371 research outputs found
Classifying Aerosols Based on Fuzzy Clustering and Their Optical and Microphysical Properties Study in Beijing, China
Classification of Beijing aerosol is carried out based on clustering optical properties obtained from three Aerosol Robotic Network (AERONET) sites. The fuzzy c-mean (FCM) clustering algorithm is used to classify fourteen-year (2001–2014) observations, totally of 6,732 records, into six aerosol types. They are identified as fine particle nonabsorbing, two kinds of fine particle moderately absorbing (fine-MA1 and fine-MA2), fine particle highly absorbing, polluted dust, and desert dust aerosol. These aerosol types exhibit obvious optical characteristics difference. While five of them show similarities with aerosol types identified elsewhere, the polluted dust aerosol has no comparable prototype. Then the membership degree, a significant parameter provided by fuzzy clustering, is used to analyze internal variation of optical properties of each aerosol type. Finally, temporal variations of aerosol types are investigated. The dominant aerosol types are polluted dust and desert dust in spring, fine particle nonabsorbing aerosol in summer, and fine particle highly absorbing aerosol in winter. The fine particle moderately absorbing aerosol occurs during the whole year. Optical properties of the six types can also be used for radiative forcing estimation and satellite aerosol retrieval. Additionally, methodology of this study can be applied to identify aerosol types on a global scale
Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation
Post-click Conversion Rate (CVR) prediction task plays an essential role in
industrial applications, such as recommendation and advertising. Conventional
CVR methods typically suffer from the data sparsity problem as they rely only
on samples where the user has clicked. To address this problem, researchers
have introduced the method of multi-task learning, which utilizes non-clicked
samples and shares feature representations of the Click-Through Rate (CTR) task
with the CVR task. However, it should be noted that the CVR and CTR tasks are
fundamentally different and may even be contradictory. Therefore, introducing a
large amount of CTR information without distinction may drown out valuable
information related to CVR. This phenomenon is called the curse of knowledge
problem in this paper. To tackle this issue, we argue that a trade-off should
be achieved between the introduction of large amounts of auxiliary information
and the protection of valuable information related to CVR. Hence, we propose a
Click-aware Structure Transfer model with sample Weight Assignment, abbreviated
as CSTWA. It pays more attention to the latent structure information, which can
filter the input information that is related to CVR, instead of directly
sharing feature representations. Meanwhile, to capture the representation
conflict between CTR and CVR, we calibrate the representation layer and
reweight the discriminant layer to excavate the click bias information from the
CTR tower. Moreover, it incorporates a sample weight assignment algorithm
biased towards CVR modeling, to make the knowledge from CTR would not mislead
the CVR. Extensive experiments on industrial and public datasets have
demonstrated that CSTWA significantly outperforms widely used and competitive
models
What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation
kNN-MT presents a new paradigm for domain adaptation by building an external
datastore, which usually saves all target language token occurrences in the
parallel corpus. As a result, the constructed datastore is usually large and
possibly redundant. In this paper, we investigate the interpretability issue of
this approach: what knowledge does the NMT model need? We propose the notion of
local correctness (LAC) as a new angle, which describes the potential
translation correctness for a single entry and for a given neighborhood.
Empirical study shows that our investigation successfully finds the conditions
where the NMT model could easily fail and need related knowledge. Experiments
on six diverse target domains and two language-pairs show that pruning
according to local correctness brings a light and more explainable memory for
kNN-MT domain adaptation
Leveraging writing systems changes for deep learning based Chinese affective analysis
Affective analysis of social media text is in great demand. Online text written in Chinese communities often contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and minor text using Latin letters, an alphabet-based writing system. This phenomenon is referred to as writing systems changes (WSCs). Past studies have shown that WSCs often reflect unfiltered immediate affections. However, the use of WSCs poses more challenges in Natural Language Processing tasks because WSCs can break the syntax of the major text. In this work, we present our work to use WSCs as an effective feature in a hybrid deep learning model with attention network. The WSCs scripts are first identified by their encoding range. Then, the document representation of the text is learned through a Long Short-Term Memory model and the minor text is learned by a separate Convolution Neural Network model. To further highlight the WSCs components, an attention mechanism is adopted to re-weight the feature vector before the classification layer. Experiments show that the proposed hybrid deep learning method which better incorporates WSCs features can further improve performance compared to the state-of-the-art classification models. The experimental result indicates that WSCs can serve as effective information in affective analysis of the social media text
Beyond Generic: Enhancing Image Captioning with Real-World Knowledge using Vision-Language Pre-Training Model
Current captioning approaches tend to generate correct but "generic"
descriptions that lack real-world knowledge, e.g., named entities and
contextual information. Considering that Vision-Language Pre-Training (VLP)
models master massive such knowledge from large-scale web-harvested data, it is
promising to utilize the generalizability of VLP models to incorporate
knowledge into image descriptions. However, using VLP models faces challenges:
zero-shot inference suffers from knowledge hallucination that leads to
low-quality descriptions, but the generic bias in downstream task fine-tuning
hinders the VLP model from expressing knowledge. To address these concerns, we
propose a simple yet effective method called Knowledge-guided Replay
(K-Replay), which enables the retention of pre-training knowledge during
fine-tuning. Our approach consists of two parts: (1) a knowledge prediction
task on automatically collected replay exemplars to continuously awaken the VLP
model's memory about knowledge, thus preventing the model from collapsing into
the generic pattern; (2) a knowledge distillation constraint to improve the
faithfulness of generated descriptions hence alleviating the knowledge
hallucination. To evaluate knowledge-enhanced descriptions, we construct a
novel captioning benchmark KnowCap, containing knowledge of landmarks, famous
brands, special foods and movie characters. Experimental results show that our
approach effectively incorporates knowledge into descriptions, outperforming
strong VLP baseline by 20.9 points (78.7->99.6) in CIDEr score and 20.5
percentage points (34.0%->54.5%) in knowledge recognition accuracy. Our code
and data is available at https://github.com/njucckevin/KnowCap.Comment: Accepted at ACM Multimedia (ACMMM) 202
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