Nevus-Like Appearance of Primary Malignant Melanoma of the Esophagus

The primary malignant melanoma of the esophagus (PMME) is a rare malignant disease, accounting for only 0.1–0.2% of all esophageal neoplasms, and the majority of the patients are diagnosed at advanced stages with poor prognosis. We present here a case of 56-year-old woman with epigastric pain and her endoscopic finding revealed several flat and black pigmented mucosal lesions within the distal portion of the esophagus which looked like flat nevus. The histopathology and immunohistochemical profile of the tissue specimens were diagnostic of malignant melanoma

Convergence of an iterative algorithm for systems of variational inequalities and nonexpansive mappings with applications

AbstractIn this paper, we consider the problem of convergence of an iterative algorithm for a system of generalized variational inequalities and a nonexpansive mapping. Strong convergence theorems are established in the framework of real Banach spaces

Development Study of Evaluation Indexes for Internet Business Models

Under the prerequisite that virtual markets need a unit of analysis other than traditional markets, business model is set as the unit of analysis of this research. In this research, in order to help choose Internet business model that creates the most value, evaluation indexes for valuecreation potential of Internet business models are developed. As research methods, deductive method and analytic hierarchy process (AHP) are used. As the first stage of deduction process, the improved profits and the reduced costs, which factors are classified and quantitative and qualitative evaluation indexes of two dimensions are extracted by related studies. Then, evaluation indexes are corrected, complemented and verified through the expert interview, and analytic hierarchy is documented. As the result, the improved profits dimension outweighed the reduced costs dimension, and each qualitative effect outweighed each quantitative effect. The overall consistency index showed to be 2%, which means that all the experts are determined to have rational consistency

Strong convergence of shrinking projection methods for quasi-ϕ-nonexpansive mappings and equilibrium problems

AbstractThe purpose of this paper is to consider the convergence of a shrinking projection method for a finite family of quasi-ϕ-nonexpansive mappings and an equilibrium problem. Strong convergence theorems are established in a uniformly smooth and strictly convex Banach space which also enjoys the Kadec–Klee property

The avian-specific small heat shock protein HSP25 is a constitutive protector against environmental stresses during blastoderm dormancy

ArticleScientific Reports. 6: 36704 (2016).journal articl

Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning

While many FSCIL studies have been undertaken, achieving satisfactory performance, especially during incremental sessions, has remained challenging. One prominent challenge is that the encoder, trained with an ample base session training set, often underperforms in incremental sessions. In this study, we introduce a novel training framework for FSCIL, capitalizing on the generalizability of the Contrastive Language-Image Pre-training (CLIP) model to unseen classes. We achieve this by formulating image-object-specific (IOS) classifiers for the input images. Here, an IOS classifier refers to one that targets specific attributes (like wings or wheels) of class objects rather than the image's background. To create these IOS classifiers, we encode a bias prompt into the classifiers using our specially designed module, which harnesses key-prompt pairs to pinpoint the IOS features of classes in each session. From an FSCIL standpoint, our framework is structured to retain previous knowledge and swiftly adapt to new sessions without forgetting or overfitting. This considers the updatability of modules in each session and some tricks empirically found for fast convergence. Our approach consistently demonstrates superior performance compared to state-of-the-art methods across the miniImageNet, CIFAR100, and CUB200 datasets. Further, we provide additional experiments to validate our learned model's ability to achieve IOS classifiers. We also conduct ablation studies to analyze the impact of each module within the architecture.Comment: 8 pages, 4 figures, 4 table