DropMix: Reducing Class Dependency in Mixed Sample Data Augmentation

Mixed sample data augmentation (MSDA) is a widely used technique that has been found to improve performance in a variety of tasks. However, in this paper, we show that the effects of MSDA are class-dependent, with some classes seeing an improvement in performance while others experience a decline. To reduce class dependency, we propose the DropMix method, which excludes a specific percentage of data from the MSDA computation. By training on a combination of MSDA and non-MSDA data, the proposed method not only improves the performance of classes that were previously degraded by MSDA, but also increases overall average accuracy, as shown in experiments on two datasets (CIFAR-100 and ImageNet) using three MSDA methods (Mixup, CutMix and PuzzleMix).Comment: 17 pages, 10 figure

Test-Time Mixup Augmentation for Data and Class-Dependent Uncertainty Estimation in Deep Learning Image Classification

Uncertainty estimation of the trained deep learning networks is valuable for optimizing learning efficiency and evaluating the reliability of network predictions. In this paper, we propose a method for estimating uncertainty in deep learning image classification using test-time mixup augmentation (TTMA). To improve the ability to distinguish correct and incorrect predictions in existing aleatoric uncertainty, we introduce the TTMA data uncertainty (TTMA-DU) by applying mixup augmentation to test data and measuring the entropy of the predicted label histogram. In addition to TTMA-DU, we propose the TTMA class-dependent uncertainty (TTMA-CDU), which captures aleatoric uncertainty specific to individual classes and provides insight into class confusion and class similarity within the trained network. We validate our proposed methods on the ISIC-18 skin lesion diagnosis dataset and the CIFAR-100 real-world image classification dataset. Our experiments show that (1) TTMA-DU more effectively differentiates correct and incorrect predictions compared to existing uncertainty measures due to mixup perturbation, and (2) TTMA-CDU provides information on class confusion and class similarity for both datasets

Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery

Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learning scenario remains a challenging problem due to the lack of prior knowledge about the number and nature of new categories. Existing methods for novel category discovery are limited by their reliance on labeled datasets and prior knowledge about the number of novel categories and the proportion of novel samples in the batch. To address the limitations and more accurately reflect real-world scenarios, in this paper, we propose a novel unsupervised class incremental learning approach for discovering novel categories on unlabeled sets without prior knowledge. The proposed method fine-tunes the feature extractor and proxy anchors on labeled sets, then splits samples into old and novel categories and clusters on the unlabeled dataset. Furthermore, the proxy anchors-based exemplar generates representative category vectors to mitigate catastrophic forgetting. Experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods on fine-grained datasets under real-world scenarios.Comment: Accepted to ICCV 202

AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation

We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which regularizes the training procedure by adversarial learning and implicit distillations. Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning. The motivation is that the self-knowledge distillation methods regularize the predictive probabilities with soft targets, but the exact distributions may be hard to predict. Our method deploys a discriminator to distinguish the distributions between the pre-trained and student models while the student model is trained to fool the discriminator in the trained procedure. Thus, the student model not only can learn the pre-trained model's predictive probabilities but also align the distributions between the pre-trained and student models. We demonstrate the effectiveness of the proposed method with network architectures on multiple datasets and show the proposed method achieves better performance than state-of-the-art methods.Comment: 12 pages, 7 figure

Creep Behavior of Passive Bovine Extraocular Muscle

This paper characterized bovine extraocular muscles (EOMs) using creep, which represents long-term stretching induced by a constant force. After preliminary optimization of testing conditions, 20 fresh EOM samples were subjected to four different loading rates of 1.67, 3.33, 8.33, and 16.67%/s, after which creep was observed for 1,500 s. A published quasilinear viscoelastic (QLV) relaxation function was transformed to a creep function that was compared with data. Repeatable creep was observed for each loading rate and was similar among all six anatomical EOMs. The mean creep coefficient after 1,500 seconds for a wide range of initial loading rates was at 1.37 ± 0.03 (standard deviation, SD). The creep function derived from the relaxation-based QLV model agreed with observed creep to within 2.7% following 16.67%/s ramp loading. Measured creep agrees closely with a derived QLV model of EOM relaxation, validating a previous QLV model for characterization of EOM biomechanics

Synthesis of Cell-Adhesive Anisotropic Multifunctional Particles by Stop Flow Lithography and Streptavidin–Biotin Interactions

Cell-adhesive particles are of significant interest in biotechnology, the bioengineering of complex tissues, and biomedical research. Their applications range from platforms to increase the efficiency of anchorage-dependent cell culture to building blocks to loading cells in heterogeneous structures to clonal-population growth monitoring to cell sorting. Although useful, currently available cell-adhesive particles can accommodate only homogeneous cell culture. Here, we report the design of anisotropic hydrogel microparticles with tunable cell-adhesive regions as first step toward micropatterned cell cultures on particles. We employed stop flow lithography (SFL), the coupling reaction between amine and N-hydroxysuccinimide (NHS) and streptavidin–biotin chemistry to adjust the localization of conjugated collagen and poly-l-lysine on the surface of microscale particles. Using the new particles, we demonstrate the attachment and formation of tight junctions between brain endothelial cells. We also demonstrate the geometric patterning of breast cancer cells on particles with heterogeneous collagen coatings. This new approach avoids the exposure of cells to potentially toxic photoinitiators and ultraviolet light and decouples in time the microparticle synthesis and the cell culture steps to take advantage of the most recent advances in cell patterning available for traditional culture substrates.National Institutes of Health (U.S.) (GM092804)National Science Foundation (U.S.) (CMMI-1120724 and DMR-1006147)Samsung Scholarship Foundatio

Copper selenide film electrodes prepared by combined electrochemical/chemical bath depositions with high photo-electrochemical conversion efficiency and stability

Copper selenide (of the type Cu2-xSe) film electrodes, prepared by combined electrochemical (ECD) followed by chemical bath deposition (CBD), may yield high photo-electrochemical (PEC) conversion efficiency (~14.6%) with no further treatment. The new ECD/CBD-copper selenide film electrodes show enhanced PEC characteristics and exhibit high stability under PEC conditions, compared to the ECD or the CBD films deposited separately. The electrodes combine the advantages of both ECD-copper selenide electrodes (in terms of good adherence to FTO surface and high surface uniformity) and CBD-copper selenide electrodes (suitable film thickness). Effect of annealing temperature, on the ECD/CBD film electrode composition and efficiency, is discussed.The results of this work are partly based on K. Murtada M.Sc. Thesis, under direct supervision of H.S. Hilal. Other experimental measurements and calculations, including dark current experiments, film thickness measurement, electrical conductivity, SEM analysis, XRD &AFM analysis revisions were performed by A. Zyoud after the thesis completion. Additional film electrode stability experiments under PEC conditions, were also performed by A. Zyoud after the Thesis completion. SEM micrographs and EDX spectra were measured by T.W. Kim and H-J.C. at the KIER, Korea. The XRD patterns were measured by D-H. Park and H. Kwon at PUK. M.H.S. Helal and H. Bsharat contributed with literature search, discussions and modeling. M. Faroun measured AFM micrographs at Al-Quds University. H.S. Hilal acknowledges financial support from ANU, Islamic Development Bank, Al-Maqdisi Project and Union of Arab Universities. T.W. Kim and H-J. Choi acknowledge financial support from the framework of the Research and Development Program of the Korea Institute of Energy Research (B6-2523)

Investigation of Formation Behaviour of Al–Cu Intermetallic Compounds in Al–50vol%Cu Composites Prepared by Spark Plasma Sintering under High Pressure

Al–Cu matrix composites with excellent mechanical and thermal properties are among the most promising materials for realising high performance in thermal management systems. However, intermetallic compounds (ICs) formed at the Al/Cu interfaces prevent direct contact between the metals and severely deteriorate the thermal conductivity of the composite. In this study, we systemically investigated the formation behaviour of Al–Cu ICs as a function of compaction pressure at a low temperature of 380 °C. The phases of the Al–Cu ICs formed during sintering were detected via X-ray diffraction, and the layer thickness and average area fraction of each IC at different compaction pressures were analysed via micro-scale observations of the cross-sections of the Al–Cu composites. The ICs were partially formed along the Al/Cu interfaces at high pressures, and the formation region was related to the direction of applied pressure. The Vickers hardness of the Al–Cu composites with ICs was nearly double those calculated using the rule of mixtures. On the other hand, the thermal conductivity of the composites increased with compaction pressure and reached 201 W·m−1·K−1. This study suggests the possibility of employing Al–Cu matrix composites with controlled IC formation in thermal management applications

Interdiffusion and Intermetallic Compounds at Al/Cu Interfaces in Al-50vol.%Cu Composite Prepared by Solid-State Sintering

Al–Cu composites have attracted significant interest recently owing to their lightweight nature and remarkable thermal properties. Understanding the interdiffusion mechanism at the numerous Al/Cu interfaces is crucial to obtain Al–Cu composites with high thermal conductivities. The present study systematically investigates the interdiffusion mechanism at Al/Cu interfaces in relation to the process temperature. Al-50vol.%Cu composite powder, where Cu particles were encapsulated in a matrix of irregular Al particles, was prepared and then sintered at various temperatures from 340 to 500 °C. Intermetallic compounds (ICs) such as CuAl2 and Cu9Al4 were formed at the Al/Cu interfaces during sintering. Microstructural analysis showed that the thickness of the interdiffusion layer, which comprised the CuAl2 and Cu9Al4 ICs, drastically increased above 400 °C. The Vickers hardness of the Al-50vol.%Cu composite sintered at 380 °C was 79 HV, which was 1.5 times that of the value estimated by the rule of mixtures. A high thermal conductivity of 150 W∙m−1∙K−1 was simultaneously obtained. This result suggests that the Al-50vol.%Cu composite material with large number of Al/Cu interfaces, as well as good mechanical strength and heat conductance, can be prepared by solid-state sintering at a low temperature