scholarly journals Leverage electron properties to predict phonon properties via transfer learning for semiconductors

2020 ◽  
Vol 6 (45) ◽  
pp. eabd1356
Author(s):  
Zeyu Liu ◽  
Meng Jiang ◽  
Tengfei Luo
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Materials Informatics ◽  
Learning Models ◽  
Materials By Design ◽  
Property Relation ◽  
Composition Property ◽  
The Mean ◽  
Machine Learning Models ◽  
Phonon Properties

Electron properties are usually easier to obtain than phonon properties. The ability to leverage electron properties to help predict phonon properties can thus greatly benefit materials by design for applications like thermoelectrics and electronics. Here, we demonstrate the ability of using transfer learning (TL), where knowledge learned from training machine learning models on electronic bandgaps of 1245 semiconductors is transferred to improve the models, trained using only 124 data, for predicting various phonon properties (phonon bandgap, group velocity, and heat capacity). Compared to directly trained models, TL reduces the mean absolute errors of prediction by 65, 14, and 54% respectively, for the three phonon properties. The TL models are further validated using several semiconductors outside of the 1245 database. Results also indicate that TL can leverage not-so-accurate proxy properties, as long as they encode composition-property relation, to improve models for target properties, a notable feature to materials informatics in general.

scholarly journals Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structurebased Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate<br>structure-based virtual screening using machine learning models. It has been validated using<br>datasets both from literature (12 datasets, each containing three million molecules docked<br>with FRED) and in-house sources (one dataset of four million compounds docked with<br>Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of<br>the top one percent scoring molecules after docking 10 % of the dataset for the literature data,<br>whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be<br>used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

Intra-domain and cross-domain transfer learning for time series

2021 ◽  
Author(s):  
Erik Otović ◽  
Marko Njirjak ◽  
Dario Jozinović ◽  
Goran Mauša ◽  
Alberto Michelini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Transfer Learning ◽  
Time Series Data ◽  
The Other ◽  
Series Data ◽  
Sound Recognition ◽  
Transfer Of Knowledge ◽  
Learning Models ◽  
Machine Learning Models

&lt;p&gt;In this study, we compared the performance of machine learning models trained using transfer learning and those that were trained from scratch - on time series data. Four machine learning models were used for the experiment. Two models were taken from the field of seismology, and the other two are general-purpose models for working with time series data. The accuracy of selected models was systematically observed and analyzed when switching within the same domain of application (seismology), as well as between mutually different domains of application (seismology, speech, medicine, finance). In seismology, we used two databases of local earthquakes (one in counts, and the other with the instrument response removed) and a database of global earthquakes for predicting earthquake magnitude; other datasets targeted classifying spoken words (speech), predicting stock prices (finance) and classifying muscle movement from EMG signals (medicine).&lt;br&gt;In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model. Therefore, in our experiment, we use reduced data sets of 1,500 and 9,000 data instances to mimic such conditions. Using the same scaled-down datasets, we trained two sets of machine learning models: those that used transfer learning for training and those that were trained from scratch. We compared the performances between pairs of models in order to draw conclusions about the utility of transfer learning. In order to confirm the validity of the obtained results, we repeated the experiments several times and applied statistical tests to confirm the significance of the results. The study shows when, within the set experimental framework, the transfer of knowledge brought improvements in terms of model accuracy and in terms of model convergence rate.&lt;br&gt;&lt;br&gt;Our results show that it is possible to achieve better performance and faster convergence by transferring knowledge from the domain of global earthquakes to the domain of local earthquakes; sometimes also vice versa. However, improvements in seismology can sometimes also be achieved by transferring knowledge from medical and audio domains. The results show that the transfer of knowledge between other domains brought even more significant improvements, compared to those within the field of seismology. For example, it has been shown that models in the field of sound recognition have achieved much better performance compared to classical models and that the domain of sound recognition is very compatible with knowledge from other domains. We came to similar conclusions for the domains of medicine and finance. Ultimately, the paper offers suggestions when transfer learning is useful, and the explanations offered can provide a good starting point for knowledge transfer using time series data.&lt;/p&gt;

The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models

2020 ◽  
pp. 219-226
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment

2020 ◽  
Vol 13 (7) ◽  
pp. 155
Author(s):  
Zhenlong Jiang ◽  
Ran Ji ◽  
Kuo-Chu Chang
Keyword(s):  
Machine Learning ◽  
Risk Aversion ◽  
Financial Data ◽  
Rolling Horizon ◽  
Learning Models ◽  
Market Index ◽  
Out Of Sample ◽  
The Mean ◽  
Mean Risk ◽  
Machine Learning Models

We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by machine learning models. We employ Gini’s Mean Difference (GMD) to specify the risk of a portfolio and use a set of technical indicators generated from a market index (e.g., S&P 500 index) to feed the machine learning models to predict market movements. Using a rolling-horizon approach, we conduct a series of computational tests with real financial data to evaluate the performance of the machine learning integrated portfolio rebalance framework. The empirical results show that the XGBoost model provides the best prediction of market movement, while the proposed portfolio rebalance strategy generates portfolios with superior out-of-sample performances in terms of average returns, time-series cumulative returns, and annualized returns compared to the benchmarks.

Predicting High-Flow Nasal Cannula Failure in an ICU Using a Recurrent Neural Network with Transfer Learning and Input Data Perseveration: A Retrospective Analysis (Preprint)

2021 ◽  
Author(s):  
George Pappy ◽  
Melissa Aczon ◽  
Randall Wetzel ◽  
David Ledbetter
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Airway Pressure ◽  
Input Data ◽  
Positive Airway Pressure ◽  
High Flow ◽  
Learning Models ◽  
High Flow Nasal Cannula ◽  
Non Invasive ◽  
Machine Learning Models

BACKGROUND High Flow Nasal Cannula (HFNC) provides non-invasive respiratory support for critically ill children who may tolerate it more readily than other Non-Invasive (NIV) techniques such as Bilevel Positive Airway Pressure (BiPAP) and Continuous Positive Airway Pressure (CPAP). Moreover, HFNC may preclude the need for mechanical ventilation (intubation). Nevertheless, NIV or intubation may ultimately be necessary for certain patients. Timely prediction of HFNC failure can provide an indication for increasing respiratory support. OBJECTIVE This work developed and compared machine learning models to predict HFNC failure. METHODS A retrospective study was conducted using the Virtual Pediatric Intensive Care Unit database of Electronic Medical Records (EMR) of patients admitted to a tertiary pediatric ICU from January 2010 to February 2020. Patients <19 years old, without apnea, and receiving HFNC treatment were included. A Long Short-Term Memory (LSTM) model using 517 variables (vital signs, laboratory data and other clinical parameters) was trained to generate a continuous prediction of HFNC failure, defined as escalation to NIV or intubation within 24 hours of HFNC initiation. For comparison, seven other models were trained: a Logistic Regression (LR) using the same 517 variables, another LR using only 14 variables, and five additional LSTM-based models using the same 517 variables as the first LSTM and incorporating additional ML techniques (transfer learning, input perseveration, and ensembling). Performance was assessed using the area under the receiver operating curve (AUROC) at various times following HFNC initiation. The sensitivity, specificity, positive and negative predictive values (PPV, NPV) of predictions at two hours after HFNC initiation were also evaluated. These metrics were also computed in a cohort with primarily respiratory diagnoses. RESULTS 834 HFNC trials [455 training, 173 validation, 206 test] met the inclusion criteria, of which 175 [103, 30, 42] (21.0%) escalated to NIV or intubation. The LSTM models trained with transfer learning generally performed better than the LR models, with the best LSTM model achieving an AUROC of 0.78, vs 0.66 for the 14-variable LR and 0.71 for the 517-variable LR, two hours after initiation. All models except for the 14-variable LR achieved higher AUROCs in the respiratory cohort than in the general ICU population. CONCLUSIONS Machine learning models trained using EMR data were able to identify children at risk for failing HFNC within 24 hours of initiation. LSTM models that incorporated transfer learning, input data perseveration and ensembling showed improved performance than the LR and standard LSTM models.

scholarly journals NIMG-68. ACCURATE PATIENT-SPECIFIC MACHINE LEARNING MODELS OF GLIOBLASTOMA INVASION USING TRANSFER LEARNING

2017 ◽  
Vol 19 (suppl_6) ◽  
pp. vi157-vi158 ◽  
Author(s):  
Leland Hu ◽  
Hyunsoo Yoon ◽  
Jennifer Eschbacher ◽  
Leslie Baxter ◽  
Kris Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Patient Specific ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structure-based Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
Accelerate Structure ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate structure-based virtual screening using machine learning models. It has been validated using datasets both from literature (12 datasets, each containing three million molecules docked with FRED) and in-house sources (one dataset of four million compounds docked with Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of the top one percent scoring molecules after docking 10 % of the dataset for the literature data, whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structurebased Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate<br>structure-based virtual screening using machine learning models. It has been validated using<br>datasets both from literature (12 datasets, each containing three million molecules docked<br>with FRED) and in-house sources (one dataset of four million compounds docked with<br>Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of<br>the top one percent scoring molecules after docking 10 % of the dataset for the literature data,<br>whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be<br>used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

Sign in / Sign up

Export Citation Format

Share Document