The Devil of Face Recognition is in the Noise

The growing scale of face recognition datasets empowers us to train strong convolutional networks for face recognition. While a variety of architectures and loss functions have been devised, we still have a limited understanding of the source and consequence of label noise inherent in existing datasets. We make the following contributions: 1) We contribute cleaned subsets of popular face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new large-scale noise-controlled IMDb-Face dataset. 2) With the original datasets and cleaned subsets, we profile and analyze label noise properties of MegaFace and MS-Celeb-1M. We show that a few orders more samples are needed to achieve the same accuracy yielded by a clean subset. 3) We study the association between different types of noise, i.e., label flips and outliers, with the accuracy of face recognition models. 4) We investigate ways to improve data cleanliness, including a comprehensive user study on the influence of data labeling strategies to annotation accuracy. The IMDb-Face dataset has been released on https://github.com/fwang91/IMDb-Face.Comment: accepted to ECCV'1

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

The research on railway freight volume forecast is of great significance to the allocation of railway freight transport resources, the formulation of transport plans and the organization of railway freight transport. This study, by fully mining the railway freight ticket data information, put forward the precise forecast model of railway freight volume under different types of freight. Firstly, the railway freight ticket data are cleaned, regulated and integrated, and the time series of the daily number of railway freight trains are constructed, get the trend, periodicity and holiday data of railway traffic data, and set the parameters of Chinese holidays and rest days according to the demand characteristics of different categories. Secondly, the forecasting result of the Prophet is taken as a cooperative parameter. DeepAR is used to forecast, and a combined model of the Prophet-DeepAR is constructed. Finally, the combined model was validated with Shanghai Railway Bureau data from January 1, 2015 to December 31, 2018 for the food and tobacco category, and with Prophet-DeepAR, LSTM, Wavelet, BILSTM, and Prophet-LSTM, prophet-wavelet and Prophet-Bilstm are used to compare the prediction results. The results show that the Prophet-DeepAR model can extract the multi-dimensional periodicity of freight traffic and mine the trend information of freight traffic, and get the prediction result with high precision. It has better accuracy than other models

A sense of unfairness reduces charitable giving to a third -party:Evidence from behavioral and electrophysiological data

Unfairness commonly impacts human economic decision-making. However, whether inequity aversion impairs pro-social decisions and the corresponding neural processes, is poorly understood. Here, we conducted two experiments to investigate whether human gifting behavior and brain activity are affected by inequity aversion. In experiment 1, participants played as a responder in a joint donation game in which they were asked to decide whether or not to accept a donation proposal made by the proposer. In experiment 2, participants played a donation game similar to experiment 1, but the charity projects were classified as high-deservingness and low-deservingness projects. The results in both of two experiments showed that the participants were more likely to reject an unfair donation proposal and the late positivity potential (LPP)/P300 elicited by fair offers was more positive than moderately unfair and highly unfair offers regardless of charity deservingness. Moreover, after principal component analysis, the differences in P300 amplitude between fair and highly unfair conditions were positively correlated with the acceptance rates in experiment 2. Taken together, our study revealed that late positivity (LPP/P300) reflected the evaluation of fairness of proposals, and could predict subsequent pro-social decisions. This study is the first to demonstrate that inequity aversion reduces pro-social motivation to help innocent third party

A Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation

Human gaze is essential for various appealing applications. Aiming at more accurate gaze estimation, a series of recent works propose to utilize face and eye images simultaneously. Nevertheless, face and eye images only serve as independent or parallel feature sources in those works, the intrinsic correlation between their features is overlooked. In this paper we make the following contributions: 1) We propose a coarse-to-fine strategy which estimates a basic gaze direction from face image and refines it with corresponding residual predicted from eye images. 2) Guided by the proposed strategy, we design a framework which introduces a bi-gram model to bridge gaze residual and basic gaze direction, and an attention component to adaptively acquire suitable fine-grained feature. 3) Integrating the above innovations, we construct a coarse-to-fine adaptive network named CA-Net and achieve state-of-the-art performances on MPIIGaze and EyeDiap.Comment: 9 pages, 7figures, AAAI-2

Endoscopic Approaches to the Treatment of Variceal Hemorrhage in Hemodialysis-Dependent Patients

Background. Esophagogastric variceal hemorrhage leads to challenging situation in chronic kidney disease patients on maintenance hemodialysis. Aims. To determine the safety and efficacy of endoscopic approaches to patients with hemodialysis-dependent concomitant with esophagogastric varices. Methods. Medical records were reviewed from January 1, 2004, to December 31, 2015, in our hospital. Five consecutive hemodialysis-dependent patients with variceal hemorrhage who underwent endoscopic treatments were retrospectively studied. Results. The median age of the patients was 54 years (range 34–67 years) and the median follow-up period was 21.3 months (range 7–134 months). All the patients received a total of three times heparin-free hemodialysis 24 hours before and no more than 24 hours and 72 hours after endoscopic treatment. They successfully had endoscopic variceal ligation, endoscopic injection sclerotherapy, and/or N-butyl cyanoacrylate injection. The short-term efficacy is satisfying and long-term follow-up showed episodes of rebleeding. Conclusions. Endoscopic approaches are the alternative options in the treatment of upper gastroenterology variceal hemorrhage in hemodialysis-dependent patients without severe complications

Prompt-enhanced Hierarchical Transformer Elevating Cardiopulmonary Resuscitation Instruction via Temporal Action Segmentation

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a custom CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, thereby devising an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone of the model preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which accounts for the excavation of the segmentation pipeline on the CPR dataset. In general, we unprecedentedly probe into a feasible pipeline that genuinely elevates the CPR instruction qualification via action segmentation in conjunction with cutting-edge deep learning techniques. Associated experiments advocate our implementation with multiple metrics surpassing 91.0%.Comment: Transformer for Cardiopulmonary Resuscitatio

Therapeutic potential of human umbilical cord mesenchymal stem cells in the treatment of rheumatoid arthritis

Introduction: Rheumatoid arthritis (RA) is a T-cell-mediated systemic autoimmune disease, characterized by synovium inflammation and articular destruction. Bone marrow mesenchymal stem cells (MSCs) could be effective in the treatment of several autoimmune diseases. However, there has been thus far no report on umbilical cord (UC)-MSCs in the treatment of RA. Here, potential immunosuppressive effects of human UC-MSCs in RA were evaluated. Methods: The effects of UC-MSCs on the responses of fibroblast-like synoviocytes (FLSs) and T cells in RA patients were explored. The possible molecular mechanism mediating this immunosuppressive effect of UC-MSCs was explored by addition of inhibitors to indoleamine 2,3-dioxygenase (IDO), Nitric oxide (NO), prostaglandin E2 (PGE2), transforming growth factor beta 1 (TGF-beta 1) and interleukin 10 (IL-10). The therapeutic effects of systemic infusion of human UC-MSCs on collagen-induced arthritis (CIA) in a mouse model were explored. Results: In vitro, UC-MSCs were capable of inhibiting proliferation of FLSs from RA patients, via IL-10, IDO and TGF-beta 1. Furthermore, the invasive behavior and IL-6 secretion of FLSs were also significantly suppressed. On the other hand, UC-MSCs induced hyporesponsiveness of T cells mediated by PGE2, TGF-beta 1 and NO and UC-MSCs could promote the expansion of CD4(+) Foxp3(+) regulatory T cells from RA patients. More importantly, systemic infusion of human UC-MSCs reduced the severity of CIA in a mouse model. Consistently, there were reduced levels of proinflammatory cytokines and chemokines (TNF-alpha, IL-6 and monocyte chemoattractant protein-1) and increased levels of the anti-inflammatory/regulatory cytokine (IL-10) in sera of UC-MSCs treated mice. Moreover, such treatment shifted Th1/Th2 type responses and induced Tregs in CIA. Conclusions: In conclusion, human UC-MSCs suppressed the various inflammatory effects of FLSs and T cells of RA in vitro, and attenuated the development of CIA in vivo, strongly suggesting that UC-MSCs might be a therapeutic strategy in RA. In addition, the immunosuppressive activitiy of UC-MSCs could be prolonged by the participation of Tregs.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000287517000020&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701RheumatologySCI(E)PubMed64ARTICLE6R2101

Texture Recognition Based on Perception Data from a Bionic Tactile Sensor

Texture recognition is important for robots to discern the characteristics of the object surface and adjust grasping and manipulation strategies accordingly. It is still challenging to develop texture classification approaches that are accurate and do not require high computational costs. In this work, we adopt a bionic tactile sensor to collect vibration data while sliding against materials of interest. Under a fixed contact pressure and speed, a total of 1000 sets of vibration data from ten different materials were collected. With the tactile perception data, four types of texture recognition algorithms are proposed. Three machine learning algorithms, including support vector machine, random forest, and K-nearest neighbor, are established for texture recognition. The test accuracy of those three methods are 95%, 94%, 94%, respectively. In the detection process of machine learning algorithms, the asamoto and polyester are easy to be confused with each other. A convolutional neural network is established to further increase the test accuracy to 98.5%. The three machine learning models and convolutional neural network demonstrate high accuracy and excellent robustness