The global attractor of the damped forced Ostrovsky equation

AbstractThe existence of the global attractor of the damped forced Ostrovsky equation in L˜2(R) is proved for the forces in L˜2(R). Moreover, the global attractor of the equation in L˜2(R) is actually a compact set in H˜3(R)

Soil heavy metal contamination and acid deposition: experimental approach on two forest soils in Hunan, Southern China

In 1985, a tailing dam collapsed in Hunan province (southern China) leading to soil contamination by heavy metals from the tailings waste. Moreover, acid deposition becomes more and more serious in this area. In this context, two forest soils (a red soil and a yellow red soil, typically and commonly found in southern China) were collected from Hunan. The objectives are (i) to determine releases and changes in speciation fractions of heavy metals (especially Cd, Cu, and Zn) when the soils are contaminated with heavy metals and affected by simulated acid deposition, and (ii) to study effects of soil heavy metals and acid deposition on releases of soil Ca2+, Mg2+, and Al3+. The soil samples were soaked in the solutions of CdCl2, CuCl2, and ZnCl2 for 15 days to make contaminated soils containing 200 mg kg1 of Cd, Cu, and Zn. Then the contaminated soils and the original soils were extracted with five simulated acid deposition solutions (pH ranged from 5.6 to 3.0 and total dissolved salts increased). The experimental results indicate that acid deposition leads to great releases of soil heavy metals due to complicated soil chemical processes, mostly cation exchange and partly dissolution of minerals at pH lower than 4.2. These released heavy metals come mainly from soil exchangeable pools and other labile fractions. Releases of heavy metals are closely controlled by pH values or, in some cases, total cation contents in acid deposition; meanwhile, concentrations of heavy metals are negatively related to the relevant pH values in soil equilibrium solutions when pH values are in a range of 4.2–5.1. From the point of view of heavy metal releases, Zn is the most sensitive to acid deposition, followed by Cd and Cu. Compared with the original soils, the contaminated soils could probably release more base cations Ca2+ and Mg2+ and less Al3+. Greater amounts of Cd, Cu, Zn, and Al released from Soil B show that this soil is more sensitive to acid deposition, and we could expect serious environmental contamination in Soil B area if mining activities and acid deposition are not under control in the future

How is Gaze Influenced by Image Transformations? Dataset and Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified UNet is proposed as the generator of the GazeGAN, which combines classic skip connections with a novel center-surround connection (CSC), in order to leverage multi level features. We also propose a histogram loss based on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. Our code and data are available at: https://github.com/CZHQuality/Sal-CFS-GAN

Parallel implementation of 3D global MHD simulations for Earth’s magnetosphere

AbstractThis paper presents a dynamic domain decomposition (D3) technique for implementing the parallelization of the piecewise parabolic method (PPM) for solving the ideal magnetohydrodynamics (MHD) equations. The key point of D3 is distributing the work dynamically among processes during the execution of the PPM algorithm. This parallel code utilizes D3 with a message passing interface (MPI) in order to permit efficient implementation on clusters of distributed memory machines and may also simultaneously exploit threading for multiprocessing shared address space architectures. 3D global MHD simulation results for the Earth’s magnetosphere on the massively parallel supercomputers Deepcomp 1800 and 6800 demonstrate the scalability and efficiency of our parallelization strategy

Seeing through the Mask: Multi-task Generative Mask Decoupling Face Recognition

The outbreak of COVID-19 pandemic make people wear masks more frequently than ever. Current general face recognition system suffers from serious performance degradation,when encountering occluded scenes. The potential reason is that face features are corrupted by occlusions on key facial regions. To tackle this problem, previous works either extract identity-related embeddings on feature level by additional mask prediction, or restore the occluded facial part by generative models. However, the former lacks visual results for model interpretation, while the latter suffers from artifacts which may affect downstream recognition. Therefore, this paper proposes a Multi-task gEnerative mask dEcoupling face Recognition (MEER) network to jointly handle these two tasks, which can learn occlusionirrelevant and identity-related representation while achieving unmasked face synthesis. We first present a novel mask decoupling module to disentangle mask and identity information, which makes the network obtain purer identity features from visible facial components. Then, an unmasked face is restored by a joint-training strategy, which will be further used to refine the recognition network with an id-preserving loss. Experiments on masked face recognition under realistic and synthetic occlusions benchmarks demonstrate that the MEER can outperform the state-ofthe-art methods

The role of ncRNAs in solid tumors prognosis: from laboratory to clinical utility

Today we know that non-coding RNAs (ncRNAs) represent most of the transcribed human genome and participate in relevant cellular processes. NcRNAs regulate from RNA transcription to protein translation, have important epigenetic roles or facilitate protein–protein interactions among other functions. In consequence, their dysregulation has been associated with tumor development and progression. Recently, their expression has also been detected in body fluids, opening the use of circulating ncRNAs for diagnosis and for evaluation and monitoring cancer prognosis