NaMemo: Enhancing Lecturers' Interpersonal Competence of Remembering Students' Names

Addressing students by their names helps a teacher to start building rapport with students and thus facilitates their classroom participation. However, this basic yet effective skill has become rather challenging for university lecturers, who have to handle large-sized (sometimes exceeding 100) groups in their daily teaching. To enhance lecturers' competence in delivering interpersonal interaction, we developed NaMemo, a real-time name-indicating system based on a dedicated face-recognition pipeline. This paper presents the system design, the pilot feasibility test, and our plan for the following study, which aims to evaluate NaMemo's impacts on learning and teaching, as well as to probe design implications including privacy considerations.Comment: DIS '20 Companio

PT{\cal PT} Symmetry and PT{\cal PT}-Enhanced Quantum Sensing in a Spin-Boson System

Open systems, governed by non-Hermitian Hamiltonians, evolve fundamentally differently from their Hermitian counterparts and facilitate many unusual applications. Although non-Hermitian but parity-time (${\cal PT}$) symmetric dynamics has been realized in a variety of classical or semiclassical systems, its fully quantum-mechanical demonstration is still lacking. Here we ingeniously engineer a highly controllable anti-Hermitian spin-boson model in a circuit quantum-electrodynamical structure composed of a decaying artificial atom (pseudospin) interacting with a bosonic mode stored in a microwave resonator. Besides observing abrupt changes in the spin-boson entanglement evolution and bifurcation transition in quantum Rabi splitting, we demonstrate super-sensitive quantum sensing by mapping the observable of interest to a hitherto unobserved ${\cal PT}$-manifested entanglement evolution. These results pave the way for exploring non-Hermitian entanglement dynamics and ${\cal PT}$-enhanced quantum sensing empowered by nonclassical correlations.Comment: 25 pages, 19 figure

Decoding the spermatogonial stem cell niche under physiological and recovery conditions in adult mice and humans

The intricate interaction between spermatogonial stem cell (SSC) and testicular niche is essential for maintaining SSC homeostasis; however, this interaction remains largely uncharacterized. In this study, to characterize the underlying signaling pathways and related paracrine factors, we delineated the intercellular interactions between SSC and niche cell in both adult mice and humans under physiological conditions and dissected the niche-derived regulation of SSC maintenance under recovery conditions, thus uncovering the essential role of C-C motif chemokine ligand 24 and insulin-like growth factor binding protein 7 in SSC maintenance. We also established the clinical relevance of specific paracrine factors in human fertility. Collectively, our work on decoding the adult SSC niche serves as a valuable reference for future studies on the aetiology, diagnosis, and treatment of male infertility.</p

Genetic diversity fuels gene discovery for tobacco and alcohol use

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury(1-4). These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries(5). Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.Peer reviewe

Degradation Mechanism of Concrete Subjected to External Sulfate Attack: Comparison of Different Curing Conditions

Sulfate induced degradation of concrete brings great damage to concrete structures in saline or offshore areas. The degradation mechanism of cast-in-situ concrete still remains unclear. This paper investigates the degradation process and corresponding mechanism of cast-in-situ concrete when immersed in sulfate-rich corrosive environments. Concrete samples with different curing conditions were prepared and immersed in sulfate solutions for 12 months to simulate the corrosion of precast and cast-in-situ concrete structures, respectively. Tests regarding the changes of physical, chemical, and mechanical properties of concrete samples were conducted and recorded continuously during the immersion. Micro-structural and mineral methods were performed to analyze the changes of concrete samples after immersion. Results indicate that the corrosion process of cast-in-situ concrete is much faster than the degradation of precast concrete. Chemical attack is the main cause of degradation for both precast and cast-in-situ concrete. Concrete in the environment with higher sulfate concentration suffers more severe degradation. The water/cement ratio has a significant influence on the durability of concrete. A lower water/cement ratio results in obviously better resistance against sulfate attack for both precast and cast-in-situ concrete

The application of deep learning algorithm reconstruction in low tube voltage coronary CT angiography

Objective: To compare the quality of low tube voltage coronary CT angiography (CCTA) images reconstructed with deep learning-based image reconstruction (DLIR) and with filter back projection (FBP) and with adaptive statistical iterative reconstruction-veo (ASiR-V). Methods: One hundred patients who underwent CCTA were included. The CCTA tube voltage were set as 70 kVp (n=50, BMI&#x02264;26 kg/m2) and 80 kVp (n=50, BMI&gt;26 kg/m2) according to body mass index(BMI). The images were reconstructed with FBP (Group A), ASIR-V 50% (Group B), DLIR at medium (DLIR-M, Group C) and high DLIR(DLIR-H, Group D) levels, respectively. Objective evaluation indice including CT attenuation, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio(CNR) were measured or calculated between groups, and Likert 5-point scale was adopted for subjective image quality assessment. Results: There were significant differences in image noise, SNR, CNR among the 4 groups(P&lt;0.05), and Group D had the highest SNR and CNR, and lowest noise. There was no significant difference between Group C and Group D in subjective scores, but Group C and D both had higher subjective scores than those of Group A and B (P&lt;0.05). Conclusions: For low tube voltage CCTA, images reconstructed with DLIR generate higher quality,and DLIR may be suitable to apply in low tube voltage CCTA

Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case

Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images

A Recurrent Adaptive Network: Balanced Learning for Road Crack Segmentation with High-Resolution Images

Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks. In this paper, we first evaluate the mainstream convolutional neural network structure in the road crack segmentation task. Second, inspired by the second law of thermodynamics, an improved method called a recurrent adaptive network for a pixelwise road crack segmentation task is proposed to solve the extreme imbalance between positive and negative samples. We achieved a flow between precision and recall, similar to the conduction of temperature repetition. During the training process, the recurrent adaptive network (1) dynamically evaluates the degree of imbalance, (2) determines the positive and negative sampling rates, and (3) adjusts the loss weights of positive and negative features. By following these steps, we established a channel between precision and recall and kept them balanced as they flow to each other. A dataset of high-resolution road crack images with annotations (named HRRC) was built from a real road inspection scene. The images in HRRC were collected on a mobile vehicle measurement platform by high-resolution industrial cameras and were carefully labeled at the pixel level. Therefore, this dataset has sufficient data complexity to objectively evaluate the real performance of convolutional neural networks in highway patrol scenes. Our main contribution is a new method of solving the data imbalance problem, and the method of guiding model training by analyzing precision and recall is experimentally demonstrated to be effective. The recurrent adaptive network achieves state-of-the-art performance on this dataset

Polymorphism and stability of nanostructures of three types of collagens from bovine flexor tendon, rat tail, and tilapia skin

Many types of collagens from different sources have been used in food, pharmaceutics, biomedicine, tissue engineering, etc. Their physicochemical properties have been widely investigated to understand their behaviors and functions. However, the polymorphism and stability of collagen nanostructures have not been systematically studied. In the current manuscript, polymorphism and stability of nanostructures of three types of collagens from bovine flexor tendon, rat tail, and tilapia skin are characterized by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), atomic force microscopy (AFM), and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectrometry. SDS-PAGE results show loading volumes have no influence on the protein bands of three types of collagens except the intensity, whereas collagen concentrations have obvious effects. AFM results show all the three types of collagens have multiple nanostructures, which are concentration-dependent. AFM results also show collagen nanostructures change with incubation time at 37 °C. According to the ATR-FTIR results, the nanostructures changes are associated with the change of protein secondary structures. These results demonstrate three types of collagens have different nanostructures, stability, protein secondary structures, and SDS-PAGE behaviors. This work also indicates that the nanostructures and secondary structures of collagens can be controlled by adjusting concentration and incubation time for the three types of collagens, which provide simple ways to design and prepared desired nanostructures of collagen-based foods. It will be also beneficial to fundamental understanding of the collagen nanoscale structure formation in different collagen-based foods

Resorbable polymer electrospun nanofibers: history, shapes and application for tissue engineering

Resorbable polymer electrospun nanofiber-based materials/devices have high surface-to-volume ratio and often have a porous structure with excellent pore interconnectivity, which are suitable for growth and development of different types of cells. Due to the huge advantages of both resorbable polymers and electrospun nanofibers, resorbable polymer electrospun nanofibers (RPENs) have been widely applied in the field of tissue engineering. In this paper, we will mainly introduce RPENs for tissue engineering. Firstly, the electrospinning technique and electrospun nanofiber architectures are briefly introduced. Secondly, the application of RPENs in the field of tissue engineering is mainly reviewed. Finally, the advantages and disadvantages of RPENs for tissue engineering are discussed. This review will provide a comprehensive guide to apply resorbable polymer electrospun nanofibers for tissue engineering