756 research outputs found
Orientation and Motion of Water Molecules at Air/Water Interface
Analysis of SFG vibrational spectra of OH stretching bands in four
experimental configurations shows that orientational motion of water molecule
at air/water interface is libratory within a limited angular range. This
picture is significantly different from the previous conclusion that the
interfacial water molecule orientation varies over a broad range within the
vibrational relaxation time, the only direct experimental evidence for
ultrafast and broad orientational motion of a liquid interface by Wei et al.
[Phys. Rev. Lett. 86, 4799, (2001)] using single SFG experimental
configuration
Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection
Multi-Label Few-Shot Aspect Category Detection (FS-ACD) is a new sub-task of
aspect-based sentiment analysis, which aims to detect aspect categories
accurately with limited training instances. Recently, dominant works use the
prototypical network to accomplish this task, and employ the attention
mechanism to extract keywords of aspect category from the sentences to produce
the prototype for each aspect. However, they still suffer from serious noise
problems: (1) due to lack of sufficient supervised data, the previous methods
easily catch noisy words irrelevant to the current aspect category, which
largely affects the quality of the generated prototype; (2) the
semantically-close aspect categories usually generate similar prototypes, which
are mutually noisy and confuse the classifier seriously. In this paper, we
resort to the label information of each aspect to tackle the above problems,
along with proposing a novel Label-Driven Denoising Framework (LDF). Extensive
experimental results show that our framework achieves better performance than
other state-of-the-art methods.Comment: Finding of EMNLP 2022 camera-read
Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA,
which aims to extract the corresponding opinion words for a given opinion
target in a sentence. Recently, neural network methods have been applied to
this task and achieve promising results. However, the difficulty of annotation
causes the datasets of TOWE to be insufficient, which heavily limits the
performance of neural models. By contrast, abundant review sentiment
classification data are easily available at online review sites. These reviews
contain substantial latent opinions information and semantic patterns. In this
paper, we propose a novel model to transfer these opinions knowledge from
resource-rich review sentiment classification datasets to low-resource task
TOWE. To address the challenges in the transfer process, we design an effective
transformation method to obtain latent opinions, then integrate them into TOWE.
Extensive experimental results show that our model achieves better performance
compared to other state-of-the-art methods and significantly outperforms the
base model without transferring opinions knowledge. Further analysis validates
the effectiveness of our model.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020
EUCLIA - Exploring the UV/optical continuum lag in active galactic nuclei. I. a model without light echoing
The tight inter-band correlation and the lag-wavelength relation among
UV/optical continua of active galactic nuclei have been firmly established.
They are usually understood within the widespread reprocessing scenario,
however, the implied inter-band lags are generally too small. Furthermore, it
is challenged by new evidences, such as the X-ray reprocessing yields too much
high frequency UV/optical variations as well as it fails to reproduce the
observed timescale-dependent color variations among {\it Swift} lightcurves of
NGC 5548. In a different manner, we demonstrate that an upgraded inhomogeneous
accretion disk model, whose local {\it independent} temperature fluctuations
are subject to a speculated {\it common} large-scale temperature fluctuation,
can intrinsically generate the tight inter-band correlation and lag across
UV/optical, and be in nice agreement with several observational properties of
NGC 5548, including the timescale-dependent color variation. The emergent lag
is a result of the {\it differential regression capability} of local
temperature fluctuations when responding to the large-scale fluctuation. An
average speed of propagations as large as of the speed of light
may be required by this common fluctuation. Several potential physical
mechanisms for such propagations are discussed. Our interesting
phenomenological scenario may shed new light on comprehending the UV/optical
continuum variations of active galactic nuclei.Comment: 18 pages, 8 figures. ApJ accepted. Further comments are very welcome
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis
Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained
Sentiment Analysis task, which has attracted growing research interests
recently. Existing work mainly utilizes image information to improve the
performance of MABSA task. However, most of the studies overestimate the
importance of images since there are many noise images unrelated to the text in
the dataset, which will have a negative impact on model learning. Although some
work attempts to filter low-quality noise images by setting thresholds, relying
on thresholds will inevitably filter out a lot of useful image information.
Therefore, in this work, we focus on whether the negative impact of noisy
images can be reduced without modifying the data. To achieve this goal, we
borrow the idea of Curriculum Learning and propose a Multi-grained
Multi-curriculum Denoising Framework (M2DF), which can achieve denoising by
adjusting the order of training data. Extensive experimental results show that
our framework consistently outperforms state-of-the-art work on three sub-tasks
of MABSA.Comment: Accepted by EMNLP 202
Psychological Stress Induces Temporary Masticatory Muscle Mechanical Sensitivity in Rats
To explore the relationship between psychological stress and masticatory muscle pain, we created a communication stress animal model to determine whether psychological stress could induce increased mechanical sensitivity in masticatory muscles and to study the changes of mechanical nociceptive thresholds after stress removal. Forty-eight male Sprague-Dawley rats were divided into a control group (CON), a foot-shocked group (FS, including 3 subgroups recorded as FS-1, FS-2, and FS-3), a psychological stress group (PS), and a drug treatment group (DT). PS and DT rats were confined in a communication box for one hour a day to observe the psychological responses of neighboring FS rats.Measurements of the mechanical nociceptive thresholds of the bilateral temporal and masseter muscles showed a stimulus-response relationship between psychological stress and muscle mechanical sensitivity. The DT rats, who received a diazepam injection, showed almost the same mechanical sensitivity of the masticatory muscles to that of the control in response to psychological stress. Fourteen days after the psychological stressor was removed, the mechanical nociceptive thresholds returned to normal. These findings suggest that psychological stress is directly related to masticatory muscle pain. Removal of the stressor could be a useful method for relieving mechanical sensitivity increase induced by psychological stress
Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is widely used in various
applications. However, existing ASTE datasets are limited in their ability to
represent real-world scenarios, hindering the advancement of research in this
area. In this paper, we introduce a new dataset, named DMASTE, which is
manually annotated to better fit real-world scenarios by providing more diverse
and realistic reviews for the task. The dataset includes various lengths,
diverse expressions, more aspect types, and more domains than existing
datasets. We conduct extensive experiments on DMASTE in multiple settings to
evaluate previous ASTE approaches. Empirical results demonstrate that DMASTE is
a more challenging ASTE dataset. Further analyses of in-domain and cross-domain
settings provide promising directions for future research. Our code and dataset
are available at https://github.com/NJUNLP/DMASTE.Comment: 15pages, 5 figures, ACL202
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