Características del turismo de luna de miel: aproximación al turista chino

En el presente trabajo, enmarcado en el turismo alternativo, se estudia el turismo de luna de miel, la elección del destino turístico y los turistas chinos en el mercado internacional. Se concluye que el turismo de luna de miel es un tipo de turismo alternativo específico con 5 características distintivas: larga duración y planificación, temporada propia, público objetivo joven, necesidades particulares y toma de decisión compartida. La investigación muestra que para los turistas chinos este tipo de turismo presenta sus propias peculiaridades

Transceiver Design for MIMO DCO-OFDM in Visible Light Communication

Direct current-biased optical-orthogonal frequency-division multiplexing (DCO-OFDM) is a simple yet spectrally efficient multicarrier modulation scheme for visible light communication (VLC). But in multiple-input multiple-output (MIMO) scenario, which is more practical for VLC due to the LED deployment, the research on DCO-OFDM is still limited and calls for in-depth investigation. In this chapter, we first study the basic modulation scheme of DCO-OFDM, including the design of conventional receiver without considering the clipping noise. Secondly, we present a novel receiver for combating clipping distortion in the DCO-OFDM system, which can reconstruct the clipping noise and subtract it from the received signal. Thirdly, we generalize the results to MIMO scenario and investigate the preliminary transceiver design, which is based on the minimum mean-square error (MMSE) criteria. Based on this, we propose a precoding algorithm to further enhance the performance. Finally, the symbol error rate performance is compared through computer simulations to give the reader a whole picture of the performance of MIMO VLC system

Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images

Automatic table detection in PDF documents has achieved a great success but tabular data extraction are still challenging due to the integrity and noise issues in detected table areas. The accurate data extraction is extremely crucial in finance area. Inspired by this, the aim of this research is proposing an automated table detection and tabular data extraction from financial PDF documents. We proposed a method that consists of three main processes, which are detecting table areas with a Faster R-CNN (Region-based Convolutional Neural Network) model with Feature Pyramid Network (FPN) on each page image, extracting contents and structures by a compounded layout segmentation technique based on optical character recognition (OCR) and formulating regular expression rules for table header separation. The tabular data extraction feature is embedded with rule-based filtering and restructuring functions that are highly scalable. We annotate a new Financial Documents dataset with table regions for the experiment. The excellent table detection performance of the detection model is obtained from our customized dataset. The main contributions of this paper are proposing the Financial Documents dataset with table-area annotations, the superior detection model and the rule-based layout segmentation technique for the tabular data extraction from PDF files

An Evidence-Based Review of Related Metabolites and Metabolic Network Research on Cerebral Ischemia

In recent years, metabolomics analyses have been widely applied to cerebral ischemia research. This paper introduces the latest proceedings of metabolomics research on cerebral ischemia. The main techniques, models, animals, and biomarkers of cerebral ischemia will be discussed. With analysis help from the MBRole website and the KEGG database, the altered metabolites in rat cerebral ischemia were used for metabolic pathway enrichment analyses. Our results identify the main metabolic pathways that are related to cerebral ischemia and further construct a metabolic network. These results will provide useful information for elucidating the pathogenesis of cerebral ischemia, as well as the discovery of cerebral ischemia biomarkers

Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction

Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what to generate but ignore what not to generate. We argue that considering the negative samples also leads to potential benefits. In this work, we propose a template-agnostic method to control the token-level generation, which boosts original learning and reduces mistakes simultaneously. Specifically, we introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models, acquiring the noises and errors. We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to balance the effects of marginalized unlikelihood learning. Extensive experiments on four public datasets demonstrate the effectiveness of our approach on various generation templates1

Dynamic Graph Representation Learning via Graph Transformer Networks

Dynamic graph representation learning is an important task with widespread applications. Previous methods on dynamic graph learning are usually sensitive to noisy graph information such as missing or spurious connections, which can yield degenerated performance and generalization. To overcome this challenge, we propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) with spatial-temporal encoding to effectively learn graph topology and capture implicit links. To improve the generalization ability, we introduce two complementary self-supervised pre-training tasks and show that jointly optimizing the two pre-training tasks results in a smaller Bayesian error rate via an information-theoretic analysis. We also propose a temporal-union graph structure and a target-context node sampling strategy for efficient and scalable training. Extensive experiments on real-world datasets illustrate that DGT presents superior performance compared with several state-of-the-art baselines

Neutrophil extracellular traps mediate deep vein thrombosis: from mechanism to therapy

Deep venous thrombosis (DVT) is a part of venous thromboembolism (VTE) that clinically manifests as swelling and pain in the lower limbs. The most serious clinical complication of DVT is pulmonary embolism (PE), which has a high mortality rate. To date, its underlying mechanisms are not fully understood, and patients usually present with clinical symptoms only after the formation of the thrombus. Thus, it is essential to understand the underlying mechanisms of deep vein thrombosis for an early diagnosis and treatment of DVT. In recent years, many studies have concluded that Neutrophil Extracellular Traps (NETs) are closely associated with DVT. These are released by neutrophils and, in addition to trapping pathogens, can mediate the formation of deep vein thrombi, thereby blocking blood vessels and leading to the development of disease. Therefore, this paper describes the occurrence and development of NETs and discusses the mechanism of action of NETs on deep vein thrombosis. It aims to provide a direction for improved diagnosis and treatment of deep vein thrombosis in the near future

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive uncertainty for classification tasks. However, directly applying EDL to NER applications faces two challenges, i.e., the problems of sparse entities and OOV/OOD entities in NER tasks. To address these challenges, we propose a trustworthy NER framework named E-NER by introducing two uncertainty-guided loss terms to the conventional EDL, along with a series of uncertainty-guided training strategies. Experiments show that E-NER can be applied to multiple NER paradigms to obtain accurate uncertainty estimation. Furthermore, compared to state-of-the-art baselines, the proposed method achieves a better OOV/OOD detection performance and better generalization ability on OOV entities.Comment: accepted by ACL Findings (2023