カーボンナノチューブ強化複合材料の機械特性向上に向けた変形・破壊機構の反応分子動力学シミュレーション

要約のみTohoku University久保百司課

Detecting Beneficial Feature Interactions for Recommender Systems

Feature interactions are essential for achieving high accuracy in recommender systems. Many studies take into account the interaction between every pair of features. However, this is suboptimal because some feature interactions may not be that relevant to the recommendation result, and taking them into account may introduce noise and decrease recommendation accuracy. To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy. The automatic feature interaction detection is achieved via edge prediction with an L0 activation regularization. Our proposed model is proved to be effective through the information bottleneck principle and statistical interaction theory. Experimental results show that our model (i) outperforms existing baselines in terms of accuracy, and (ii) automatically identifies beneficial feature interactions.Comment: 14 pages, 7 figures, 5 table

Thermal conductivity, structure and mechanical properties of konjac glucomannan/starch based aerogel strengthened by wheat straw

This study presents the preparation and property characterization of a konjac glucomannan (KGM)/starch based aerogel as a thermal insulation material. Wheat straw powders (a kind of agricultural waste) and starch are used to enhance aerogel physical properties such as mechanical strength and pore size distribution. Aerogel samples were made using environmentally friendly sol–gel and freeze drying methods. Results show that starch addition could strengthen the mechanical strength of aerogel significantly, and wheat straw addition could decrease aerogel pore size due to its special micron-cavity structure, with appropriate gelatin addition as the stabilizer. The aerogel formula was optimized to achieve lowest thermal conductivity and good thermal stability. Within the experimental range, aerogel with the optimized formula had a thermal conductivity 0.04641 Wm−1 K−1, a compression modulus 67.5 kPa and an elasticity 0.27. The results demonstrate the high potential of KGM/starch based aerogels enhanced with wheat straw for application in thermal insulation

Polystyrene nanoplastics mediated the toxicity of silver nanoparticles in zebrafish embryos

The widespread distribution of nanoplastics and nanomaterials in aquatic environments is of great concern. Nanoplastics have been found to modulate the toxicity of other environmental pollutants in organisms, while few studies have focused on their influences on nanomaterials. Thus, this study evaluated the influences of polystyrene (PS) nanoplastics on the toxicity of silver nanoparticles (AgNPs) to zebrafish (Danio rerio) embryos, including acute toxicity, oxidative stress, apoptosis, immunotoxicity, and metabolic capability. The results showed that the presence of PS nanoplastics could act as a carrier of the co-existing AgNPs in waters. The release ratio of Ag+ from AgNPs was up to 4.23%. The lethal effects of AgNPs on zebrafish embryos were not significantly changed by the co-added PS nanoplastics. Whereas, the alterations in gene expression related to antioxidant and metabolic capability in zebrafish (sod1, cat, mt2, mtf-1, and cox1) caused by AgNPs were significantly enhanced by the presence of PS nanoplastics, which simultaneously lowered the apoptosis and immunotoxicity (caspase9, nfkβ, cebp, and il-1β) induced by AgNPs. It suggests the presence of PS nanoplastics suppressed the AgNPs-induced genotoxicity in zebrafish. The released Ag+ from AgNPs may be responsible for the toxicity of AgNPs in zebrafish, while the subsequent absorption and agglomeration of AgNPs and the released Ag+ on PS nanoplastics may alleviate the toxicity

AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models

The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this task. However, to our best knowledge, existing methods all require manually crafted seed alignments, which are expensive to obtain. In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments. Specifically, for predicate embeddings, AutoAlign constructs a predicate-proximity-graph with the help of large language models to automatically capture the similarity between predicates across two KGs. For entity embeddings, AutoAlign first computes the entity embeddings of each KG independently using TransE, and then shifts the two KGs' entity embeddings into the same vector space by computing the similarity between entities based on their attributes. Thus, both predicate alignment and entity alignment can be done without manually crafted seed alignments. AutoAlign is not only fully automatic, but also highly effective. Experiments using real-world KGs show that AutoAlign improves the performance of entity alignment significantly compared to state-of-the-art methods.Comment: 14 pages, 5 figures, 4 tables. arXiv admin note: substantial text overlap with arXiv:2210.0854