In this paper we consider the modeling of measurement error for fund returns data. In particular, given access to a time-series of discretely observed log-returns and the associated maximum over the observation period...
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In this study, we use the log-linear link function and propose a generalized fused Lasso (GFL) Poisson regression model in which the nonlinear trend is discretely represented by categorical covariates in the additive ...
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Train is a popular mode of ground transportation due to the ability to accommodate a large number of passenger, save time, avoid traffic congestion, offer cost-effective fares, and provide a relatively high level of s...
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Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) ...
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ISBN:
(数字)9798350356816
ISBN:
(纸本)9798350356823
Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions.
An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of ...
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An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.
Models for the observational appearance of astrophysical black holes rely critically on accurate general-relativistic ray tracing and radiation transport to compute the intensity measured by a distant observer. In thi...
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Models for the observational appearance of astrophysical black holes rely critically on accurate general-relativistic ray tracing and radiation transport to compute the intensity measured by a distant observer. In this paper, we illustrate how the choice of coordinates and initial conditions affect this process. In particular, we show that propagating rays from the camera to the source leads to different solutions if the spatial part of the momentum of the photon points towards the horizon or away from it. In doing this, we also show that coordinates that are well suited for numerical general-relativistic magnetohydrodynamic (GRMHD) simulations are typically not optimal for generic ray tracing. We discuss the implications for black hole images and show that radiation transport in optimal and nonoptimal spacetime coordinates lead to the same images up to numerical errors and algorithmic choices.
作者:
Arai, KeisukeTakai, YuukiDepartment of Mathematics
School of Science and Technology for Future Life Tokyo Denki University 5 Senju Asahi-cho Adachi-ku Tokyo120-8551 Japan Mathematics
Science Data Science and AI Program Academic Foundations Programs Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan
In this paper, we give an equivalent condition for an abelian variety over a finite field to have multiplication by a quaternion algebra over a number field. We prove the result by combining Tate’s classification of ...
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The genetic information coded in DNA leads to trait innovation via a gene regulatory network(GRN)in ***,we developed a conserved non-coding element interpretation method to integrate multi-omics data into gene regulat...
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The genetic information coded in DNA leads to trait innovation via a gene regulatory network(GRN)in ***,we developed a conserved non-coding element interpretation method to integrate multi-omics data into gene regulatory network(CNEReg)to investigate the ruminant multi-chambered stomach *** generated paired expression and chromatin accessibility data during rumen and esophagus development in sheep,and revealed 1601 active ruminantspecific conserved non-coding elements(active-RSCNEs).To interpret the function of these activeRSCNEs,we defined toolkit transcription factors(TTFs)and modeled their regulation on rumenspecific genes via batteries of active-RSCNEs during *** developmental GRN revealed 18 TTFs and 313 active-RSCNEs regulating 7 rumen functional ***,6 TTFs(OTX1,SOX21,HOXC8,SOX2,TP63,and PPARG),as well as 16 active-RSCNEs,functionally distinguished the rumen from the *** study provides a systematic approach to understanding how gene regulation evolves and shapes complex traits by putting evo-devo concepts into practice with developmental multi-omics data.
Functional data analysis (FDA) and ensemble learning can be powerful tools for analyzing complex environmental time series. Recent literature has highlighted the key role of diversity in enhancing accuracy and reducin...
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