UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition

Recently proposed robust 3D face alignment methods establish either dense or sparse correspondence between a 3D face model and a 2D facial image. The use of these methods presents new challenges as well as opportunities for facial texture analysis. In particular, by sampling the image using the fitted model, a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map is always incomplete. In this paper, we propose a framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images. To this end, we first gather complete UV maps by fitting a 3D Morphable Model (3DMM) to various multiview image and video datasets, as well as leveraging on a new 3D dataset with over 3,000 identities. Second, we devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model. We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance. Experiments on both controlled and in-the-wild UV datasets prove the effectiveness of our adversarial UV completion model. We achieve state-of-the-art verification accuracy, $94.05\%$, under the CFP frontal-profile protocol only by combining pose augmentation during training and pose discrepancy reduction during testing. We will release the first in-the-wild UV dataset (we refer as WildUV) that comprises of complete facial UV maps from 1,892 identities for research purposes

Application of Flipped Class Model in Teaching Elemental Compounds of Inorganic Chemistry-The Example of Teaching Practice on Carbon Group Elements

The flipped class model is applied to the teaching process of carbon group elements, which fully reflects the effective teaching of "student-oriented and teacher-led". The students complete the "learning first" according to their self-study task list before class. The class's QQ group, wechat group and other social softwares communicate and interact with teachers and students online, and the cloud class topic database is used to detect the feedback effect of learning first. In class, teachers collect the feedback information of students' "learning first", carefully design the teaching process of "fixed teaching" problem inquiry, and encourage students to participate actively. After class, teachers and students can further communicate and interact online. Students can conduct self-awareness, self-reflection, self-evaluation and self-regulation of the learned knowledge unit, complete knowledge construction and improve students' metacognitive ability. Keywords: Flipping class model; Mindmap; Elemental compounds; Inorganic chemistry DOI: 10.7176/JEP/14-18-05 Publication date:June 30th 202

Micro/nanoscale magnetic robots for biomedical applications

Magnetic small-scale robots are devices of great potential for the biomedical field because of the several benefits of this method of actuation. Recent work on the development of these devices has seen tremendous innovation and refinement toward ​improved performance for potential clinical applications. This review briefly details recent advancements in small-scale robots used for biomedical applications, covering their design, fabrication, applications, and demonstration of ability, and identifies the gap in studies and the difficulties that have persisted in the optimization of the use of these devices. In addition, alternative biomedical applications are also suggested for some of the technologies that show potential for other functions. This study concludes that although the field of small-scale robot research is highly innovative ​there is need for more concerted efforts to improve functionality and reliability of these devices particularly in clinical applications. Finally, further suggestions are made toward ​the achievement of commercialization for these devices

Personalized Estimate of Chemotherapy-Induced Nausea and Vomiting: Development and External Validation of a Nomogram in Cancer Patients Receiving Highly/Moderately Emetogenic Chemotherapy.

Chemotherapy-induced nausea and vomiting (CINV) is presented in over 30% of cancer patients receiving highly/moderately emetogenic chemotherapy (HEC/MEC). The currently recommended antiemetic therapy is merely based on the emetogenic level of chemotherapy, regardless of patient's individual risk factors. It is, therefore, critical to develop an approach for personalized management of CINV in the era of precision medicine.A number of variables were involved in the development of CINV. In the present study, we pooled the data from 2 multi-institutional investigations of CINV due to HEC/MEC treatment in Asian countries. Demographic and clinical variables of 881 patients were prospectively collected as defined previously, and 862 of them had full documentation of variables of interest. The data of 548 patients from Chinese institutions were used to identify variables associated with CINV using multivariate logistic regression model, and then construct a personalized prediction model of nomogram; while the remaining 314 patients out of China (Singapore, South Korea, and Taiwan) entered the external validation set. C-index was used to measure the discrimination ability of the model.The predictors in the final model included sex, age, alcohol consumption, history of vomiting pregnancy, history of motion sickness, body surface area, emetogenicity of chemotherapy, and antiemetic regimens. The C-index was 0.67 (95% CI, 0.62-0.72) for the training set and 0.65 (95% CI, 0.58-0.72) for the validation set. The C-index was higher than that of any single predictor, including the emetogenic level of chemotherapy according to current antiemetic guidelines. Calibration curves showed good agreement between prediction and actual occurrence of CINV.This easy-to-use prediction model was based on chemotherapeutic regimens as well as patient's individual risk factors. The prediction accuracy of CINV occurrence in this nomogram was well validated by an independent data set. It could facilitate the assessment of individual risk, and thus improve the personalized management of CINV

Self-Retracting Motion of Graphite Microflakes

We report the observation of a novel phenomenon, the self-retracting motion of graphite, in which tiny flakes of graphite, after being displaced to various suspended positions from islands of highly orientated pyrolytic graphite, retract back onto the islands under no external influences. Our repeated probing and observing such flakes of various sizes indicate the existence of a critical size of flakes, approximately 35 micrometer, above which the self-retracting motion does not occur under the operation. This helps to explain the fact that the self-retracting motion of graphite has not been reported, because samples of natural graphite are typical larger than this critical size. In fact, reports of this phenomenon have not been found in the literature for single crystals of any kinds. A model that includes the static and dynamic shear strengths, the van der Waals interaction force, and the edge dangling bond interaction effect, was used to explain the observed phenomenon. These findings may conduce to create nano-electromechanical systems with a wide range of mechanical operating frequency from mega to giga hertzs

Engineering multiple defect sites on ultrathin graphitic carbon nitride for efficiently photocatalytic conversion of lignin into monomeric aromatics via selective C–C bond scission

Lignin depolymerisation via photocatalytic cleavage of the selective interunit linkage in lignin could be a sustainable approach to produce monomeric aromatic chemicals. However, the insufficient investigation of interunit C–C bond fragmentation has obstructed the rational design of efficient photocatalytic system and further limit the yields of aromatic monomers from lignin depolymerisation. Herein, this work developed the ultrathin g-C3N4 with multiple defective sites by simple self-assembly process and in-situ thermal gas-shocking/etching process to catalyse the cleavage of lignin C–C bonds under visible light irradiation. Compared with the pristine g-C3N4, the developed g-C3N4 photocatalyst exhibited a superior catalytic activity (improved 102 %) and selectivity (∼90 %) in the cleavage of C–C bonds in lignin. This study demonstrated that the defects construction and ultrathin structure can optimise the electronic structures of g-C3N4 for better separation and transfer of photoinduced charges. And the control experiments and DFT calculation indicated that the created defect sites can promote the generation of essential reactive radicals (e.g., the activation of O2) and radical intermediates (C–H activation). The present work provides useful insights for the rational use of defect engineering in designing the efficient photocatalytic system for the conversion of lignin into aromatic monomers via the C–C bond cleavage