Artificial intelligence (AI) has transformed medical imaging, driving advancements in radiology and endoscopy. Semantic segmentation, a pixel-level technique crucial for delineating pathological features, become cornerstone of digital pathology. Pathology segmentation AI models are often trained using annotations generated by pathologists. Despite the meticulous care typically exercised, these frequently contain empirical label noise. However, specific types noise pathology data their impact...

In eukaryotes, the process of intron removal from nuclear pre-mRNA is performed by spliceosome, a dynamic molecular machine composed small ribonucleoproteins (snRNPs; U1, U2, U4, U5, and U6) dozens other protein splicing factors. The U6 snRNP contains snRNA proteins Prp24 Lsm2-8 heteroheptamer. A key feature modified 3' end, which in S. cerevisiae (yeast) phosphate. plays an essential role splicing, must be completely disassembled for to occur. Once finished, then reassembled participate...

Reference fatigue crack growth rate curves for austenitic stainless steels in pressurized water reactors (PWR) environments were prescribed JSME S NA1-2004(1) Japan. The reference curve PWR environment had been determined as a function of stress intensity factor range, temperature, load rising time and ratio. In order to confirm the applicability under high ratio, low propagation tests 316, 316 weld metal, 304 metal carried out. It is concluded that applicable predict this study test conditions.

The fatigue life reduces remarkably with reduction in strain rate simulated light water reactor (LWR) but the effects of wave form on this are still not clear. This paper provides data obtained from stepwise change tests, sine tests and holding tests. varying can be estimated very well by modified approach (MRA) method case step wise changing as shown authors’ previous papers [1, 2, 3, 4, 5]. In wave, however, is much less compared to that predicted MRA method. mechanism such difference...

In recent years, the development of deep learning has contributed to various areas machine learning. However, requires a huge amount data train model, and collection techniques such as web crawling can easily generate incorrect labels. If training dataset noisy labels, generalization performance significantly decreases. Some works have successfully divided into samples with clean labels ones light these studies, we propose novel expansion framework robustly models on attention mechanisms....

The fatigue life in elevated temperature water is strongly affected by chemistry, and strain rate. effects of these parameters on reduction have been investigated experimentally. In transient condition an actual plant, however, such as rate are not constant. order to evaluate damage plant the basis experimental results under constant condition, modified approach method was developed. As a part EFT (Environmental Fatigue Tests) project, study conducted applicability case where varied...

In this work, we will consider the dimension reduction of set time series, such as economic data, to find meaningful basis vector for and indicate which data use vector. Usually each series is analyzed independently in economics but here analyze simultaneously. Since some are measured positive values want decompose them a mixture parts, apply non-negative matrix factorization data. Non-negative can compress dimensions by approximating with product two matrices. The matrices called...

Abstract In recent years, the dimensionality reduction has become more important as number of dimensions data used in various tasks such regression and classification increased. As popular nonlinear methods, t-distributed stochastic neighbor embedding (t-SNE) uniform manifold approximation projection (UMAP) have been proposed. However, former outputs only one low-dimensional space determined by t-distribution latter is difficult to control distribution distance between each pair samples...

The advantage of emergence is that various solutions are emerged. However, it takes large computation cost to emerge them due the number iterations simulation required. So we try reduces without losing variety by introducing abstraction technique in artificial intelligence. This paper presents an isomorphism based reinforcement learning actions solutions. Isomorphism one concepts enumerative combinatorics mathematics. First explain actions, then behaviors. isomorphic behaviors which perform...

It is known that the fatigue life in elevated temperature water substantially reduced compared with air (1–4). Although key parameters have an effect on lives are strain rate and PWR environment, it necessary to consider other factors for accurate evaluation. The effects of many been investigated experimentally EFT project Japan Nuclear Energy Safety Organization (JNES). Many tests done carbon, low alloy, stainless steel nickel-based environmental equation evaluates quantitative factor...

In the recent years, deep learning has achieved significant results in various areas of machine learning. Deep requires a huge amount data to train model, and collection techniques such as web crawling have been developed. However, there is risk that these may generate incorrect labels. If model for image classification trained on dataset with noisy labels, generalization performance significantly decreases. This problem called Learning Noisy Labels (LNL). One researches LNL, DivideMix [1],...

In recent years, deep neural networks (DNNs) have made a significant impact on variety of research fields and applications. One drawback DNNs is that it requires huge amount dataset for training. Since very expensive to ask experts label the data, many non-expert data collection methods such as web crawling been proposed. However, created by non-experts often contain corrupted labels, trained are unreliable. an enormous number parameters, tends overfit noisy resulting in poor generalization...

The fatigue life of steel light water reactor (LWR) in elevated temperature is affected by the composition environmental water, decreasing strain rate and increasing temperature. effects these parameters on reduction have been investigated experimentally. One problem to be discussed fact that previous studies which leaded main results were generally executed experimental constant. On other hand, an actual plant, such as are changing transient. In order evaluate damage plant basis under...

In recent years, deeper and wider neural networks have shown excellent performance in computer vision tasks, while their enormous amount of parameters results increased computational cost overfitting. Several methods been proposed to compress the size without reducing network performance. Network pruning can reduce redundant unnecessary from a network. Knowledge distillation transfer knowledge smaller networks. The obtained by these is bounded predefined Neural architecture search has...

Deep learning has achieved great success in recent years various areas of machine learning, but it requires an enormous amount data for training. Data collection techniques such as web crawling have been developed to solve this problem, these run the risk generating incorrect labels. When a deep model is trained image classification problem with dataset that contains noisy labels, its generalization performance severely degraded. To we proposed two approaches: one correct labels using graph...

In recent years, deeper and wider neural networks have shown excellent performance in computer vision tasks, while their enormous amount of parameters results increased computational cost overfitting. Several methods been proposed to compress the size without reducing network performance. Network pruning can reduce redundant unnecessary from a network. Knowledge distillation transfer knowledge smaller networks. The obtained by these is bounded predefined Neural architecture search has...