Minimal training time in supervised retinal vessel segmentation

In this paper, we perform comparative analysis between different classifiers using the same experimental setup for supervised retinal vessel segmentation. The aim of this paper is to find supervised classifier that can obtain good segmentation accuracy with minimal training time. Minimizing the training time is essential when dealing with biomedical images. The more samples introduced to a learning model, the better it can adapt to the unseen data. The results indicate a trade-off between accuracy and training time can be obtained in a classifier trained by a Neural Network. When tested with a publicly available database, the learning model only requires less than 2 minutes in the learning phase and achieves overall accuracy of 94.54%

Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension

Multi-hop machine reading comprehension is a challenging task in natural language processing, which requires more reasoning ability and explainability. Spectral models based on graph convolutional networks grant the inferring abilities and lead to competitive results, however, part of them still face the challenge of analyzing the reasoning in a human-understandable way. Inspired by the concept of the Grandmother Cells in cognitive neuroscience, a spatial graph attention framework named crname, imitating the procedure was proposed. This model is designed to assemble the semantic features in multi-angle representations and automatically concentrate or alleviate the information for reasoning. The name "crname" is a metaphor for the pattern of the model: regard the subjects of queries as the start points of clues, take the reasoning entities as bridge points, and consider the latent candidate entities as the grandmother cells, and the clues end up in candidate entities. The proposed model allows us to visualize the reasoning graph and analyze the importance of edges connecting two entities and the selectivity in the mention and candidate nodes, which can be easier to be comprehended empirically. The official evaluations in open-domain multi-hop reading dataset WikiHop and Drug-drug Interactions dataset MedHop prove the validity of our approach and show the probability of the application of the model in the molecular biology domain

DeepSurveyCam — A Deep Ocean Optical Mapping System

Underwater photogrammetry and in particular systematic visual surveys of the deep sea are by far less developed than similar techniques on land or in space. The main challenges are the rough conditions with extremely high pressure, the accessibility of target areas (container and ship deployment of robust sensors, then diving for hours to the ocean floor), and the limitations of localization technologies (no GPS). The absence of natural light complicates energy budget considerations for deep diving flash-equipped drones. Refraction effects influence geometric image formation considerations with respect to field of view and focus, while attenuation and scattering degrade the radiometric image quality and limit the effective visibility. As an improvement on the stated issues, we present an AUV-based optical system intended for autonomous visual mapping of large areas of the seafloor (square kilometers) in up to 6000 m water depth. We compare it to existing systems and discuss tradeoffs such as resolution vs. mapped area and show results from a recent deployment with 90,000 mapped square meters of deep ocean floor

A UX model for the evaluation of learners' experience on lms platforms over time

Although user experience (UX) is dynamic and evolves over time, prior research reported that the learners' experience models developed so far were only for the static evaluation of learners' experiences. So far, no model has been developed for the dynamic summative evaluation of the UX of LMS platforms over time. The objective of this study is to build a UX model that will be used to evaluate learners' experience on LMS over time. The study reviewed relevant literature with the goal of conceptualizing a theoretical model. The Stimuli-Organism-Response (SOR) framework was deployed to model the experience engineering process. To verify the model, 6 UX experts were involved. The model was also validated using a quasi-experimental design involving 900 students. The evaluation was conducted in four time points, once a week for four weeks. From the review, a conceptual UX model was developed for the evaluation of learners' experience with LMS design over time. The outcome of the model verification shows that the experts agreed that the model is adequate for the evaluation of learners' experience on LMS. The results of the model validation indicate that the model was highly statistically significant over time (Week 1: x2(276) = 273 I 9.339, Week2: x2(276) = 23419.626, Week3: x2(276) =18941.900, Week4: x2(276) = 27580.397, p=000<0.01). Each design quality had strong positive effects on the learners' cognitive, sensorimotor and affective states respectively. Furthermore, each of the three organismic states: cognitive, sensorimotor, and affective, had strong positive influence on learners' overall learning experience. These results imply that the experience engineering process was successful. The study fills a significant gap in knowledge by contributing a novel UX model for the evaluation of learners' experience on LMS platforms over time. UX quality assurance practitioners can also utilize the model in the verification and validation of learner experience over tim

Migration of young Croatian scientists

The migration from Croatia during transitions period assumed a form of drain rather than circulation of labor. The exact scale of brain drain remains unknown because the topic of external migration of scientists had been neglected for years and is still insufficiently investigated. Scientists who migrate have high educational qualifications as well as specific motives (self–development and educational reasons for leaving). The multiple regression analysis (N=536) and predictors achieved show that potential migrants are mostly young scientists that are satisfied with their positioning within the current system of opportunities but not satisfied with their perspectives in Croatia. E-researches and the new kinds of collaboration help the transformation of brain drain process into brain circulation. The EU has been developing positive immigration policies, especially favorable to highly qualified immigrants from the field of natural sciences and computer technology. It could be expected that Croatia will experience in the next few years both circulation of labor and outgoing/ingoing brain drain; the latter from surrounding Balkan countries. The problem is still persisting inadequate social valorization of science subsystem in Croatia

Official Congressional Directory for the use of the United States Congress

Congressional Directory. 5 Dec. SMD 13 (pts. 1-3), 51-1, vl, 765p. [2697] Duties of the CIA listed

Portland Daily Press: September 15,1868

https://digitalmaine.com/pdp_1868/1271/thumbnail.jp