Comparing Sample-wise Learnability Across Deep Neural Network Models

Estimating the relative importance of each sample in a training set has important practical and theoretical value, such as in importance sampling or curriculum learning. This kind of focus on individual samples invokes the concept of sample-wise learnability: How easy is it to correctly learn each sample (cf. PAC learnability)? In this paper, we approach the sample-wise learnability problem within a deep learning context. We propose a measure of the learnability of a sample with a given deep neural network (DNN) model. The basic idea is to train the given model on the training set, and for each sample, aggregate the hits and misses over the entire training epochs. Our experiments show that the sample-wise learnability measure collected this way is highly linearly correlated across different DNN models (ResNet-20, VGG-16, and MobileNet), suggesting that such a measure can provide deep general insights on the data's properties. We expect our method to help develop better curricula for training, and help us better understand the data itself.Comment: Accepted to AAAI 2019 Student Abstrac

The economic opportunities and constraints of green growth

노트 : Asie.Visions is an electronic publication dedicated to Asia. With contributions by French and international experts, Asie.Visions deals with economic, strategic, and political issues. The collection aims to contribute to the global debate and to a better understanding of the regional issues at stake. It is published in French and/or in English and upholds Ifri’s standards of quality (editing and anonymous peerreview)

Fermi Surface Spin Texture and Topological Superconductivity in Spin-Orbit Free Non-Collinear Antiferromagnets

We explore the relationship among the magnetic ordering in real space, the resulting spin texture on the Fermi surface, and the related superconducting gap structure in non-collinear antiferromagnetic metals without spin-orbit coupling. Via a perturbative approach, we show that a non-collinear magnetic ordering in a metal can generate a momentum-dependent spin texture on its Fermi surface, even in the absence of spin-orbit coupling, if the metal has more than three sublattices in its magnetic unit cell. Thus, our theory naturally extends the idea of altermagnetism to non-collinear spin structures. When superconductivity is developed in a magnetic metal, as the gap-opening condition is strongly constrained by the spin texture, the nodal structure of the superconducting state is also enforced by the magnetism-induced spin texture. Taking the non-collinear antiferromagnet on the kagome lattice as a representative example, we demonstrate how the Fermi surface spin texture induced by noncollinear antiferromagnetism naturally leads to odd-parity spin-triplet superconductivity with nontrivial topological properties

Flow-Induced Voltage Generation Over Monolayer Graphene in the Presence of Herringbone Grooves

While flow-induced voltage over a graphene layer has been reported, its origin remains unclear. In our previous study, we suggested different mechanisms for different experimental configurations: phonon dragging effect for the parallel alignment and an enhanced out-of-plane phonon mode for the perpendicular alignment (Appl. Phys. Lett. 102:063116, 2011). In order to further examine the origin of flow-induced voltage, we introduced a transverse flow component by integrating staggered herringbone grooves in the microchannel. We found that the flow-induced voltage decreased significantly in the presence of herringbone grooves in both parallel and perpendicular alignments. These results support our previous interpretation

Mobile Shopping Behavior among Fashion Adoption Groups

The purpose of the study was to examine differences among fashion adoption groups regarding mobile shopping behaviors

Public Service Motivation (PSM) and Attitudes toward Purchasing Fashion Counterfeits

Counterfeits are reproductions that appear identical to legitimate products in appearance, including packaging, trademarks, and labeling (Ang et al, 2001). In 2007, trade in counterfeits was estimated to be more than US $600 billion a year – 5 to 7$60 billion (The Korea Times, 2010). Fashion products (clothing, shoes, watches, leather goods, and jewelry) are the most popular counterfeit products