Creative Haptic Interface Design for the Aging Population

Audiovisual human-computer-interfaces still make up the majority of content to the public; however, haptic interfaces offer unique advantage over the dominant information infrastructure, particularly for users with a disability or diminishing cognitive and physical skills like the elderly. The tactile sense allows users to integrate new, unobstructive channels for digital information into their sensorium, one that is less likely to be overwhelmed compared to vision and audition. Haptics research focus on the development of hardware, improving resolution, modality, and fidelity of the actuators. Despite the technological limitations, haptic interfaces are shown to reinforce physical skill acquisition, therapy, and communication. This chapter will present key characteristics intuitive tactile interfaces should capture for elderly end-users; sample projects will showcase unique applications and designs that identify the limitations of the UI

An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification

Sparsity regularized loss minimization problems play an important role in various fields including machine learning, data mining, and modern statistics. Proximal gradient descent method and coordinate descent method are the most popular approaches to solving the minimization problem. Although existing methods can achieve implicit model identification, aka support set identification, in a finite number of iterations, these methods still suffer from huge computational costs and memory burdens in high-dimensional scenarios. The reason is that the support set identification in these methods is implicit and thus cannot explicitly identify the low-complexity structure in practice, namely, they cannot discard useless coefficients of the associated features to achieve algorithmic acceleration via dimension reduction. To address this challenge, we propose a novel accelerated doubly stochastic gradient descent (ADSGD) method for sparsity regularized loss minimization problems, which can reduce the number of block iterations by eliminating inactive coefficients during the optimization process and eventually achieve faster explicit model identification and improve the algorithm efficiency. Theoretically, we first prove that ADSGD can achieve a linear convergence rate and lower overall computational complexity. More importantly, we prove that ADSGD can achieve a linear rate of explicit model identification. Numerically, experimental results on benchmark datasets confirm the efficiency of our proposed method

Discrete element simulation of powder layer thickness in laser additive manufacturing

The optimisation of the laser additive manufacturing (AM) process is a challenging task when a new material is considered. Compared to the selection of other process parameters such as laser power, scanning speed and hatch spacing, the optimisation of powder layer thickness is much more time-consuming and costly because a new run is normally needed when the layer thickness value is changed. In practice, the layer thickness is fixed to a value that is slightly higher than the average particle size. This paper introduces a systematic approach to layer thickness optimisation based on a theoretical model of the interactions between the particles, the wiper and the build plate during the powder deposition. The focus is on a systematic theoretical and experimental investigation of the effect of powder layer thickness on various powder bed characteristics during single-layer and multi-layer powder deposition. The theoretical model was tested experimentally using Hastelloy X (HX) with an average particle size of 34.4 μm. The experimental results validated the simulation model, which predicted a uniform powder bed deposition when employing a 40 μm layer thickness value. Lower (30 μm) and higher (50 μm) layer thickness values resulted in large voids and short-feed defects, respectively. The subsequent optimisation of the scanning speed and hatch spacing parameters was executed using a 40 μm layer thickness. The optimum process parameters were then used to examine the microstructure and tensile performance of the as-fabricated HX. This study provides an improved understanding of the powder deposition process and offers insights into the selection of suitable powder layer thicknesses in laser AM

Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers

Current arbitrary style transfer models are limited to either image or video domains. In order to achieve satisfying image and video style transfers, two different models are inevitably required with separate training processes on image and video domains, respectively. In this paper, we show that this can be precluded by introducing UniST, a Unified Style Transfer framework for both images and videos. At the core of UniST is a domain interaction transformer (DIT), which first explores context information within the specific domain and then interacts contextualized domain information for joint learning. In particular, DIT enables exploration of temporal information from videos for the image style transfer task and meanwhile allows rich appearance texture from images for video style transfer, thus leading to mutual benefits. Considering heavy computation of traditional multi-head self-attention, we present a simple yet effective axial multi-head self-attention (AMSA) for DIT, which improves computational efficiency while maintains style transfer performance. To verify the effectiveness of UniST, we conduct extensive experiments on both image and video style transfer tasks and show that UniST performs favorably against state-of-the-art approaches on both tasks. Code is available at https://github.com/NevSNev/UniST.Comment: Conference on International Conference on Computer Vision.(ICCV 2023