Equatorial waves simulated by the NCAR community climate model

The equatorial planetary waves simulated by the NCAR CCM1 general circulation model were investigated in terms of space-time spectral analysis (Kao, 1968; Hayashi, 1971, 1973) and energetic analysis (Hayashi, 1980). These analyses are particularly applied to grid-point data on latitude circles. In order to test some physical factors which may affect the generation of tropical transient planetary waves, three different model simulations with the CCM1 (the control, the no-mountain, and the no-cloud experiments) were analyzed

Fracture toughness prediction of eutectic ceramic composite considering damage effect and transformation toughening

The toughness of eutectic ceramic composites is obtained by multiple toughening mechanisms involving crack-bridging and pull-out of rod-shaped eutectics, as well as stress-induced transformation toughening. In the loading procedure, damage will emerge in the rod-shaped eutectic. Firstly, the damage variables are defined by the microstructure of rod-shaped eutectic with aligned nano/micro- fibers. The maximum strain criterion is used for determining the loading function. According to the attenuation characteristic of eutectic rigidity, the critical fracture stress of the damage rod-shaped eutectic is obtained by damage variable maximizing. Secondly, we imagine the bridging load carried by the damage rod-shaped eutectics in the crack wake to produce a crack-closing force. The latter reduces the stress intensity in front of the crack. The pull-out work is given by the integral of the frictional force over the pull-out length. Bridging toughening mechanism and pull-out toughening mechanism of damage rod-shaped eutectics are constructed. Thirdly, defining a parabola transformation yield function, the transformation plastic strain increment is gotten by transformation plastic potential function. The screening impact of transformation particles for mixed-mode I-II crack is gained. And lastly, based on the crack-bridging and pull-out of rod-shaped eutectics, as well as stress-induced transformation toughening mechanisms, the added toughness scale with the inherent matrix toughness, the theoretical formula of fracture toughness of the eutectic ceramics composite is determined. The result shows that the fracture toughness is dependent on the aspect ratio of rod-shaped eutectic: the fracture toughness is minimum as the aspect ratio is equal to 0.3 and maximizing when the aspect ratio is equal to 14. The damages inside eutectics enlarge the incremental range of variation of the fracture toughness. The transformation particles exert a slight influence on the fracture toughness due to its less content

Strength Prediction Model of Eutectic Composite Ceramics Mainly Composed of Rod-Shaped Crystals

In this paper, the effect of preexisting defects on strength of eutectic composite ceramic mainly composed of rod-Shaped crystals is considered. In line with the microstructure characteristic of eutectic composite ceramic, its micromechanical model is set up. Defects are assumed to be lamellar, and matrix around is transversely isotropic. Then, the damage stress field is obtained by Chaboche’s damage theory. Finally, the micromechanical strength of eutectic ceramic composite is predicted, and its influencing factor is analyzed

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues. However, existing methods either extract the sentence-level features coupling all words or focus more on object names, which would lose the word-level information or neglect other attributes. To alleviate these issues, we present EDA that Explicitly Decouples the textual attributes in a sentence and conducts Dense Alignment between such fine-grained language and point cloud objects. Specifically, we first propose a text decoupling module to produce textual features for every semantic component. Then, we design two losses to supervise the dense matching between two modalities: position alignment loss and semantic alignment loss. On top of that, we further introduce a new visual grounding task, locating objects without object names, which can thoroughly evaluate the model's dense alignment capacity. Through experiments, we achieve state-of-the-art performance on two widely-adopted 3D visual grounding datasets, ScanRefer and SR3D/NR3D, and obtain absolute leadership on our newly-proposed task. The source code will be available at https://github.com/yanmin-wu/EDA.Comment: 16 pages with 5 pages of supplementary materia

Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification

Person re-identification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. Most existing methods tackle this challenge by aligning spatial features of body parts according to external semantic cues or feature similarities but this alignment approach is complicated and sensitive to noises. We design DRL-Net, a disentangled representation learning network that handles occluded re-ID without requiring strict person image alignment or any additional supervision. Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via global reasoning of local features of occluded person images. It measures image similarity by automatically disentangling the representation of undefined semantic components, e.g., human body parts or obstacles, under the guidance of semantic preference object queries in the transformer. In addition, we design a decorrelation constraint in the transformer decoder and impose it over object queries for better focus on different semantic components. To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features. Extensive experiments over occluded and holistic re-ID benchmarks (Occluded-DukeMTMC, Market1501 and DukeMTMC) show that the DRL-Net achieves superior re-ID performance consistently and outperforms the state-of-the-art by large margins for Occluded-DukeMTMC

FBM Model Based Network-Wide Performance Analysis with Service Differentiation

ABSTRACT In this paper, we demonstrate that traffic modeling with the fractional Brownian motion (FBM) process is an efficient tool for end-to-end performance analysis over a network provisioning differentiated services (DiffServ). The FBM process is a parsimonious model involving only three parameters to describe the Internet traffic showing the property of selfsimilarity or long-range dependence (LRD). As a foundation for network-wide performance analysis, the FBM modeling can significantly facilitate the single-hop performance analysis. While accurate FBM based queueing analysis for an infinite/finite first-in-first-out (FIFO) buffer is available in the existing literature, we develop a generic FBM based analysis for multiclass single-hop analysis where both inter-buffer priority and intra-buffer priority are used for service differentiation. Moreover, we present both theoretical and simulation studies to reveal the preservation of the self-similarity, when the traffic process is multiplexed or randomly split, or goes through a queueing system. It is such self-similar preservation that enables the concatenation of FBM based single-hop analysis into a network-wide performance analysis

Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts

Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies. However, current methods struggle to generate correct 3D content for a complex prompt in semantics, i.e., a prompt describing multiple interacted objects binding with different attributes. In this work, we propose a general framework named Progressive3D, which decomposes the entire generation into a series of locally progressive editing steps to create precise 3D content for complex prompts, and we constrain the content change to only occur in regions determined by user-defined region prompts in each editing step. Furthermore, we propose an overlapped semantic component suppression technique to encourage the optimization process to focus more on the semantic differences between prompts. Extensive experiments demonstrate that the proposed Progressive3D framework generates precise 3D content for prompts with complex semantics and is general for various text-to-3D methods driven by different 3D representations.Comment: Project Page: https://cxh0519.github.io/projects/Progressive3D

LncRNA LINC01857 drives pancreatic adenocarcinoma progression via modulating miR-19a-3p/SMOC2

Objectives: Emerging evidence has demonstrated that LINC01857 exerts a pivotal function in many cancers. However, its function in Pancreatic Ductal Adenocarcinoma (PDAC) still remains unclear. This study was designed to investigate the regulatory character of LINC01857 in PDAC. Methods: Bioinformatic tools and databases were used to seek potential miRNAs and mRNAs. Gene expression was evaluated by Reverse Transcription quantitative real-time Polymerase Chain Reaction (RT-qPCR), and western blot was used for protein level detection. A subcellular fraction assay was done to ascertain the location of LINC01857 in PANC-1 and BxPC-3 human pancreatic cancer cells. CCK-8, EdU, wound healing and Transwell assays were performed to inquire into the influence of LINC01857, and SPARC -related Modular Calcium-binding protein-2 (SMOC2) on cell viability, proliferation, migration, and invasion, respectively. The interaction between LINC01857 and its downstream genes was explored by RNA immunoprecipitation and luciferase reporter assays. Results: LINC01857 levels were significantly elevated in PDAC. Knockdown of LINC01857 significantly restrained the proliferation, migration, invasion, and Epithelial-Mesenchymal Transition (EMT) process of PDAC cells. MiR-19a-3p was a downstream target of LINC01857, and miR-19a-3p levels were significantly decreased in PDAC cells. In addition, SMOC2 expression had a negative correlation with that of miR-19a-3p, and SMOC2 was a downstream target of miR-19a-3p. Furthermore, SMOC2 upregulation partially abolished the inhibitive influence of LINC01857 downregulation on cell proliferation, migration, invasion, and the EMT process. Conclusion: LINC01857 promotes malignant phenotypes of PDAC cells via upregulation of SMOC2 by interacting with miR-19a-3p

Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission and processing power. Encoding the image information in quantum-mechanical systems instead of classical ones and replacing classical with quantum information processing may alleviate some of these challenges. By encoding and processing the image information in quantum-mechanical systems, we here demonstrate the framework of quantum image processing, where a pure quantum state encodes the image information: we encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Our quantum image representation reduces the required number of qubits compared to existing implementations, and we present image processing algorithms that provide exponential speed-up over their classical counterparts. For the commonly used task of detecting the edge of an image, we propose and implement a quantum algorithm that completes the task with only one single-qubit operation, independent of the size of the image. This demonstrates the potential of quantum image processing for highly efficient image and video processing in the big data era.Comment: 13 pages, including 9 figures and 5 appendixe