525 research outputs found
Multivariable Scaling for the Anomalous Hall Effect
We derive a general scaling relation for the anomalous Hall effect in
ferromagnetic metals involving multiple competing scattering mechanisms,
described by a quadratic hypersurface in the space spanned by the partial
resistivities. We also present experimental findings, which show strong
deviation from previously found scaling forms when different scattering
mechanism compete in strength but can be nicely explained by our theory
DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations
In e-commerce platforms, the relevant recommendation is a unique scenario
providing related items for a trigger item that users are interested in.
However, users' preferences for the similarity and diversity of recommendation
results are dynamic and vary under different conditions. Moreover, individual
item-level diversity is too coarse-grained since all recommended items are
related to the trigger item. Thus, the two main challenges are to learn
fine-grained representations of similarity and diversity and capture users'
dynamic preferences for them under different conditions. To address these
challenges, we propose a novel method called the Dynamic Preference-based and
Attribute-aware Network (DPAN) for predicting Click-Through Rate (CTR) in
relevant recommendations. Specifically, based on Attribute-aware Activation
Values Generation (AAVG), Bi-dimensional Compression-based Re-expression (BCR)
is designed to obtain similarity and diversity representations of user
interests and item information. Then Shallow and Deep Union-based Fusion (SDUF)
is proposed to capture users' dynamic preferences for the diverse degree of
recommendation results according to various conditions. DPAN has demonstrated
its effectiveness through extensive offline experiments and online A/B testing,
resulting in a significant 7.62% improvement in CTR. Currently, DPAN has been
successfully deployed on our e-commerce platform serving the primary traffic
for relevant recommendations. The code of DPAN has been made publicly
available
Evidence of the side jump mechanism in the anomalous Hall effect in paramagnets
Persistent confusion has existed between the intrinsic (Berry curvature) and
the side jump mechanisms of anomalous Hall effect (AHE) in ferromagnets. We
provide unambiguous identification of the side jump mechanism, in addition to
the skew scattering contribution in epitaxial paramagnetic NiCu
thin films, in which the intrinsic contribution is by definition excluded.
Furthermore, the temperature dependence of the AHE further reveals that the
side jump mechanism is dominated by the elastic scattering
A Meta-Analysis of the Relationship Between Corporate Social Responsibility and Consumer Response in the Chinese Context
Consumers’ attitudes toward corporate social responsibility (CSR) and their response levels can significantly affect a firm’s behaviors. Based on 61 research papers addressing CSR responses to consumers in China, this study conducted a meta-analysis on three variables: CSR type, CSR characteristics, and CSR publicity behavior, which impact the CSR relationship. The following conclusions are drawn: Corporate social responsibility (encompassing private and public moral dimensions) elicits a positive consumer response, with private moral behavior having a greater positive effect compared to public moral behavior. The four dimensions of CSR characteristics (CSR commitment, CSR level, CSR correlation, and CSR timing) all lead to positive consumer responses. Positive consumer responses also arise from CSR publicity behavior, including publicity initiative and publicity intensity. Additionally, product type, sample time, and sample source have significant moderating effects on these relationships
Protoplast transformation as a potential platform for exploring gene function in Verticillium dahliae
Position of siRNAs along the Vta2 gene of V. dahliae. The position of different siRNAs designed to target this gene is shown in this figure. Sequence underlined with different colors shows different siRNAs. (JPG 7797 kb
Coreference Resolution in Biomedical Texts: a Machine Learning Approach
Motivation: Coreference resolution, the process of identifying different
mentions of an entity, is a very important component in a
text-mining system. Compared with the work in news articles, the
existing study of coreference resolution in biomedical texts is quite
preliminary by only focusing on specific types of anaphors like pronouns
or definite noun phrases, using heuristic methods, and running
on small data sets. Therefore, there is a need for an in-depth
exploration of this task in the biomedical domain.
Results: In this article, we presented a learning-based approach
to coreference resolution in the biomedical domain. We made three
contributions in our study. Firstly, we annotated a large scale coreference
corpus, MedCo, which consists of 1,999 medline abstracts
in the GENIA data set. Secondly, we proposed a detailed framework
for the coreference resolution task, in which we augmented the traditional
learning model by incorporating non-anaphors into training.
Lastly, we explored various sources of knowledge for coreference
resolution, particularly, those that can deal with the complexity of
biomedical texts. The evaluation on the MedCo corpus showed promising
results. Our coreference resolution system achieved a high
precision of 85.2% with a reasonable recall of 65.3%, obtaining an
F-measure of 73.9%. The results also suggested that our augmented
learning model significantly boosted precision (up to 24.0%) without
much loss in recall (less than 5%), and brought a gain of over 8% in
F-measure
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