Music Genre Classification With Neural Networks: An Examination Of Several Impactful Variables

There have been several attempts to classify music with content-based machine learning approaches. Most of these projects followed a similar procedure with a Deep Belief Network. In this project, we examined the performance of convolutional neural networks (CNN) and recurrent neural networks (RNN) as well as other components of a classification architecture, such as the choice of dataset, pre-processing techniques, and the sample size. Under a controlled environment, we discovered that the most successful architecture was a Mel-spectrogram combined with a CNN. Although our results fell behind the state-of-the-art performance, we outperform other music classification studies that use a CNN by a large margin. By performing binary classification, we also discovered individuality across genres that caused inconsistent performance

HO CHI MINH’S MULTICULTURAL THOUGHTS

Ho Chi Minh is one of influential personages in the history of Vietnam. His thoughts became the crucial guideline in the anti-colonial, anti-imperialist and independence movements of Vietnam. So far, quite a few scholars have conducted in-depth analysis of Ho Chi Minh‟s thoughts from the perspectives of politics, sociology and philosophy, but few scholars have studied Ho‟s thoughts from the perspective of multiculturalism. Given that multiculturalism is a theory that firstly put forward by western scholars in the western world, whether the theory is applicable to traditional and communitarian oriental world has become a hot topic in academic circles. From the perspective of morality, the elements of liberty, equality and justice of multiculturalism have the function of anti-colonialism and anti-imperialism in Southeast Asian countries including Vietnam. In the colonial times, French deliberately isolated Vietnamese ethnic groups through ideological control and geographical isolation, but under the guideline of Ho Chi Minh‟s thought, the Vietnamese broken the barrier of colonial and successfully achieve national unity and ethnic unity. This paper intends to prove that multiculturalism has the function of anti-colonialism and anti-imperialism by researching the Ho Chi Minh‟s multicultural thoughts, which is the best practice of western multiculturalism in southeast Asia

Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs

Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) is a promising paradigm to address the exploration-exploitation dilemma in reinforcement learning. It decomposes the source task into subgoal conditional subtasks and conducts exploration and exploitation in the subgoal space. The effectiveness of GCHRL heavily relies on subgoal representation functions and subgoal selection strategy. However, existing works often overlook the temporal coherence in GCHRL when learning latent subgoal representations and lack an efficient subgoal selection strategy that balances exploration and exploitation. This paper proposes HIerarchical reinforcement learning via dynamically building Latent Landmark graphs (HILL) to overcome these limitations. HILL learns latent subgoal representations that satisfy temporal coherence using a contrastive representation learning objective. Based on these representations, HILL dynamically builds latent landmark graphs and employs a novelty measure on nodes and a utility measure on edges. Finally, HILL develops a subgoal selection strategy that balances exploration and exploitation by jointly considering both measures. Experimental results demonstrate that HILL outperforms state-of-the-art baselines on continuous control tasks with sparse rewards in sample efficiency and asymptotic performance. Our code is available at https://github.com/papercode2022/HILL.Comment: Accepted by the conference of International Joint Conference on Neural Networks (IJCNN) 202

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accurate clinical decision making. However, omics data are normally high dimensional with large number of molecular features and relatively small number of available samples with clinical labels. The "dimensionality curse" makes it challenging to train a machine learning model using high dimensional omics data like DNA methylation and gene expression profiles. Here we propose an end-to-end deep learning model called OmiVAE to extract low dimensional features and classify samples from multi-omics data. OmiVAE combines the basic structure of variational autoencoders with a classification network to achieve task-oriented feature extraction and multi-class classification. The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier. During the unsupervised phase, a hierarchical cluster structure of samples can be automatically formed without the need for labels. And in the supervised phase, OmiVAE achieved an average classification accuracy of 97.49% after 10-fold cross-validation among 33 tumour types and normal samples, which shows better performance than other existing methods. The OmiVAE model learned from multi-omics data outperformed that using only one type of omics data, which indicates that the complementary information from different omics datatypes provides useful insights for biomedical tasks like cancer classification.Comment: 7 pages, 4 figure

Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records

The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods.Comment: Accepted by BIBM 2019 (Regular

Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain

The previous state-of-the-art (SOTA) method achieved a remarkable execution accuracy on the Spider dataset, which is one of the largest and most diverse datasets in the Text-to-SQL domain. However, during our reproduction of the business dataset, we observed a significant drop in performance. We examined the differences in dataset complexity, as well as the clarity of questions' intentions, and assessed how those differences could impact the performance of prompting methods. Subsequently, We develop a more adaptable and more general prompting method, involving mainly query rewriting and SQL boosting, which respectively transform vague information into exact and precise information and enhance the SQL itself by incorporating execution feedback and the query results from the database content. In order to prevent information gaps, we include the comments, value types, and value samples for columns as part of the database description in the prompt. Our experiments with Large Language Models (LLMs) illustrate the significant performance improvement on the business dataset and prove the substantial potential of our method. In terms of execution accuracy on the business dataset, the SOTA method scored 21.05, while our approach scored 65.79. As a result, our approach achieved a notable performance improvement even when using a less capable pre-trained language model. Last but not least, we also explore the Text-to-Python and Text-to-Function options, and we deeply analyze the pros and cons among them, offering valuable insights to the community

Local Gromov-Witten Invariants are Log Invariants

We prove a simple equivalence between the virtual count of rational curves in the total space of an anti-nef line bundle and the virtual count of rational curves maximally tangent to a smooth section of the dual line bundle. We conjecture a generalization to direct sums of line bundles.Comment: 15 pages, version accepted for publication in Advances in Mathematic