Impacts of Large-Scale Circulation on Convection: A 2-D Cloud Resolving Model Study

Studies of impacts of large-scale circulation on convection, and the roles of convection in heat and water balances over tropical region are fundamentally important for understanding global climate changes. Heat and water budgets over warm pool (SST=29.5 C) and cold pool (SST=26 C) were analyzed based on simulations of the two-dimensional cloud resolving model. Here the sensitivity of heat and water budgets to different sizes of warm and cold pools is examined

Failure Assessment for the High-Strength Pipelines with Constant-Depth Circumferential Surface Cracks

In the oil and gas transportation system over long distance, application of high-strength pipeline steels can efficiently reduce construction and operation cost by increasing operational pressure and reducing the pipe wall thickness. Failure assessment is an important issue in the design, construction, and maintenance of the pipelines. The small circumferential surface cracks with constant depth in the welded pipelines are of practical interest. This work provides an engineering estimation procedure based upon the GE/EPRI method to determine the J-integral for the thin-walled pipelines with small constant-depth circumferential surface cracks subject to tension and bending loads. The values of elastic influence functions for stress intensity factor and plastic influence functions for fully plastic J-integral estimation are derived in tabulated forms through a series of three-dimensional finite element calculations for different crack geometries and material properties. To check confidence of the J-estimation solution in practical application, J-integral values obtained from detailed finite element (FE) analyses are compared with those estimated from the new influence functions. Excellent agreement of FE results with the proposed J-estimation solutions for both tension and bending loads indicates that the new solutions can be applied for accurate structural integrity assessment of high-strength pipelines with constant-depth circumferential surface cracks

Spatiotemporal double-phase hologram for complex-amplitude holographic displays

This Letter describes an approach to encode complex-amplitude light waves with spatiotemporal double-phase holograms (DPHs) for overcoming the limit of the space-bandwidth product (SBP) delivered by existing methods. To construct DPHs, two spatially macro-pixel encoded phase components are employed in the SBP-preserved resampling of complex holograms. Four generated sub-DPHs are displayed sequentially in time for high-quality holographic image reconstruction without reducing the image size or discarding any image terms when the DPHs are interweaved. The reconstructed holographic images contain more details and less speckle noise, with their signal-to-noise ratio and structure similarity index being improved by 14.64% and 78.79%, respectively

Static detection of control-flow-related vulnerabilities using graph embedding

© 2019 IEEE. Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges

Flow2Vec: Value-flow-based precise code embedding

© 2020 Owner/Author. Code embedding, as an emerging paradigm for source code analysis, has attracted much attention over the past few years. It aims to represent code semantics through distributed vector representations, which can be used to support a variety of program analysis tasks (e.g., code summarization and semantic labeling). However, existing code embedding approaches are intraprocedural, alias-unaware and ignoring the asymmetric transitivity of directed graphs abstracted from source code, thus they are still ineffective in preserving the structural information of code. This paper presents Flow2Vec, a new code embedding approach that precisely preserves interprocedural program dependence (a.k.a value-flows). By approximating the high-order proximity, i.e., the asymmetric transitivity of value-flows, Flow2Vec embeds control-flows and alias-aware data-flows of a program in a low-dimensional vector space. Our value-flow embedding is formulated as matrix multiplication to preserve context-sensitive transitivity through CFL reachability by filtering out infeasible value-flow paths. We have evaluated Flow2Vec using 32 popular open-source projects. Results from our experiments show that Flow2Vec successfully boosts the performance of two recent code embedding approaches codevec and codeseq for two client applications, i.e., code classification and code summarization. For code classification, Flow2Vec improves codevec with an average increase of 21.2%, 20.1% and 20.7% in precision, recall and F1, respectively. For code summarization, Flow2Vec outperforms codeseq by an average of 13.2%, 18.8% and 16.0% in precision, recall and F1, respectively

A modified protocol for the detection of three different mRNAs with a new-generation in situ hybridization chain reaction on frozen sections

A new multiple fluorescence in situ hybridization method based on hybridization chain reaction was recently reported, enabling simultaneous mapping of multiple target mRNAs within intact zebrafish and mouse embryos. With this approach, DNA probes complementary to target mRNAs trigger chain reactions in which metastable fluorophore-labeled DNA hairpins self-assemble into fluorescent amplification polymers. The formation of the specific polymers enhances greatly the sensitivity of multiple fluorescence in situ hybridization. In this study we describe the optimal parameters (hybridization chain reaction time and temperature, hairpin and salt concentration) for multiple fluorescence in situ hybridization via amplification of hybridization chain reaction for frozen tissue sections. The combined use of fluorescence in situ hybridization and immunofluorescence, together with other control experiments (sense probe, neutralization and competition, RNase treatment, and anti-sense probe without initiator) confirmed the high specificity of the fluorescence in situ hybridization used in this study. Two sets of three different mRNAs for oxytocin, vasopressin and somatostatin or oxytocin, vasopressin and thyrotropin releasing hormone were successfully visualized via this new method. We believe that this modified protocol for multiple fluorescence in situ hybridization via hybridization chain reaction would allow researchers to visualize multiple target nucleic acids in the future

Acoustic phonon recycling for photocarrier generation in graphene-WS_{2} heterostructures

Electron-phonon scattering is the key process limiting the efficiency of modern nanoelectronic and optoelectronic devices, in which most of the incident energy is converted to lattice heat and finally dissipates into the environment. Here, we report an acoustic phonon recycling process in graphene-WS_{2} heterostructures, which couples the heat generated in graphene back into the carrier distribution in WS_{2}. This recycling process is experimentally recorded by spectrally resolved transient absorption microscopy under a wide range of pumping energies from 1.77 to 0.48 eV and is also theoretically described using an interfacial thermal transport model. The acoustic phonon recycling process has a relatively slow characteristic time (>100 ps), which is beneficial for carrier extraction and distinct from the commonly found ultrafast hot carrier transfer (~1 ps) in graphene-WS_{2} heterostructures. The combination of phonon recycling and carrier transfer makes graphene-based heterostructures highly attractive for broadband high-efficiency electronic and optoelectronic applications

Atmospheric reactive nitrogen concentrations at ten sites with contrasting land use in an arid region of central Asia

Atmospheric concentrations of reactive nitrogen (N<sub>r</sub>) species from 2009 to 2011 are reported for ten sites in Xinjiang, China, an arid region of central Asia. Concentrations of NH<sub>3</sub>, NO<sub>2</sub>, particulate ammonium and nitrate (<i>p</i>NH<sub>4</sub><sup>+</sup> and <i>p</i>NO<sub>3</sub><sup>−</sup>) showed large spatial and seasonal variation and averaged 7.71, 9.68, 1.81 and 1.13 μg N m<sup>−3</sup>, and PM<sub>10</sub> concentrations averaged 249.2 μg m<sup>−3</sup> across all sites. Lower NH<sub>3</sub> concentrations and higher NO<sub>2</sub>, <i>p</i>NH<sub>4</sub><sup>+</sup> and <i>p</i>NO<sub>3</sub><sup>−</sup> concentrations were found in winter, reflecting serious air pollution due to domestic heating in winter and other anthropogenic sources such as increased emissions from motor traffic and industry. The increasing order of total concentrations of N<sub>r</sub> species was alpine grassland; desert, desert-oasis ecotone; desert in an oasis; farmland; suburban and urban ecosystems. Lower ratios of secondary particles (NH<sub>4</sub><sup>+</sup> and NO<sub>3</sub><sup>−</sup>) were found in the desert and desert-oasis ecotone, while urban and suburban areas had higher ratios, which implied that anthropogenic activities have greatly influenced local air quality and must be controlled

DeepWukong: Statically Detecting Software Vulnerabilities Using Deep Graph Neural Network

Static bug detection has shown its effectiveness in detecting well-defined memory errors, e.g., memory leaks, buffer overflows, and null dereference. However, modern software systems have a wide variety of vulnerabilities. These vulnerabilities are extremely complicated with sophisticated programming logic, and these bugs are often caused by different bad programming practices, challenging existing bug detection solutions. It is hard and labor-intensive to develop precise and efficient static analysis solutions for different types of vulnerabilities, particularly for those that may not have a clear specification as the traditional well-defined vulnerabilities. This article presents DeepWukong, a new deep-learning-based embedding approach to static detection of software vulnerabilities for C/C++ programs. Our approach makes a new attempt by leveraging advanced recent graph neural networks to embed code fragments in a compact and low-dimensional representation, producing a new code representation that preserves high-level programming logic (in the form of control-and data-flows) together with the natural language information of a program. Our evaluation studies the top 10 most common C/C++ vulnerabilities during the past 3 years. We have conducted our experiments using 105,428 real-world programs by comparing our approach with four well-known traditional static vulnerability detectors and three state-of-the-art deep-learning-based approaches. The experimental results demonstrate the effectiveness of our research and have shed light on the promising direction of combining program analysis with deep learning techniques to address the general static code analysis challenges