Stability and bifurcation analysis of Westwood+ TCP congestion control model in mobile cloud computing networks

In this paper, we first build up a Westwood+ TCP congestion control model with communication delay in mobile cloud computing networks. We then study the dynamics of this model by analyzing the distribution ranges of eigenvalues of its characteristic equation. Taking communication delay as the bifurcation parameter, we derive the linear stability criteria depending on communication delay. Furthermore, we study the direction of Hopf bifurcation as well as the stability of periodic solution for the Westwood+ TCP congestion control model with communication delay. We find that the Hopf bifurcation occurs when the communication delay passes a sequence of critical values. The stability and direction of the Hopf bifurcation are determined by the normal form theory and the center manifold theorem. Finally, numerical simulation is done to verify the theoretical results

Characterization of mineral and pore evolution under CO2-brine-rock interaction at in-situ conditions

Herein, a method of physical modeling of CO2-brine-rock interaction and in-situ characterization of mineral and pore evolution is established. The nested preparation and installation of the same sample with different sizes could protect and keep the integrality of the millimeter-size sample in conventional high-temperature and high-pressure reactors. This paper establishes a procedure to obtain the three-dimensional in-situ comparison of minerals and pores before and after the reaction. The resolution is updated from 5-10 µ m to 10 nm, which could be helpful for modeling CO2-brine-rock interaction in unconventional tight reservoirs. This method could be applied to CO2-enhanced oil recovery as well as CO2 capture, utilization, and storage scientific research. Furthermore, it may shed light on the carbon sequestration schemes in the Chinese petroleum industry.Cited as: Wu, S., Yu, C., Hu, X., Yu, Z., Jiang, X. Characterization of mineral and pore evolution under CO2-brine-rock interaction at in-situ conditions. Advances in Geo-Energy Research, 2022, 6(2): 177-178. https://doi.org/10.46690/ager.2022.02.0

Generalization Ability of Wide Residual Networks

In this paper, we study the generalization ability of the wide residual network on $\mathbb{S}^{d-1}$ with the ReLU activation function. We first show that as the width $m\rightarrow\infty$, the residual network kernel (RNK) uniformly converges to the residual neural tangent kernel (RNTK). This uniform convergence further guarantees that the generalization error of the residual network converges to that of the kernel regression with respect to the RNTK. As direct corollaries, we then show $i)$ the wide residual network with the early stopping strategy can achieve the minimax rate provided that the target regression function falls in the reproducing kernel Hilbert space (RKHS) associated with the RNTK; $ii)$ the wide residual network can not generalize well if it is trained till overfitting the data. We finally illustrate some experiments to reconcile the contradiction between our theoretical result and the widely observed ``benign overfitting phenomenon''Comment: 28 pages, 3 figure

Functional Slicing-free Inverse Regression via Martingale Difference Divergence Operator

Functional sliced inverse regression (FSIR) is one of the most popular algorithms for functional sufficient dimension reduction (FSDR). However, the choice of slice scheme in FSIR is critical but challenging. In this paper, we propose a new method called functional slicing-free inverse regression (FSFIR) to estimate the central subspace in FSDR. FSFIR is based on the martingale difference divergence operator, which is a novel metric introduced to characterize the conditional mean independence of a functional predictor on a multivariate response. We also provide a specific convergence rate for the FSFIR estimator. Compared with existing functional sliced inverse regression methods, FSFIR does not require the selection of a slice number. Simulations demonstrate the efficiency and convenience of FSFIR

Probabilistic Single-Valued (Interval) Neutrosophic Hesitant Fuzzy Set and Its Application in Multi-Attribute Decision Making

The uncertainty and concurrence of randomness are considered when many practical problems are dealt with. To describe the aleatory uncertainty and imprecision in a neutrosophic environment and prevent the obliteration of more data, the concept of the probabilistic single-valued (interval) neutrosophic hesitant fuzzy set is introduced

Cavity-coupled telecom atomic source in silicon

Atomic defects in solid-state materials are promising candidates for quantum interconnect and networking applications. Recently, a series of atomic defects have been identified in the silicon platform, where scalable device integration can be enabled by mature silicon photonics and electronics technologies. In particular, T centers hold great promise due to their telecom band optical transitions and the doublet ground state electronic spin manifold with long coherence times. However, an open challenge for advancing the T center platform is to enhance its weak and slow zero phonon line emission. In this work, we demonstrate the cavity-enhanced fluorescence emission from a single T center. This is realized by integrating single T centers with a low-loss, small mode-volume silicon photonic crystal cavity, which results in an enhancement of the fluorescence decay rate by a factor of $F$ = 6.89. Efficient photon extraction enables the system to achieve an average photon outcoupling rate of 73.3 kHz at the zero phonon line. The dynamics of the coupled system is well modeled by solving the Lindblad master equation. These results represent a significant step towards building efficient T center spin-photon interfaces for quantum information processing and networking applications

Model-Free Algorithm with Improved Sample Efficiency for Zero-Sum Markov Games

The problem of two-player zero-sum Markov games has recently attracted increasing interests in theoretical studies of multi-agent reinforcement learning (RL). In particular, for finite-horizon episodic Markov decision processes (MDPs), it has been shown that model-based algorithms can find an $\epsilon$-optimal Nash Equilibrium (NE) with the sample complexity of $O(H^3SAB/\epsilon^2)$, which is optimal in the dependence of the horizon $H$ and the number of states $S$ (where $A$ and $B$ denote the number of actions of the two players, respectively). However, none of the existing model-free algorithms can achieve such an optimality. In this work, we propose a model-free stage-based Q-learning algorithm and show that it achieves the same sample complexity as the best model-based algorithm, and hence for the first time demonstrate that model-free algorithms can enjoy the same optimality in the $H$ dependence as model-based algorithms. The main improvement of the dependency on $H$ arises by leveraging the popular variance reduction technique based on the reference-advantage decomposition previously used only for single-agent RL. However, such a technique relies on a critical monotonicity property of the value function, which does not hold in Markov games due to the update of the policy via the coarse correlated equilibrium (CCE) oracle. Thus, to extend such a technique to Markov games, our algorithm features a key novel design of updating the reference value functions as the pair of optimistic and pessimistic value functions whose value difference is the smallest in the history in order to achieve the desired improvement in the sample efficiency

Seasonal and interannual ice velocity changes of Polar Record Glacier, East Antarctica

We present a study of seasonal and interannual ice velocity changes at Polar Record Glacier, East Antarctica, using ERS-1/2, Envisat and PALSAR data with D-InSAR and intensity tracking. Ice flow showed seasonal variations at the front of the glacier tongue. Velocities in winter were 19% less than velocities during summer. No significant interannual changes were detected. Ice velocities in the grounding zone and grounded glacier did not show clear seasonal or interannual changes. The distributio of the seasonal variations suggests that the cause for the changes should be localized. Possible causes are seasonal sea-ice changes and iceberg blocking. Satellite images show that the sea ice surrounding Polar Record Glacier undergoes seasonal changes. Frozen sea ice in winter slowed the huge iceberg, and provided increased resistance to the glacier flow. The interaction between the glacier tongue, ice berg and sea ice significantly influences their flow pattern