149 research outputs found
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
Characterization, activity, and computer modeling of a molecular inclusion complex containing rifaldazine
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
Improved biological properties and hypouricemic effects of uricase from Candida utilis loaded in novel alkaline enzymosomes
- …