Synergy of Physics-based Reasoning and Machine Learning in Biomedical Applications: Towards Unlimited Deep Learning with Limited Data

Technological advancements enable collecting vast data, i.e., Big Data, in science and industry including biomedical field. Increased computational power allows expedient analysis of collected data using statistical and machine-learning approaches. Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. Advancements in deep learning (DL) frameworks based on deep neural networks (DNN) improved accuracy in image recognition, natural language processing, and other applications yet severe data limitations and/or absence of transfer-learning-relevant problems drastically reduce advantages of DNN-based DL. Our earlier works demonstrate that hierarchical data representation can be alternatively implemented without NN, using boosting-like algorithms for utilization of existing domain knowledge, tolerating significant data incompleteness, and boosting accuracy of low-complexity models within the classifier ensemble, as illustrated in physiological-data analysis. Beyond obvious use in initial-factor selection, existing simplified models are effectively employed for generation of realistic synthetic data for later DNN pre-training. We review existing machine learning approaches, focusing on limitations caused by training-data incompleteness. We outline our hybrid framework that leverages existing domain-expert models/knowledge, boosting-like model combination, DNN-based DL and other machine learning algorithms for drastic reduction of training-data requirements. Applying this framework is illustrated in context of analyzing physiological data

Improvements in short-term forecasting of geomagnetic activity

We have improved our space weather forecasting algorithms to now predict Dst and AE in addition to Kp for up to 6 h of forecast times. These predictions can be accessed in real time at http://mms.rice. edu/realtime/forecast.html. In addition, in the event of an ongoing or imminent activity, e-mail “alerts” based on key discriminator levels have been going out to our subscribers since October 2003. The neural network–based algorithms utilize ACE data to generate full 1, 3, and 6 h ahead predictions of these indices from the Boyle index, an empirical approximation that estimates the Earth’s polar cap potential using solar wind parameters. Our models yield correlation coefficients of over 0.88, 0.86, and 0.83 for 1 h predictions of Kp, Dst, and AE, respectively, and 0.86, 0.84, and 0.80 when predicting the same but 3 h ahead. Our 6 h ahead predictions, however, have slightly higher uncertainties. Furthermore, the paper also tests other solar wind functions—the Newell driver, the Borovsky control function, and adding solar wind pressure term to the Boyle index—for their ability to predict geomagnetic activity

Resonant-to-nonresonant transition in electrostatic ion-cyclotron wave phase velocity

Because of the implications for plasmas in the laboratory and in space, attention has been drawn to inhomogeneous energy-density driven (IEDD) waves that are sustained by velocity-shear-induced inhomogeneity in cross-field plasma flow. These waves have a frequency vr in the lab frame within an order of magnitude of the ion gyrofrequency vci, propagate nearly perpendicular to the magnetic field (kz/k^ \u3c\u3c 1), and can be Landau resonant (0 \u3c v1/kz \u3c nd) with a parallel drifting electron population (drift speed nd), where subscripts 1 and r indicate frequency in the frame of flowing ions and in the lab frame, respectively, and kz is the parallel component of the wavevector. A transition in phase velocity from 0 \u3c v1/kz \u3c nd to 0 \u3e v1/kz \u3e nd for a pair of IEDD eigenmodes is observed as the degree of in-homogeneity in the transverse E × B flow is increased in a magnetized plasma column. For weaker velocity shear, both eigenmodes are dissipative, i.e. in Landau resonance, with kz nd \u3e 0. For stronger shear, both eigenmodes become reactive, with one\u27s wavevector component kz remaining parallel, but with v1/kz \u3e nd , and the other\u27s wavevector component kz becoming anti-parallel, so that 0 \u3e v1/kz . For both eigenmodes, the transition (1) involves a small frequency shift and (2) does not involve a sign change in the wave energy density, which is proportional to vr v1, both of which are previously unrecognized aspects of inhomogeneous energy-density driven waves

Observations of large-amplitude, parallel, electrostatic waves associated with the Kelvin-Helmholtz instability by the magnetospheric multiscale mission

On 8 September 2015, the four Magnetospheric Multiscale spacecraft encountered a Kelvin-Helmholtz unstable magnetopause near the dusk flank. The spacecraft observed periodic compressed current sheets, between which the plasma was turbulent. We present observations of large-amplitude (up to 100 mV/m) oscillations in the electric field. Because these oscillations are purely parallel to the background magnetic field, electrostatic, and below the ion plasma frequency, they are likely to be ion acoustic-like waves. These waves are observed in a turbulent plasma where multiple particle populations are intermittently mixed, including cold electrons with energies less than 10 eV. Stability analysis suggests a cold electron component is necessary for wave growth

Ensemble learning frameworks for the discovery of multi-component quantitative models in biomedical applications

Abstrac

Resonant-to-nonresonant transition in electrostatic ion-cyclotron wave phase velocity

Because of the implications for plasmas in the laboratory and in space, attention has been drawn to inhomogeneous energy-density driven (IEDD) waves that are sustained by velocity-shear-induced inhomogeneity in cross-field plasma flow. These waves have a frequency vr in the lab frame within an order of magnitude of the ion gyrofrequency vci, propagate nearly perpendicular to the magnetic field (kz /k^ v1/kz nd) with a parallel drifting electron population (drift speed nd), where subscripts 1 and r indicate frequency in the frame of flowing ions and in the lab frame, respectively, and kz is the parallel component of the wavevector. A transition in phase velocity from 0 v1/kz nd to 0 > v1/kz > nd for a pair of IEDD eigenmodes is observed as the degree of in-homogeneity in the transverse E × B flow is increased in a magnetized plasma column. For weaker velocity shear, both eigenmodes are dissipative, i.e. in Landau resonance, with kz nd > 0. For stronger shear, both eigenmodes become reactive, with one's wavevector component kz remaining parallel, but with v1/kz > nd , and the other's wavevector component kz becoming anti-parallel, so that 0 > v1/kz . For both eigenmodes, the transition (1) involves a small frequency shift and (2) does not involve a sign change in the wave energy density, which is proportional to vr v1, both of which are previously unrecognized aspects of inhomogeneous energy-density driven waves

Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany

The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection device that iteratively adds the predictors with the largest contribution to the fit. Using data for the United States, the euro area and Germany, we assess the performance of boosting when forecasting a wide range of macroeconomic variables. Moreover, we analyse to what extent its forecasting accuracy depends on the method used for determining its key regularisation parameter, the number of iterations. We find that boosting mostly outperforms the autoregressive benchmark, and that K-fold cross-validation works much better as stopping criterion than the commonly used information criteria