Abstract. With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into preidentified morphological classes and to discover new classes that appear as the detectors arXiv:1611.04596v2 [gr-qc]

In this study, we achieved a major step forward in the analysis of firing patterns of populations of motoneurons, through remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. The ability to implement these extensive parameter searches seem ideally matched to understanding the many parameters that define the inputs to neuron populations that generate these patterns. Therefore, we investigated the feasibility of using supercomputer-based models of spinal motoneurons as a basis for reverse engineering (RE) their firing patterns to identify the organization of their inputs, which we defined as the amplitudes and patterns of excitation, inhibition, and neuromodulation. This study combines two advances: 1) highly-realistic motoneuron models based on extensive in situ voltage and current clamp studies focused on neuromodulatory actions, and 2) implementation of these models using the Laboratory Computing Resource Center at Argonne National Laboratory to carry thousands (soon millions) of simulations simultaneously. The goal for computing and performing RE on over 300,000 combinations of excitatory, inhibitory, and neuromodulatory inputs was twofold: 1) to estimate the synaptic input to the motoneuron pool and 2) to generate training data for identifying the excitatory, inhibitory, and neuromodulatory inputs based on output firing patterns. As with other neural systems, any given motoneuron firing pattern could potentially be non-unique with respect to the excitatory, inhibitory, and neuromodulatory input combination (many input combinations produce similar outputs). However, our initial results show that the neuromodulatory input makes the motoneuron input-output properties so nonlinear that the effective RE solution space is restricted. The RE approach we demonstrate in this work is successful in generating estimates of the actual simulated patterns of excitation, inhibition, and neuromodulation with variances accounted for ranging from 75% to 90%. It was striking that the nonlinearities induced in firing patterns by the neuromodulation inputs did not impede RE, but instead generated distinctive features in firing patterns that aided RE. These simulations demonstrate the potential of this form of RE analysis. It is likely that the ever-increasing power of supercomputers will allow increasingly accurate RE of neuron inputs from their firing patterns from many neural systems.

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Background: Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it to alternating current in the 100 kHz-800 kHz frequency range and heating it until it is destroyed via coagulative necrosis. Ablation treatment is gaining momentum especially in cancer research, where the undesired tissue is a malignant tumor. While ablating the tumor with an electrode or catheter is an easy task, real-time monitoring the ablation process is a must in order to maintain the reliability of the treatment. Common methods for this monitoring task have proven to be accurate, however, they are all time-consuming or require expensive equipment, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure. Methods: A machine learning (ML) approach is presented that aims to reduce the monitoring time while keeping the accuracy of the conventional methods. Two different hardware setups are used to perform the ablation and collect impedance data at the same time and different ML algorithms are tested to predict the ablation depth in 3 dimensions, based on the collected data. Results: Both the random forest and adaptive boosting (adaboost) models had over 98% R 2 on the data collected with the embedded system-based hardware instrumentation setup, outperforming Neural Network-based models. Conclusions: It is shown that an optimal pair of hardware setup and ML algorithm (Adaboost) is able to control the ablation by estimating the lesion depth within a test average of 0.3mm while keeping the estimation time within 10ms on a Â86-64 workstation.

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