When Backpressure Meets Predictive Scheduling

Motivated by the increasing popularity of learning and predicting human user behavior in communication and computing systems, in this paper, we investigate the fundamental benefit of predictive scheduling, i.e., predicting and pre-serving arrivals, in controlled queueing systems. Based on a lookahead window prediction model, we first establish a novel equivalence between the predictive queueing system with a \emph{fully-efficient} scheduling scheme and an equivalent queueing system without prediction. This connection allows us to analytically demonstrate that predictive scheduling necessarily improves system delay performance and can drive it to zero with increasing prediction power. We then propose the \textsf{Predictive Backpressure (PBP)} algorithm for achieving optimal utility performance in such predictive systems. \textsf{PBP} efficiently incorporates prediction into stochastic system control and avoids the great complication due to the exponential state space growth in the prediction window size. We show that \textsf{PBP} can achieve a utility performance that is within $O(\epsilon)$ of the optimal, for any $\epsilon>0$, while guaranteeing that the system delay distribution is a \emph{shifted-to-the-left} version of that under the original Backpressure algorithm. Hence, the average packet delay under \textsf{PBP} is strictly better than that under Backpressure, and vanishes with increasing prediction window size. This implies that the resulting utility-delay tradeoff with predictive scheduling beats the known optimal $[O(\epsilon), O(\log(1/\epsilon))]$ tradeoff for systems without prediction

Spin alignment of vector mesons from quark dynamics in a rotating medium

Vorticities in heavy-ion collisions (HICs) are supposed to induce spin alignment and polarization phenomena of quarks and mesons. In this work, we analyze the spin alignment of vector mesons $\phi$ and $\rho$ induced by rotation from quark dynamics in the framework of the Nambu-Jona-Lasinio (NJL) model. The rotating angular velocity induces mass splitting of spin components for vector $\phi,\rho$ mesons $M_{\phi,\rho}(\Omega)\simeq M_{\phi,\rho}(\Omega=0)-s_{z}\Omega$. This behavior contributes to the spin alignment of vector mesons $\phi,\rho$ in an equilibrium medium and naturally explains the negative deviation of $\rho_{00}-1/3$ for vector mesons. Incidentally, the positive deviation of $\rho_{00}-1/3$ under the magnetic field can also be easily understood from quark dynamics.Comment: 12 pages, 8 figure

Ginkgetin aglycone exerts anti-osteoporotic effect via regulation of NOX4/Akt/PI3K pathway

Purpose: To investigate the protective effect of Ginkgetin aglycone (GA) on ovariectomy-induced osteoporosis in rats, as well as the mechanism of action involved. Methods: Adult female Wistar rats (n = 40) were separated into four group: normal control, ovariectomy (OVR), 100 mg GA/kg dose, and 200 mg GA/kg dose. The rats were ovariectomized using standard procedures, except for those in normal control group. Rats in the two treatment groups received 100 or 200 mg GA/kg orally for a period of 12 weeks. Biochemical assays were performed on the urine and blood. Markers of bone formation and mediators of inflammation were assessed. Bone microarchitectural changes were examined using micro-CT scanner, while Western blotting was used to determine the expressions of NOX4, NF-κB p65, PI3K, Akt and JNK proteins in rat femurs. Results: Phosphorus and calcium levels in the serum varied among different groups. Levels of calcium, phosphorus and creatinine decreased (p < 0.01) significantly to a greater extent in the urine of GA group than in that of OVR group (p < 0.05). Interleukin-1β (IL-1β), tumor necrosis factor α (TNF-α) and osteocalcin (OC) levels and the activity of alkaline phosphatase (ALP) decreased more in GA group than in OVR group. In GA-treated group, bone mineral density (BMD) was enhanced in a dose dependent manner than OVR group (p < 0.05). Treatment with GA ameliorated altered bone microarchitecture in OVR rats. Treatment of osteoporotic rats with GA led to significant and dosedependent decrease in the expressions of JNK, NOX4, NF-κB p65 and PI3K, and (p < 0.05) increase in the expression of Akt in femur tissue. Conclusion: In conclusion, result of study proves the anti-osteoporotic activity of GA is exerted via regulation of NOX4/PI3K/Akt pathway

Assessment of Long-Term Watershed Management on Reservoir Phosphorus Concentrations and Export Fluxes.

Source water nutrient management to prevent eutrophication requires critical strategies to reduce watershed phosphorus (P) loadings. Shanxi Drinking-Water Source Area (SDWSA) in eastern China experienced severe water quality deterioration before 2010, but showed considerable improvement following application of several watershed management actions to reduce P. This paper assessed the changes in total phosphorus (TP) concentrations and fluxes at the SDWSA outlet relative to watershed anthropogenic P sources during 2005⁻2016. Overall anthropogenic P inputs decreased by 21.5% over the study period. Domestic sewage, livestock, and fertilizer accounted for (mean ± SD) 18.4 ± 0.6%, 30.1 ± 1.9%, and 51.5 ± 1.5% of total anthropogenic P inputs during 2005⁻2010, compared to 24.3 ± 2.7%, 8.8 ± 10.7%, and 66.9 ± 8.0% for the 2011⁻2016 period, respectively. Annual average TP concentrations in SDWSA decreased from 0.041 ± 0.019 mg/L in 2009 to 0.025 ± 0.013 mg/L in 2016, a total decrease of 38.2%. Annual P flux exported from SDWSA decreased from 0.46 ± 0.04 kg P/(ha·a) in 2010 to 0.25 ± 0.02 kg P/(ha·a) in 2016, a decrease of 44.9%. The success in reducing TP concentrations was mainly due to the development of domestic sewage/refuse collection/treatment and improved livestock management. These P management practices have prevented harmful algal blooms, providing for safe drinking water

Decentralized Federated Learning with Asynchronous Parameter Sharing for Large-scale IoT Networks

Federated learning (FL) enables wireless terminals to collaboratively learn a shared parameter model while keeping all the training data on devices per se. Parameter sharing consists of synchronous and asynchronous ways: the former transmits parameters as blocks or frames and waits until all transmissions finish, whereas the latter provides messages about the status of pending and failed parameter transmission requests. Whatever synchronous or asynchronous parameter sharing is applied, the learning model shall adapt to distinct network architectures as an improper learning model will deteriorate learning performance and, even worse, lead to model divergence for the asynchronous transmission in resource-limited large-scale Internet-of-Things (IoT) networks. This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks. This decentralized learning model approaches a convex function as the number of nodes increases, and its learning process converges to a global stationary point with a higher probability than the centralized FL model. Moreover, by jointly accounting for the convergence bound of federated learning and the transmission delay of wireless communications, we develop a node scheduling and bandwidth allocation algorithm to minimize the transmission delay. Extensive simulation results corroborate the effectiveness of the distributed algorithm in terms of fast learning model convergence and low transmission delay.Comment: 17 pages, 8 figures, to appear in IEEE Internet of Things Journa