Wilhelmsenhaahr2050

The data imbalance problem in classification is a frequent but challenging task. In real-world datasets, numerous class distributions are imbalanced and the classification result under such condition reveals extreme bias in the majority data class. Recently, the potential of GAN as a data augmentation method on minority data has been studied. In this paper, we propose a classification enhancement generative adversarial networks (CEGAN) to enhance the quality of generated synthetic minority data and more importantly, to improve the prediction accuracy in data imbalanced condition. In addition, we propose an ambiguity reduction method using the generated synthetic minority data for the case of multiple similar classes that are degenerating the classification accuracy. The proposed method is demonstrated with five benchmark datasets. The results indicate that approximating the real data distribution using CEGAN improves the classification performance significantly in data imbalanced conditions compared with various standard data augmentation methods.As an effective convex relaxation of the rank minimization model, the tensor nuclear norm minimization based multi-view clustering methods have been attracting more and more interest in recent years. However, most existing clustering methods regularize each singular value equally, restricting their capability and flexibility in tackling many practical problems, where the singular values should be treated differently. To address this problem, we propose a novel weighted tensor nuclear norm minimization (WTNNM) based method for multi-view spectral clustering. Specifically, we firstly calculate a set of transition probability matrices from different views, and construct a 3-order tensor whose lateral slices are composed of probability matrices. Secondly, we learn a latent high-order transition probability matrix by using our proposed weighted tensor nuclear norm, which directly considers the prior knowledge of singular values. Finally, clustering is performed on the learned transition probability matrix, which well characterizes both the complementary information and high-order information embedded in multi-view data. An efficient optimization algorithm is designed to solve the optimal solution. Extensive experiments on five benchmarks demonstrate that our method outperforms the state-of-the-art methods.Conversational sentiment analysis is an emerging, yet challenging subtask of the sentiment analysis problem. It aims to discover the affective state and sentimental change in each person in a conversation based on their opinions. There exists a wealth of interaction information that affects speaker sentiment in conversations. STS inhibitor mw However, existing sentiment analysis approaches are insufficient in dealing with this subtask due to two primary reasons the lack of benchmark conversational sentiment datasets and the inability to model interactions between individuals. To address these issues, in this paper, we first present a new conversational dataset that we created and made publicly available, named ScenarioSA, to support the development of conversational sentiment analysis models. Then, we investigate how interaction dynamics are associated with conversations and study the multidimensional nature of interactions, which is understandability, credibility and influence. Finally, we propose an interactive long short-term memory (LSTM) network for conversational sentiment analysis to model interactions between speakers in a conversation by (1) adding a confidence gate before each LSTM hidden unit to estimate the credibility of the previous speakers and (2) combining the output gate with the learned influence scores to incorporate the influences of the previous speakers. Extensive experiments are conducted on ScenarioSA and IEMOCAP, and the results show that our model outperforms a wide range of strong baselines and achieves competitive results with the state-of-art approaches.This paper considers the prespecified-time synchronization issue of switched coupled neural networks (SCNNs) under some smooth controllers. Different from the traditional finite-time synchronization (FTS), the synchronization time obtained in this paper is independent of control gains, initial values or network topology, which can be pre-set as to the task requirements. Moreover, unlike the existing nonsmooth or even discontinuous FTS control strategies, the new proposed control protocols are fully smooth, which abandon the common fractional power feedbacks or signum functions. Finally, two illustrative examples are provided to illustrate the effectiveness of the theoretical results.The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused more than 1 million deaths in the first 6 months of the pandemic and huge economic and social upheaval internationally. An efficacious vaccine is essential to prevent further morbidity and mortality. Although some countries might deploy COVID-19 vaccines on the strength of safety and immunogenicity data alone, the goal of vaccine development is to gain direct evidence of vaccine efficacy in protecting humans against SARS-CoV-2 infection and COVID-19 so that manufacture of efficacious vaccines can be selectively upscaled. A candidate vaccine against SARS-CoV-2 might act against infection, disease, or transmission, and a vaccine capable of reducing any of these elements could contribute to disease control. However, the most important efficacy endpoint, protection against severe disease and death, is difficult to assess in phase 3 clinical trials. In this Review, we explore the challenges in assessing the efficacy of candidate SARS-CoV-2 vaccines, discuss the caveats needed to interpret reported efficacy endpoints, and provide insight into answering the seemingly simple question, "Does this COVID-19 vaccine work?"The spread of Plasmodium falciparum isolates carrying mutations in the kelch13 (Pfkelch13) gene associated with artemisinin resistance (PfART-R) in southeast Asia threatens malaria control and elimination efforts. Emergence of PfART-R in Africa would result in a major public health problem. In this systematic review, we investigate the frequency and spatial distribution of Pfkelch13 mutants in Africa, including mutants linked to PfART-R in southeast Asia. Seven databases were searched (PubMed, Embase, Scopus, African Journal Online, African Index Medicus, Bioline, and Web of Science) for relevant articles about polymorphisms of the Pfkelch13 gene in Africa before January, 2019. Following PRISMA guidelines, 53 studies that sequenced the Pfkelch13 gene of 23 100 sample isolates in 41 sub-Saharan African countries were included. The Pfkelch13 sequence was highly polymorphic (292 alleles, including 255 in the Pfkelch13-propeller domain) but with mutations occurring at very low relative frequencies. Non-synonymous mutations were found in only 626 isolates (2·7%) from west, central, and east Africa.