Optimising Distributions with Natural Gradient Surrogates

Natural gradient methods have been used to optimise the parameters of probability distributions in a variety of settings, often resulting in fast-converging procedures. Unfortunately, for many distributions of interest, computing the natural gradient has a number of challenges. In this work we propose a novel technique for tackling such issues, which involves reframing the optimisation as one with respect to the parameters of a surrogate distribution, for which computing the natural gradient is easy. We give several examples of existing methods that can be interpreted as applying this technique, and propose a new method for applying it to a wide variety of problems. Our method expands the set of distributions that can be efficiently targeted with natural gradients. Furthermore, it is fast, easy to understand, simple to implement using standard autodiff software, and does not require lengthy model-specific derivations. We demonstrate our method on maximum likelihood estimation and variational inference tasks

Identifiable Feature Learning for Spatial Data with Nonlinear ICA

Recently, nonlinear ICA has surfaced as a popular alternative to the many heuristic models used in deep representation learning and disentanglement. An advantage of nonlinear ICA is that a sophisticated identifiability theory has been developed; in particular, it has been proven that the original components can be recovered under sufficiently strong latent dependencies. Despite this general theory, practical nonlinear ICA algorithms have so far been mainly limited to data with one-dimensional latent dependencies, especially time-series data. In this paper, we introduce a new nonlinear ICA framework that employs $t$-process (TP) latent components which apply naturally to data with higher-dimensional dependency structures, such as spatial and spatio-temporal data. In particular, we develop a new learning and inference algorithm that extends variational inference methods to handle the combination of a deep neural network mixing function with the TP prior, and employs the method of inducing points for computational efficacy. On the theoretical side, we show that such TP independent components are identifiable under very general conditions. Further, Gaussian Process (GP) nonlinear ICA is established as a limit of the TP Nonlinear ICA model, and we prove that the identifiability of the latent components at this GP limit is more restricted. Namely, those components are identifiable if and only if they have distinctly different covariance kernels. Our algorithm and identifiability theorems are explored on simulated spatial data and real world spatio-temporal data.Comment: Work under revie

Formation and removal of alkylthiolate self-assembled monolayers on gold in aqueous solutions

We report the development of novel reagents and approaches for generating recyclable biosensors. The use of aqueous media for the formation of protein binding alkylthiolate monolayers on Au surfaces results in accelerated alkylthiolate monolayer formation and improvement in monolayer integrity as visualized by fluorescence microscopy and CV techniques. We have also developed an electrocleaning protocol that is compatible with microfluidics devices, and this technique serves as an on-chip method for cleaning Au substrates both before and after monolayer formation. The techniques for the formation and dissociation of biotinylated SAMs from aqueous solvents reported here may be applied towards the development of Au-based sensor devices and microfluidics chips in the future. A potential use of these devices includes the specific capture and triggered release of target cells, proteins, or small molecules from liquid samples

Trajectories of Objectively Measured Physical Activity among Secondary Students in Canada in the Context of a Province-Wide Physical Education Policy: A Longitudinal Analysis

Lower levels of physical activity are associated with childhood obesity. School physical education (PE) policies have been identified as critical to improve child and adolescent physical activity levels but there has been little evaluation of such policies. In the province of Manitoba, Canada, the government implemented a mandatory PE policy in secondary schools designed to increase the daily physical activity levels of adolescents. The objective of this study was to examine the longitudinal changes in and the factors associated with the physical activity trajectories of adolescents in Manitoba during their tenure as secondary school students in the context of this school PE policy. The results found, despite the PE policy, a grade-related decline in the physical activity trajectories of adolescents; however, the decline in physical activity was attenuated among adolescents with low and moderate baseline physical activity compared to adolescents with high baseline physical activity and among adolescents who attended schools in neighbourhoods of low compared to high socioeconomic status. There are several possible explanations for these findings, including the influence of the PE policy on the PA patterns of adolescent subpopulations that tend to be at higher risk for inactivity in both childhood and adult life

Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA

Peer reviewe

Trekking the Educator Track at a Research-Intensive University: Five Accounts of Different Career Levels

In this paper, we offer personal accounts along the Educator Track from Instructor to Associate Professor as members of an English Language Centre at a leading research-intensive university in Asia. The Educator Track is a career pathway growing in significance and status and now boasts a full professorial grade. Our narratives provide an overview of what we and our institution deem as excellence in scholarly teaching leading to our recent promotions along the track. We also detail some of our identity construction processes as practitioners and how our Scholarship of Teaching and Learning (SoTL) has progressed over our careers. We draw on three frameworks. The first, Kern et al.’s (2015) Dimensions of Activities Related to Teaching, enables us to map what we do. The second, Shulman’s (2005) Habits of Mind, Hand, and Heart, is used to present important elements of how we teach our content and rationalize why we teach it. The last, Quinlan’s (2014) concept of Leadership of Teaching for Student Learning links the Associate Professor role to engagement in the wider community beyond the classroom. We hope that these accounts might help further understanding of what it means to be on the Educator Track at a research-intensive university

Trekking the Educator Track at a Research-Intensive University: Five Accounts of Different Career Levels

In this paper, we offer personal accounts along the Educator Track from Instructor to Associate Professor as members of an English Language Centre at a leading research-intensive university in Asia. The Educator Track is a career pathway growing in significance and status and now boasts a full professorial grade. Our narratives provide an overview of what we and our institution deem as excellence in scholarly teaching leading to our recent promotions along the track. We also detail some of our identity construction processes as practitioners and how our Scholarship of Teaching and Learning (SoTL) has progressed over our careers. We draw on three frameworks. The first, Kern et al.’s (2015) Dimensions of Activities Related to Teaching, enables us to map what we do. The second, Shulman’s (2005) Habits of Mind, Hand, and Heart, is used to present important elements of how we teach our content and rationalize why we teach it. The last, Quinlan’s (2014) concept of Leadership of Teaching for Student Learning links the Associate Professor role to engagement in the wider community beyond the classroom. We hope that these accounts might help further understanding of what it means to be on the Educator Track at a research-intensive university

Neural Fields for Robotic Object Manipulation from a Single Image

We present a unified and compact representation for object rendering, 3D reconstruction, and grasp pose prediction that can be inferred from a single image within a few seconds. We achieve this by leveraging recent advances in the Neural Radiance Field (NeRF) literature that learn category-level priors and fine-tune on novel objects with minimal data and time. Our insight is that we can learn a compact shape representation and extract meaningful additional information from it, such as grasping poses. We believe this to be the first work to retrieve grasping poses directly from a NeRF-based representation using a single viewpoint (RGB-only), rather than going through a secondary network and/or representation. When compared to prior art, our method is two to three orders of magnitude smaller while achieving comparable performance at view reconstruction and grasping. Accompanying our method, we also propose a new dataset of rendered shoes for training a sim-2-real NeRF method with grasping poses for different widths of grippers.Comment: Submitted to ICRA 202

Time dependent decomposition of ammonia borane for the controlled production of 2D hexagonal boron nitride.

Ammonia borane (AB) is among the most promising precursors for the large-scale synthesis of hexagonal boron nitride (h-BN) by chemical vapour deposition (CVD). Its non-toxic and non-flammable properties make AB particularly attractive for industry. AB decomposition under CVD conditions, however, is complex and hence has hindered tailored h-BN production and its exploitation. To overcome this challenge, we report in-depth decomposition studies of AB under industrially safe growth conditions. In situ mass spectrometry revealed a time and temperature-dependent release of a plethora of NxBy-containing species and, as a result, significant changes of the N:B ratio during h-BN synthesis. Such fluctuations strongly influence the formation and morphology of 2D h-BN. By means of in situ gas monitoring and regulating the precursor temperature over time we achieve uniform release of volatile chemical species over many hours for the first time, paving the way towards the controlled, industrially viable production of h-BN

TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation

Test-time adaptation methods have been gaining attention recently as a practical solution for addressing source-to-target domain gaps by gradually updating the model without requiring labels on the target data. In this paper, we propose a method of test-time adaptation for category-level object pose estimation called TTA-COPE. We design a pose ensemble approach with a self-training loss using pose-aware confidence. Unlike previous unsupervised domain adaptation methods for category-level object pose estimation, our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime. Extensive experimental results demonstrate that the proposed pose ensemble and the self-training loss improve category-level object pose performance during test time under both semi-supervised and unsupervised settings. Project page: https://taeyeop.com/ttacopeComment: Accepted to CVPR 2023, Project page: https://taeyeop.com/ttacop