Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference

Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Probabilistic models and stochastic neural networks can explicitly handle uncertainty in data and allow adaptive learning-on-the-fly, but their implementation in a low-power substrate remains a challenge. Here, we introduce a novel hardware fabric that implements a new class of stochastic NN called Neural-Sampling-Machine that exploits stochasticity in synaptic connections for approximate Bayesian inference. Harnessing the inherent non-linearities and stochasticity occurring at the atomic level in emerging materials and devices allows us to capture the synaptic stochasticity occurring at the molecular level in biological synapses. We experimentally demonstrate in-silico hybrid stochastic synapse by pairing a ferroelectric field-effect transistor -based analog weight cell with a two-terminal stochastic selector element. Such a stochastic synapse can be integrated within the well-established crossbar array architecture for compute-in-memory. We experimentally show that the inherent stochastic switching of the selector element between the insulator and metallic state introduces a multiplicative stochastic noise within the synapses of NSM that samples the conductance states of the FeFET, both during learning and inference. We perform network-level simulations to highlight the salient automatic weight normalization feature introduced by the stochastic synapses of the NSM that paves the way for continual online learning without any offline Batch Normalization. We also showcase the Bayesian inferencing capability introduced by the stochastic synapse during inference mode, thus accounting for uncertainty in data. We report 98.25%accuracy on standard image classification task as well as estimation of data uncertainty in rotated samples

Utilizing Light-field Imaging Technology in Neurosurgery

Traditional still cameras can only focus on a single plane for each image while rendering everything outside of that plane out of focus. However, new light-field imaging technology makes it possible to adjust the focus plane after an image has already been captured. This technology allows the viewer to interactively explore an image with objects and anatomy at varying depths and clearly focus on any feature of interest by selecting that location during post-capture viewing. These images with adjustable focus can serve as valuable educational tools for neurosurgical residents. We explore the utility of light-field cameras and review their strengths and limitations compared to other conventional types of imaging. The strength of light-field images is the adjustable focus, as opposed to the fixed-focus of traditional photography and video. A light-field image also is interactive by nature, as it requires the viewer to select the plane of focus and helps with visualizing the three-dimensional anatomy of an image. Limitations include the relatively low resolution of light-field images compared to traditional photography and video. Although light-field imaging is still in its infancy, there are several potential uses for the technology to complement traditional still photography and videography in neurosurgical education

Utilizing Light-field Imaging Technology in Neurosurgery

Traditional still cameras can only focus on a single plane for each image while rendering everything outside of that plane out of focus. However, new light-field imaging technology makes it possible to adjust the focus plane after an image has already been captured. This technology allows the viewer to interactively explore an image with objects and anatomy at varying depths and clearly focus on any feature of interest by selecting that location during post-capture viewing. These images with adjustable focus can serve as valuable educational tools for neurosurgical residents. We explore the utility of light-field cameras and review their strengths and limitations compared to other conventional types of imaging. The strength of light-field images is the adjustable focus, as opposed to the fixed-focus of traditional photography and video. A light-field image also is interactive by nature, as it requires the viewer to select the plane of focus and helps with visualizing the three-dimensional anatomy of an image. Limitations include the relatively low resolution of light-field images compared to traditional photography and video. Although light-field imaging is still in its infancy, there are several potential uses for the technology to complement traditional still photography and videography in neurosurgical education

Stabilization of Dakota Sandstone surface of the Faris Cave petroglyphs, Kanopolis Lake Project, Kansas

This study was conducted as part of Work Unit 32357, entitled Field Preservation of Cultural Sites, of the Environmental Impact Research Program (EIRP). The EIRP is sponsored by the Headquarters, US Army Corps of Engineers (HQUSACE), and is managed by the Environmental Laboratory (EL) of the US Army Engineer Waterways Experiment Station (WES). Technical Monitors were Dr. John Bushman, Mr. David P. Buelow, and Mr. Dave Mathis of HQUSACE. Mr. Paul Rubenstein of HQUSACE also provided technical guidance and review. Drs. Fred- erick Briuer, WES, and Roger Grosser, US Army Engineer District, Kansas City, provided technical reviews of the report. Dr. Roger T. Saucier, EL, WES, was the EIRP Program Manager. The work was conducted under provisions of Contract No. DACW39-90-M-0445 between WES and the Kansas Geological Survey, University of Kansas. Dr. David A. Grisafe of the Survey was the Principal Investigator and prepared the report. The study was conducted under the direct supervision of Dr. Paul R. Nickens, Resource Analysis Group (RAG), Environmental Resources Division (ERD), EL. General supervision was provided by Mr. H. Roger Hamilton, Chief, RAG; Dr. Conrad J. Kirby, Chief, ERD; and Dr. John Harrison, Director, EL. At the time of publication of this report, Director of WES was Dr. Robert W. Whalin. Commander and Deputy Director was COL Leonard G. Hassell, EN. Open Access Northup Database Collection See Extended description for more information

Plasmonics-based detection of H2 and CO: discrimination between reducing gases facilitated by material control

Monitoring emissions in high-temperature-combustion applications is very important for regulating the discharge of gases such as NO2 and CO as well as unburnt fuel into the environment. This work reports the detection of H2 and CO gases by employing a metal–metal oxide nanocomposite (gold–yttria stabilized zirconia (Au–YSZ)) film fabricated through layer-by-layer physical vapor deposition (PVD). The change in the peak position of the localized surface plasmon resonance (LSPR) was monitored as a function of time and gas concentration. The responses of the films were preferential towards H2, as observed from the results of exposing the films to the gases at temperatures of 500 °C in a background of dry air. Characterization of the samples by XRD and SEM enabled the correlation of material properties with the differences in the CO- and H2-induced LSPR peak shifts, including the relative desensitization towards NO2. Sensing characteristics of films with varying support thicknesses and metal-particle diameters have been studied, and the results are presented. A comparison has been made to films fabricated through co-sputtered PVD, and the calibration curves of the sensing response show a preferential response towards H2. The distinction between H2 and CO responses is also seen through the use of principal-component analysis (PCA). Such material arrangements, which can be tuned for their selectivity by changing certain parameters such as particle size, support thickness, etc., have direct applications within optical chemical sensors for turbine engines, solid-oxide fuel cells, and other high-temperature applications