Advances in machine learning age progression technology offer the unique opportunity to better understand the public's perception on the aging face. To compare how observers perceive attractiveness and traditional gender traits in faces created with a machine learning model. Eight surveys were developed, each with 10 sets of photographs that were progressively aged with a machine learning model. Respondents rated attractiveness and masculinity or femininity of each photograph using a sliding scale (range: 0-100). Mean attractiveness scores were calculated and compared between men and women as well as between age groups. A total of 315 respondents (51% men, 49% women) completed the survey. Accuracy of the facial age progression model was 85%. Females were considered significantly less attractive (-10.43, < 0.01) and less feminine (-7.59, < 0.01) per decade with the greatest drop over age 40 years. Male attractiveness and masculinity were relatively preserved until age 50 years where attractiveness scores were significantly lower (-5.45, = 0.39). In this study, observers were found to perceive attractiveness at older ages differently between men and women.
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http://dx.doi.org/10.1089/fpsam.2022.0273 | DOI Listing |
Philos Trans A Math Phys Eng Sci
December 2024
Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC, Campus Universitat Illes Balears, Palma de Mallorca 07122, Spain.
Quantum optical networks are instrumental in addressing the fundamental questions and enable applications ranging from communication to computation and, more recently, machine learning (ML). In particular, photonic artificial neural networks (ANNs) offer the opportunity to exploit the advantages of both classical and quantum optics. Photonic neuro-inspired computation and ML have been successfully demonstrated in classical settings, while quantum optical networks have triggered breakthrough applications such as teleportation, quantum key distribution and quantum computing.
View Article and Find Full Text PDFElife
December 2024
Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
An unprecedented amount of SARS-CoV-2 data has been accumulated compared with previous infectious diseases, enabling insights into its evolutionary process and more thorough analyses. This study investigates SARS-CoV-2 features as it evolved to evaluate its infectivity. We examined viral sequences and identified the polarity of amino acids in the receptor binding motif (RBM) region.
View Article and Find Full Text PDFFront Vet Sci
December 2024
Information Systems Department, University of Haifa, Haifa, Israel.
Facial landmarks, widely studied in human affective computing, are beginning to gain interest in the animal domain. Specifically, landmark-based geometric morphometric methods have been used to objectively assess facial expressions in cats, focusing on pain recognition and the impact of breed-specific morphology on facial signaling. These methods employed a 48-landmark scheme grounded in cat facial anatomy.
View Article and Find Full Text PDFFront Immunol
December 2024
Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
Introduction: Necroptosis has emerged as a promising biomarker for predicting immunotherapy responses across various cancer types. Its role in modulating immune activation and therapeutic outcomes offers potential for precision oncology.
Methods: A comprehensive pan-cancer analysis was performed using bulk RNA sequencing data to develop a necroptosis-related gene signature, termed Necroptosis.
Front Comput Neurosci
December 2024
Department of Mathematics, Informatics and Geoscience, University of Trieste, Trieste, Italy.
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