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 $\gtrsim 15\%$ 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