An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

Named Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using mono-lingual corpora

Analyzing the Connotation of Science from the View of Criticizing the Omnipotence of Science

This paper argues that before critiquing the theory, the first task is to thoroughly understand the connotation of science, and proposes the theory of science in a dynamic and static way, which examines the development of science as a system of knowledge and its delimitation in two aspects, while the static level focuses on the scientific spirit and scientific attitude to The static level focuses on understanding science in terms of scientific spirit and attitude. This is followed by a deeper understanding of science in terms of its role in society as a ‘ transforming agent’ for social development and disagreement, thus providing a different perspective on scientific omnipotence and, by extension, on current social disagreements, from the perspective of science as a ‘ transforming agent’ . ‘ transforming agent’ perspective to provide new ways out of the current social disagreements, and in this way to play the benign social role of science

Exploiting Pseudo Image Captions for Multimodal Summarization

Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI between anchors and their positives, while we theoretically prove that MI involving negatives also matters when noises commonly exist. Guided by a more general lower bound form for optimization, we propose a contrastive learning strategy regulated by progressively refined cross-modal similarity, to more accurately optimize MI between an image/text anchor and its negative texts/images instead of improperly minimizing it. Our method performs competitively on four downstream cross-modal tasks and systematically balances the beneficial and harmful effects of (partial) false negative samples under theoretical guidance.Comment: Accepted at ACL2023 Finding

BMAD: Benchmarks for Medical Anomaly Detection

Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To bridge this gap, we introduce a comprehensive evaluation benchmark for assessing anomaly detection methods on medical images. This benchmark encompasses six reorganized datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT, chest X-ray, and digital histopathology) and three key evaluation metrics, and includes a total of fourteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables comprehensive comparisons among recently proposed anomaly detection methods. It will facilitate the community to conduct a fair comparison and advance the field of AD on medical imaging. More information on BMAD is available in our GitHub repository: https://github.com/DorisBao/BMA

When Parameter-efficient Tuning Meets General-purpose Vision-language Models

Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate multimodal information for creative applications. However, existing works still face two main limitations: the high training costs and heavy computing resource dependence of full model fine-tuning, and the lack of semantic information in instructions, which hinders multimodal alignment. Addressing these challenges, this paper proposes a novel approach to utilize Parameter-Efficient Tuning for generAl-purpose vision-Language models, namely PETAL. PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique, which significantly reduces the training costs and reliance on heavy computing resources. Furthermore, PETAL enhances the semantic depth of instructions in two innovative ways: 1) by introducing adaptive instruction mixture-of-experts(MOEs), and 2) by fortifying the score-based linkage between parameter-efficient tuning and mutual information. Our extensive experiments across five multimodal downstream benchmarks reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness. Additionally, our approach demonstrates remarkable advantages in few-shot settings, backed by comprehensive visualization analyses. Our source code is available at: https://github. com/melonking32/PETAL

Self-Supervised Intensity-Event Stereo Matching

Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption. Despite these advantages, event cameras cannot be directly applied to computational imaging tasks due to the inability to obtain high-quality intensity and events simultaneously. This paper aims to connect a standalone event camera and a modern intensity camera so that the applications can take advantage of both two sensors. We establish this connection through a multi-modal stereo matching task. We first convert events to a reconstructed image and extend the existing stereo networks to this multi-modality condition. We propose a self-supervised method to train the multi-modal stereo network without using ground truth disparity data. The structure loss calculated on image gradients is used to enable self-supervised learning on such multi-modal data. Exploiting the internal stereo constraint between views with different modalities, we introduce general stereo loss functions, including disparity cross-consistency loss and internal disparity loss, leading to improved performance and robustness compared to existing approaches. The experiments demonstrate the effectiveness of the proposed method, especially the proposed general stereo loss functions, on both synthetic and real datasets. At last, we shed light on employing the aligned events and intensity images in downstream tasks, e.g., video interpolation application.Comment: This paper has been accepted by the Journal of Imaging Science & Technolog

Variations in CD14 Gene Are Associated With Autoimmune Thyroid Diseases in the Chinese Population

Autoimmune thyroid diseases (AITDs) are chronic organ-specific autoimmune diseases and mainly include Graves' disease (GD) and Hashimoto's thyroiditis (HT). CD14 is an important component of the immune system as a receptor for gram-negative lipopolysaccharide (LPS). The genetic polymorphisms of CD14 have been confirmed to be associated with a variety of autoimmune diseases. However, its relationship with AITDs is still unclear. The study was aimed to determine whether four single nucleotide polymorphisms (rs2915863, rs2569190, rs2569192, and rs2563298) of CD14 are associated with AITDs and its subgroups of GD and HT. The results showed significant association of rs2915863 and rs2569190 with GD. The frequencies of rs2915863 genotypes and T allele in patients with GD differed significantly from their controls (P = 0.007 and P = 0.021, respectively). For rs2569190, frequencies of genotypes and G allele in GD patients also showed positive P-values (P = 0.038 and P = 0.027, respectively). The correlations between these two loci and GD are more pronounced in female GD patients and patients with a family history. In genetic model analysis, the allele model, recessive model, and homozygous model of rs2569190 and rs2915863 embodied strong correlations with GD after the adjusting of age and gender (P = 0.014, P = 0.015, P = 0.009, and P = 0.014, P = 0.001, P = 0.006, respectively). However, these four sites are not related to HT. We firstly discovered the relationship between CD14 gene polymorphism and GD, and the results indicate that CD14 is an important risk locus for AITD and its SNPs may contribute to host's genetic predisposition to GD