Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains

Rapid discovery of new diseases, such as COVID-19 can enable a timely epidemic response, preventing the large-scale spread and protecting public health. However, limited research efforts have been taken on this problem. In this paper, we propose a contrastive learning-based modeling approach for COVID-19 coughing and breathing pattern discovery from non-COVID coughs. To validate our models, extensive experiments have been conducted using four large audio datasets and one image dataset. We further explore the effects of different factors, such as domain relevance and augmentation order on the pre-trained models. Our results show that the proposed model can effectively distinguish COVID-19 coughing and breathing from unlabeled data and labeled non-COVID coughs with an accuracy of up to 0.81 and 0.86, respectively. Findings from this work will guide future research to detect an outbreak of a new disease early.Comment: Accepted by Proceedings of INTERSPEECH 202

A secured framework for SDN-based edge computing in IoT-enabled healthcare system

The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record*

Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images

Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resources and have attracted extensive research interests in industry and research communities. We present a new method for real-time SR for computer graphics, namely Super-Resolution by Predicting Offsets (SRPO). Our algorithm divides the image into two parts for processing, i.e., sharp edges and flatter areas. For edges, different from the previous SR methods that take the anti-aliased images as inputs, our proposed SRPO takes advantage of the characteristics of rasterized images to conduct SR on the rasterized images. To complement the residual between HR and low-resolution (LR) rasterized images, we train an ultra-efficient network to predict the offset maps to move the appropriate surrounding pixels to the new positions. For flat areas, we found simple interpolation methods can already generate reasonable output. We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image. The proposed network only contains 8,434 parameters and can be accelerated by network quantization. Extensive experiments show that the proposed SRPO can achieve superior visual effects at a smaller computational cost than the existing state-of-the-art methods.Comment: This article has been accepted by ECCV202

Security and blockchain convergence with internet of multimedia things : current trends, research challenges and future directions

The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications. © 2020 Elsevier Lt

Neuroepithelial Body Microenvironment Is a Niche for a Distinct Subset of Clara-Like Precursors in the Developing Airways

Clara cells of mammalian airways have multiple functions and are morphologically heterogeneous. Although Notch signaling is essential for the development of these cells, it is unclear how Notch influences Clara cell specification and if diversity is established among Clara cell precursors. Here we identify expression of the secretoglobin Scgb3a2 and Notch activation as early events in a program of secretory cell fate determination in developing murine airways. We show that Scgb3a2 expression in vivo is Notch-dependent at early stages and ectopically induced by constitutive Notch1 activation, and also that in vitro Notch signaling together with the pan-airway transcription factor Ttf1 (Nkx2.1) synergistically regulate secretoglobin gene transcription. Furthermore, we identified a subpopulation of secretory precursors juxtaposed to presumptive neuroepithelial bodies (NEBs), distinguished by their strong Scgb3a2 and uroplakin 3a (Upk3a) signals and reduced Ccsp (Scgb1a1) expression. Genetic ablation of Ascl1 prevented NEB formation and selectively interfered with the formation of this subpopulation of cells. Lineage labeling of Upk3a-expressing cells during development showed that these cells remain largely uncommitted during embryonic development and contribute to Clara and ciliated cells in the adult lung. Together, our findings suggest a role for Notch in the induction of a Clara cell-specific program of gene expression, and reveals that the NEB microenvironment in the developing airways is a niche for a distinct subset of Clara-like precursors.Molecular and Cellular Biolog

Manipulation of electronic property of epitaxial graphene on SiC substrate by Pb intercalation

Manipulating the electronic properties of graphene has been a subject of great interest since it can aid material design to extend the applications of graphene to many different areas. In this paper, we systematically investigate the effect of lead (Pb) intercalation on the structural and electronic properties of epitaxial graphene on the SiC(0001) substrate. We show that the band structure of Pb-intercalated few-layer graphene can be effectively tuned through changing intercalation conditions, such as coverage, location of Pb, and the initial number of graphene layers. Lead intercalation at the interface between the buffer layer (BL) and the SiC substrate decouples the BL from the substrate and transforms the BL into a p-doped graphene layer. We also show that Pb atoms tend to donate electrons to neighboring layers, leading to an n-doping graphene layer and a small gap in the Dirac cone under a sufficiently high Pb coverage. This paper provides useful guidance for manipulating the electronic properties of graphene layers on the SiC substrate

Crosstalk-free achromatic full Stokes imaging polarimetry metasurface enabled by polarization-dependent phase optimization

Imaging polarimetry is one of the most widely used analytical technologies for object detection and analysis. To date, most metasurface-based polarimetry techniques are severely limited by narrow operating bandwidths and inevitable crosstalk, leading to detrimental effects on imaging quality and measurement accuracy. Here, we propose a crosstalk-free broadband achromatic full Stokes imaging polarimeter consisting of polarization-sensitive dielectric metalenses, implemented by the principle of polarization-dependent phase optimization. Compared with the single-polarization optimization method, the average crosstalk has been reduced over three times under incident light with arbitrary polarization ranging from 9 μm to 12 μm, which guarantees the measurement of the polarization state more precisely. The experimental results indicate that the designed polarization-sensitive metalenses can effectively eliminate the chromatic aberration with polarization selectivity and negligible crosstalk. The measured average relative errors are 7.08%, 8.62%, 7.15%, and 7.59% at 9.3, 9.6, 10.3, and 10.6 μm, respectively. Simultaneously, the broadband full polarization imaging capability of the device is also verified. This work is expected to have potential applications in wavefront detection, remote sensing, light-field imaging, and so forth

Microsatellite Development for an Endangered Bream Megalobrama pellegrini (Teleostei, Cyprinidae) Using 454 Sequencing

Megalobrama pellegrini is an endemic fish species found in the upper Yangtze River basin in China. This species has become endangered due to the construction of the Three Gorges Dam and overfishing. However, the available genetic data for this species is limited. Here, we developed 26 polymorphic microsatellite markers from the M. pellegrini genome using next-generation sequencing techniques. A total of 257,497 raw reads were obtained from a quarter-plate run on 454 GS-FLX titanium platforms and 49,811 unique sequences were generated with an average length of 404 bp; 24,522 (49.2%) sequences contained microsatellite repeats. Of the 53 loci screened, 33 were amplified successfully and 26 were polymorphic. The genetic diversity in M. pellegrini was moderate, with an average of 3.08 alleles per locus, and the mean observed and expected heterozygosity were 0.47 and 0.51, respectively. In addition, we tested cross-species amplification for all 33 loci in four additional breams: M. amblycephala, M. skolkovii, M. terminalis, and Sinibrama wui. The cross-species amplification showed a significant high level of transferability (79%–97%), which might be due to their dramatically close genetic relationships. The polymorphic microsatellites developed in the current study will not only contribute to further conservation genetic studies and parentage analyses of this endangered species, but also facilitate future work on the other closely related species