Moderately Supervised Learning: Definition, Framework and Generality

Learning with supervision has achieved remarkable success in numerous artificial intelligence (AI) applications. In the current literature, by referring to the properties of the labels prepared for the training data set, learning with supervision is categorized as supervised learning (SL) and weakly supervised learning (WSL). SL concerns the situation where the training data set is assigned with ideal labels, while WSL concerns the situation where the training data set is assigned with non-ideal labels. However, without considering the properties of the transformation from the given labels to learnable targets, the definition of SL is relatively abstract, which conceals some details that can be critical to building the appropriate solutions for specific SL tasks. Thus, it is desirable to reveal these details more concretely. This article attempts to achieve this goal by expanding the categorization of SL and investigating the sub-type that plays the central role in SL. More specifically, taking into consideration the properties of the transformation from the given labels to learnable targets, we firstly categorize SL into three narrower sub-types. Then we focus on the moderately supervised learning (MSL) sub-type that concerns the situation where the given labels are ideal, but due to the simplicity in annotation, careful designs are required to transform the given labels into learnable targets. From the perspectives of the definition, framework and generality, we comprehensively illustrate MSL and reveal what details are concealed by the abstractness of the definition of SL. At the meantime, the whole presentation of this paper as well establishes a tutorial for AI application engineers to refer to viewing a problem to be solved from the mathematicians' vision.Comment: 26 pages,8 figure

One-Step Abductive Multi-Target Learning with Diverse Noisy Samples: An Application to Tumour Segmentation for Breast Cancer

One-step abductive multi-target learning (OSAMTL) is an approach proposed to handle complex noisy labels. However, OSAMTL is not suitable for the situation where diverse noisy samples (DNS) are provided for a learning task. In this paper, giving definition of DNS, we propose one-step abductive multi-target learning with DNS (OSAMTL-DNS) to expand the original OSAMTL to a wider range of tasks that handle complex noisy labels. Applying OSAMTL-DNS to tumour segmentation for breast cancer in medical histopathology whole slide image analysis, we show that OSAMTL-DNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve significantly more rational predictions.Comment: The proofs provide in the supplementary needs further careful consideratio

Prediction of superconducting properties of materials based on machine learning models

The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which not only increases the cost of experiments but also prolongs the period of discovering new superconducting materials. In recent years, machine learning has been increasingly applied to materials science. Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials. Compared with our known similar literature, all the above algorithms reach state-of-the-art. Finally, this manuscript uses the above models to search the COD public dataset and identify 50 candidate superconducting materials with possible critical temperature greater than 90 K

Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning

Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text. However, FET poses a major challenge known as the noise labeling problem, whereby current methods rely on estimating noise distribution to identify noisy labels but are confused by diverse noise distribution deviation. To address this limitation, we introduce Co-Prediction Prompt Tuning for noise correction in FET, which leverages multiple prediction results to identify and correct noisy labels. Specifically, we integrate prediction results to recall labeled labels and utilize a differentiated margin to identify inaccurate labels. Moreover, we design an optimization objective concerning divergent co-predictions during fine-tuning, ensuring that the model captures sufficient information and maintains robustness in noise identification. Experimental results on three widely-used FET datasets demonstrate that our noise correction approach significantly enhances the quality of various types of training samples, including those annotated using distant supervision, ChatGPT, and crowdsourcing.Comment: Accepted by Findings of EMNLP 2023, 11 page

A Boundary Offset Prediction Network for Named Entity Recognition

Named entity recognition (NER) is a fundamental task in natural language processing that aims to identify and classify named entities in text. However, span-based methods for NER typically assign entity types to text spans, resulting in an imbalanced sample space and neglecting the connections between non-entity and entity spans. To address these issues, we propose a novel approach for NER, named the Boundary Offset Prediction Network (BOPN), which predicts the boundary offsets between candidate spans and their nearest entity spans. By leveraging the guiding semantics of boundary offsets, BOPN establishes connections between non-entity and entity spans, enabling non-entity spans to function as additional positive samples for entity detection. Furthermore, our method integrates entity type and span representations to generate type-aware boundary offsets instead of using entity types as detection targets. We conduct experiments on eight widely-used NER datasets, and the results demonstrate that our proposed BOPN outperforms previous state-of-the-art methods.Comment: Accepted by Findings of EMNLP 2023, 13 page

Progress of Exogenous Enzymes Application in Black Tea Processing

Black tea is the most important in the world tea production, and is very popular in the world tea market. China has abundant tea resources, especially unused tea leaves in summer and autumn, and there are a few new ways to develop them, such as processing black tea or deep processed products. Application of exogenous enzymes with good functional characteristics, can improve the quality of summer and autumn black teas, and their deep processed products. This article reviews that, the current status and existing problems of exogenous enzymes application in black tea processing in recent years, as well as the types and acting mechanisms of frequently-used exogenous enzymes, transformation of tea components catalyzed by exogenous enzymes during withering, rolling, and fermentation in black tea processing. Utilizing exogenous enzymes to promote the synthesis of theaflavins, a unique component of black tea, and the quality changes in deep-processing of black tea, are also summarized. Exploring the enzymatic processing methods, could promote the efficient utilization of tea resources, and regulate quality of summer and autumn black tea, as well as their deep-processed products, in the future

Active Encoding of Flexural Wave with Non-Diffractive Talbot Effect

This study employs the theory of conformal transformation to devise a Mikaelian lens for flexural waves manipulation. We investigate the propagation patterns of flexural waves in the lens under scenarios of plane wave and point source incidence. Additionally, the study explores the Talbot effect generated by interference patterns of multiple sources. Within the Mikaelian lens, the Talbot effect displays non diffractive characteristics, facilitating propagation over considerable distances. Leveraging the non-diffractive attributes of the Talbot effect in the Mikaelian lens, the paper discusses the feasibility of encoding flexural waves based on active interference sources. Simulation and experimental validation attest to the lens's effective active encoding. This research introduces novel perspectives on flexural wave encoding, showcasing potential applications in flexural wave communication, detection, and related fields