A Case Study of New Media Literacies in an English Language Learning Program

This case study was conducted in a four-week winter program of an English language center affiliated to a university that is situated in a city in Ontario. Underpinned by theories on new media literacies, actor-network theory, and curriculum, the thesis examines human and nonhuman actors that enabled and constrained students’ new media literacies practices in the English language learning program. Despite the fact that there are emergent studies on new media literacies, there is scarce literature on human and nonhuman actors that influence students’ new media literacies practices. In this study, sources of data included curricular documents, students’ artifacts, classroom observations of 12 student participants and two instructors, and semi-structured interviews with six student participants. Findings show that students’ new media literacies practices of transmedia navigation, appropriation, judgment, and distributed cognition were enabled in the program whereas the practices of networking, participatory culture, and collective intelligence were constrained to a certain degree. The study also identified human and nonhuman actors that shaped students’ new media literacies practices, such as program design, materiality of classrooms, and individual differences of student participants. This study provides curricular and pedagogical suggestions to English language learning programs in order to enable and expand students’ new media literacies practices and bolster their language learning

Porous calcium phosphate ceramics prepared by coating polyurethane foams with calcium phosphate cements

Porous calcium phosphates have important biomedical applications such as bone defect fillers, tissue engineering scaffolds, and drug delivery systems. While a number of methods to produce the porous calcium phosphate ceramics have been reported, this study aimed to develop a new fabrication method. The new method involved the use of polyurethane foams to produce highly porous calcium phosphate cements (CPCs). By firing the porous CPCs at 1200 degrees Celsius, the polyurethane foams were burnt off and the CPCs prepared at room temperature were converted into sintered porous hydroxyapatite-based calcium phosphate ceramics. The sintered porous calcium phosphate ceramics could then be coated with a layer of the CPC at room temperature, resulting in high porosity, high pore interconnectivity, and controlled pores size

Weak Signal Inclusion Under Dependence and Applications in Genome-wide Association Study

Motivated by the inquiries of weak signals in underpowered genome-wide association studies (GWASs), we consider the problem of retaining true signals that are not strong enough to be individually separable from a large amount of noise. We address the challenge from the perspective of false negative control and present false negative control (FNC) screening, a data-driven method to efficiently regulate false negative proportion at a user-specified level. FNC screening is developed in a realistic setting with arbitrary covariance dependence between variables. We calibrate the overall dependence through a parameter whose scale is compatible with the existing phase diagram in high-dimensional sparse inference. Utilizing the new calibration, we asymptotically explicate the joint effect of covariance dependence, signal sparsity, and signal intensity on the proposed method. We interpret the results using a new phase diagram, which shows that FNC screening can efficiently select a set of candidate variables to retain a high proportion of signals even when the signals are not individually separable from noise. Finite sample performance of FNC screening is compared to those of several existing methods in simulation studies. The proposed method outperforms the others in adapting to a user-specified false negative control level. We implement FNC screening to empower a two-stage GWAS procedure, which demonstrates substantial power gain when working with limited sample sizes in real applications.Comment: arXiv admin note: text overlap with arXiv:2006.1566

RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Different from most existing works dedicated to adaptive refinement of cost volumes, we opt to directly optimize the depth value along each camera ray, mimicking the range finding of a laser scanner. This reduces the MVS problem to ray-based depth optimization which is much more light-weight than full cost volume optimization. In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth. This sequential modeling, conducted based on transformer features, essentially learns the epipolar line search in traditional multi-view stereo. We devise a multi-task learning for better optimization convergence and depth accuracy. We found the monotonicity property of the SDFs along each ray greatly benefits the depth estimation. Our method ranks top on both the DTU and the Tanks & Temples datasets over all previous learning-based methods, achieving an overall reconstruction score of 0.33mm on DTU and an F-score of 59.48% on Tanks & Temples. It is able to produce high-quality depth estimation and point cloud reconstruction in challenging scenarios such as objects/scenes with non-textured surface, severe occlusion, and highly varying depth range. Further, we propose RayMVSNet++ to enhance contextual feature aggregation for each ray through designing an attentional gating unit to select semantically relevant neighboring rays within the local frustum around that ray. RayMVSNet++ achieves state-of-the-art performance on the ScanNet dataset. In particular, it attains an AbsRel of 0.058m and produces accurate results on the two subsets of textureless regions and large depth variation.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv admin note: substantial text overlap with arXiv:2204.0132

Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum

This paper applies an idea of adaptive momentum for the nonlinear conjugate gradient to accelerate optimization problems in sparse recovery. Specifically, we consider two types of minimization problems: a (single) differentiable function and the sum of a non-smooth function and a differentiable function. In the first case, we adopt a fixed step size to avoid the traditional line search and establish the convergence analysis of the proposed algorithm for a quadratic problem. This acceleration is further incorporated with an operator splitting technique to deal with the non-smooth function in the second case. We use the convex $\ell_1$ and the nonconvex $\ell_1-\ell_2$ functionals as two case studies to demonstrate the efficiency of the proposed approaches over traditional methods