scholarly journals Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Entong Zhao ◽  
Jeongwon Lee ◽  
Chengdong He ◽  
Zejian Ren ◽  
Elnur Hajiyev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spin Symmetry ◽  
High Sensitivity ◽  
Density Fluctuations ◽  
Fermi Liquids ◽  
Many Body ◽  
Thermodynamic Studies ◽  
Learning Framework ◽  
Quantum Simulator ◽  
Learning Analysis

AbstractThe power of machine learning (ML) provides the possibility of analyzing experimental measurements with a high sensitivity. However, it still remains challenging to probe the subtle effects directly related to physical observables and to understand physics behind from ordinary experimental data using ML. Here, we introduce a heuristic machinery by using machine learning analysis. We use our machinery to guide the thermodynamic studies in the density profile of ultracold fermions interacting within SU(N) spin symmetry prepared in a quantum simulator. Although such spin symmetry should manifest itself in a many-body wavefunction, it is elusive how the momentum distribution of fermions, the most ordinary measurement, reveals the effect of spin symmetry. Using a fully trained convolutional neural network (NN) with a remarkably high accuracy of ~94% for detection of the spin multiplicity, we investigate how the accuracy depends on various less-pronounced effects with filtered experimental images. Guided by our machinery, we directly measure a thermodynamic compressibility from density fluctuations within the single image. Our machine learning framework shows a potential to validate theoretical descriptions of SU(N) Fermi liquids, and to identify less-pronounced effects even for highly complex quantum matter with minimal prior understanding.

scholarly journals Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

2020 ◽  
Author(s):  
Chenglong Liu ◽  
Xiaoyang Wang ◽  
Chenbin Liu ◽  
Qingfeng Sun ◽  
Wenxian Peng
Keyword(s):  
Machine Learning ◽  
Medical Image Analysis ◽  
High Sensitivity ◽  
Ct Images ◽  
Chest Ct ◽  
Learning Framework ◽  
Novel Coronavirus ◽  
Sensitivity Specificity ◽  
Level Performance ◽  
Fold Cross Validation

Abstract Background Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods To develop and validate an integrated machine learning framework on chest CT images for differentiating COVID-19 from common pneumonia (CP). Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed common pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. Statistical textual features of COVID-19 and CP images were extracted. After feature selection, the reserved features were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers with 10-fold cross-validation. Results The classification accuracy, precision, sensitivity, specificity and F1 score of our proposed method are 91.66%, 97.91%, 85.26%, 98.15% and 91.15% respectively. The AUC of its receiver operating characteristic is 0.98. Conclusions The experimental results indicate that the EBT algorithm with statistical textural features on chest CT for differentiating COVID-19 from common pneumonia achieved high transferability, efficiency, specificity, and impressive accuracy.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

scholarly journals Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

2021 ◽  
Vol 14 (3) ◽  
pp. 101016 ◽  
Author(s):  
Jim Abraham ◽  
Amy B. Heimberger ◽  
John Marshall ◽  
Elisabeth Heath ◽  
Joseph Drabick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Type ◽  
Learning Analysis

Simulation of sports movement training based on machine learning and brain-computer interface

2020 ◽  
pp. 1-12
Author(s):  
Linuo Wang
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Joint Learning ◽  
Scientific Methods ◽  
Learning Framework ◽  
Brain Functions ◽  
Movement Training ◽  
Practical Effect ◽  
Machine Interface ◽  
The Brain

Injuries and hidden dangers in training have a greater impact on athletes ’careers. In particular, the brain function that controls the motor function area has a greater impact on the athlete ’s competitive ability. Based on this, it is necessary to adopt scientific methods to recognize brain functions. In this paper, we study the structure of motor brain-computer and improve it based on traditional methods. Moreover, supported by machine learning and SVM technology, this study uses a DSP filter to convert the preprocessed EEG signal X into a time series, and adjusts the distance between the time series to classify the data. In order to solve the inconsistency of DSP algorithms, a multi-layer joint learning framework based on logistic regression model is proposed, and a brain-machine interface system of sports based on machine learning and SVM is constructed. In addition, this study designed a control experiment to improve the performance of the method proposed by this study. The research results show that the method in this paper has a certain practical effect and can be applied to sports.

scholarly journals Physiological sleep measures predict time to 15‐year mortality in community adults: Application of a novel machine learning framework

2021 ◽  
Author(s):  
Meredith L. Wallace ◽  
Timothy S. Coleman ◽  
Lucas K. Mentch ◽  
Daniel J. Buysse ◽  
Jessica L. Graves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Framework ◽  
Physiological Sleep
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