Development of a Reference Wafer for On-Wafer Testing of Extreme Impedance Devices

This paper describes the design, fabrication, and testing of an on-wafer substrate that has been developed specifically for measuring extreme impedance devices using an on-wafer probe station. Such devices include carbon nano-tubes (CNTs) and structures based on graphene which possess impedances in the κ Ω range and are generally realised on the nano-scale rather than the micro-scale that is used for conventional on-wafer measurement. These impedances are far removed from the conventional 50- reference impedance of the test equipment. The on-wafer substrate includes methods for transforming from the micro-scale towards the nano-scale and reference standards to enable calibrations for extreme impedance devices. The paper includes typical results obtained from the designed wafer

Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semisupervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by Li- DAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a 2.3× reduction in model parameters and 641× fewer multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More)

Tackling Data Bias in Painting Classification with Style Transfer

It is difficult to train classifiers on paintings collections due to model bias from domain gaps and data bias from the uneven distribution of artistic styles. Previous techniques like data distillation, traditional data augmentation and style transfer improve classifier training using task specific training datasets or domain adaptation. We propose a system to handle data bias in small paintings datasets like the Kaokore dataset while simultaneously accounting for domain adaptation in fine-tuning a model trained on real world images. Our system consists of two stages which are style transfer and classification. In the style transfer stage, we generate the stylized training samples per class with uniformly sampled content and style images and train the style transformation network per domain. In the classification stage, we can interpret the effectiveness of the style and content layers at the attention layers when training on the original training dataset and the stylized images. We can tradeoff the model performance and convergence by dynamically varying the proportion of augmented samples in the majority and minority classes. We achieve comparable results to the SOTA with fewer training epochs and a classifier with fewer training parameters

Vortex liquid correlations induced by in-plane field in underdoped Bi2Sr2CaCu2O8+d

By measuring the Josephson Plasma Resonance, we have probed the influence of an in-plane magnetic field on the pancake vortex correlations along the c-axis in heavily underdoped Bi2Sr2CaCu2O8+d (Tc = 72.4 +/- 0.6 K) single crystals both in the vortex liquid and in the vortex solid phase. Whereas the in-plane field enhances the interlayer phase coherence in the liquid state close to the melting line, it slightly depresses it in the solid state. This is interpreted as the result of an attractive force between pancake vortices and Josephson vortices, apparently also present in the vortex liquid state. The results unveil a boundary between a correlated vortex liquid in which pancakes adapt to Josephson vortices, and the usual homogeneous liquid.Comment: 2 pages, submitted to the Proceedings of M2S HTSC VIII Dresde

Role of pair-breaking and phase fluctuations in c-axis tunneling in underdoped Bi2_{2}Sr2_{2}CaCu2_{2}O8+δ_{8+\delta}

The Josephson Plasma Resonance is used to study the c-axis supercurrent in the superconducting state of underdoped Bi$_{2}$Sr$_{2}$CaCu$_{2}$O$_{8+\delta}$ with varying degrees of controlled point-like disorder, introduced by high-energy electron irradiation. As disorder is increased, the Josephson Plasma frequency decreases proportionally to the critical temperature. The temperature dependence of the plasma frequency does not depend on the irradiation dose, and is in quantitative agreement with a model for quantum fluctuations of the superconducting phase in the CuO$_{2}$ layers.Comment: 2 pages, submitted to the Proceedings of M2S-HTSC VIII Dresde

Facial reshaping operator for controllable face beautification

Posting attractive facial photos is part of everyday life in the social media era. Motivated by the demand, we propose a lightweight method to automatically and efficiently beautify the shapes of both portrait and non-portrait faces in photos, while allowing users to customize the beautification of individual facial features. Previous methods focus on the beautification of mostly frontal and neutral faces, without incorporating user controllability in the beautification process. To address these restrictions, we propose the Facial Reshaping Operator representation, which is affine-invariant, captures the pairwise geometric configuration of facial landmarks, and allows for efficient face beautification with the user-specified weights of individual facial parts. We also propose an unsupervised beautification method in the operator space of faces, where an input face is iteratively pulled towards a local nearby density mode with improved attractiveness. Our method distinguishes itself from the commercial beautification tools in that it mildly enhances facial shapes without altering makeups or complexions, which complements these tools that lack fine-grained control on the attractiveness of facial shapes for users. The experimental results show that our method improves facial shape attractiveness for a large range of poses and expressions, demonstrating the potential of applicability to photos seen on the social media such as Facebook and Instagram everyday

Evaluating distribution of foveal avascular zone parameters corrected by lateral magnification and their associations with retinal thickness

Purpose To examine the distribution of foveal avascular zone (FAZ) parameters, with and without correction for lateral magnification, in a large cohort of healthy young adults. Design Cross-sectional, observational cohort study. Participants A total of 504 healthy adults, 27 to 30 years of age. Methods Participants underwent a comprehensive ophthalmic examination including axial length measurement and OCT angiography (OCTA) imaging of the macula. OCT angiography images of combined superficial and deep retinal vessel plexuses were processed via a custom software to extract foveal avascular zone area (FAZA) and foveal density-300 (FD-300), the vessel density in a 300-μm wide annulus surrounding the FAZ, with and without correction for lateral magnification. Bland–Altman analyses were performed to examine the effect of lateral magnification on FAZA and FD-300, as well as to evaluate the interocular agreement in both parameters. Linear mixed-effects models were used to examine the relationship between retinal thicknesses and OCTA parameters. Main Outcome Measures The FAZA and FD-300, corrected for lateral magnification. Results The mean (standard deviation [SD]) of laterally corrected FAZA and FD-300 was 0.22 mm2 (0.10 mm2) and 51.9% (3.2%), respectively. Relative to uncorrected data, 55.6% of corrected FAZA showed a relative change > 5%, whereas all FD-300 changes were within 5%. There was good interocular symmetry (mean right eye–left eye difference, 95% limits of agreement [LoA]) in both FAZA (0.006 mm2, -0.05 mm2, to 0.07 mm2) and FD-300 (-0.05%, -5.39%, to 5.30%). There were significant negative associations between central retinal thickness and FAZA (β = -0.0029), as well as between central retinal thickness and FD-300 (β = -0.044), with the relationships driven by inner, not outer, retina. Conclusions We reported lateral magnification adjusted normative values for FAZA and FD-300 in a large cohort of young, healthy eyes. Clinicians should strongly consider accounting for lateral magnification when evaluating FAZA. Good interocular agreement in FAZA and FD-300 suggests the contralateral eye can be used as control data

A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis

As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle involvement for diagnosis or rehabilitation monitoring. On the one hand, human diagnosis is not scalable and may be subject to personal bias. On the other hand, automatic action quality assessment (AQA) algorithms cannot guarantee 100% accuracy, making them not suitable for biomedical applications. As a solution, we propose a video-based augmented reality system for human-in-the-loop muscle strength assessment of children with JDM. We first propose an AQA algorithm for muscle strength assessment of JDM using contrastive regression trained by a JDM dataset. Our core insight is to visualize the AQA results as a virtual character facilitated by a 3D animation dataset, so that users can compare the real-world patient and the virtual character to understand and verify the AQA results. To allow effective comparisons, we propose a video-based augmented reality system. Given a feed, we adapt computer vision algorithms for scene understanding, evaluate the optimal way of augmenting the virtual character into the scene, and highlight important parts for effective human verification. The experimental results confirm the effectiveness of our AQA algorithm, and the results of the user study demonstrate that humans can more accurately and quickly assess the muscle strength of children using our system