Shared-memory multiprocessors have dominated all platforms from high-end to desktop computers. On such platforms, it is well known that the interconnect between the processors and the main memory has become a major bottleneck. The bandwidth-aware job scheduling is an effective and relatively easy-to-implement way to relieve the bandwidth contention. Previous policies understood that bandwidth saturation hurt the throughput of parallel jobs so they scheduled the jobs to let the total bandwidth requirement equal to the system peak bandwidth. However, we found that intra-quantum fine-grained bandwidth contention still happened due to a program's irregular fluctuation in memory access intensity, which is mostly ignored in previous policies.In this paper, we quantify the impact of bandwidth contention on overall performance. We found that concurrent jobs could achieve a higher memory bandwidth utilization at the expense of super-linear performance degradation. Based on such an observation, we proposed a new workload scheduling policy. Its basic idea is that interference due to bandwidth contention could be minimized when bandwidth utilization is maintained at the level of average bandwidth requirement of the workload. Our evaluation is based on both SPEC 2006 and NPB workloads. The evaluation results on randomly generated workloads show that our policy could improve the system throughput by 4.1% on average over the native OS scheduler, and up to 11.7% improvement has been observed.

Neuromodulation is a fundamental method for obtaining basic information about neuronal circuits for use in treatments for neurological and psychiatric disorders. Ultrasound stimulation has become a promising approach for noninvasively inducing neuromodulation in animals and humans. However, the previous investigations were subject to substantial limitations, due to most of them involving anesthetized and fixed small-animal models. Studies of awake and freely moving animals are needed, but the currently used ultrasound devices are too bulky to be applied to a freely moving animal. This study is the first time to design and fabricate a miniature and lightweight head-mounted ultrasound stimulator for inducing neuromodulation in freely moving mice. The main components of the stimulator include a miniature piezoelectric ceramic, a concave epoxy acoustic lens, and housing and connection components. The device was able to induce action potentials recorded and evoke head-turning behaviors by stimulating the primary somatosensory cortex barrel field of the mouse. These findings indicate that the proposed method can be used to induce noninvasive neuromodulation in freely moving mice. This novel method could potentially lead to the application of ultrasonic neuromodulation in more-extensive neuroscience investigations.

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Competition for shared memory resources on multiprocessors is the most dominant cause for slowing down applications and makes their performance varies unpredictably. It exacerbates the need for Quality of Service (QoS) on such systems. In this paper, we propose a fair-progress process scheduling (FPS) policy to improve system fairness. Its strategy is to force the equallyweighted applications to have the same amount of slowdown when they run concurrently. The basic approach is to monitor the progress of all applications at runtime. When we find an application suffered more slowdown and accumulated less effective work than others, we allocate more CPU time to give it a better parity. Our policy also allows different weights to different threads, and provides an effective and robust tuner that allows the OS to freely make tradeoffs between system fairness and higher throughput.Evaluation results show that FPS can significantly improve system fairness by an average of 53.5% and 65.0% on a 4-core processor with a private cache and a 4-core processor with a shared cache, respectively. The penalty is about 1.1% and 1.6% of the system throughput. For memory-intensive workloads, FPS also improves system fairness by an average of 45.2% and 21.1% on 4-core and 8-core system respectively at the expense of a throughput loss of about 2%. 1

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A new drimane sesquiterpenoid, 3-keto-drimenol (1), were isolated from cultures of the basidiomycete Clitocybe conglobata together with 3b b-hydroxy-11-acetyldrimene (2), 3b b-hydroxydrimenol (3), 11,12-dihydroxydrimene (4), 3b b-hydroxy-11,12-O-isopropyldrimene (5), and 3b b,11,12-trihydroxydrimene (6). Their structures were established from MS and NMR experiments. Compounds 1, 3, and 6 showed inhibitory activities against two isozymes of 11b b-hydroxysteroid dehydrogenases with IC 50 1.7-8.0 m mg/ml (human 11b b-HSD1), 10.7-24.1 m mg/ml (mouse 11b b-HSD1); 177.0-220.0 m mg/ml (human 11b b-HSD2), 250.5-500.2 m mg/ml (mouse 11b b-HSD2), respectively, which catalyze the interconversion of active cortisol and inactive cortisone.

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.Entropy 2020, 22, 290 2 of 28 functions (IMFs) by EMD [10]. Aiming at the shortcomings of the EMD, such as modal mixture and end effect, which affect the accuracy of signal decomposition [11], Wu et al. [12] improved the EMD method with auxiliary noise and proposed the ensemble empirical mode decomposition (EEMD) method.The EEMD method results in signal decomposition with anti-noise characteristics, reduces reconstruction errors, and improves the quality of IMFs by adding Gaussian white noise to the original signal. Therefore, the EEMD method is widely used in signal processing and fault diagnosis [13]. However, when the EEMD method is applied independently for decomposing signals and reducing noise, the information in the high-frequency components is also lost as some IMFs are discarded [14]. The WT method has a good performance on the suppression of random noise by having the properties of multi-scale, low entropy, and decorrelation [15]. Jumah et al. [16] proposed a method using wavelet transform and various thresholding techniques, which has a good effect on removing one-dimensional Gaussian white noise. However, there is no uniform standard for the operation of the wavelet threshold method, which has a greater impact on the final result. The wavelet hard threshold denoising method generates discontinuous points and loses some vital information [17]. The wavelet soft threshold denoising method causes distortion phenomena, such as edge blur effect [18].Although the wavelet threshold denoising method effectively removes noise from high-frequency signals, the effect is not ideal, and the useful signals still mix with noise signals. Nevertheless, the wavelet semi-soft threshold with the advantages of the hard and soft threshold can not only preserve the integrity of the signal but al...

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