Convolution layer with nonlinear kernel of square of subtraction for dark-direction-free recognition of images

A nonlinear kernel with a bias is proposed here in the convolutional neural network. Negative square of subtraction between input image pixel numbers and the kernel coefficients are convolved to conform new feature map through the convolution layer in convolutional neural network. The operation is nonlinear from the input pixel point of view, as well as from the kernel weight coefficient point of view. Maximum-pooling may follow the feature map, and the results are finally fully connected to the output nodes of the network. While using gradient descent method to train relevant coefficients and biases, the gradient of the square of subtraction term appears in the whole gradient over each kernel coefficient. The new subtraction kernel is applied to two sets of images, and shows better performance than the existing linear convolution kernel. Each coefficient of the nonlinear subtraction kernel has quite image-equivalent meaning on top of pure mathematical number. The subtraction kernel works equally for a given black and white image set and its reversed version or for a given gray image set and its reversed version. This attribute becomes important when patterns are mixed with light color and dark color, or mixed with background color, and still both sides are equally important

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Prediction of surface morphology and reflection spectrum of laser-induced periodic surface structures using deep learning

Laser-induced periodic surface structures have been extensively explored as an enabling tool for fabricating various optical surfaces because the resulting surface ripples can effectively modify surface reflectance. Here, we propose a deep-learning-based model for predicting high-quality surface scanning electron microscopy (SEM) images with detailed surface morphology corresponding to unexplored process conditions. In addition, the reflectance value at 550 nm and reflection spectra from the generated (or original) SEM images were predicted. To obtain training data, stainless steel 304 specimens were processed with femtosecond laser pulses on a large process window consisting of 32 process conditions to obtain SEM images with various surface morphologies and corresponding reflection spectra. The image prediction model is based on a conditional generative adversarial network, which generates a surface SEM image from the laser fluence and scanning speed values. The reflectance prediction model was developed based on ResNet152 and Long-Short Term Memory network. The average accuracies for the pattern period and ripple width were 98.2 and 94.6 %, respectively, and the L2 norm error for the reflection spectra was less than 4 %. It was demonstrated that the reflection spectra can be accurately predicted using only surface images, which can also be accurately generated from process parameters

Characteristics of Resuspended Road Dust with Traffic and Atmospheric Environment in South Korea

Characterizing the influencing factors of resuspended dust on paved roads according to the atmospheric environment and traffic conditions is important to provide a basis for road atmospheric pollution control measures suitable for various road environments in the future. This study attempts to identify factors in the concentration of resuspended dust according to the level of road dust loading and PM10 emission characteristics according to atmospheric weather environment and traffic conditions using real-time vehicle-based resuspended PM10 concentration measuring equipment. This study mainly focuses on the following main topics: (1) the increased level of resuspended dust according to vehicle speed and silt loading (sL) level; (2) difference between atmospheric pollution at adjacent monitoring station concentration and background concentration levels on roads due to atmospheric weather changes; (3) the correlation between traffic and weather factors with resuspended dust levels; (4) the evaluation of resuspended dust levels by road section. Based on the results, the necessity of research to more appropriately set the focus of analysis in order to characterize the resuspended dust according to changes in the traffic and weather environment in urban areas is presented

Development of an Estimation Method for Depth of Spalling Damage in Concrete Pavement by Ultrasonic Velocity Measurement

As the amount of aged pavement increases, functional damage, such as spalling, occurs frequently on Portland Cement Concrete pavement (PCC) in South Korea. However, the existing management method does not properly reflect the scope of deterioration of the pavement causing early damage. To overcome the problem of the existing repair method, this study evaluated the deterioration of functional damage on the surface of the slab as soundness through ultrasonic velocity measurement method among non-destructive testing (NDT) techniques and suggested a method to estimate the depth of deterioration. To develop a method for estimating the depth of the deterioration a slab, a preliminary investigation was conducted to check the range of ultrasonic velocity of concrete pavement in South Korea and to evaluate the variability of NDT equipment. Based on the ultrasonic velocity, the sound rating of concrete pavement was graded from 5 for “very good” to 0 for “very poor”, and the tendency of the ultrasonic velocity to increase according to the depth of the deteriorated areas was confirmed

End-to-End Multi-Object Detection with a Regularized Mixture Model

Recent end-to-end multi-object detectors simplify the inference pipeline by removing hand-crafted processes such as non-maximum suppression (NMS). However, during training, they still heavily rely on heuristics and hand-crafted processes which deteriorate the reliability of the predicted confidence score. In this paper, we propose a novel framework to train an end-to-end multi-object detector consisting of only two terms: negative log-likelihood (NLL) and a regularization term. In doing so, the multi-object detection problem is treated as density estimation of the ground truth bounding boxes utilizing a regularized mixture density model. The proposed \textit{end-to-end multi-object Detection with a Regularized Mixture Model} (D-RMM) is trained by minimizing the NLL with the proposed regularization term, maximum component maximization (MCM) loss, preventing duplicate predictions. Our method reduces the heuristics of the training process and improves the reliability of the predicted confidence score. Moreover, our D-RMM outperforms the previous end-to-end detectors on MS COCO dataset.Comment: Accepted at ICML 202

Model-based cost-effectiveness analysis of oral antivirals against SARS-CoV-2 in Korea

OBJECTIVES Many countries have authorized the emergency use of oral antiviral agents for patients with mild-to-moderate cases of coronavirus disease 2019 (COVID-19). We assessed the cost-effectiveness of these agents for reducing the number of severe COVID-19 cases and the burden on Korea’s medical system. METHODS Using an existing model, we estimated the number of people who would require hospital/intensive care unit (ICU) admission in Korea in 2022. The treatment scenarios included (1) all adult patients, (2) elderly patients only, and (3) adult patients with underlying diseases only, compared to standard care. Based on the current health system capacity, we calculated the incremental costs per severe case averted and hospital admission for each scenario. RESULTS We estimated that 236,510 COVID-19 patients would require hospital/ICU admission in 2022 with standard care only. Nirmatrelvir/ritonavir (87% efficacy) was predicted to reduce this number by 80%, 24%, and 17% when targeting all adults, adults with underlying diseases, and elderly patients (25, 8, and 4%, respectively, for molnupiravir, with 30% efficacy). Nirmatrelvir/ritonavir use is likely to be cost-effective, with predicted costs of US$8,878, US$8,964, and US$1,454, per severe patient averted for the target groups listed above, respectively, while molnupiravir is likely to be less cost-effective, with costs of US$28,492, US$29,575, and US$7,915, respectively. CONCLUSIONS In Korea, oral treatment using nirmatrelvir/ritonavir for symptomatic COVID-19 patients targeting elderly patients would be highly cost-effective and would substantially reduce the demand for hospital admission to below the capacity of the health system if targeted to all adult patients instead of standard care

Porous silicon nanowires for lithium rechargeable batteries

Porous silicon nanowire is fabricated by a simple electrospinning process combined with a magnesium reduction; this material is investigated for use as an anode material for lithium rechargeable batteries. We find that the porous silicon nanowire electrode from the simple and scalable method can deliver a high reversible capacity with an excellent cycle stability. The enhanced performance in terms of cycling stability is attributed to the facile accommodation of the volume change by the pores in the interconnect and the increased electronic conductivity due to a multi-level carbon coating during the fabrication process.