487 research outputs found
The global attractor of the damped forced Ostrovsky equation
AbstractThe existence of the global attractor of the damped forced Ostrovsky equation in LËś2(R) is proved for the forces in LËś2(R). Moreover, the global attractor of the equation in LËś2(R) is actually a compact set in HËś3(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
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