506 research outputs found
Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora
Much of scientific progress stems from previously published findings, but
searching through the vast sea of scientific publications is difficult. We
often rely on metrics of scholarly authority to find the prominent authors but
these authority indices do not differentiate authority based on research
topics. We present Latent Topical-Authority Indexing (LTAI) for jointly
modeling the topics, citations, and topical authority in a corpus of academic
papers. Compared to previous models, LTAI differs in two main aspects. First,
it explicitly models the generative process of the citations, rather than
treating the citations as given. Second, it models each author's influence on
citations of a paper based on the topics of the cited papers, as well as the
citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS,
and Citeseer. We compare the performance of LTAI against various baselines,
starting with the latent Dirichlet allocation, to the more advanced models
including author-link topic model and dynamic author citation topic model. The
results show that LTAI achieves improved accuracy over other similar models
when predicting words, citations and authors of publications.Comment: Accepted by Transactions of the Association for Computational
Linguistics (TACL); to appea
Stable cheapest nonconforming finite elements for the Stokes equations
We introduce two pairs of stable cheapest nonconforming finite element space
pairs to approximate the Stokes equations. One pair has each component of its
velocity field to be approximated by the nonconforming quadrilateral
element while the pressure field is approximated by the piecewise constant
function with globally two-dimensional subspaces removed: one removed space is
due to the integral mean--zero property and the other space consists of global
checker--board patterns. The other pair consists of the velocity space as the
nonconforming quadrilateral element enriched by a globally
one--dimensional macro bubble function space based on
(Douglas-Santos-Sheen-Ye) nonconforming finite element space; the pressure
field is approximated by the piecewise constant function with mean--zero space
eliminated. We show that two element pairs satisfy the discrete inf-sup
condition uniformly. And we investigate the relationship between them. Several
numerical examples are shown to confirm the efficiency and reliability of the
proposed methods
Robust Evaluation of Diffusion-Based Adversarial Purification
We question the current evaluation practice on diffusion-based purification
methods. Diffusion-based purification methods aim to remove adversarial effects
from an input data point at test time. The approach gains increasing attention
as an alternative to adversarial training due to the disentangling between
training and testing. Well-known white-box attacks are often employed to
measure the robustness of the purification. However, it is unknown whether
these attacks are the most effective for the diffusion-based purification since
the attacks are often tailored for adversarial training. We analyze the current
practices and provide a new guideline for measuring the robustness of
purification methods against adversarial attacks. Based on our analysis, we
further propose a new purification strategy improving robustness compared to
the current diffusion-based purification methods.Comment: Accepted by ICCV 2023, Oral presentatio
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