196 research outputs found
Splitting Method for Support Vector Machine in Reproducing Kernel Banach Space with Lower Semi-continuous Loss Function
In this paper, we use the splitting method to solve support vector machine in
reproducing kernel Banach space with lower semi-continuous loss function. We
equivalently transfer support vector machines in reproducing kernel Banach
space with lower semi-continuous loss function to a finite-dimensional tensor
Optimization and propose the splitting method based on alternating direction
method of multipliers. By Kurdyka-Lojasiewicz inequality, the iterative
sequence obtained by this splitting method is globally convergent to a
stationary point if the loss function is lower semi-continuous and subanalytic.
Finally, several numerical performances demonstrate the effectiveness.Comment: arXiv admin note: text overlap with arXiv:2208.1252
Similarity-Aware Multimodal Prompt Learning for Fake News Detection
The standard paradigm for fake news detection mainly utilizes text
information to model the truthfulness of news. However, the discourse of online
fake news is typically subtle and it requires expert knowledge to use textual
information to debunk fake news. Recently, studies focusing on multimodal fake
news detection have outperformed text-only methods. Recent approaches utilizing
the pre-trained model to extract unimodal features, or fine-tuning the
pre-trained model directly, have become a new paradigm for detecting fake news.
Again, this paradigm either requires a large number of training instances, or
updates the entire set of pre-trained model parameters, making real-world fake
news detection impractical. Furthermore, traditional multimodal methods fuse
the cross-modal features directly without considering that the uncorrelated
semantic representation might inject noise into the multimodal features. This
paper proposes a Similarity-Aware Multimodal Prompt Learning (SAMPLE)
framework. First, we incorporate prompt learning into multimodal fake news
detection. Prompt learning, which only tunes prompts with a frozen language
model, can reduce memory usage significantly and achieve comparable
performances, compared with fine-tuning. We analyse three prompt templates with
a soft verbalizer to detect fake news. In addition, we introduce the
similarity-aware fusing method to adaptively fuse the intensity of multimodal
representation and mitigate the noise injection via uncorrelated cross-modal
features. For evaluation, SAMPLE surpasses the F1 and the accuracies of
previous works on two benchmark multimodal datasets, demonstrating the
effectiveness of the proposed method in detecting fake news. In addition,
SAMPLE also is superior to other approaches regardless of few-shot and
data-rich settings
The Outbreak Evaluation of COVID-19 in Wuhan District of China
There were 27 novel coronavirus pneumonia cases found in Wuhan, China in
December 2019, named as 2019-nCoV temporarily and COVID-19 formally by WHO on
11 February, 2020. In December 2019 and January 2020, COVID-19 has spread in
large scale among the population, which brought terrible disaster to the life
and property of the Chinese people. In this paper, we will first analyze the
feature and pattern of the virus transmission, and discuss the key impact
factors and uncontrollable factors of epidemic transmission based on public
data. Then the virus transmission can be modelled and used for the inflexion
and extinction period of epidemic development so as to provide theoretical
support for the Chinese government in the decision-making of epidemic
prevention and recovery of economic production. Further, this paper
demonstrates the effectiveness of the prevention methods taken by the Chinese
government such as multi-level administrative region isolation. It is of great
importance and practical significance for the world to deal with public health
emergencies.Comment: 7 pages, 18 figure
Existence and Exponential Stability of Periodic Solution for a Class of Generalized Neural Networks with Arbitrary Delays
By the continuation theorem of coincidence degree and M-matrix theory, we obtain some sufficient conditions for the existence and exponential stability of periodic solutions for a class of generalized neural networks with arbitrary delays, which are milder and less restrictive than those of previous known criteria. Moreover our results generalize and improve many existing ones
Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors
[EN] Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.This research was funded by the National Key R&D Program, grant number 2019YFC0119700, and the National Natural Science Foundation of China, grant number U20A20388.Zhang, Y.; Hao, D.; Yang, L.; Zhou, X.; Ye Lin, Y.; Yang, Y. (2022). Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors. Sensors. 22(9):1-18. https://doi.org/10.3390/s2209335211822
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Apical-Basal Polarity Signaling Components, Lgl1 and aPKCs, Control Glutamatergic Synapse Number and Function.
Normal synapse formation is fundamental to brain function. We show here that an apical-basal polarity (A-BP) protein, Lgl1, is present in the postsynaptic density and negatively regulates glutamatergic synapse numbers by antagonizing the atypical protein kinase Cs (aPKCs). A planar cell polarity protein, Vangl2, which inhibits synapse formation, was decreased in synaptosome fractions of cultured cortical neurons from Lgl1 knockout embryos. Conditional knockout of Lgl1 in pyramidal neurons led to reduction of AMPA/NMDA ratio and impaired plasticity. Lgl1 is frequently deleted in Smith-Magenis syndrome (SMS). Lgl1 conditional knockout led to increased locomotion, impaired novel object recognition and social interaction. Lgl1+/- animals also showed increased synapse numbers, defects in open field and social interaction, as well as stereotyped repetitive behavior. Social interaction in Lgl1+/- could be rescued by NMDA antagonists. Our findings reveal a role of apical-basal polarity proteins in glutamatergic synapse development and function and also suggest a potential treatment for SMS patients with Lgl1 deletion
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