Decorrelation of Neutral Vector Variables: Theory and Applications

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate Gaussian distributed, the conventional principal component analysis (PCA) cannot yield mutually independent scalar variables. With the two proposed transformations, a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom. We also evaluate the decorrelation performances for the vectors generated from a single Dirichlet distribution and a mixture of Dirichlet distributions. The mutual independence is verified with the distance correlation measurement. The advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations

Soccer training: an effective exercise mode to prevent and treat childhood obesity?

Exercise training has been recognized as an effective treatment for childhood obesity. Clinical experience has shown that great efforts are needed to train children around 10 years old when using traditional aerobic exercise modes, such as walking and running. To seek more attractive training methods for children, in this paper, we review the current literature to evaluate the effectiveness of soccer training on childhood obesity prevention and treatment. Future research direction and sport injury prevention are also discussed

Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance

Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language Models (PLMs) have achieved substantial advancements in the field of natural language processing. However, in real-world scenarios, data labels are often noisy due to the complex annotation process, making it essential to develop strategies for fine-tuning PLMs with such noisy labels. To this end, we introduce an innovative approach for fine-tuning PLMs using noisy labels, which incorporates the guidance of Large Language Models (LLMs) like ChatGPT. This guidance assists in accurately distinguishing between clean and noisy samples and provides supplementary information beyond the noisy labels, thereby boosting the learning process during fine-tuning PLMs. Extensive experiments on synthetic and real-world noisy datasets further demonstrate the superior advantages of our framework over the state-of-the-art baselines.Comment: EMNLP Findings 202

Virtual Node Tuning for Few-shot Node Classification

Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer structural knowledge from base classes with abundant labels to target novel classes. However, existing solutions become ineffective or inapplicable when base classes have no or limited labeled nodes. To address this challenge, we propose an innovative method dubbed Virtual Node Tuning (VNT). Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task. A unique feature of VNT is that, by incorporating a Graph-based Pseudo Prompt Evolution (GPPE) module, VNT-GPPE can handle scenarios with sparse labels in base classes. Experimental results on four datasets demonstrate the superiority of the proposed approach in addressing FSNC with unlabeled or sparsely labeled base classes, outperforming existing state-of-the-art methods and even fully supervised baselines.Comment: Accepted to KDD 202

Aerobic exercise training at maximal fat oxidation intensity improves body composition, glycemic control, and physical capacity in older people with type 2 diabetes

Background: Aerobic training has been used as one of the common treatments for type 2 diabetes; however, further research on the individualized exercise program with the optimal intensity is still necessary. The purpose of this study was to investigate the effects of supervised exercise training at the maximal fat oxidation (FATmax) intensity on body composition, glycemic control, lipid profile, and physical capacity in older people with type 2 diabetes. Methods: Twenty-four women and 25 men with type 2 diabetes, aged 60–69 years. The exercise groups trained at the individualized FATmax intensity for 1 h/day for 3 days/week over 16 weeks. No dietary intervention was introduced during the experimental period. Whole body fat, abdominal fat, oral glucose tolerance test, lipid profile, and physical capacity were measured before and after the interventions. Results: FATmax intensity was at 41.3 ± 3.2% VO2max for women and 46.1 ± 10.3% VO2max for men. Exercise groups obtained significant improvements in body composition, with a special decrease in abdominal obesity; decreased resting blood glucose concentration and HbA1c; and increased VO2max, walking ability, and lower body strength, compared to the non-exercising controls. Daily energy intake and medication remained unchanged for all participants during the experimental period. Conclusion: Beside the improvements in the laboratory variables, the individualized FATmax training can also benefit daily physical capacity of older people with type 2 diabetes

NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition

Long-tailed visual recognition has received increasing attention in recent years. Due to the extremely imbalanced data distribution in long-tailed learning, the learning process shows great uncertainties. For example, the predictions of different experts on the same image vary remarkably despite the same training settings. To alleviate the uncertainty, we propose a Nested Collaborative Learning (NCL++) which tackles the long-tailed learning problem by a collaborative learning. To be specific, the collaborative learning consists of two folds, namely inter-expert collaborative learning (InterCL) and intra-expert collaborative learning (IntraCL). In-terCL learns multiple experts collaboratively and concurrently, aiming to transfer the knowledge among different experts. IntraCL is similar to InterCL, but it aims to conduct the collaborative learning on multiple augmented copies of the same image within the single expert. To achieve the collaborative learning in long-tailed learning, the balanced online distillation is proposed to force the consistent predictions among different experts and augmented copies, which reduces the learning uncertainties. Moreover, in order to improve the meticulous distinguishing ability on the confusing categories, we further propose a Hard Category Mining (HCM), which selects the negative categories with high predicted scores as the hard categories. Then, the collaborative learning is formulated in a nested way, in which the learning is conducted on not just all categories from a full perspective but some hard categories from a partial perspective. Extensive experiments manifest the superiority of our method with outperforming the state-of-the-art whether with using a single model or an ensemble. The code will be publicly released.Comment: arXiv admin note: text overlap with arXiv:2203.1535

Liquid chromatography coupled with time-of-flight and ion trap mass spectrometry for qualitative analysis of herbal medicines

AbstractWith the expansion of herbal medicine (HM) market, the issue on how to apply up-to-date analytical tools on qualitative analysis of HMs to assure their quality, safety and efficacy has been arousing great attention. Due to its inherent characteristics of accurate mass measurements and multiple stages analysis, the integrated strategy of liquid chromatography (LC) coupled with time-of-flight mass spectrometry (TOF-MS) and ion trap mass spectrometry (IT-MS) is well-suited to be performed as qualitative analysis tool in this field. The purpose of this review is to provide an overview on the potential of this integrated strategy, including the review of general features of LC-IT-MS and LC-TOF-MS, the advantages of their combination, the common procedures for structure elucidation, the potential of LC-hybrid-IT-TOF/MS and also the summary and discussion of the applications of the integrated strategy for HM qualitative analysis (2006–2011). The advantages and future developments of LC coupled with IT and TOF-MS are highlighted