Asymptotic Properties for Bayesian Neural Network in Besov Space

Neural networks have shown great predictive power when dealing with various unstructured data such as images and natural languages. The Bayesian neural network captures the uncertainty of prediction by putting a prior distribution for the parameter of the model and computing the posterior distribution. In this paper, we show that the Bayesian neural network using spike-and-slab prior has consistency with nearly minimax convergence rate when the true regression function is in the Besov space. Even when the smoothness of the regression function is unknown the same posterior convergence rate holds and thus the spike-and-slab prior is adaptive to the smoothness of the regression function. We also consider the shrinkage prior, which is more feasible than other priors, and show that it has the same convergence rate. In other words, we propose a practical Bayesian neural network with guaranteed asymptotic properties

Detecting Bladder Biomarkers for Closed-Loop Neuromodulation: A Technological Review

Neuromodulation was introduced for patients with poor outcomes from the existing traditional treatment approaches. It is well-established as an alternative, novel treatment option for voiding dysfunction. The current system of neuromodulation uses an open-loop system that only delivers continuous stimulation without considering the patient’s state changes. Though the conventional open-loop system has shown positive clinical results, it can cause problems such as decreased efficacy over time due to neural habituation, higher risk of tissue damage, and lower battery life. Therefore, there is a need for a closed-loop system to overcome the disadvantages of existing systems. The closed-loop neuromodulation includes a system to monitor and stimulate micturition reflex pathways from the lower urinary tract, as well as the central nervous system. In this paper, we reviewed the current technological status to measure biomarker for closed-loop neuromodulation systems for voiding dysfunction

Analysis of several VERA benchmark problems with the photon transport capability of STREAM

STREAM - a lattice transport calculation code with method of characteristics for the purpose of light water reactor analysis - has been developed by the Computational Reactor Physics and Experiment laboratory (CORE) of the Ulsan National Institute of Science and Technology (UNIST). Recently, efforts have been taken to develop a photon module in STREAM to assess photon heating and the influence of gamma photon transport on power distributions, as only neutron transport was considered in previous STREAM versions. A multi-group photon library is produced for STREAM based on the ENDF/B-VII.1 library with the use of the library-processing code NJOY. The developed photon solver for the computation of 2D and 3D distributions of photon flux and energy deposition is based on the method of characteristics like the neutron solver. The photon library and photon module produced and implemented for STREAM are verified on VERA pin and assembly problems by comparison with the Monte Carlo code MCS - also developed at UNIST. A short analysis of the impact of photon transport during depletion and thermal hydraulics feedback is presented for a 2D core also from the VERA benchmark. (C) 2022 Korean Nuclear Society, Published by Elsevier Korea LLC

Analysis of Nociceptive Information Encoded in the Temporal Discharge Patterns of Cutaneous C-Fibers

The generation of pain signals from primary afferent neurons is explained by a labeled-line code. However, this notion cannot apply in a simple way to cutaneous C-fibers, which carry signals from a variety of receptors that respond to various stimuli including agonist chemicals. To represent the discharge patterns of C-fibers according to different agonist chemicals, we have developed a quantitative approach using three consecutive spikes. By using this method, the generation of pain in response to chemical stimuli is shown to be dependent on the temporal aspect of the spike trains. Furthermore, under pathological conditions, gamma-aminobutyric acid resulted in pain behavior without change of spike number but with an altered discharge pattern. Our results suggest that information about the agonist chemicals may be encoded in specific temporal patterns of signals in C-fibers, and nociceptive sensation may be influenced by the extent of temporal summation originating from the temporal patterns.open0

Innovative Design Thinking Process with TRIZ

Part 7: TRIZ Combined with other ApproachesInternational audienceThis paper describes an innovative design thinking process with simplified TRIZ that can be used to resolve contradictions in all domains with words such as “dilemma”, “conflict”, “contradiction” and “paradox”. The design thinking process that have been used at d.school at Stanford and Potsdam University are popular as a human-centered innovation process with “Empathy” and “Define” stages for human-centered problem finding. However, many results may be not innovative because it uses mostly “Group brainstorming” at “Ideate” stage.TRIZ users have complained that the Russian conventional TRIZ is so difficult to learn and apply. They consider that it is useful in manufacturing and mechanical fields mostly. This paper suggests an innovative design thinking process with simplified and generally usable the step-by-step TRIZ. Its effectiveness of the innovative design thinking process with the simplified and step-by-step general TRIZ is explained in the development case study, a smart wind free air conditioner

Verification of Graphite Isotope Ratio Method Combined With Polynomial Regression for the Estimation of Cumulative Plutonium Production in a Graphite-Moderated Reactor

Graphite Isotope Ratio Method (GIRM) can be used to estimate plutonium production in a graphite-moderated reactor. This study presents verification results for the GIRM combined with a 3-D polynomial regression function to estimate cumulative plutonium production in a graphite-moderated reactor. Using the 3-D Monte-Carlo method, verification was done by comparing the cumulative plutonium production with the GIRM. The GIRM can estimate plutonium production for specific sampling points using a function that is based on an isotope ratio of impurity elements. In this study, the 10B/11B isotope ratio was chosen and calculated for sampling points. Then, 3-D polynomial regression was used to derive a function that represents a whole core cumulative plutonium production map. To verify the accuracy of the GIRM with polynomial regression, the reference value of plutonium production was calculated using a Monte-Carlo code, MCS, up to 4250 days of depletion. Moreover, the amount of plutonium produced in certain axial layers and fuel pins at 1250, 2250, and 3250 days of depletion was obtained and used for additional verification. As a result, the difference in the total cumulative plutonium production based on the MCS and GIRM results was found below 3.1% with regard to the root mean square (RMS) error