For the following mixed bivariate probability distribution between a discrete random variable X and a continuous random variable Λ: where α, β > 0, 0 < p = 1 ? q < 1, x=0,1,2,...,a canonical expansion is obtained in terms of the Laguerre and Meixner orthogonal polynomials. The chance mechanisms giving rise to this mixed bivariate distribution are also discussed. 相似文献
In this paper, we investigate a class of linear continuous-time systems with Markovian jump parameters. An integral part of the system dynamics is a delayed state with time-varying and bounded delays. The jumping parameters are modeled as a continuous-time, discrete-state Markov process. Employing norm-bounded parametric uncertainties and utilizing the second-method of Lyapunov, we examine the problem of designing a mixed controller which minimizes a quadratic performance measure while satisfying a prescribed -norm bound on the closed-loop system. It is established that sufficient conditions for the existence of the mixed controller and the associated performance upper bound could be cast in the form of linear matrix inequalities. 相似文献
Sequential recommendation models a user’s historical sequence to predict future items. Existing studies utilize deep learning methods and contrastive learning for data augmentation to alleviate data sparsity. However, these existing methods cannot learn accurate high-quality item representations while augmenting data. In addition, they usually ignore data noise and user cold-start issues. To solve the above issues, we investigate the possibility of Generative Adversarial Network (GAN) with contrastive learning for sequential recommendation to balance data sparsity and noise. Specifically, we propose a new framework, Enhanced Contrastive Learning with Generative Adversarial Network for Sequential Recommendation (ECGAN-Rec), which models the training process as a GAN and recommendation task as the main task of the discriminator. We design a sequence augmentation module and a contrastive GAN module to implement both data-level and model-level augmentations. In addition, the contrastive GAN learns more accurate high-quality item representations to alleviate data noise after data augmentation. Furthermore, we propose an enhanced Transformer recommender based on GAN to optimize the performance of the model. Experimental results on three open datasets validate the efficiency and effectiveness of the proposed model and the ability of the model to balance data noise and data sparsity. Specifically, the improvement of ECGAN-Rec in two evaluation metrics (HR@N and NDCG@N) compared to the state-of-the-art model performance on the Beauty, Sports and Yelp datasets are 34.95%, 36.68%, and 13.66%, respectively. Our implemented model is available via https://github.com/nishawn/ECGANRec-master. 相似文献
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specific domains (like politics or health care), little work is found comparing false news across domains. In this article, we investigate false news across nine domains on Weibo, the largest Twitter-like social media platform in China, from 2009 to 2019. The newly collected data comprise 44,728 posts in the nine domains, published by 40,215 users, and reposted over 3.4 million times. Based on the distributions and spreads of the multi-domain dataset, we observe that false news in domains that are close to daily life like health and medicine generated more posts but diffused less effectively than those in other domains like politics, and that political false news had the most effective capacity for diffusion. The widely diffused false news posts on Weibo were associated strongly with certain types of users — by gender, age, etc. Further, these posts provoked strong emotions in the reposts and diffused further with the active engagement of false-news starters. Our findings have the potential to help design false news detection systems in suspicious news discovery, veracity prediction, and display and explanation. The comparison of the findings on Weibo with those of existing work demonstrates nuanced patterns, suggesting the need for more research on data from diverse platforms, countries, or languages to tackle the global issue of false news. The code and new anonymized dataset are available at https://github.com/ICTMCG/Characterizing-Weibo-Multi-Domain-False-News. 相似文献
This commentary supplements the work of a creative practice research project that generates new ways of thinking about innovation and entrepreneurial processes. Our creative method, underwritten by the logic of sensation and presented in film format, operates as an alternative form of research in these fields, where results are normally conveyed in book or journal paper. Film-based research has developed distinctive qualitative, empirical and theoretical vocabularies that can expand the nature and range of evidence, argument and expression across the broad range of innovation and entrepreneurship studies. 600 Mills, the film that accompanies this paper, is available at https://doi.org/10.1080/08109028.2017.1336011. 相似文献
Text simplification and text summarisation are related, but different sub-tasks in Natural Language Generation. Whereas summarisation attempts to reduce the length of a document, whilst keeping the original meaning, simplification attempts to reduce the complexity of a document. In this work, we combine both tasks of summarisation and simplification using a novel hybrid architecture of abstractive and extractive summarisation called HTSS. We extend the well-known pointer generator model for the combined task of summarisation and simplification. We have collected our parallel corpus from the simplified summaries written by domain experts published on the science news website EurekaAlert (www.eurekalert.org). Our results show that our proposed HTSS model outperforms neural text simplification (NTS) on SARI score and abstractive text summarisation (ATS) on the ROUGE score. We further introduce a new metric (CSS1) which combines SARI and Rouge and demonstrates that our proposed HTSS model outperforms NTS and ATS on the joint task of simplification and summarisation by 38.94% and 53.40%, respectively. We provide all code, models and corpora to the scientific community for future research at the following URL: https://github.com/slab-itu/HTSS/. 相似文献
Conventional downlink non-orthogonal multiple access (NOMA) uses the power domain for multiple access and applies successive interference cancellation (SIC) to mitigate intra-cluster interference. It requires sophisticated user terminals (UTs) clustering, moreover, imperfect SIC results in error propagation that severely degrades system capacity. In this paper, a novel code-aided multiuser multiple-input multiple-output NOMA (MIMONOMA) framework for downlink transmission is proposed. Since the total number of UTs in a cell is in general more than the number of transmit antennas at the base station (BS), purely zero-forcing (ZF) downlink beamforming is unable to mitigate multiuser interference (MUI). We first propose a simple grouping algorithm that separate all UTs in a cell into several clusters, with cluster number less than or equal to BS antennas. ZF-based multiuser beamforming is then employed to remove the inter-cluster interference. To mitigate the intra-cluster interference, we propose to modulate the downlink data for each UT within the same cluster by orthogonal codes, thereby, instead of SIC, matched-filtering can be employed at each UT for interference suppression. Computer simulation results quantify the capacity gain of the proposed coded-aided MIMONOMA method over conventional MIMONOMA schemes. 相似文献
Multimodal sentiment analysis aims to judge the sentiment of multimodal data uploaded by the Internet users on various social media platforms. On one hand, existing studies focus on the fusion mechanism of multimodal data such as text, audio and visual, but ignore the similarity of text and audio, text and visual, and the heterogeneity of audio and visual, resulting in deviation of sentiment analysis. On the other hand, multimodal data brings noise irrelevant to sentiment analysis, which affects the effectness of fusion. In this paper, we propose a Polar-Vector and Strength-Vector mixer model called PS-Mixer, which is based on MLP-Mixer, to achieve better communication between different modal data for multimodal sentiment analysis. Specifically, we design a Polar-Vector (PV) and a Strength-Vector (SV) for judging the polar and strength of sentiment separately. PV is obtained from the communication of text and visual features to decide the sentiment that is positive, negative, or neutral sentiment. SV is gained from the communication between the text and audio features to analyze the sentiment strength in the range of 0 to 3. Furthermore, we devise an MLP-Communication module (MLP-C) composed of several fully connected layers and activation functions to make the different modal features fully interact in both the horizontal and the vertical directions, which is a novel attempt to use MLP for multimodal information communication. Finally, we mix PV and SV to obtain a fusion vector to judge the sentiment state. The proposed PS-Mixer is tested on two publicly available datasets, CMU-MOSEI and CMU-MOSI, which achieves the state-of-the-art (SOTA) performance on CMU-MOSEI compared with baseline methods. The codes are available at: https://github.com/metaphysicser/PS-Mixer. 相似文献
Similarity search with hashing has become one of the fundamental research topics in computer vision and multimedia. The current researches on semantic-preserving hashing mainly focus on exploring the semantic similarities between pointwise or pairwise samples in the visual space to generate discriminative hash codes. However, such learning schemes fail to explore the intrinsic latent features embedded in the high-dimensional feature space and they are difficult to capture the underlying topological structure of data, yielding low-quality hash codes for image retrieval. In this paper, we propose an ordinal-preserving latent graph hashing (OLGH) method, which derives the objective hash codes from the latent space and preserves the high-order locally topological structure of data into the learned hash codes. Specifically, we conceive a triplet constrained topology-preserving loss to uncover the ordinal-inferred local features in binary representation learning. By virtue of this, the learning system can implicitly capture the high-order similarities among samples during the feature learning process. Moreover, the well-designed latent subspace learning is built to acquire the noise-free latent features based on the sparse constrained supervised learning. As such, the latent under-explored characteristics of data are fully employed in subspace construction. Furthermore, the latent ordinal graph hashing is formulated by jointly exploiting latent space construction and ordinal graph learning. An efficient optimization algorithm is developed to solve the resulting problem to achieve the optimal solution. Extensive experiments conducted on diverse datasets show the effectiveness and superiority of the proposed method when compared to some advanced learning to hash algorithms for fast image retrieval. The source codes of this paper are available at https://github.com/DarrenZZhang/OLGH . 相似文献
Multi-modal hashing can encode the large-scale social geo-media multimedia data from multiple sources into a common discrete hash space, in which the heterogeneous correlations from multiple modalities could be well explored and preserved into the objective semantic-consistent hash codes. The current researches on multi-modal hashing mainly focus on performing common data reconstruction, but they fail to effectively distill the intrinsic and consensus structures of multi-modal data and fully exploit the inherent semantic knowledge to capture semantic-consistent information across multiple modalities, leading to unsatisfactory retrieval performance. To facilitate this problem and develop an efficient multi-modal geographical retrieval method, in this article, we propose a discriminative multi-modal hashing framework named Cognitive Multi-modal Consistent Hashing (CMCH), which can progressively pursue the structure consensus over heterogeneous multi-modal data and simultaneously explore the informative transformed semantics. Specifically, we construct a parameter-free collaborative multi-modal fusion module to incorporate and excavate the underlying common components from multi-source data. Particularly, our formulation seeks for a joint multi-modal compatibility among multiple modalities under a self-adaptive weighting manner, which can take full advantages of their complementary properties. Moreover, a cognitive self-paced learning policy is further leveraged to conduct progressive feature aggregation, which can coalesce multi-modal data onto the established common latent space in a curriculum learning mode. Furthermore, deep semantic transform learning is elaborated to generate flexible semantics for interactively guiding collaborative hash codes learning. An efficient discrete learning algorithm is devised to address the resulting optimization problem, which obtains stable solutions when dealing with large-scale multi-modal retrieval tasks. Sufficient experiments performed on four large-scale multi-modal datasets demonstrate the encouraging performance of the proposed CMCH method in comparison with the state-of-the-arts over multi-modal information retrieval and computational efficiency. The source codes of this work could be available at https://github.com/JunfengAn1998a/CMCH . 相似文献
