4,547 research outputs found
The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge
This article describes the final solution of team monkeytyping, who finished
in second place in the YouTube-8M video understanding challenge. The dataset
used in this challenge is a large-scale benchmark for multi-label video
classification. We extend the work in [1] and propose several improvements for
frame sequence modeling. We propose a network structure called Chaining that
can better capture the interactions between labels. Also, we report our
approaches in dealing with multi-scale information and attention pooling. In
addition, We find that using the output of model ensemble as a side target in
training can boost single model performance. We report our experiments in
bagging, boosting, cascade, and stacking, and propose a stacking algorithm
called attention weighted stacking. Our final submission is an ensemble that
consists of 74 sub models, all of which are listed in the appendix.Comment: Submitted to the CVPR 2017 Workshop on YouTube-8M Large-Scale Video
Understandin
Zero-Mode Contribution in Nucleon-Delta Transition
We investigate the transition form factors between nucleon and (1232)
particles by using a covariant quark-spectator-diquark field theory model in
(3+1) dimensions. Performing a light-front calculation in parallel with the
manifestly covariant calculation in light-front helicity basis, we examine the
light-front zero-mode contribution to the helicity components of light-front
good ("+") current matrix elements. Choosing the light-front gauge
() with circular polarization in Drell-Yan-West frame, we
find that only the helicity components and of the good current receive the zero-mode contribution. Taking
into account the zero-mode, we find the prescription independence in obtaining
the light-front solution of form factors from any three helicity matrix
elements with smeared light-front wavefunctions. The angular condition, which
guarantees the full covariance of different schemes, is recovered.Comment: 16 latex pages, 7 figures, to appear in PR
Optimal control-based inverse determination of electrode distribution for electroosmotic micromixer
This paper presents an optimal control-based inverse method used to determine
the distribution of the electrodes for the electroosmotic micromixers with
external driven flow from the inlet. Based on the optimal control method, one
Dirichlet boundary control problem is constructed to inversely find the optimal
distribution of the electrodes on the sidewalls of electroosmotic micromixers
and achieve the acceptable mixing performance. After solving the boundary
control problem, the step-shaped distribution of the external electric
potential imposed on the sidewalls can be obtained and the distribution of
electrodes can be inversely determined according to the obtained external
electric potential. Numerical results are also provided to demonstrate the
effectivity of the proposed method
Thermoelectric Transport in Holographic Quantum Matter under Shear Strain
We study the thermoelectric transport under shear strain in two spatial
dimensional quantum matter using the holographic duality. General analytic
formulae for the DC thermoelectric conductivities subjected to finite shear
strain are obtained in terms of the black hole horizon data. Off-diagonal terms
in the conductivity matrix appear also at zero magnetic field, resembling an
emergent electronic nematicity which cannot nevertheless be identified with the
presence of an anomalous Hall effect. For an explicit model study, we
numerically construct a family of strained black holes and obtain the
corresponding nonlinear stress-strain curves. We then compute all electric,
thermoelectric, and thermal conductivities and discuss the effects of strain.
While the shear elastic deformation does not affect the temperature dependence
of thermoelectric and thermal conductivities quantitatively, it can strongly
change the behavior of the electric conductivity. For both shear hardening and
softening cases, we find a clear metal-insulator transition driven by the shear
deformation. Moreover, the violation of the previously conjectured thermal
conductivity bound is observed for large shear deformation.Comment: 35 pages, 13 figure
CMNER: A Chinese Multimodal NER Dataset based on Social Media
Multimodal Named Entity Recognition (MNER) is a pivotal task designed to
extract named entities from text with the support of pertinent images.
Nonetheless, a notable paucity of data for Chinese MNER has considerably
impeded the progress of this natural language processing task within the
Chinese domain. Consequently, in this study, we compile a Chinese Multimodal
NER dataset (CMNER) utilizing data sourced from Weibo, China's largest social
media platform. Our dataset encompasses 5,000 Weibo posts paired with 18,326
corresponding images. The entities are classified into four distinct
categories: person, location, organization, and miscellaneous. We perform
baseline experiments on CMNER, and the outcomes underscore the effectiveness of
incorporating images for NER. Furthermore, we conduct cross-lingual experiments
on the publicly available English MNER dataset (Twitter2015), and the results
substantiate our hypothesis that Chinese and English multimodal NER data can
mutually enhance the performance of the NER model
Finding Answers to Definition Questions Using Web Knowledge Bases
PACLIC 23 / City University of Hong Kong / 3-5 December 200
Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning
PACLIC 23 / City University of Hong Kong / 3-5 December 200
Evidence of Environmental Quenching at Redshift z ~ 2
We report evidence of environmental quenching among galaxies at redshift ~ 2,
namely the probability that a galaxy quenches its star formation activity is
enhanced in the regions of space in proximity of other quenched, more massive
galaxies. The effect is observed as strong clustering of quiescent galaxies
around quiescent galaxies on angular scales \theta < 20 arcsec, corresponding
to a proper(comoving) scale of 168 (502) kpc at z = 2. The effect is observed
only for quiescent galaxies around other quiescent galaxies; the probability to
find star-forming galaxies around quiescent or around star-forming ones is
consistent with the clustering strength of galaxies of the same mass and at the
same redshift, as observed in dedicated studies of galaxy clustering. The
effect is mass dependent in the sense that the quenching probability is
stronger for galaxies of smaller mass () than for more
massive ones, i.e. it follows the opposite trend with mass relative to
gravitational galaxy clustering. The spatial scale where the effect is observed
suggests these environments are massive halos, in which case the observed
effect would likely be satellite quenching. The effect is also redshift
dependent in that the clustering strength of quiescent galaxies around other
quiescent galaxies at z = 1.6 is ~ 1.7 times larger than that of the galaxies
with the same stellar mass at z = 2.6. This redshift dependence allows for a
crude estimate of the time scale of environmental quenching of low-mass
galaxies, which is in the range 1.5 - 4 Gyr, in broad agreement with other
estimates and with our ideas on satellite quenching.Comment: 12 pages, 9 figures, Accepted for publication in Ap
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition
The joint task of Dialog Sentiment Classification (DSC) and Act Recognition
(DAR) aims to predict the sentiment label and act label for each utterance in a
dialog simultaneously. However, current methods encode the dialog context in
only one direction, which limits their ability to thoroughly comprehend the
context. Moreover, these methods overlook the explicit correlations between
sentiment and act labels, which leads to an insufficient ability to capture
rich sentiment and act clues and hinders effective and accurate reasoning. To
address these issues, we propose a Bi-directional Multi-hop Inference Model
(BMIM) that leverages a feature selection network and a bi-directional
multi-hop inference network to iteratively extract and integrate rich sentiment
and act clues in a bi-directional manner. We also employ contrastive learning
and dual learning to explicitly model the correlations of sentiment and act
labels. Our experiments on two widely-used datasets show that BMIM outperforms
state-of-the-art baselines by at least 2.6% on F1 score in DAR and 1.4% on F1
score in DSC. Additionally, Our proposed model not only improves the
performance but also enhances the interpretability of the joint sentiment and
act prediction task.Comment: Accepted by NLPCC 202
- …