1,458 research outputs found
Zoology of domain walls in quasi-2D correlated charge density wave of 1T-TaS2
Domain walls in correlated charge density wave compounds such as 1T-TaS2 can
have distinct localized states which govern physical properties and
functionalities of emerging quantum phases. However, detailed atomic and
electronic structures of domain walls have largely been elusive. We identify
using scanning tunneling microscope and density functional theory calculations
the atomic and electronic structures for a plethora of discommensuration domain
walls in 1T-TaS2 quenched metastably with nanoscale domain wall networks. The
domain walls exhibit various in-gap states within the Mott gap but metallic
states appear in only particular types of domain walls. A systematic
understanding of the domain-wall electronic property requests not only the
electron counting but also including various intertwined interactions such as
structural relaxation, electron correlation, and charge transfer. This work
guides the domain wall engineering of the functionality in correlated van der
Waals materials.Comment: 7 pages, 4 figure
Quantifying the Influence of Climate Variation on Typhoon Rainfalls and Streamflows in South Korea
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv
Toward a Better Understanding of Loss Functions for Collaborative Filtering
Collaborative filtering (CF) is a pivotal technique in modern recommender
systems. The learning process of CF models typically consists of three
components: interaction encoder, loss function, and negative sampling. Although
many existing studies have proposed various CF models to design sophisticated
interaction encoders, recent work shows that simply reformulating the loss
functions can achieve significant performance gains. This paper delves into
analyzing the relationship among existing loss functions. Our mathematical
analysis reveals that the previous loss functions can be interpreted as
alignment and uniformity functions: (i) the alignment matches user and item
representations, and (ii) the uniformity disperses user and item distributions.
Inspired by this analysis, we propose a novel loss function that improves the
design of alignment and uniformity considering the unique patterns of datasets
called Margin-aware Alignment and Weighted Uniformity (MAWU). The key novelty
of MAWU is two-fold: (i) margin-aware alignment (MA) mitigates
user/item-specific popularity biases, and (ii) weighted uniformity (WU) adjusts
the significance between user and item uniformities to reflect the inherent
characteristics of datasets. Extensive experimental results show that MF and
LightGCN equipped with MAWU are comparable or superior to state-of-the-art CF
models with various loss functions on three public datasets.Comment: Accepted by CIKM 202
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