Effect of chloride and sulfate ions in simulated boiler water on pitting corrosion behavior of 13Cr steel

The pitting corrosion behavior of 13Cr steel was investigated in simulated boiler waters containing chloride ions (Cl-) and sulfate ions (SO42-) using potentiodynamic and potentiostatic polarization tests in addition to pit morphology analysis. The presence of 100 ppm cl(-) in the water caused pitting corrosion of the steel. Pit initiation was inhibited by the addition of 50 ppm or 100 ppm SO42- into the water containing 100 ppm Cl-. Pit growth was also suppressed by the presence of 50 ppm SO42- in the water with 100 ppm Cl-; however, it was conversely promoted in the presence of 100 ppm SO42-. (C) 2015 Elsevier Ltd. All rights reserved.ArticleCORROSION SCIENCE. 96:171-177 (2015)journal articl

NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search

Deep convolutional neural networks (CNNs) have been widely used in surface defect detection. However, no CNN architecture is suitable for all detection tasks and designing effective task-specific requires considerable effort. The neural architecture search (NAS) technology makes it possible to automatically generate adaptive data-driven networks. Here, we propose a new method called NAS-ASDet to adaptively design network for surface defect detection. First, a refined and industry-appropriate search space that can adaptively adjust the feature distribution is designed, which consists of repeatedly stacked basic novel cells with searchable attention operations. Then, a progressive search strategy with a deep supervision mechanism is used to explore the search space faster and better. This method can design high-performance and lightweight defect detection networks with data scarcity in industrial scenarios. The experimental results on four datasets demonstrate that the proposed method achieves superior performance and a relatively lighter model size compared to other competitive methods, including both manual and NAS-based approaches

Direct Adversarial Training: A New Approach for Stabilizing The Training Process of GANs

Generative Adversarial Networks (GANs) are the most popular models for image generation by optimizing discriminator and generator jointly and gradually. However, instability in training process is still one of the open problems for all GAN-based algorithms. In order to stabilize training, some regularization and normalization techniques have been proposed to make discriminator meet the Lipschitz continuity constraint. In this paper, a new approach inspired by works on adversarial attack is proposed to stabilize the training process of GANs. It is found that sometimes the images generated by the generator play a role just like adversarial examples for discriminator during the training process, which might be a part of the reason of the unstable training. With this discovery, we propose to introduce a adversarial training method into the training process of GANs to improve its stabilization. We prove that this DAT can limit the Lipschitz constant of the discriminator adaptively. The advanced performance of the proposed method is verified on multiple baseline and SOTA networks, such as DCGAN, WGAN, Spectral Normalization GAN, Self-supervised GAN and Information Maximum GAN

FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations

While the majority of existing pre-trained models from code learn source code features such as code tokens and abstract syntax trees, there are some other works that focus on learning from compiler intermediate representations (IRs). Existing IR-based models typically utilize IR features such as instructions, control and data flow graphs (CDFGs), call graphs, etc. However, these methods confuse variable nodes and instruction nodes in a CDFG and fail to distinguish different types of flows, and the neural networks they use fail to capture long-distance dependencies and have over-smoothing and over-squashing problems. To address these weaknesses, we propose FAIR, a Flow type-Aware pre-trained model for IR that involves employing (1) a novel input representation of IR programs; (2) Graph Transformer to address over-smoothing, over-squashing and long-dependencies problems; and (3) five pre-training tasks that we specifically propose to enable FAIR to learn the semantics of IR tokens, flow type information, and the overall representation of IR. Experimental results show that FAIR can achieve state-of-the-art results on four code-related downstream tasks.Comment: ICSE 2024 First Cycl

Corrosion Resistance of Modified Heat-Treated 16Cr-4Ni Steel for Geothermal Steam Turbine Blades

Immersion corrosion tests and electrochemical corrosion tests were carried out in the simulated geothermal water on a modified heat-treated material of 16Cr-4Ni steel, which is used for geothermal steam turbine blades. Incidentally, the purpose of the modified heat treatment is for reducing the susceptibility of stress corrosion cracking (SCC) of the steel in the geothermal fluids. For comparison, a conventional heat-treated material of the 16Cr-4Ni steel was also used in the corrosion tests. In the immersion corrosion tests up to 4,000 hours, the modified heat-treated material showed lower corrosion rates than those of the conventional heat-treated one in the test water. In the electrochemical corrosion tests, the modified heat-treated material exhibited noble and stable corrosion potential behavior. The XPS analysis results exhibited that the passive films formed on the materials were consisted mainly of Cr-oxides (CrOOH, CrO3) and Fe-oxides (FeO, Fe2O3). Furthermore, the composition ratio of Cr-oxides in the outermost surface of the passive film formed on the modified heat-treated material was higher than that on the conventional heat-treated one. It was suggested that the better corrosion resistance of the modified heat-treated 16Cr-4Ni steel was contributed to the formation of the passive film with higher compositions of Cr-oxides

DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions

Multiple object tracking (MOT) tends to become more challenging when severe occlusions occur. In this paper, we analyze the limitations of traditional Convolutional Neural Network-based methods and Transformer-based methods in handling occlusions and propose DNMOT, an end-to-end trainable DeNoising Transformer for MOT. To address the challenge of occlusions, we explicitly simulate the scenarios when occlusions occur. Specifically, we augment the trajectory with noises during training and make our model learn the denoising process in an encoder-decoder architecture, so that our model can exhibit strong robustness and perform well under crowded scenes. Additionally, we propose a Cascaded Mask strategy to better coordinate the interaction between different types of queries in the decoder to prevent the mutual suppression between neighboring trajectories under crowded scenes. Notably, the proposed method requires no additional modules like matching strategy and motion state estimation in inference. We conduct extensive experiments on the MOT17, MOT20, and DanceTrack datasets, and the experimental results show that our method outperforms previous state-of-the-art methods by a clear margin.Comment: ACM Multimedia 202

YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design

The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14$\times$ compression rate of YOLOv4 with 49.0 mAP. Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme, the inference speed is increased to 19.1 FPS, and outperforms the original YOLOv4 by 5$\times$ speedup. Source code is at: \url{https://github.com/nightsnack/YOLObile}

Plant buffering against the high-light stress-induced accumulation of CsGA2ox8 transcripts via alternative splicing to finely tune gibberellin levels and maintain hypocotyl elongation

Ajuts: this study was supported by The National Key Research and Development Program of China (2019YFD1000300), the International Postdoctoral Exchange Fellowship Program from the China Postdoctoral Council (20170053), the Technology System Construction of Modern Agricultural Industry of Shanghai (19Z113040008), and the Presidential Foundation of Guangdong Academy of Agricultural Sciences (BZ201901).In plants, alternative splicing (AS) is markedly induced in response to environmental stresses, but it is unclear why plants generate multiple transcripts under stress conditions. In this study, RNA-seq was performed to identify AS events in cucumber seedlings grown under different light intensities. We identified a novel transcript of the gibberellin (GA)-deactivating enzyme Gibberellin 2-beta-dioxygenase 8 (CsGA2ox8). Compared with canonical CsGA2ox8.1, the CsGA2ox8.2 isoform presented intron retention between the second and third exons. Functional analysis proved that the transcript of CsGA2ox8.1 but not CsGA2ox8.2 played a role in the deactivation of bioactive GAs. Moreover, expression analysis demonstrated that both transcripts were upregulated by increased light intensity, but the expression level of CsGA2ox8.1 increased slowly when the light intensity was >400 µmol·m −2 ·s −1 PPFD (photosynthetic photon flux density), while the CsGA2ox8.2 transcript levels increased rapidly when the light intensity was >200 µmol·m −2 ·s −1 PPFD. Our findings provide evidence that plants might finely tune their GA levels by buffering against the normal transcripts of CsGA2ox8 through AS

Radiative transitions in charmonium from Nf=2N_f=2 twisted mass lattice QCD

We present a study for charmonium radiative transitions: $J/\psi\rightarrow\eta_c\gamma$, $\chi_{c0}\rightarrow J/\Psi\gamma$ and $h_c\rightarrow\eta_c\gamma$ using $N_f=2$ twisted mass lattice QCD gauge configurations. The single-quark vector form factors for $\eta_c$ and $\chi_{c0}$ are also determined. The simulation is performed at a lattice spacing of $a= 0.06666$ fm and the lattice size is $32^3\times 64$. After extrapolation of lattice data at nonzero $Q^2$ to 0, we compare our results with previous quenched lattice results and the available experimental values.Comment: typeset with revtex, 15 pages, 11 figures, 4 table