Image Anomaly Detection and Localization with Position and Neighborhood Information

Anomaly detection and localization are essential in many areas, where collecting enough anomalous samples for training is almost impossible. To overcome this difficulty, many existing methods use a pre-trained network to encode input images and non-parametric modeling to estimate the encoded feature distribution. In the modeling process, however, they overlook that position and neighborhood information affect the distribution of normal features. To use the information, in this paper, the normal distribution is estimated with conditional probability given neighborhood features, which is modeled with a multi-layer perceptron network. At the same time, positional information can be used by building a histogram of representative features at each position. While existing methods simply resize the anomaly map into the resolution of an input image, the proposed method uses an additional refine network that is trained from synthetic anomaly images to perform better interpolation considering the shape and edge of the input image. For the popular industrial dataset, MVTec AD benchmark, the experimental results show \textbf{99.52\%} and \textbf{98.91\%} AUROC scores in anomaly detection and localization, which is state-of-the-art performance

Ductile Fracture Simulation of Full-scale Circumferential Cracked Pipes: (II) Stainless Steel

AbstractThis paper reports ductile fracture simulation of full-scale circumferentially cracked pipes using finite element (FE) damage analysis. In the structural integrity, without experimental investigations or with few ones, it is not an easy task to properly evaluate the crack initiation and crack propagation of large-scale components with a crack-like defect. Unfortunately, from an economic perspective, performing experiments of large-scale components would be consequently unfavorable. For these reasons, ductile fracture simulation using FE damage analysis to predict crack behavior is one efficient way to replace the test procedures. In order to simulate ductile tearing of large-scale cracked pipes, element-size-dependent critical damage model based on the stress-modified fracture strain model is proposed. To evaluate fracture behavior of full-scale cracked pipes, tensile and C(T) specimens are calibrated by FE analysis technique. Tensile properties and fracture toughness of stainless steel at 288oC are taken from Battelle Pipe Fracture Encyclopedia. After calibrations, simulated results of the full-scale pipes with a circumferential crack are compared with test data to validate the proposed method

Green growth and green new deal policies in Korea

노트 : A Paper for the GURN/ITUC workshop on "A Green Economy that Works for Social Progress

Estimation of Fluctuation Characterizations by USV-Operation Simulations in Sea State 3

This paper proposes a method based on simulation techniques for fluctuation characterizations of unmanned surface vehicle (USV) operations under Sea State 3. In order to simulate the operations of a USV in Sea State 3, we generated the data of sea surfaces using linear wave theory and utilized the motion equation. Fluctuation analysis results by the proposed simulation method could provide crucial information for designing the stabilization system for the critical equipment on a USV. Through these works, it was verified that the design specifications such as range of motion, maximum speed, and acceleration could be estimated using the simulation data

Sound of Story: Multi-modal Storytelling with Audio

Storytelling is multi-modal in the real world. When one tells a story, one may use all of the visualizations and sounds along with the story itself. However, prior studies on storytelling datasets and tasks have paid little attention to sound even though sound also conveys meaningful semantics of the story. Therefore, we propose to extend story understanding and telling areas by establishing a new component called "background sound" which is story context-based audio without any linguistic information. For this purpose, we introduce a new dataset, called "Sound of Story (SoS)", which has paired image and text sequences with corresponding sound or background music for a story. To the best of our knowledge, this is the largest well-curated dataset for storytelling with sound. Our SoS dataset consists of 27,354 stories with 19.6 images per story and 984 hours of speech-decoupled audio such as background music and other sounds. As benchmark tasks for storytelling with sound and the dataset, we propose retrieval tasks between modalities, and audio generation tasks from image-text sequences, introducing strong baselines for them. We believe the proposed dataset and tasks may shed light on the multi-modal understanding of storytelling in terms of sound. Downloading the dataset and baseline codes for each task will be released in the link: https://github.com/Sosdatasets/SoS_Dataset.Comment: Findings of EMNLP 2023, project: https://github.com/Sosdatasets/SoS_Dataset

Development of Freeze-Thaw Tolerant Lactobacillus rhamnosus GG by Adaptive Laboratory Evolution

The industrial application of microorganisms as starters or probiotics requires their preservation to assure viability and metabolic activity. Freezing is routinely used for this purpose, but the cold damage caused by ice crystal formation may result in severe decrease in microbial activity. In this study, adaptive laboratory evolution (ALE) technique was applied to a lactic acid bacterium to select tolerant strains against freezing and thawing stresses. Lactobacillus rhamnosus GG was subjected to freeze-thaw-growth (FTG) for 150 cycles with four replicates. After 150 cycles, FTG-evolved mutants showed improved fitness (survival rates), faster growth rate, and shortened lag phase than those of the ancestor. Genome sequencing analysis of two evolved mutants showed genetic variants at distant loci in six genes and one intergenic space. Loss-of-function mutations were thought to alter the structure of the microbial cell membrane (one insertion in cls), peptidoglycan (two missense mutations in dacA and murQ), and capsular polysaccharides (one missense mutation in wze), resulting in an increase in cellular fluidity. Consequently, L. rhamnosus GG was successfully evolved into stress-tolerant mutants using FTG-ALE in a concerted mode at distal loci of DNA. This study reports for the first time the functioning of dacA and murQ in freeze-thaw sensitivity of cells and demonstrates that simple treatment of ALE designed appropriately can lead to an intelligent genetic changes at multiple target genes in the host microbial cell

A multicenter, prospective, randomized, controlled trial evaluating the safety and efficacy of intracoronary cell infusion mobilized with granulocyte colony-stimulating factor and darbepoetin after acute myocardial infarction: study design and rationale of the 'MAGIC cell-5-combination cytokine trial'

<p>Abstract</p> <p>Background</p> <p>Bone marrow derived stem/progenitor cell transplantation after acute myocardial infarction is safe and effective for improving left ventricular systolic function. However, the improvement of left ventricular systolic function is limited. This study will evaluate novel stem/progenitor cell therapy with combination cytokine treatment of the long-acting erythropoietin analogue, darbepoetin, and granulocyte colony-stimulating factor (G-CSF) in patients with acute myocardial infarction.</p> <p>Methods</p> <p>The 'MAGIC Cell-5-Combination Cytokine Trial' is a multicenter, prospective, randomized, 3-arm, controlled trial with blind evaluation of the endpoints. A total of 116 patients will randomly receive one of the following three treatments: an intravenous darbepoetin infusion and intracoronary infusion of peripheral blood stem cells mobilized with G-CSF (n = 58), an intracoronary infusion of peripheral blood stem cells mobilized with G-CSF alone (n = 29), or conventional therapy (n = 29) at phase I. Patients with left ventricular ejection fraction < 45% at 6 months, in the patients who received stem cell therapy at phase I, will receive repeated cell therapy at phase II. The objectives of this study are to evaluate the safety and efficacy of combination cytokine therapy with erythropoietin and G-CSF (phase I) and repeated progenitor/stem cell treatment (phase II).</p> <p>Discussion</p> <p>This is the first study to evaluate the safety and efficacy of combination cytokine based progenitor/stem cell treatment.</p> <p>Trial registration</p> <p><url>http://www.ClinicalTrials.gov</url> identifier: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00501917">NCT00501917</a>.</p

Identification of DNA methylation changes associated with human gastric cancer

<p>Abstract</p> <p>Background</p> <p>Epigenetic alteration of gene expression is a common event in human cancer. DNA methylation is a well-known epigenetic process, but verifying the exact nature of epigenetic changes associated with cancer remains difficult.</p> <p>Methods</p> <p>We profiled the methylome of human gastric cancer tissue at 50-bp resolution using a methylated DNA enrichment technique (methylated CpG island recovery assay) in combination with a genome analyzer and a new normalization algorithm.</p> <p>Results</p> <p>We were able to gain a comprehensive view of promoters with various CpG densities, including CpG Islands (CGIs), transcript bodies, and various repeat classes. We found that gastric cancer was associated with hypermethylation of 5' CGIs and the 5'-end of coding exons as well as hypomethylation of repeat elements, such as short interspersed nuclear elements and the composite element SVA. Hypermethylation of 5' CGIs was significantly correlated with downregulation of associated genes, such as those in the <it>HOX </it>and histone gene families. We also discovered long-range epigenetic silencing (LRES) regions in gastric cancer tissue and identified several hypermethylated genes (<it>MDM2</it>, <it>DYRK2</it>, and <it>LYZ</it>) within these regions. The methylation status of CGIs and gene annotation elements in metastatic lymph nodes was intermediate between normal and cancerous tissue, indicating that methylation of specific genes is gradually increased in cancerous tissue.</p> <p>Conclusions</p> <p>Our findings will provide valuable data for future analysis of CpG methylation patterns, useful markers for the diagnosis of stomach cancer, as well as a new analysis method for clinical epigenomics investigations.</p

Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models

The South Korean government has recently focused on environmental protection efforts to improve water quality which has been degraded by nonpoint sources of water pollution from runoff. In order to take care of environmental issues, many physically-based models have been used. However, the physically-based models take a large amount of work to carry out site simulations, and there is a need to find faster and more efficient approaches. For an alternative approach for sediment management using the physically-based models, the machine learning-based models were used for estimating sediment trapping efficiency of vegetative filter strips. The seven nonlinear regression algorithms of machine learning models (e.g., decision tree, multilayer perceptron, k-nearest neighbors, support vector machine, random forest, AdaBoost and gradient boosting) were applied to select the model which best estimates the sediment trapping efficiency of vegetative filter strips. The sediment trapping efficiencies calculated by the machine learning models showed similar results as those of vegetative filter strip modeling system (VFSMOD-W) model. As a result of the accuracy evaluation among the seven machine learning models, the multilayer perceptron model-derived the best fit with VFSMOD-W model. It is expected that the sediment trapping efficiency of the vegetative filter strips in various cases in agricultural fields in South Korea can be predicted easier, faster and accurately by the machine learning models developed in this study. Machine learning models can be used to evaluate sediment trapping efficiency without complicated physically-based model design and high computational cost. Therefore, decision makers can maximize the quality of their outputs by minimizing their efforts in the decision-making process.</p