scholarly journals Stochasticity invariance control in Pr1-xCaxMnO3 RRAM to enable large-scale stochastic recurrent neural networks

2021 ◽  
Author(s):  
Vivek Saraswat ◽  
Udayan Ganguly
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Time Distribution ◽  
Large Scale ◽  
Internal State ◽  
External Input ◽  
Problem Size ◽  
Boltzmann Machines ◽  
Wide Range ◽  
The Impact

Abstract Emerging non-volatile memories have been proposed for a wide range of applications, from easing the von-Neumann bottleneck to neuromorphic applications. Specifically, scalable RRAMs based on Pr1-xCaxMnO3 (PCMO) exhibit analog switching have been demonstrated as an integrating neuron, an analog synapse, and a voltage-controlled oscillator. More recently, the inherent stochasticity of memristors has been proposed for efficient hardware implementations of Boltzmann Machines. However, as the problem size scales, the number of neurons increases and controlling the stochastic distribution tightly over many iterations is necessary. This requires parametric control over stochasticity. Here, we characterize the stochastic Set in PCMO RRAMs. We identify that the Set time distribution depends on the internal state of the device (i.e., resistance) in addition to external input (i.e., voltage pulse). This requires the confluence of contradictory properties like stochastic switching as well as deterministic state control in the same device. Unlike ‘stochastic-everywhere’ filamentary memristors, in PCMO RRAMs, we leverage the (i) stochastic Set in negative polarity and (ii) deterministic analog Reset in positive polarity to demonstrate 100× reduced Set time distribution drift. The impact on Boltzmann Machines’ performance is analyzed and as opposed to the “fixed external input stochasticity”, the “state-monitored stochasticity” can solve problems 20× larger in size. State monitoring also tunes out the device-to-device variability effect on distributions providing 10× better performance. In addition to the physical insights, this study establishes the use of experimental stochasticity in PCMO RRAMs in stochastic recurrent neural networks reliably over many iterations.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

scholarly journals A perspective on graph theory-based stability analysis of impulsive stochastic recurrent neural networks with time-varying delays

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
M. Iswarya ◽  
R. Raja ◽  
G. Rajchakit ◽  
J. Cao ◽  
J. Alzabut ◽  
...  
Keyword(s):  
Neural Networks ◽  
Graph Theory ◽  
Exponential Stability ◽  
Recurrent Neural Networks ◽  
Lyapunov Functional ◽  
Large Scale ◽  
Sufficient Conditions ◽  
Distributed Delay ◽  
Theoretic Approach ◽  
Discrete Time Delay

AbstractIn this work, the exponential stability problem of impulsive recurrent neural networks is investigated; discrete time delay, continuously distributed delay and stochastic noise are simultaneously taken into consideration. In order to guarantee the exponential stability of our considered recurrent neural networks, two distinct types of sufficient conditions are derived on the basis of the Lyapunov functional and coefficient of our given system and also to construct a Lyapunov function for a large scale system a novel graph-theoretic approach is considered, which is derived by utilizing the Lyapunov functional as well as graph theory. In this approach a global Lyapunov functional is constructed which is more related to the topological structure of the given system. We present a numerical example and simulation figures to show the effectiveness of our proposed work.

scholarly journals OncoThreads: Visualization of Large Scale Longitudinal Cancer Molecular Data

2021 ◽  
Author(s):  
Theresa A Harbig ◽  
Sabrina Nusrat ◽  
Tali Mazor ◽  
Qianwen Wang ◽  
Alexander Thomson ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Large Scale ◽  
Cancer Genomics ◽  
Temporal Patterns ◽  
Molecular Data ◽  
Liquid Biopsies ◽  
Sequencing Technologies ◽  
Molecular Features ◽  
Wide Range ◽  
The Impact

Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, e.g. emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2-4 timepoints. OncoThreads is freely available at http://oncothreads.gehlenborglab.org and implemented in Javascript using the cBioPortal web API as a backend.

Ivan Kavaleridze`s dramaturgy on the Sumy stage

2021 ◽  
Author(s):  
D.V. Budianskyi
Keyword(s):  
Large Scale ◽  
Musical Comedy ◽  
Historical Figures ◽  
Wide Range ◽  
Socialist Realist ◽  
History Of ◽  
Characteristic Features ◽  
Old Men ◽  
The Impact ◽  
First Time

The characteristic features of I. Kavaleridze’s drama is considered in the article. It is noted that there are signs of the artist’s individuality, attraction to expressionist forms, artistic techniques characteristic for the art of sculpture: symbolism, monumentality, hyperbole. I. Kavaleridze was well versed in the drama laws, understood the specifics of the stage events construction, had a large arsenal of literary means, thanks to which the characters’ monologues and dialogues were extremely expressive and colorful. In his work, he implemented original solutions that were ahead of time. Therefore, many of the artist’s ideas and achievements received due recognition only after his death. I. Kavaleridze’s creative heritage covers a wide range of both purely artistic and general philosophical problems. Among them the formation of the era of modernism and its features in the Ukrainian art of the early XX century, the impact of revolutionary ideas on the work of the 1920s, the role of spiritual leaders of the Ukrainian people T. Shevchenko and G. Skovoroda in the formation of national consciousness, political and ideological pressure on figurative art language and the formation of a socialist-realist canon, etc. The analysis of the productions of I. Kavalerizde’s plays “The First Furrow” and “Gregory and Paraskeva” on the stage of the Mykhailo Shchepkin Sumy Theater of Drama and Musical Comedy in 1970-1972. The article notes that these plays were staged in Sumy for the first time in the history of Ukrainian theater. The premiere of “The First Furrow” (the play was called “Old Men”) took place on March 19, 1970. The figure of the national genius Hryhoriy Skov oroda was als o embodied for the first time on t he stage in Sumy in th e play “Hryhoriy and Paraskeva”. It premiered on October 21, 1972. I. Rybchynsky, Honored Artist of the USSR, performed the production. Creating generalized historical outlines of people’s life, features of life at that time, depicting psychological portraits of people in various, sometimes-dramatic collisions, in the productions of I. Kavaleridze’s plays on the Sumy stage the emphasis was on universal values such as virtue, love. The main character was the Ukrainian people, who nurtured such large-scale historical figures, gave them strength and wisdom for great achievements. Based on publications in periodicals of that time, memoirs of Ukrainian directors, the peculiarities of the director’s interpretation, stenographic and musical design of these plays on the Sumy stage are considered. Considerable attention is paid to the analysis of acting works in I. Kavaleridze’s plays. In particular, the peculiarities of the actor’s embodiment of the image of the national genius Hryhoriy Skovoroda on the stage are presented. It is noted that I. Kavaleridze’s plays, created in a difficult political, social and ideological context, are rightly considered to be highly artistic works of Ukrainian drama. Their staging was carried out on various theatrical stages, including Mykhailo Shchepkin Sumy Theater of Drama and Musical Comedy is an important page of national theatrical art.

scholarly journals Economics of the disintegration of the Greenland ice sheet

2019 ◽  
Vol 116 (25) ◽  
pp. 12261-12269 ◽  
Author(s):  
William Nordhaus
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Social Cost ◽  
Climate Models ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Reduced Form ◽  
Small Contribution ◽  
Wide Range ◽  
The Impact

Concerns about the impact on large-scale earth systems have taken center stage in the scientific and economic analysis of climate change. The present study analyzes the economic impact of a potential disintegration of the Greenland ice sheet (GIS). The study introduces an approach that combines long-run economic growth models, climate models, and reduced-form GIS models. The study demonstrates that social cost–benefit analysis and damage-limiting strategies can be usefully extended to illuminate issues with major long-term consequences, as well as concerns such as potential tipping points, irreversibility, and hysteresis. A key finding is that, under a wide range of assumptions, the risk of GIS disintegration makes a small contribution to the optimal stringency of current policy or to the overall social cost of climate change. It finds that the cost of GIS disintegration adds less than 5% to the social cost of carbon (SCC) under alternative discount rates and estimates of the GIS dynamics.

scholarly journals InSAR Greece with Parallelized Persistent Scatterer Interferometry: A National Ground Motion Service for Big Copernicus Sentinel-1 Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3207
Author(s):  
Ioannis Papoutsis ◽  
Charalampos Kontoes ◽  
Stavroula Alatza ◽  
Alexis Apostolakis ◽  
Constantinos Loupasakis
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Large Scale ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterers ◽  
Processing Chain ◽  
Persistent Scatterer ◽  
Basin Scale ◽  
Wide Range ◽  
The Impact

Advances in synthetic aperture radar (SAR) interferometry have enabled the seamless monitoring of the Earth’s crust deformation. The dense archive of the Sentinel-1 Copernicus mission provides unprecedented spatial and temporal coverage; however, time-series analysis of such big data volumes requires high computational efficiency. We present a parallelized-PSI (P-PSI), a novel, parallelized, and end-to-end processing chain for the fully automated assessment of line-of-sight ground velocities through persistent scatterer interferometry (PSI), tailored to scale to the vast multitemporal archive of Sentinel-1 data. P-PSI is designed to transparently access different and complementary Sentinel-1 repositories, and download the appropriate datasets for PSI. To make it efficient for large-scale applications, we re-engineered and parallelized interferogram creation and multitemporal interferometric processing, and introduced distributed implementations to best use computing cores and provide resourceful storage management. We propose a new algorithm to further enhance the processing efficiency, which establishes a non-uniform patch grid considering land use, based on the expected number of persistent scatterers. P-PSI achieves an overall speed-up by a factor of five for a full Sentinel-1 frame for processing in a 20-core server. The processing chain is tested on a large-scale project to calculate and monitor deformation patterns over the entire extent of the Greek territory—our own Interferometric SAR (InSAR) Greece project. Time-series InSAR analysis was performed on volumes of about 12 TB input data corresponding to more than 760 Single Look Complex Sentinel-1A and B images mostly covering mainland Greece in the period of 2015–2019. InSAR Greece provides detailed ground motion information on more than 12 million distinct locations, providing completely new insights into the impact of geophysical and anthropogenic activities at this geographic scale. This new information is critical to enhancing our understanding of the underlying mechanisms, providing valuable input into risk assessment models. We showcase this through the identification of various characteristic geohazard locations in Greece and discuss their criticality. The selected geohazard locations, among a thousand, cover a wide range of catastrophic events including landslides, land subsidence, and structural failures of various scales, ranging from a few hundredths of square meters up to the basin scale. The study enriches the large catalog of geophysical related phenomena maintained by the GeObservatory portal of the Center of Earth Observation Research and Satellite Remote Sensing BEYOND of the National Observatory of Athens for the opening of new knowledge to the wider scientific community.

scholarly journals Activity-dependent myelination: A glial mechanism of oscillatory self-organization in large-scale brain networks

2020 ◽  
Vol 117 (24) ◽  
pp. 13227-13237 ◽  
Author(s):  
Rabiya Noori ◽  
Daniel Park ◽  
John D. Griffiths ◽  
Sonya Bells ◽  
Paul W. Frankland ◽  
...  
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Phase Synchronization ◽  
Brain Networks ◽  
Self Organization ◽  
Conduction Delay ◽  
Neural Communication ◽  
Wide Range ◽  
The Impact ◽  
Activity Dependent

Communication and oscillatory synchrony between distributed neural populations are believed to play a key role in multiple cognitive and neural functions. These interactions are mediated by long-range myelinated axonal fiber bundles, collectively termed as white matter. While traditionally considered to be static after development, white matter properties have been shown to change in an activity-dependent way through learning and behavior—a phenomenon known as white matter plasticity. In the central nervous system, this plasticity stems from oligodendroglia, which form myelin sheaths to regulate the conduction of nerve impulses across the brain, hence critically impacting neural communication. We here shift the focus from neural to glial contribution to brain synchronization and examine the impact of adaptive, activity-dependent changes in conduction velocity on the large-scale phase synchronization of neural oscillators. Using a network model based on primate large-scale white matter neuroanatomy, our computational and mathematical results show that such plasticity endows white matter with self-organizing properties, where conduction delay statistics are autonomously adjusted to ensure efficient neural communication. Our analysis shows that this mechanism stabilizes oscillatory neural activity across a wide range of connectivity gain and frequency bands, making phase-locked states more resilient to damage as reflected by diffuse decreases in connectivity. Critically, our work suggests that adaptive myelination may be a mechanism that enables brain networks with a means of temporal self-organization, resilience, and homeostasis.

Motion Simulation Experiments for Driver Behavior and Road Vehicle Dynamics

2011 ◽  
Vol 11 (4) ◽  
Author(s):  
Kemper Lewis ◽  
Kevin Hulme ◽  
Edward Kasprzak ◽  
Deborah Moore-Russo ◽  
Gregory Fabiano
Keyword(s):  
Vehicle Dynamics ◽  
Large Scale ◽  
Driving Simulation ◽  
Vehicle Design ◽  
Simulation Environment ◽  
Learning Mechanisms ◽  
Road Vehicle ◽  
Wide Range ◽  
Traffic Systems ◽  
The Impact

This paper discusses the design and development of a motion-based driving simulation and its integration into driving simulation research. The integration of the simulation environment into a road vehicle dynamics curriculum is also presented. The simulation environment provides an immersive experience to conduct a wide range of research on driving behavior, vehicle design and intelligent traffic systems. From an education perspective, the environment is designed to promote hands-on student participation in real-world engineering experiences that enhance conventional learning mechanisms for road vehicle dynamics and engineering systems analysis. The paper assesses the impact of the environment on student learning objectives in an upper level vehicle dynamics course and presents results from research involving teenage drivers. The paper presents an integrated framework for the use of real-time simulation and large-scale visualization to both study driving behaviors and to discover the impact that design decisions have on vehicle design using a realistic simulated driving interface.

scholarly journals Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population

SLEEP ◽  
2020 ◽  
Vol 43 (9) ◽  
Author(s):  
Pedro Fonseca ◽  
Merel M van Gilst ◽  
Mustafa Radha ◽  
Marco Ross ◽  
Arnaud Moreau ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Disorders ◽  
Recurrent Neural Networks ◽  
Clinical Diagnostics ◽  
Sleep Staging ◽  
Substantial Agreement ◽  
Body Movements ◽  
Wide Range

Abstract Study Objectives To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients. Methods We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center. Results The classifier achieved substantial agreement on four-class sleep staging (wake/N1–N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%. Conclusions This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.

scholarly journals Simple framework for constructing functional spiking recurrent neural networks

2019 ◽  
Vol 116 (45) ◽  
pp. 22811-22820 ◽  
Author(s):  
Robert Kim ◽  
Yinghao Li ◽  
Terrence J. Sejnowski
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Continuous Variable ◽  
Model Parameters ◽  
Additional Parameter ◽  
Wide Range ◽  
Close Relationship ◽  
Rate Coding ◽  
One To One ◽  
The One

Cortical microcircuits exhibit complex recurrent architectures that possess dynamically rich properties. The neurons that make up these microcircuits communicate mainly via discrete spikes, and it is not clear how spikes give rise to dynamics that can be used to perform computationally challenging tasks. In contrast, continuous models of rate-coding neurons can be trained to perform complex tasks. Here, we present a simple framework to construct biologically realistic spiking recurrent neural networks (RNNs) capable of learning a wide range of tasks. Our framework involves training a continuous-variable rate RNN with important biophysical constraints and transferring the learned dynamics and constraints to a spiking RNN in a one-to-one manner. The proposed framework introduces only 1 additional parameter to establish the equivalence between rate and spiking RNN models. We also study other model parameters related to the rate and spiking networks to optimize the one-to-one mapping. By establishing a close relationship between rate and spiking models, we demonstrate that spiking RNNs could be constructed to achieve similar performance as their counterpart continuous rate networks.

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