E^2TAD: An Energy-Efficient Tracking-based Action Detector

Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of video action detection in object detection, i.e., firstly generating person proposals and then classifying their actions. However, none of the existing solutions could provide fine-grained action detection to the "who-when-where-what" level. This paper presents a tracking-based solution to accurately and efficiently localize predefined key actions spatially (by predicting the associated target IDs and locations) and temporally (by predicting the time in exact frame indices). This solution won first place in the UAV-Video Track of 2021 Low-Power Computer Vision Challenge (LPCVC)

When Internet of Things meets blockchain: challenges in distributed consensus

Blockchain has been regarded as a promising technology for IoT, since it provides significant solutions for decentralized networks that can address trust and security concerns, high maintenance cost problems, and so on. The decentralization provided by blockchain can be largely attributed to the use of a consensus mechanism, which enables peer-to-peer trading in a distributed manner without the involvement of any third party. This article starts by introducing the basic concept of blockchain and illustrating why a consensus mechanism plays an indispensable role in a blockchain enabled IoT system. Then we discuss the main ideas of two famous consensus mechanisms, PoW and PoS, and list their limitations in IoT. Next, two mainstream DAG based consensus mechanisms, the Tangle and Hashgraph, are reviewed to show why DAG consensus is more suitable for IoT system than PoW and PoS. Potential issues and challenges of DAG based consensus mechanisms to be addressed in the future are discussed in the last section

Transcriptome analysis of the hepatopancreas from the Litopenaeus vannamei infected with different flagellum types of Vibrio alginolyticus strains

Vibrio alginolyticus, one of the prevalently harmful Vibrio species found in the ocean, causes significant economic damage in the shrimp farming industry. Its flagellum serves as a crucial virulence factor in the invasion of host organisms. However, the processes of bacteria flagella recognition and activation of the downstream immune system in shrimp remain unclear. To enhance comprehension of this, a ΔflhG strain was created by in-frame deletion of the flhG gene in V. alginolyticus strain HN08155. Then we utilized the transcriptome analysis to examine the different immune responses in Litopenaeus vannamei hepatopancreas after being infected with the wild type and the mutant strains. The results showed that the ΔflhG strain, unlike the wild type, lost its ability to regulate flagella numbers negatively and displayed multiple flagella. When infected with the hyperflagella-type strain, the RNA-seq revealed the upregulation of several immune-related genes in the shrimp hepatopancreas. Notably, two C-type lectins (CTLs), namely galactose-specific lectin nattectin and macrophage mannose receptor 1, and the TNF receptor-associated factor (TRAF) 6 gene were upregulated significantly. These findings suggested that C-type lectins were potentially involved in flagella recognition in shrimp and the immune system was activated through the TRAF6 pathway after flagella detection by CTLs

Effect of rubber particles and fibers on the dynamic compressive behavior of novel ultra-lightweight cement composites:Numerical simulations and metamodeling

This paper presents, first, a finite element (FE) model for a rubberized ultra-lightweight cement composite (RULCC), which uses a modified Holmquist-Johnson-Concrete (H-J-C) constitutive law that is calibrated and validated by new Split Hopkinson pressure bar (SHPB) tests on the material. The validated FE model is used then as the core of a cloud computing platform using a multi node cloud simulation framework to carry out the parametric simulations, which generate required data to develop a meta-model to predict the dynamic impact strength of the RULCC. Design of experiment (DoE) and Generic Programming techniques are the main instruments in developing meta-models with reduced size of data. Finally, a meta-model of explicit expression, which is the first of its kind and considers the effect of rubber ratio, fiber ratio and dynamic impact strain rate, is proposed to predict the dynamic impact strength of the RULCC

Photometric Variability in the CSTAR Field: Results From the 2008 Data Set

The Chinese Small Telescope ARray (CSTAR) is the first telescope facility built at Dome A, Antarctica. During the 2008 observing season, the installation provided long-baseline and high-cadence photometric observations in the i-band for 18,145 targets within 20 deg2 CSTAR field around the South Celestial Pole for the purpose of monitoring the astronomical observing quality of Dome A and detecting various types of photometric variability. Using sensitive and robust detection methods, we discover 274 potential variables from this data set, 83 of which are new discoveries. We characterize most of them, providing the periods, amplitudes and classes of variability. The catalog of all these variables is presented along with the discussion of their statistical properties.Comment: 38 pages, 11 figures, 4 tables; Accepted for publication in ApJ