked. We port Autoware.Auto to Xronos and show that it avoids the identified problems with manageable cost in end-to-end latency. Furthermore, we compare the maximum throughput of Xronos to ROS 2 and MQTT using microbenchmarks under different settings, including on three different hardware configurations, and find that it can match or exceed those frameworks in terms of throughput. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09555v1-abstract-full').style.display = 'none'; document.getElementById('2207.09555v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.00834">arXiv:2205.00834</a> <span> [<a href="https://arxiv.org/pdf/2205.00834">pdf</a>, <a href="https://arxiv.org/format/2205.00834">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Convex Augmentation for Total Variation Based Phase Retrieval </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Niu%2C+J">Jianwei Niu</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">Hok Shing Wong</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+T">Tieyong Zeng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.00834v1-abstract-short" style="display: inline;"> Phase retrieval is an important problem with significant physical and industrial applications. In this paper, we consider the case where the magnitude of the measurement of an underlying signal is corrupted by Gaussian noise. We introduce a convex augmentation approach for phase retrieval based on total variation regularization. In contrast to popular convex relaxation models like PhaseLift, our m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.00834v1-abstract-full').style.display = 'inline'; document.getElementById('2205.00834v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.00834v1-abstract-full" style="display: none;"> Phase retrieval is an important problem with significant physical and industrial applications. In this paper, we consider the case where the magnitude of the measurement of an underlying signal is corrupted by Gaussian noise. We introduce a convex augmentation approach for phase retrieval based on total variation regularization. In contrast to popular convex relaxation models like PhaseLift, our model can be efficiently solved by a modified semi-proximal alternating direction method of multipliers (sPADMM). The modified sPADMM is more general and flexible than the standard one, and its convergence is also established in this paper. Extensive numerical experiments are conducted to showcase the effectiveness of the proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.00834v1-abstract-full').style.display = 'none'; document.getElementById('2205.00834v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.11914">arXiv:2005.11914</a> <span> [<a href="https://arxiv.org/pdf/2005.11914">pdf</a>, <a href="https://arxiv.org/format/2005.11914">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Deep Tensor CCA for Multi-view Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">Hok Shing Wong</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+L">Li Wang</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+R">Raymond Chan</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+T">Tieyong Zeng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.11914v1-abstract-short" style="display: inline;"> We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The high-order correlation of given multiple views is modeled by covariance tensor, which is different from most CCA formulations relying solely on the pairwise cor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.11914v1-abstract-full').style.display = 'inline'; document.getElementById('2005.11914v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.11914v1-abstract-full" style="display: none;"> We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The high-order correlation of given multiple views is modeled by covariance tensor, which is different from most CCA formulations relying solely on the pairwise correlations. Parameters of transformations of each view are jointly learned by maximizing the high-order canonical correlation. To solve the resulting problem, we reformulate it as the best sum of rank-1 approximation, which can be efficiently solved by existing tensor decomposition method. DTCCA is a nonlinear extension of tensor CCA (TCCA) via deep networks. The transformations of DTCCA are parametric functions, which are very different from implicit mapping in the form of kernel function. Comparing with kernel TCCA, DTCCA not only can deal with arbitrary dimensions of the input data, but also does not need to maintain the training data for computing representations of any given data point. Hence, DTCCA as a unified model can efficiently overcome the scalable issue of TCCA for either high-dimensional multi-view data or a large amount of views, and it also naturally extends TCCA for learning nonlinear representation. Extensive experiments on three multi-view data sets demonstrate the effectiveness of the proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.11914v1-abstract-full').style.display = 'none'; document.getElementById('2005.11914v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1105.6277">arXiv:1105.6277</a> <span> [<a href="https://arxiv.org/pdf/1105.6277">pdf</a>, <a href="https://arxiv.org/format/1105.6277">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Incremental Top-k List Comparison Approach to Robust Multi-Structure Model Fitting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wong%2C+H+S">Hoi Sim Wong</a>, <a href="/search/cs?searchtype=author&query=Chin%2C+T">Tat-Jun Chin</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jin Yu</a>, <a href="/search/cs?searchtype=author&query=Suter%2C+D">David Suter</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1105.6277v1-abstract-short" style="display: inline;"> Random hypothesis sampling lies at the core of many popular robust fitting techniques such as RANSAC. In this paper, we propose a novel hypothesis sampling scheme based on incremental computation of distances between partial rankings (top-$k$ lists) derived from residual sorting information. Our method simultaneously (1) guides the sampling such that hypotheses corresponding to all true structures… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1105.6277v1-abstract-full').style.display = 'inline'; document.getElementById('1105.6277v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1105.6277v1-abstract-full" style="display: none;"> Random hypothesis sampling lies at the core of many popular robust fitting techniques such as RANSAC. In this paper, we propose a novel hypothesis sampling scheme based on incremental computation of distances between partial rankings (top-$k$ lists) derived from residual sorting information. Our method simultaneously (1) guides the sampling such that hypotheses corresponding to all true structures can be quickly retrieved and (2) filters the hypotheses such that only a small but very promising subset remain. This permits the usage of simple agglomerative clustering on the surviving hypotheses for accurate model selection. The outcome is a highly efficient multi-structure robust estimation technique. Experiments on synthetic and real data show the superior performance of our approach over previous methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1105.6277v1-abstract-full').style.display = 'none'; document.getElementById('1105.6277v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 May, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2011. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a 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