300 research outputs found
Optimal Distributed Resource Allocation for Decode-and-Forward Relay Networks
This paper presents a distributed resource allocation algorithm to jointly
optimize the power allocation, channel allocation and relay selection for
decode-and-forward (DF) relay networks with a large number of sources, relays,
and destinations. The well-known dual decomposition technique cannot directly
be applied to resolve this problem, because the achievable data rate of DF
relaying is not strictly concave, and thus the local resource allocation
subproblem may have non-unique solutions. We resolve this non-strict concavity
problem by using the idea of the proximal point method, which adds quadratic
terms to make the objective function strictly concave. However, the proximal
solution adds an extra layer of iterations over typical duality based
approaches, which can significantly slow down the speed of convergence. To
address this key weakness, we devise a fast algorithm without the need for this
additional layer of iterations, which converges to the optimal solution. Our
algorithm only needs local information exchange, and can easily adapt to
variations of network size and topology. We prove that our distributed resource
allocation algorithm converges to the optimal solution. A channel resource
adjustment method is further developed to provide more channel resources to the
bottleneck links and realize traffic load balance. Numerical results are
provided to illustrate the benefits of our algorithm
Unstructured Mixed Grid and SIMPLE Algorithm based Model for 2D-SWE
AbstractA 2D depth-averaged flow model was developed using implicit schemes on unstructured mixed grid. The implicit time-marching algorithm is adopted to make the model much stable. To suppress the numerical oscillation, the TVD (total-variation diminishing) based second-order convection scheme is employed in the framework of finite volume method. The new model is validated using measured data and compared with YGLai model (newly developed by Lai (2010)). Results show that the new model is consistent with the measured data fairly well. The comparison with YGLai model indicates that our new model is generally better with respect to accuracy
Toward Unbiased Multiple-Target Fuzzing with Path Diversity
In this paper, we propose a novel directed fuzzing solution named AFLRun,
which features target path-diversity metric and unbiased energy assignment.
Firstly, we develop a new coverage metric by maintaining extra virgin map for
each covered target to track the coverage status of seeds that hit the target.
This approach enables the storage of waypoints into the corpus that hit a
target through interesting path, thus enriching the path diversity for each
target. Additionally, we propose a corpus-level energy assignment strategy that
guarantees fairness for each target. AFLRun starts with uniform target weight
and propagates this weight to seeds to get a desired seed weight distribution.
By assigning energy to each seed in the corpus according to such desired
distribution, a precise and unbiased energy assignment can be achieved.
We built a prototype system and assessed its performance using a standard
benchmark and several extensively fuzzed real-world applications. The
evaluation results demonstrate that AFLRun outperforms state-of-the-art fuzzers
in terms of vulnerability detection, both in quantity and speed. Moreover,
AFLRun uncovers 29 previously unidentified vulnerabilities, including 8 CVEs,
across four distinct programs
Stress-driven crystallization via shear-diffusion transformations in a metallic glass at very low temperatures
At elevated temperatures, glasses crystallize via thermally activated diffusion. However, metallic glasses can also undergo deformation-induced crystallization at very low temperatures. Here we demonstrate the crystallization of Al[subscript 50]Fe[subscript 50] metallic glasses under cyclic deformation at 50 K using molecular dynamics simulations and reveal the underlying atomic-scale processes. We demonstrate that stress-driven nonaffine atomic rearrangements, or shear diffusion transformation (SDT) events, lead to successive metabasin-to-metabasin transitions and long-range ordering. We also illustrate that the nucleation and growth of the crystal proceed via collective attachment of ordered clusters, advancing the amorphous/crystal interface in an intermittent manner. The cooperative nature of the steplike crystallization is attributed to the large activation volume of Eshelby transformations which generate as a by-product nonaffine diffusive atomic displacements that accumulate over loading cycles. The dual nature of shear (affine) and diffusion (nonaffine) in low-temperature stress-driven SDT events thus unifies inelasticity with crystallization.National Basic Research Program of China (973 Program) (Grant 2012CB619402)National Basic Research Program of China (111 Program) (Grant B06025)National Science Foundation (U.S.) (Grant DMR-1120901)National Science Foundation (U.S.) (Grant DMR-1410636
Denoising Magnetic Resonance Spectroscopy (MRS) Data Using Stacked Autoencoder for Improving Signal-to-Noise Ratio and Speed of MRS
Background: Magnetic resonance spectroscopy (MRS) enables non-invasive
detection and measurement of biochemicals and metabolites. However, MRS has low
signal-to-noise ratio (SNR) when concentrations of metabolites are in the range
of the million molars. Standard approach of using a high number of signal
averaging (NSA) to achieve sufficient NSR comes at the cost of a long
acquisition time. Purpose: We propose to use deep-learning approaches to
denoise MRS data without increasing the NSA. Methods: The study was conducted
using data collected from the brain spectroscopy phantom and human subjects. We
utilized a stack auto-encoder (SAE) network to train deep learning models for
denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high
SNR data collected with high NSA (NSA=192) which were also used to obtain the
ground truth. We applied both self-supervised and fully-supervised training
approaches and compared their performance of denoising low NSA data based on
improved SNRs. Results: With the SAE model, the SNR of low NSA data (NSA = 1)
obtained from the phantom increased by 22.8% and the MSE decreased by 47.3%.
For low NSA images of the human parietal and temporal lobes, the SNR increased
by 43.8% and the MSE decreased by 68.8%. In all cases, the chemical shift of
NAA in the denoised spectra closely matched with the high SNR spectra,
suggesting no distortion to the spectra from denoising. Furthermore, the
denoising performance of the SAE model was more effective in denoising spectra
with higher noise levels. Conclusions: The reported SAE denoising method is a
model-free approach to enhance the SNR of low NSA MRS data. With the denoising
capability, it is possible to acquire MRS data with a few NSA, resulting in
shorter scan times while maintaining adequate spectroscopic information for
detecting and quantifying the metabolites of interest
Multi-robot hunting in dynamic environments
This paper is concerned with multi-robot hunting in dynamic environments. A BCSLA approach is proposed to allow mobile robots to capture an intelligent evader. During the process of hunting, four states including dispersion-random-search, surrounding, catch and prediction are employed. In order to ensure each robot appropriate movement in each state, a series of strategies are developed in this paper. The dispersion-search strategy enables the robots to find the evader effectively. The leader-adjusting strategy aims to improve the hunting robots’ response to environmental changes and the outflank strategy is proposed for the hunting robots to force the evader to enter a besieging circle. The catch strategy is designed for shrinking the besieging circle to catch the evader. The predict strategy allows the robots to predict the evader’s position when they lose the tracking information about the evader. A novel collision-free motion strategy is also presented in this paper, which is called the direction-optimization strategy. To test the effect of cooperative hunting, the target to be captured owns a safety-motion strategy, which helps it to escape being captured. The computer simulations support the rationality of the approach.<br /
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