Neural Spectro-polarimetric Fields

Modeling the spatial radiance distribution of light rays in a scene has been extensively explored for applications, including view synthesis. Spectrum and polarization, the wave properties of light, are often neglected due to their integration into three RGB spectral bands and their non-perceptibility to human vision. Despite this, these properties encompass substantial material and geometric information about a scene. In this work, we propose to model spectro-polarimetric fields, the spatial Stokes-vector distribution of any light ray at an arbitrary wavelength. We present Neural Spectro-polarimetric Fields (NeSpoF), a neural representation that models the physically-valid Stokes vector at given continuous variables of position, direction, and wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory efficiency, and preserves physically vital signals, factors that are crucial for representing the high-dimensional signal of a spectro-polarimetric field. To validate NeSpoF, we introduce the first multi-view hyperspectral-polarimetric image dataset, comprised of both synthetic and real-world scenes. These were captured using our compact hyperspectral-polarimetric imaging system, which has been calibrated for robustness against system imperfections. We demonstrate the capabilities of NeSpoF on diverse scenes

Measuring the social value of nuclear energy using contingent valuation methodology

As one of the promising energy sources for the next few decades, nuclear energy receives more attention than before as environmental issues become more important and the supply of fossil fuels becomes unstable. One of the reasons for this attention is based on the rapid innovation of nuclear technology which solves many of its technological constraints and safety issues. However, regardless of these rapid innovations, social acceptance for nuclear energy has been relatively low and unchanged. Consequently, the social perception has often been an obstacle to the development and execution of nuclear policy requiring enormous subsidies which are not based on the social value of nuclear energy. Therefore, in this study, we estimate the social value of nuclear energy-consumers’ willingness-to-pay for nuclear energy—using the Contingent Valuation Method (CVM) and suggest that the social value of nuclear energy increases approximately 68.5% with the provision of adequate information about nuclear energy to the public. Consequently, we suggest that the social acceptance management in nuclear policy development is important along with nuclear technology innovation

Classification of skin disease using deep learning neural networks with mobilenet V2 and LSTM

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning-based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2x lesser computations than the conven-tional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity

Disposition of youth in predicting sustainable development goals using the Neuro - fuzzy and random forest algorithms

This paper evaluates the inclination of Asian youth regarding the achievement of Sustainable Development Goals (SDGs). As the young population of a country holds the key to its future development, the authors of this study aim to provide evidence of the successful application of machine learning techniques to highlight their opinions about a sustainable future. This study’s timing is critical due to rapid developments in technology which are highlighting gaps between policy and the actual aspirations of citizens. Several studies indicate the superior predictive capabilities of neuro-fuzzy techniques. At the same time, Random Forest is gaining popularity as an advanced prediction and classification tool. This study aims to build on the previous research and compare the predictive accuracy of the adaptive neuro-fuzzy inference system (ANFIS) and Random Forest models for three categories of SGDs. The study also aims to explore possible differences of opinion regarding the importance of these categories among Asian and Serbian youth. The data used in this study were collected from 425 youth respondents in India. The results of data analysis show that ANFIS is better at predicting SDGs than the Random Forest model. The SDG preference among Asian and Serbian youth was found to be highest for the environmental pillar, followed by the social and economic pillars. This paper makes both a theoretical and a practical contribution to deepening understanding of the predictive power of the two models and to devising policies for attaining the SDGs by 2030

Clinical characteristics and prognosis of Korean patients with hepatocellular carcinoma with respect to etiology

Background/Aim The profile of patients with hepatocellular carcinoma (HCC) has changed globally; the role of etiology in predicting prognosis of HCC patients remains unclear. We aimed to analyze the characteristics and prognosis of Korean patients with HCC according to disease etiology. Methods This retrospective observational study included patients diagnosed with HCC between 2010 and 2014 in a single center in Korea. Patients with HCC aged <19 years old, had coinfection with other viral hepatitis, had missing follow-up data, were Barcelona Clinic Liver Cancer stage D, or died before 1 month were excluded. Results A total of 1,595 patients with HCC were analyzed; they were classified into the hepatitis B virus (HBV) group (1,183 [74.2%]), hepatitis C virus (HCV) group (146 [9.2%]), and non-B non-C (NBNC) group (266 [16.7%]). The median overall survival of all patients was 74 months. The survival rates at 1, 3, and 5 years were 78.8%, 62.0% and 54.9% in the HBV group; 86.0%, 64.0%, and 48.6% in the HCV group; and 78.4%, 56.5%, and 45.9% in the NBNC group, respectively. NBNC-HCC has a poorer prognosis than other causes of HCC. Survival was significantly longer in the HBV group with early-stage HCC than in the NBNC group. Furthermore, survival was shorter in patients with early-stage HCC and diabetes mellitus (DM) than in those without DM. Conclusions The etiology of HCC affected clinical characteristics and prognosis to some extent. NBNC-HCC patients showed shorter overall survival than viral-related HCC patients. Additionally, the presence of DM is an additional important prognostic factor in patients with early-stage HCC

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation.

It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks