4,369 research outputs found
Study on Optimal Device Design for 2-terminal Mechanical Perovskite/Silicon Tandem Solar Cells with Transparent Conductive Adhesives
Department of Materials Science and Engineeringclos
Smartphone dependence classification using tensor factorization
Excessive smartphone use causes personal and social problems. To address this issue, we sought to derive usage patterns that were directly correlated with smartphone dependence based on usage data. This study attempted to classify smartphone dependence using a data-driven prediction algorithm. We developed a mobile application to collect smartphone usage data. A total of 41,683 logs of 48 smartphone users were collected from March 8, 2015, to January 8, 2016. The participants were classified into the control group (SUC) or the addiction group (SUD) using the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale) and a face-to-face offline interview by a psychiatrist and a clinical psychologist (SUC = 23 and SUD = 25). We derived usage patterns using tensor factorization and found the following six optimal usage patterns: 1) social networking services (SNS) during daytime, 2) web surfing, 3) SNS at night, 4) mobile shopping, 5) entertainment, and 6) gaming at night. The membership vectors of the six patterns obtained a significantly better prediction performance than the raw data. For all patterns, the usage times of the SUD were much longer than those of the SUC. From our findings, we concluded that usage patterns and membership vectors were effective tools to assess and predict smartphone dependence and could provide an intervention guideline to predict and treat smartphone dependence based on usage data.112Ysciescopu
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition
There is growing interest in the challenging visual perception task of
learning from long-tailed class distributions. The extreme class imbalance in
the training dataset biases the model to prefer recognizing majority class data
over minority class data. Furthermore, the lack of diversity in minority class
samples makes it difficult to find a good representation. In this paper, we
propose an effective data augmentation method, referred to as bilateral mixup
augmentation, which can improve the performance of long-tailed visual
recognition. The bilateral mixup augmentation combines two samples generated by
a uniform sampler and a re-balanced sampler and augments the training dataset
to enhance the representation learning for minority classes. We also reduce the
classifier bias using class-wise temperature scaling, which scales the logits
differently per class in the training phase. We apply both ideas to the
dual-branch network (DBN) framework, presenting a new model, named dual-branch
network with bilateral mixup (DBN-Mix). Experiments on popular long-tailed
visual recognition datasets show that DBN-Mix improves performance
significantly over baseline and that the proposed method achieves
state-of-the-art performance in some categories of benchmarks
Superconducting transition of a two-dimensional Josephson junction array in weak magnetic fields
The superconducting transition of a two-dimensional (2D) Josephson junction
array exposed to weak magnetic fields has been studied experimentally.
Resistance measurements reveal a superconducting-resistive phase boundary in
serious disagreement with the theoretical and numerical expectations. Critical
scaling analyses of the characteristics indicate contrary to the
expectations that the superconducting-to-resistive transition in weak magnetic
fields is associated with a melting transition of magnetic-field-induced
vortices directly from a pinned-solid phase to a liquid phase. The expected
depinning transition of vortices from a pinned-solid phase to an intermediate
floating-solid phase was not observed. We discuss effects of the
disorder-induced random pinning potential on phase transitions of vortices in a
2D Josephson junction array.Comment: 9 pages, 7 figures (EPS+JPG format), RevTeX
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