Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation

The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes. To resolve this, recent works focus on developing weaker forms of supervision -- e.g., hyperparameters discovered with a small number of validation samples with spurious attribute annotation -- but none of the methods retain comparable performance to methods using full supervision on the spurious attribute. In this paper, instead of searching for weaker supervisions, we ask: Given access to a fixed number of samples with spurious attribute annotations, what is the best achievable worst-group loss if we "fully exploit" them? To this end, we propose a pseudo-attribute-based algorithm, coined Spread Spurious Attribute (SSA), for improving the worst-group accuracy. In particular, we leverage samples both with and without spurious attribute annotations to train a model to predict the spurious attribute, then use the pseudo-attribute predicted by the trained model as supervision on the spurious attribute to train a new robust model having minimal worst-group loss. Our experiments on various benchmark datasets show that our algorithm consistently outperforms the baseline methods using the same number of validation samples with spurious attribute annotations. We also demonstrate that the proposed SSA can achieve comparable performances to methods using full (100%) spurious attribute supervision, by using a much smaller number of annotated samples -- from 0.6% and up to 1.5%, depending on the dataset.Comment: ICLR 2022 camera read

Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for graph-structured data (e.g., scene graph matching, code searching, and drug-drug interaction prediction). To this end, recent works have shifted their focus to learning the interaction between pairs of graphs. Despite their improved performance, these works were still limited in that the interactions were considered at the node-level, resulting in high computational costs and suboptimal performance. To address this issue, we propose a novel and efficient graph-level approach for extracting interaction representations using co-attention in graph pooling. Our method, Co-Attention Graph Pooling (CAGPool), exhibits competitive performance relative to existing methods in both classification and regression tasks using real-world datasets, while maintaining lower computational complexity.Comment: Published at IEEE Acces

Quantum Oscillations of the J=3/2 Fermi Surface in the Topological Semimetal Yptbi

The bismuth-based half-Heusler materials host a nontrivial topological band structure, unconventional superconductivity, and large spin-orbit coupling in a system with very low electron density. In particular, the inversion of p-orbital-derived bands with an effective angular momentum j of up to 3/2 is thought to play a central role in anomalous Cooper pairing in the cubic half-Heusler semimetal YPtBi, which is thought to be the first high-spin superconductor. Here, we report an extensive study of the angular dependence of quantum oscillations (QOs) in the electrical conductivity of YPtBi, revealing an anomalous Shubnikov-de Haas effect consistent with the presence of a coherent j=3/2 Fermi surface. The QO signal in YPtBi manifests an extreme anisotropy upon rotation of the magnetic field from the [100] to [110] crystallographic direction, where the QO amplitude vanishes. This radical anisotropy for such a highly isotropic system cannot be explained by trivial scenarios involving changes in effective mass or impurity scattering, but rather is naturally explained by the warping feature of the j=3/2 Fermi surface of YPtBi, providing direct proof of active high angular momentum quasiparticles in the half-Heusler compounds

RIDESOURCING IN MANUFACTURING SITES: A FRAMEWORK AND CASE STUDY

With the recent innovations in transportation, ridesourcing services have been proliferating in many countries. There are increasing attempts to apply ridesourcing in the corporate context. Manufacturing companies now install the Industrial Internet of Things (IIOT) sensors to vehicles to obtain real-time data on the movement of goods and materials. Despite the massive amount of data accumulated, little attention has been paid to exploiting the data for vehicle fleet management (FM). This paper proposes an analytical framework to solve two FM problems: how to group organizational units for vehicle sharing and where to deploy the groups. The framework is then validated with a case study of a Korean shipbuilder. The results indicate that grouping departments with similar spatial patterns can reduce the current fleet

Confirmation of the performance of exfoliated graphite nanoplatelets for pollutant reduction rate on wood panel

Abstract Exfoliated graphite nanoplatelets have been used as an additive for the improvement of a variety of properties. Nowadays, it is used as an adsorbent for pollutants, especially volatile organic compounds and formaldehyde. Additionally, exfoliated graphite nanoplatelets have been used as an additive in wood composites, because wood composites have a higher thermal conductivity than timber or typical wood-based panels. Therefore, exfoliated graphite nanoplatelets have been applied to high-density fiberboard which is a core material of wood flooring for the emission rate reduction of volatile organic compounds and formaldehyde and for increasing the thermal conductivity of wood flooring in radiant floor heating systems. Volatile organic compound emissions have been reduced significantly in wood composite boards according to the addition of exfoliated graphite nanoplatelets. The properties of formaldehyde emission and thermal conductivity decreased when 2% exfoliated graphite nanoplatelets are added to high-density fiberboard. However, these properties increased with the increase in the addition of exfoliated graphite nanoplatelets from 2% to 4%

The PEX7-Mediated Peroxisomal Import System Is Required for Fungal Development and Pathogenicity in Magnaporthe oryzae

In eukaryotes, microbodies called peroxisomes play important roles in cellular activities during the life cycle. Previous studies indicate that peroxisomal functions are important for plant infection in many phytopathogenic fungi, but detailed relationships between fungal pathogenicity and peroxisomal function still remain unclear. Here we report the importance of peroxisomal protein import through PTS2 (Peroxisomal Targeting Signal 2) in fungal development and pathogenicity of Magnaporthe oryzae. Using an Agrobacterium tumefaciens-mediated transformation library, a pathogenicity-defective mutant was isolated from M. oryzae and identified as a T-DNA insert in the PTS2 receptor gene, MoPEX7. Gene disruption of MoPEX7 abolished peroxisomal localization of a thiolase (MoTHL1) containing PTS2, supporting its role in the peroxisomal protein import machinery. ΔMopex7 showed significantly reduced mycelial growth on media containing short-chain fatty acids as a sole carbon source. ΔMopex7 produced fewer conidiophores and conidia, but conidial germination was normal. Conidia of ΔMopex7 were able to develop appressoria, but failed to cause disease in plant cells, except after wound inoculation. Appressoria formed by ΔMopex7 showed a defect in turgor generation due to a delay in lipid degradation and increased cell wall porosity during maturation. Taken together, our results suggest that the MoPEX7-mediated peroxisomal matrix protein import system is required for fungal development and pathogenicity M. oryzae

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts