103 research outputs found
THE EFFECT OF COMMUNITY-BASED GROUP MUSIC THERAPY ON QUALITY OF LIFE FOR INDIVIDUALS WITH DEVELOPMENTAL DISABILITIES
The purpose of this qualitative study was to gain a richer understanding of the effect of community-based group music therapy on quality of life for individuals with developmental disabilities through the insights of four sub-groups in two community-based group music therapy choirs: choir members (people with developmental disabilities), parents/caregivers, choir leaders (music therapists) and the program supervisor. The study provided valuable feedback concerning how satisfied persons with developmental disabilities were with the group music therapy services that they had received, what benefits the choir members had derived from participating in the community-based group music therapy choirs, and how the benefits were related to the choir members' quality of life.  Data were collected in group and individual interviews that were conducted with 29 individuals consisting of 18 choir members, 8 parents/caregivers, 2 choir leaders and 1 program supervisor. Key statements of each subgroup were summarized according to the transcript texts. Global themes shared by part or all of the subgroups were identified by comparing and contrasting subgroups' key statements.  The result of this study showed that there is a significant positive effect of community-based group music therapy on the quality of life for individuals with developmental disabilities in the five categories of "emotional well-being," "social inclusion," "interpersonal relations," "self-determination," "personal development" and eleven subcategories of "safety," "self-concept," "happiness," "community integration/participation," "lifestyle," "friendships," "family relationships," "personal control," "choices," "education," and "skills." Thirty global themes of the effect of community-based group music therapy on quality of life for people with developmental disabilities shared by part or all of the subgroups were identified and described.  M.M
Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning
This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/
Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning
As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us to predict the formation energy with sufficient precision of a large number of materials of which only the structural information is available. The enlarged training set is then used to train a convolutional neural network model (the screening model) with the generic Magpie elemental features with high prediction power. Extensive experiments demonstrate the superior performance of our transfer learning model and screening model compared to the baseline models. We then applied the screening model to filter out promising new perovskite materials out of 21,316 hypothetical perovskite structures with a large portion of them confirmed by existing literature
Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression
Echo cancellation and noise reduction are essential for full-duplex
communication, yet most existing neural networks have high computational costs
and are inflexible in tuning model complexity. In this paper, we introduce
time-frequency dual-path compression to achieve a wide range of compression
ratios on computational cost. Specifically, for frequency compression,
trainable filters are used to replace manually designed filters for dimension
reduction. For time compression, only using frame skipped prediction causes
large performance degradation, which can be alleviated by a post-processing
network with full sequence modeling. We have found that under fixed compression
ratios, dual-path compression combining both the time and frequency methods
will give further performance improvement, covering compression ratios from 4x
to 32x with little model size change. Moreover, the proposed models show
competitive performance compared with fast FullSubNet and DeepFilterNet. A demo
page can be found at
hangtingchen.github.io/ultra_dual_path_compression.github.io/.Comment: Accepted by Interspeech 202
Mlatticeabc: Generic Lattice Constant Prediction of Crystal Materials Using Machine Learning
Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has wide applications in crystal structure prediction and materials property prediction. Previous work has used machine learning models such as neural networks and support vector machines combined with composition features for lattice constant prediction and has achieved a maximum performance for cubic structures with an average coefficient of determination (R2) of 0.82. Other models tailored for special materials family of a fixed form such as ABX3 perovskites can achieve much higher performance due to the homogeneity of the structures. However, these models trained with small data sets are usually not applicable to generic lattice parameter prediction of materials with diverse compositions. Herein, we report MLatticeABC, a random forest machine learning model with a new descriptor set for lattice unit cell edge length (a, b, c) prediction which achieves an R2 score of 0.973 for lattice parameter a of cubic crystals with an average R2 score of 0.80 for a prediction of all crystal systems. The R2 scores are between 0.498 and 0.757 over lattice b and c prediction performance of the model, which could be used by just inputting the molecular formula of the crystal material to get the lattice constants. Our results also show significant performance improvement for lattice angle predictions. Source code and trained models can be freely accessed at https://github.com/usccolumbia/MLatticeABC
AlphaCrystal: Contact map based crystal structure prediction using deep learning
Crystal structure prediction is one of the major unsolved problems in
materials science. Traditionally, this problem is formulated as a global
optimization problem for which global search algorithms are combined with first
principle free energy calculations to predict the ground-state crystal
structure given only a material composition or a chemical system. These ab
initio algorithms usually cannot exploit a large amount of implicit
physicochemical rules or geometric constraints (deep knowledge) of atom
configurations embodied in a large number of known crystal structures. Inspired
by the deep learning enabled breakthrough in protein structure prediction,
herein we propose AlphaCrystal, a crystal structure prediction algorithm that
combines a deep residual neural network model that learns deep knowledge to
guide predicting the atomic contact map of a target crystal material followed
by reconstructing its 3D crystal structure using genetic algorithms. Based on
the experiments of a selected set of benchmark crystal materials, we show that
our AlphaCrystal algorithm can predict structures close to the ground truth
structures. It can also speed up the crystal structure prediction process by
predicting and exploiting the predicted contact map so that it has the
potential to handle relatively large systems. We believe that our deep learning
based ab initio crystal structure prediction method that learns from existing
material structures can be used to scale up current crystal structure
prediction practice. To our knowledge, AlphaCrystal is the first neural network
based algorithm for crystal structure contact map prediction and the first
method for directly reconstructing crystal structures from materials
composition, which can be further optimized by DFT calculations.Comment: 13 pages; 5 figure
Grass MicroRNA Gene Paleohistory Unveils New Insights into Gene Dosage Balance in Subgenome Partitioning after Whole-Genome Duplication
Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning
In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining 73,452 inorganic compounds from the Materials Project (MP) database and building an atomic environment matrix, we obtained a vector representation (atomic vector) of 87 atoms by singular value decomposition (SVD) of the atomic environment matrix. Then, the obtained atom vector was used to implement the coded representation of the superconductors in the order of the atoms in the chemical formula of the superconductor. The experimental results of the HNN model trained with 12,413 superconductors were compared with three benchmark neural network algorithms and multiple machine learning algorithms using two commonly used material characterization methods. The experimental results show that the HNN method proposed in this paper can eectively extract the characteristic relationships between the atoms of superconductors, and it has high accuracy in predicting the Tc
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