2,500 research outputs found
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
Due to the exponential growth of scientific publications on the Web, there is
a pressing need to tag each paper with fine-grained topics so that researchers
can track their interested fields of study rather than drowning in the whole
literature. Scientific literature tagging is beyond a pure multi-label text
classification task because papers on the Web are prevalently accompanied by
metadata information such as venues, authors, and references, which may serve
as additional signals to infer relevant tags. Although there have been studies
making use of metadata in academic paper classification, their focus is often
restricted to one or two scientific fields (e.g., computer science and
biomedicine) and to one specific model. In this work, we systematically study
the effect of metadata on scientific literature tagging across 19 fields. We
select three representative multi-label classifiers (i.e., a bag-of-words
model, a sequence-based model, and a pre-trained language model) and explore
their performance change in scientific literature tagging when metadata are fed
to the classifiers as additional features. We observe some ubiquitous patterns
of metadata's effects across all fields (e.g., venues are consistently
beneficial to paper tagging in almost all cases), as well as some unique
patterns in fields other than computer science and biomedicine, which are not
explored in previous studies.Comment: 11 pages; Accepted to WWW 202
Learning Multiplex Embeddings on Text-rich Networks with One Text Encoder
In real-world scenarios, texts in a network are often linked by multiple
semantic relations (e.g., papers in an academic network are referenced by other
publications, written by the same author, or published in the same venue),
where text documents and their relations form a multiplex text-rich network.
Mainstream text representation learning methods use pretrained language models
(PLMs) to generate one embedding for each text unit, expecting that all types
of relations between texts can be captured by these single-view embeddings.
However, this presumption does not hold particularly in multiplex text-rich
networks. Along another line of work, multiplex graph neural networks (GNNs)
directly initialize node attributes as a feature vector for node representation
learning, but they cannot fully capture the semantics of the nodes' associated
texts. To bridge these gaps, we propose METERN, a new framework for learning
Multiplex Embeddings on TExt-Rich Networks. In contrast to existing methods,
METERN uses one text encoder to model the shared knowledge across relations and
leverages a small number of parameters per relation to derive relation-specific
representations. This allows the encoder to effectively capture the multiplex
structures in the network while also preserving parameter efficiency. We
conduct experiments on nine downstream tasks in five networks from both
academic and e-commerce domains, where METERN outperforms baselines
significantly and consistently. The code is available at
https://github.com/PeterGriffinJin/METERN-submit.Comment: 9 pages, 11 appendix page
Emergent O(4) symmetry at the phase transition from plaquette-singlet to antiferromagnetic order in quasi-two-dimensional quantum magnets
Recent experiments [J. Guo et al., Phys. Rev. Lett.124,206602 (2020)] on
thermodynamic properties of the frustrated layered quantum magnet
SrCu(BO) -- the Shastry-Sutherland material -- have provided strong
evidence for a low-temperature phase transition between plaquette-singlet and
antiferromagnetic order as a function of pressure. Further motivated by the
recently discovered unusual first-order quantum phase transition with an
apparent emergent O(4) symmetry of the antiferromagnetic and plaquette-singlet
order parameters in a two-dimensional "checkerboard J-Q" quantum spin model [B.
Zhao et al., Nat. Phys. 15, 678 (2019)], we here study the same model in the
presence of weak inter-layer couplings. Our focus is on the evolution of the
emergent symmetry as the system crosses over from two to three dimensions and
the phase transition extends from strictly zero temperature in two dimensions
up to finite temperature as expected in SrCu(BO). Using quantum
Monte Carlo simulations, we map out the phase boundaries of the
plaquette-singlet and antiferromagnetic phases, with particular focus on the
triple point where these two order phases meet the paramagnetic phase for given
strength of the inter-layer coupling. All transitions are first-order in the
neighborhood of the triple points. We show that the emergent O(4) symmetry of
the coexistence state breaks down clearly when the interlayer coupling becomes
sufficiently large, but for a weak coupling, of the magnitude expected
experimentally, the enlarged symmetry can still be observed at the triple point
up to significant length scales. Thus, it is likely that the plaquette-singlet
to antiferromagnetic transition in SrCu(BO) exhibits remnants of
emergent O(4) symmetry, which should be observable due to additional weakly
gapped Goldstone modes.Comment: 14 pages, 8 figure
Quantum phases of SrCu2(BO3)2 from high-pressure thermodynamics
We report heat capacity measurements of SrCu(BO) under high
pressure along with simulations of relevant quantum spin models and map out the
phase diagram of the material. We find a first-order quantum phase
transition between the low-pressure quantum dimer paramagnet and a phase with
signatures of a plaquette-singlet state below T = K. At higher pressures,
we observe a transition into a previously unknown antiferromagnetic state below
K. Our findings can be explained within the two-dimensional
Shastry-Sutherland quantum spin model supplemented by weak inter-layer
couplings. The possibility to tune SrCu(BO) between the
plaquette-singlet and antiferromagnetic states opens opportunities for
experimental tests of quantum field theories and lattice models involving
fractionalized excitations, emergent symmetries, and gauge fluctuations.Comment: 6 pages + 8 pages supplemental informatio
Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
Instead of relying on human-annotated training samples to build a classifier,
weakly supervised scientific paper classification aims to classify papers only
using category descriptions (e.g., category names, category-indicative
keywords). Existing studies on weakly supervised paper classification are less
concerned with two challenges: (1) Papers should be classified into not only
coarse-grained research topics but also fine-grained themes, and potentially
into multiple themes, given a large and fine-grained label space; and (2) full
text should be utilized to complement the paper title and abstract for
classification. Moreover, instead of viewing the entire paper as a long linear
sequence, one should exploit the structural information such as citation links
across papers and the hierarchy of sections and paragraphs in each paper. To
tackle these challenges, in this study, we propose FUTEX, a framework that uses
the cross-paper network structure and the in-paper hierarchy structure to
classify full-text scientific papers under weak supervision. A network-aware
contrastive fine-tuning module and a hierarchy-aware aggregation module are
designed to leverage the two types of structural signals, respectively.
Experiments on two benchmark datasets demonstrate that FUTEX significantly
outperforms competitive baselines and is on par with fully supervised
classifiers that use 1,000 to 60,000 ground-truth training samples.Comment: 12 pages; Accepted to KDD 2023 (Code:
https://github.com/yuzhimanhua/FUTEX
Mechanical squeezing via fast continuous measurement
We revisit quantum state preparation of an oscillator by continuous linear
position measurement. Quite general analytical expressions are derived for the
conditioned state of the oscillator. Remarkably, we predict that quantum
squeezing is possible outside of both the backaction dominated and quantum
coherent oscillation regimes, relaxing experimental requirements even compared
to ground-state cooling. This provides a new way to generate non-classical
states of macroscopic mechanical oscillators, and opens the door to quantum
sensing and tests of quantum macroscopicity at room temperature
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