More comprehensive facial inversion for more effective expression recognition

Facial expression recognition (FER) plays a significant role in the ubiquitous application of computer vision. We revisit this problem with a new perspective on whether it can acquire useful representations that improve FER performance in the image generation process, and propose a novel generative method based on the image inversion mechanism for the FER task, termed Inversion FER (IFER). Particularly, we devise a novel Adversarial Style Inversion Transformer (ASIT) towards IFER to comprehensively extract features of generated facial images. In addition, ASIT is equipped with an image inversion discriminator that measures the cosine similarity of semantic features between source and generated images, constrained by a distribution alignment loss. Finally, we introduce a feature modulation module to fuse the structural code and latent codes from ASIT for the subsequent FER work. We extensively evaluate ASIT on facial datasets such as FFHQ and CelebA-HQ, showing that our approach achieves state-of-the-art facial inversion performance. IFER also achieves competitive results in facial expression recognition datasets such as RAF-DB, SFEW and AffectNet. The code and models are available at https://github.com/Talented-Q/IFER-master

Improvements to Self-Supervised Representation Learning for Masked Image Modeling

This paper explores improvements to the masked image modeling (MIM) paradigm. The MIM paradigm enables the model to learn the main object features of the image by masking the input image and predicting the masked part by the unmasked part. We found the following three main directions for MIM to be improved. First, since both encoders and decoders contribute to representation learning, MIM uses only encoders for downstream tasks, which ignores the impact of decoders on representation learning. Although the MIM paradigm already employs small decoders with asymmetric structures, we believe that continued reduction of decoder parameters is beneficial to improve the representational learning capability of the encoder . Second, MIM solves the image prediction task by training the encoder and decoder together , and does not design a separate task for the encoder . To further enhance the performance of the encoder when performing downstream tasks, we designed the encoder for the tasks of comparative learning and token position prediction. Third, since the input image may contain background and other objects, and the proportion of each object in the image varies, reconstructing the tokens related to the background or to other objects is not meaningful for MIM to understand the main object representations. Therefore we use ContrastiveCrop to crop the input image so that the input image contains as much as possible only the main objects. Based on the above three improvements to MIM, we propose a new model, Contrastive Masked AutoEncoders (CMAE). We achieved a Top-1 accuracy of 65.84% on tinyimagenet using the ViT-B backbone, which is +2.89 outperforming the MAE of competing methods when all conditions are equal. Code will be made available

Problems and Countermeasures of Intelligent Elderly Care Service in the Context of Fewer Children in China

With the intensification of population aging and the implementation of the three-child policy, the elderly care pressure of Chinese families continues to rise. Therefore, accelerating the construction of a new intelligent elderly care service model is an important measure to actively respond to population aging, ease the burden of family elderly care and promote high-quality economic development. In view of this, this study analyzed the intelligent elderly care service to explore the relevant countermeasures of the intelligent elderly care service in the context of fewer children

Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification

Masked autoencoders (MAEs) have displayed significant potential in the classification and semantic segmentation of medical images in the last year. Due to the high similarity of human tissues, even slight changes in medical images may represent diseased tissues, necessitating fine-grained inspection to pinpoint diseased tissues. The random masking strategy of MAEs is likely to result in areas of lesions being overlooked by the model. At the same time, inconsistencies between the pre-training and fine-tuning phases impede the performance and efficiency of MAE in medical image classification. To address these issues, we propose a medical supervised masked autoencoder (MSMAE) in this paper. In the pre-training phase, MSMAE precisely masks medical images via the attention maps obtained from supervised training, contributing to the representation learning of human tissue in the lesion area. During the fine-tuning phase, MSMAE is also driven by attention to the accurate masking of medical images. This improves the computational efficiency of the MSMAE while increasing the difficulty of fine-tuning, which indirectly improves the quality of MSMAE medical diagnosis. Extensive experiments demonstrate that MSMAE achieves state-of-the-art performance in case with three official medical datasets for various diseases. Meanwhile, transfer learning for MSMAE also demonstrates the great potential of our approach for medical semantic segmentation tasks. Moreover, the MSMAE accelerates the inference time in the fine-tuning phase by 11.2% and reduces the number of floating-point operations (FLOPs) by 74.08% compared to a traditional MAE

Problems and Countermeasures of Intelligent Elderly Care Service in the Context of Fewer Children in China

With the intensification of population aging and the implementation of the three-child policy, the elderly care pressure of Chinese families continues to rise. Therefore, accelerating the construction of a new intelligent elderly care service model is an important measure to actively respond to population aging, ease the burden of family elderly care and promote high-quality economic development. In view of this, this study analyzed the intelligent elderly care service to explore the relevant countermeasures of the intelligent elderly care service in the context of fewer children

Integration of proteomic and metabolomic characterization in atrial fibrillation-induced heart failure

Abstract Background The exact mechanism of atrial fibrillation (AF)-induced heart failure (HF) remains unclear. Proteomics and metabolomics were integrated to in this study, as to describe AF patients’ dysregulated proteins and metabolites, comparing patients without HF to patients with HF. Methods Plasma samples of 20 AF patients without HF and another 20 with HF were analyzed by multi-omics platforms. Proteomics was performed with data independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS), as metabolomics was performed with LC-MS/MS platform. Proteomic and metabolomic results were analyzed separately and integrated using univariate statistical methods, multivariate statistical methods or machine learning model. Results We found 35 up-regulated and 15 down-regulated differentially expressed proteins (DEPs) in AF patients with HF compared to AF patients without HF. Moreover, 121 up-regulated and 14 down-regulated differentially expressed metabolites (DEMs) were discovered in HF patients compared to AF patients without HF. An integrated analysis of proteomics and metabolomics revealed several significantly enriched pathways, including Glycolysis or Gluconeogenesis, Tyrosine metabolism and Pentose phosphate pathway. A total of 10 DEPs and DEMs selected as potential biomarkers provided excellent predictive performance, with an AUC of 0.94. In addition, subgroup analysis of HF classification was performed based on metabolomics, which yielded 9 DEMs that can distinguish between AF and HF for HF classification. Conclusions This study provides novel insights to understanding the mechanisms of AF-induced HF progression and identifying novel biomarkers for prognosis of AF with HF by using metabolomics and proteomics analyses

A Self-Healing PVA-Linked Phytic Acid Hydrogel-Based Electrolyte for High-Performance Flexible Supercapacitors

Flexible supercapacitors can be ideal flexible power sources for wearable electronics due to their ultra-high power density and high cycle life. In daily applications, wearable devices will inevitably cause damage or short circuit during bending, stretching, and compression. Therefore, it is necessary to develop proper energy storage devices to meet the requirements of various wearable electronic devices. Herein, Poly(vinyl alcohol) linked various content of phytic acid (PVA-PAx) hydrogels are synthesized with high transparency and high toughness by a one-step freeze-thaw method. The effects of different raw material ratios and agents on the ionic conductivity and mechanical properties of the hydrogel electrolyte are investigated. The PVA-PA21% with 2 M H2SO4 solution (PVA-PA21%-2 M H2SO4) shows a high ionic conductivity of 62.75 mS cm−1. Based on this, flexible supercapacitors fabricated with PVA-PA21%-2 M H2SO4 hydrogel present a high specific capacitance at 1 A g−1 after bending at 90° (64.8 F g−1) and for 30 times (67.3 F g−1), respectively. Moreover, the device shows energy densities of 13.5 Wh kg−1 and 14.0 Wh kg−1 at a power density of 300 W kg−1 after bending at 90° and for 30 times during 10,000 cycles. It provides inspiration for the design and development of electrolytes for related energy electrochemical devices

A Self-Healing PVA-Linked Phytic Acid Hydrogel-Based Electrolyte for High-Performance Flexible Supercapacitors

Flexible supercapacitors can be ideal flexible power sources for wearable electronics due to their ultra-high power density and high cycle life. In daily applications, wearable devices will inevitably cause damage or short circuit during bending, stretching, and compression. Therefore, it is necessary to develop proper energy storage devices to meet the requirements of various wearable electronic devices. Herein, Poly(vinyl alcohol) linked various content of phytic acid (PVA-PAx) hydrogels are synthesized with high transparency and high toughness by a one-step freeze-thaw method. The effects of different raw material ratios and agents on the ionic conductivity and mechanical properties of the hydrogel electrolyte are investigated. The PVA-PA21% with 2 M H2SO4 solution (PVA-PA21%-2 M H2SO4) shows a high ionic conductivity of 62.75 mS cm−1. Based on this, flexible supercapacitors fabricated with PVA-PA21%-2 M H2SO4 hydrogel present a high specific capacitance at 1 A g−1 after bending at 90° (64.8 F g−1) and for 30 times (67.3 F g−1), respectively. Moreover, the device shows energy densities of 13.5 Wh kg−1 and 14.0 Wh kg−1 at a power density of 300 W kg−1 after bending at 90° and for 30 times during 10,000 cycles. It provides inspiration for the design and development of electrolytes for related energy electrochemical devices

Generation of two MEN1 knockout lines from a human embryonic stem cell line

The MEN1 gene is cytogenetically located at 11q13.1 and encodes the nuclear protein menin, which is involved in cell proliferation, apoptosis, differentiation, and metabolism. Here, we generated two MEN1 knockout human embryonic stem cell lines, WAe001-A-4 and WAe001-A-5, by targeting exon-2 and exon-9 of MEN1 using the CRISPR/Cas9 technique. These cell lines maintained their pluripotency, in vitro differentiation potential, normal morphology, and karyotype. These human MEN1-mutated cell lines not only enlarge the pool of lab resources but also provide ideal models to dissect the detailed physio-pathological roles of the menin protein. (C) 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license