Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks

Data preprocessing is a crucial part of any machine learning pipeline, and it can have a significant impact on both performance and training efficiency. This is especially evident when using deep neural networks for time series prediction and classification: real-world time series data often exhibit irregularities such as multi-modality, skewness and outliers, and the model performance can degrade rapidly if these characteristics are not adequately addressed. In this work, we propose the EDAIN (Extended Deep Adaptive Input Normalization) layer, a novel adaptive neural layer that learns how to appropriately normalize irregular time series data for a given task in an end-to-end fashion, instead of using a fixed normalization scheme. This is achieved by optimizing its unknown parameters simultaneously with the deep neural network using back-propagation. Our experiments, conducted using synthetic data, a credit default prediction dataset, and a large-scale limit order book benchmark dataset, demonstrate the superior performance of the EDAIN layer when compared to conventional normalization methods and existing adaptive time series preprocessing layers

Functional connectome of arousal and motor brainstem nuclei in living humans by 7 Tesla resting-state fMRI

Brainstem nuclei play a pivotal role in many functions, such as arousal and motor control. Nevertheless, the connectivity of arousal and motor brainstem nuclei is understudied in living humans due to the limited sensitivity and spatial resolution of conventional imaging, and to the lack of atlases of these deep tiny regions of the brain. For a holistic comprehension of sleep, arousal and associated motor processes, we investigated in 20 healthy subjects the resting-state functional connectivity of 18 arousal and motor brainstem nuclei in living humans. To do so, we used high spatial-resolution 7 Tesla resting-state fMRI, as well as a recently developed in-vivo probabilistic atlas of these nuclei in stereotactic space. Further, we verified the translatability of our brainstem connectome approach to conventional (e.g. 3 Tesla) fMRI. Arousal brainstem nuclei displayed high interconnectivity, as well as connectivity to the thalamus, hypothalamus, basal forebrain and frontal cortex, in line with animal studies and as expected for arousal regions. Motor brainstem nuclei showed expected connectivity to the cerebellum, basal ganglia and motor cortex, as well as high interconnectivity. Comparison of 3 Tesla to 7 Tesla connectivity results indicated good translatability of our brainstem connectome approach to conventional fMRI, especially for cortical and subcortical (non-brainstem) targets and to a lesser extent for brainstem targets. The functional connectome of 18 arousal and motor brainstem nuclei with the rest of the brain might provide a better understanding of arousal, sleep and accompanying motor functions in living humans in health and disease

Hybrid fuzzy and sliding-mode control for motorised tether spin-up when coupled with axial vibration

A hybrid fuzzy sliding mode controller is applied to the control of motorised tether spin-up coupled with an axial oscillation phenomenon. A six degree of freedom dynamic model of a motorised momentum exchange tether is used as a basis for interplanetary payload exchange. The tether comprises a symmetrical double payload configuration, with an outrigger counter inertia and massive central facility. It is shown that including axial elasticity permits an enhanced level of performance prediction accuracy and a useful departure from the usual rigid body representations, particularly for accurate payload positioning at strategic points. A special simulation program has been devised in MATLAB and MATHEMATICA for a given initial condition data case

Two-Dimensional Electronic Spectroscopy of Chlorophyll a: Solvent Dependent Spectral Evolution

The interaction of the monomeric chlorophyll Q-band electronic transition with solvents of differing physical-chemical properties is investigated through two-dimensional electronic spectroscopy (2DES). Chlorophyll constitutes the key chromophore molecule in light harvesting complexes. It is well-known that the surrounding protein in the light harvesting complex fine-tunes chlorophyll electronic transitions to optimize energy transfer. Therefore, an understanding of the influence of the environment on the monomeric chlorophyll electronic transitions is important. The Q-band 2DES is inhomogeneous at early times, particularly in hydrogen bonding polar solvents, but also in nonpolar solvents like cyclohexane. Interestingly this inhomogeneity persists for long times, even up to the nanosecond time scale in some solvents. The reshaping of the 2DES occurs over multiple time scales and was assigned mainly to spectral diffusion. At early times the reshaping is Gaussian-like, hinting at a strong solvent reorganization effect. The temporal evolution of the 2DES response was analyzed in terms of a Brownian oscillator model. The spectral densities underpinning the Brownian oscillator fitting were recovered for the different solvents. The absorption spectra and Stokes shift were also properly described by this model. The extent and nature of inhomogeneous broadening was a strong function of solvent, being larger in H-bonding and viscous media and smaller in nonpolar solvents. The fastest spectral reshaping components were assigned to solvent dynamics, modified by interactions with the solute

Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI

Despite remarkable advances in mapping the functional connectivity of the cortex, the functional connectivity of subcortical regions is understudied in living humans. This is the case for brainstem nuclei that control vital processes, such as autonomic, limbic, nociceptive and sensory functions. This is because of the lack of precise brainstem nuclei localization, of adequate sensitivity and resolution in the deepest brain regions, as well as of optimized processing for the brainstem. To close the gap between the cortex and the brainstem, on 20 healthy subjects, we computed a correlation-based functional connectome of 15 brainstem nuclei involved in autonomic, limbic, nociceptive, and sensory function (superior and inferior colliculi, ventral tegmental area-parabrachial pigmented nucleus complex, microcellular tegmental nucleus-prabigeminal nucleus complex, lateral and medial parabrachial nuclei, vestibular and superior olivary complex, superior and inferior medullary reticular formation, viscerosensory motor nucleus, raphe magnus, pallidus, and obscurus, and parvicellular reticular nucleus – alpha part) with the rest of the brain. Specifically, we exploited 1.1mm isotropic resolution 7 Tesla resting-state fMRI, ad-hoc coregistration and physiological noise correction strategies, and a recently developed probabilistic template of brainstem nuclei. Further, we used 2.5mm isotropic resolution resting-state fMRI data acquired on a 3 Tesla scanner to assess the translatability of our results to conventional datasets. We report highly consistent correlation coefficients across subjects, confirming available literature on autonomic, limbic, nociceptive and sensory pathways, as well as high interconnectivity within the central autonomic network and the vestibular network. Interestingly, our results showed evidence of vestibulo-autonomic interactions in line with previous work. Comparison of 7 Tesla and 3 Tesla findings showed high translatability of results to conventional settings for brainstem-cortical connectivity and good yet weaker translatability for brainstem-brainstem connectivity. The brainstem functional connectome might bring new insight in the understanding of autonomic, limbic, nociceptive and sensory function in health and disease

Bacterial foraging-optimized PID control of a two-wheeled machine with a two-directional handling mechanism

This paper presents the performance of utilizing a bacterial foraging optimization algorithm on a PID control scheme for controlling a five DOF two-wheeled robotic machine with two-directional handling mechanism. The system under investigation provides solutions for industrial robotic applications that require a limited-space working environment. The system nonlinear mathematical model, derived using Lagrangian modeling approach, is simulated in MATLAB/Simulink(®) environment. Bacterial foraging-optimized PID control with decoupled nature is designed and implemented. Various working scenarios with multiple initial conditions are used to test the robustness and the system performance. Simulation results revealed the effectiveness of the bacterial foraging-optimized PID control method in improving the system performance compared to the PID control scheme

Impact of exercise-based cardiac rehabilitation in patients with heart failure (ExTraMATCH II) on mortality and hospitalisation:an individual-patient data meta-analysis of randomised trials

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordAIMS: To undertake an individual patient data (IPD) meta-analysis to assess the impact of exercise-based cardiac rehabilitation (ExCR) in patients with heart failure (HF) on mortality and hospitalisation, and differential effects of ExCR according to patient characteristics: age, sex, ethnicity, New York Heart Association functional class, ischaemic aetiology, ejection fraction, and exercise capacity. METHODS AND RESULTS: Randomised trials of exercise training for at least 3 weeks compared with no exercise control with 6-month follow-up or longer, providing IPD time to event on mortality or hospitalisation (all-cause or HF-specific). IPD were combined into a single dataset. We used Cox proportional hazards models to investigate the effect of ExCR and the interactions between ExCR and participant characteristics. We used both two-stage random effects and one-stage fixed effect models. IPD were obtained from 18 trials including 3912 patients with HF with reduced ejection fraction. Compared to control, there was no statistically significant difference in pooled time to event estimates in favour of ExCR although confidence intervals (CIs) were wide [all-cause mortality: hazard ratio (HR) 0.83, 95% CI 0.67-1.04; HF-specific mortality: HR 0.84, 95% CI 0.49-1.46; all-cause hospitalisation: HR 0.90, 95% CI 0.76-1.06; and HF-specific hospitalisation: HR 0.98, 95% CI 0.72-1.35]. No strong evidence was found of differential intervention effects across patient characteristics. CONCLUSION: Exercise-based cardiac rehabilitation did not have a significant effect on the risk of mortality and hospitalisation in HF with reduced ejection fraction. However, uncertainty around effect estimates precludes drawing definitive conclusions.This work is supported by UK National Institute for Health Research funding (HTA 15/80/30)

Revisiting the obesity paradox in heart failure:Per cent body fat as predictor of biomarkers and outcome

Aims - Obesity defined by body mass index (BMI) is characterized by better prognosis and lower plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) in heart failure. We assessed whether another anthropometric measure, per cent body fat (PBF), reveals different associations with outcome and heart failure biomarkers (NT-proBNP, high-sensitivity troponin T (hs-TnT), soluble suppression of tumorigenesis-2 (sST2)). Methods - In an individual patient dataset, BMI was calculated as weight (kg)/height (m)2, and PBF through the Jackson–Pollock and Gallagher equations. Results - Out of 6468 patients (median 68 years, 78% men, 76% ischaemic heart failure, 90% reduced ejection fraction), 24% died over 2.2 years (1.5–2.9), 17% from cardiovascular death. Median PBF was 26.9% (22.4–33.0%) with the Jackson–Pollock equation, and 28.0% (23.8–33.5%) with the Gallagher equation, with an extremely strong correlation (r = 0.996, p 2, third PBF tertile), hs-TnT and sST2, but not NT-proBNP, independently predicted outcome. Conclusion - In parallel with increasing BMI or PBF there is an improvement in patient prognosis and a decrease in NT-proBNP, but not hs-TnT or sST2. hs-TnT or sST2 are stronger predictors of outcome than NT-proBNP among obese patients