A peculiar lens-shaped structure observed in the South China Sea

Lens-shaped structures within thermocline potentially play a significant role in subsurface transport of mass, heat, and salt in the global ocean. Whilst such structures have been documented in many oceanic regions, none has been observed in the China Seas. This study reports on observations of a lens-shaped structure within thermocline in the southwestern South China Sea in September 2007. This structure had a maximum thickness of approximately 60 m and a horizontal extent exceeding 220 km. This lens was peculiar in that its size is larger than most similar structures documented in the literature. The lens core was characterized by well-mixed water with higher temperature (~28.8 °C), lower salinity (~33.3) and lower potential vorticity (PV) compared to the surrounding waters. Based on an ocean reanalysis, possible generation mechanism of the lens is explored by examining the evolution of surface and subsurface thermohaline properties, and an analysis of vertical PV flux. The lens was likely generated by a mixture of the local mixed-layer water and the water from the coastal jet separation site

Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other models. Although great efforts have been delved into the transferability across models, surprisingly, less attention has been paid to the cross-task transferability, which represents the real-world cybercriminal's situation, where an ensemble of different defense/detection mechanisms need to be evaded all at once. In this paper, we investigate the transferability of adversarial examples across a wide range of real-world computer vision tasks, including image classification, object detection, semantic segmentation, explicit content detection, and text detection. Our proposed attack minimizes the ``dispersion'' of the internal feature map, which overcomes existing attacks' limitation of requiring task-specific loss functions and/or probing a target model. We conduct evaluation on open source detection and segmentation models as well as four different computer vision tasks provided by Google Cloud Vision (GCV) APIs, to show how our approach outperforms existing attacks by degrading performance of multiple CV tasks by a large margin with only modest perturbations linf=16.Comment: arXiv admin note: substantial text overlap with arXiv:1905.0333

A neuroergonomics model to evaluating nuclear power plants operators' performance under heat stress driven by ECG time-frequency spectrums and fNIRS prefrontal cortex network: a CNN-GAT fusion model

Operators experience complicated physiological and psychological states when exposed to extreme heat stress, which can impair cognitive function and decrease performance significantly, ultimately leading to severe secondary disasters. Therefore, there is an urgent need for a feasible technique to identify their abnormal states to enhance the reliability of human-cybernetics systems. With the advancement of deep learning in physiological modeling, a model for evaluating operators' performance driven by electrocardiogram (ECG) and functional near-infrared spectroscopy (fNIRS) was proposed, demonstrating high ecological validity. The model fused a convolutional neural network (CNN) backbone and a graph attention network (GAT) backbone to extract discriminative features from ECG time-frequency spectrums and fNIRS prefrontal cortex (PFC) network respectively with deeper neuroscience domain knowledge, and eventually achieved 0.90 AUC. Results supported that handcrafted features extracted by specialized neuroscience methods can alleviate overfitting. Inspired by the small-world nature of the brain network, the fNIRS PFC network was organized as an undirected graph and embedded by GAT. It is proven to perform better in information aggregation and delivery compared to a simple non-linear transformation. The model provides a potential neuroergonomics application for evaluating the human state in vital human-cybernetics systems under industry 5.0 scenarios

Carbon monoxide poisoning deaths in Shanghai, China: A 10-year epidemiological and comparative study with the Wuhan sample

Abstract: Carbon monoxide (CO) poisoning is a common cause of death globally. However, CO poisoning deaths in the Mainland China are rarely studied. Therefore, this study aims to explore the incidence trend of CO poisoning deaths that occurred in Pudong for a 10-year period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). Using official police data, a total of 139 CO poisoning events that resulted in the death of 176 victims are collected. By comparing the data from Shanghai with the previous one from Wuhan, this study presents the most up-to date information about CO poisoning deaths that happened in China. The result indicates that the CO poisoning death rate in the study area in China is in the low level around the globe. Features of fire-related CO poisoning deaths are similar between the two mega cities, but in nonfire-related CO poisoning deaths, there are some distinguishing regional features. This study also found that the CO poisoning suicides by burning coal or charcoal is increasing sharply in recent years, especially in considering about the higher rate of burning charcoal suicides in the regions around the Mainland China. Certain precautious should be taken to prevent the growing trend of coal or charcoal burning suicides in future

In situ Observation of Sodium Dendrite Growth and Concurrent Mechanical Property Measurements Using an Environmental Transmission Electron Microscopy–Atomic Force Microscopy (ETEM-AFM) Platform

Akin to Li, Na deposits in a dendritic form to cause a short circuit in Na metal batteries. However, the growth mechanisms and related mechanical properties of Na dendrites remain largely unknown. Here we report real-time characterizations of Na dendrite growth with concurrent mechanical property measurements using an environmental transmission electron microscopy–atomic force microscopy (ETEM-AFM) platform. In situ electrochemical plating produces Na deposits stabilized with a thin Na2CO3 surface layer (referred to as Na dendrites). These Na dendrites have characteristic dimensions of a few hundred nanometers and exhibit different morphologies, including nanorods, polyhedral nanocrystals, and nanospheres. In situ mechanical measurements show that the compressive and tensile strengths of Na dendrites with a Na2CO3 surface layer vary from 36 to >203 MPa, which are much larger than those of bulk Na. In situ growth of Na dendrites under the combined overpotential and mechanical confinement can generate high stress in these Na deposits. These results provide new baseline data on the electrochemical and mechanical behavior of Na dendrites, which have implications for the development of Na metal batteries toward practical energy-storage applications

Proteome changes of lungs artificially infected with H-PRRSV and N-PRRSV by two-dimensional fluorescence difference gel electrophoresis

<p>Abstract</p> <p>Background</p> <p>Porcine reproductive and respiratory syndrome with PRRS virus (PRRSV) infection, which causes significant economic losses annually, is one of the most economically important diseases affecting swine industry worldwide. In 2006 and 2007, a large-scale outbreak of highly pathogenic porcine reproductive and respiratory syndrome (PRRS) happened in China and Vietnam. However little data is available on global host response to PRRSV infection at the protein level, and similar approaches looking at mRNA is problematic since mRNA levels do not necessarily predict protein levels. In order to improve the knowledge of host response and viral pathogenesis of highly virulent Chinese-type PRRSV (H-PRRSV) and Non-high-pathogenic North American-type PRRSV strains (N-PRRSV), we analyzed the protein expression changes of H-PRRSV and N-PRRSV infected lungs compared with those of uninfected negative control, and identified a series of proteins related to host response and viral pathogenesis.</p> <p>Results</p> <p>According to differential proteomes of porcine lungs infected with H-PRRSV, N-PRRSV and uninfected negative control at different time points using two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) and mass spectrometry identification, 45 differentially expressed proteins (DEPs) were identified. These proteins were mostly related to cytoskeleton, stress response and oxidation reduction or metabolism. In the protein interaction network constructed based on DEPs from lungs infected with H-PRRSV, HSPA8, ARHGAP29 and NDUFS1 belonged to the most central proteins, whereas DDAH2, HSPB1 and FLNA corresponded to the most central proteins in those of N-PRRSV infected.</p> <p>Conclusions</p> <p>Our study is the first attempt to provide the complex picture of pulmonary protein expression during H-PRRSV and N-PRRSV infection under the in vivo environment using 2D-DIGE technology and bioinformatics tools, provides large scale valuable information for better understanding host proteins-virus interactions of these two PRRSV strains.</p

In Situ Measurements of the Mechanical Properties of Electrochemically Deposited Li₂CO₃ and Li₂O Nanorods

Solid-electrolyte interface (SEI) is “the most important but least understood (component) in rechargeable Li-ion batteries”. The ideal SEI requires high elastic strength and can resist the penetration of a Li dendrite mechanically, which is vital for inhibiting the dendrite growth in lithium batteries. Even though Li$_{2}$CO$_{3}$ and Li$_{2}$O are identified as the major components of SEI, their mechanical properties are not well understood. Herein, SEI-related materials such as Li$_{2}$CO$_{3}$ and Li$_{2}$O were electrochemically deposited using an environmental transmission electron microscopy (ETEM), and their mechanical properties were assessed by in situ atomic force microscopy (AFM) and inverse finite element simulations. Both Li$_{2}$CO$_{3}$ and Li$_{2}$O exhibit nanocrystalline structures and good plasticity. The ultimate strength of Li$_{2}$CO$_{3}$ ranges from 192 to 330 MPa, while that of Li$_{2}$O is less than 100 MPa. These results provide a new understanding of the SEI and its related dendritic problems in lithium batteries

Aerobic Anoxygenic Phototrophic Bacteria Promote the Development of Biological Soil Crusts

Chlorophyll-containing oxygenic photoautotrophs have been well known to play a fundamental role in the development of biological soil crusts (BSCs) by harvesting solar radiations and providing fixed carbon to the BSCs ecosystems. Although the same functions can be theoretically fulfilled by the widespread bacteriochlorophyll-harboring aerobic anoxygenic phototrophic bacteria (AAnPB), whether AAnPB play a role in the formation of BSCs and how important they are to this process remain largely unknown. To address these questions, we set up a microcosm system with surface sands of the Hopq desert in northern China and observed the significant effects of near-infrared illumination on the development of BSCs. Compared to near-infrared or red light alone, the combined use of near-infrared and red lights for illumination greatly increased the thickness of BSCs, their organic matter contents and the microalgae abundance by 24.0, 103.7, and 1447.6%, respectively. These changes were attributed to the increasing abundance of AAnPB that can absorb near-infrared radiations. Our data suggest that AAnPB is a long-overlooked driver in promoting the development of BSCs in drylands

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments