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Deep multi-modal learning, a rapidly growing field with a wide range of practical applica‐ tions, aims to effectively utilize and integrate information from multiple sources, known as modalities. Despite its impressive empirical performance, the theoretical foundations of deep multi-modal learning have yet to be fully explored. In this paper, we will undertake a comprehensive survey of recent devel‐ opments in multi-modal learning theories, focusing on the fundamental properties that govern this field. Our goal is to provide a thorough collection of current theoretical tools for analyzing multi-modal learn‐ ing, to clarify their implications for practitioners, and to suggest future directions for the establishment of a solid theoretical foundation for deep multi-modal learning. [ABSTRACT FROM AUTHOR]

We investigate the problem of stochastic network optimization in the presence of state prediction and non-stationarity. Based on a novel state prediction model featured with a distribution-accuracy curve, we develop the predictive learning-aided control (PLC) algorithm, which jointly utilizes historic and predicted network state information for decision making. PLC is an online algorithm that consists of three key components, namely, sequential distribution estimation and change detection, dual learning, and online queue-based control. We show that for stationary networks, PLC achieves a near-optimal utility-delay tradeoff. For non-stationary networks, PLC obtains an utility-backlog tradeoff for distributions that last longer than a time proportional to the square of the prediction error, which is smaller than that needed by backpressure (BP) for achieving the same utility performance. Moreover, PLC detects distribution change $O(w)$ slots faster with high probability ($w$ is the prediction size) and achieves a convergence time faster than that under BP. Our results demonstrate that state prediction helps: 1) achieve faster detection and convergence and 2) obtain better utility-delay tradeoffs. They also quantify the benefits of prediction in four important performance metrics, i.e., utility (efficiency), delay (quality-of-service), detection (robustness), and convergence (adaptability) and provide new insight for joint prediction, learning, and optimization in stochastic networks [ABSTRACT FROM AUTHOR]

Increasing threats of global warming and climate changes call for an energy-efficient and sustainable design of future wireless communication systems. To this end, a novel two-scale stochastic control framework is put forth for smart-grid powered coordinated multi-point (CoMP) systems. Taking into account renewable energy sources, dynamic pricing, two-way energy trading facilities, and imperfect energy storage devices, the energy management task is formulated as an infinite-horizon optimization problem minimizing the time-averaged energy transaction cost, subject to the users’ quality of service requirements. Leveraging the Lyapunov optimization approach as well as the stochastic subgradient method, a two-scale online control (TS-OC) approach is developed for the resultant smart-grid powered CoMP systems. Using only historical data, the proposed TS-OC makes online control decisions at two timescales, and features a provably feasible and asymptotically near-optimal solution. Numerical tests further corroborate the theoretical analysis, and demonstrate the merits of the proposed approach. [ABSTRACT FROM PUBLISHER]

In this paper, we develop optimal sleep/wake scheduling algorithms for smart mobile devices that are powered by batteries and are capable of harvesting energy from the environment. Using a novel combination of the two-timescale Lyapunov optimization approach and weight perturbation, we first design the Optimal Sleep/wake scheduling Algorithm (OSA), which does not require any knowledge of the harvestable energy process. We prove that OSA is able to achieve any system performance that is within $O(\epsilon)$ of the optimal, and explicitly compute the required battery size, which is $O(1/\epsilon)$ . We then extend our results to incorporate system information into algorithm design. Specifically, we develop the Information-aided OSA algorithm (IOSA) by introducing a novel drift augmenting idea in Lyapunov optimization. We show that IOSA is able to achieve the $O(\epsilon)$ close-to-optimal utility performance and ensures that the required traffic buffer and energy storage size are $O(\log (1/\epsilon)^2)$ with high probability. [ABSTRACT FROM AUTHOR]

The explosive growth of global mobile traffic has led to rapid growth in the energy consumption in communication networks. In this paper, we focus on the energy-aware design of the network selection, subchannel, and power allocation in cellular and Wi-Fi networks, while taking into account the traffic delay of mobile users. Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm, which yields a power consumption within O\left(\frac1V \right) bound of the optimal value, and guarantees an $O\left(V \right)$ traffic delay for any positive control parameter $V$. Motivated by the recent advancement in the accurate estimation and prediction of user mobility, channel conditions, and traffic demands, we further develop a novel predictive Lyapunov optimization technique to utilize the predictive information, and propose a Predictive Energy-Aware Network Selection and Resource Allocation (P-ENSRA) algorithm. We characterize the performance bounds of P-ENSRA in terms of the power-delay tradeoff theoretically. To reduce the computational complexity, we finally propose a Greedy Predictive Energy-Aware Network Selection and Resource Allocation (GP-ENSRA) algorithm, where the operator solves the problem in P-ENSRA approximately and iteratively. Numerical results show that GP-ENSRA significantly improves the power-delay performance over ENSRA in the large delay regime. For a wide range of system parameters, GP-ENSRA reduces the traffic delay over ENSRA by 20–30% under the same power consumption. [ABSTRACT FROM PUBLISHER]

This paper considers a stochastic optimization approach for job scheduling and server management in large-scale, geographically distributed data centers. Randomly arriving jobs are routed to a choice of servers. The number of active servers depends on server activation decisions that are updated at a slow time scale, and the service rates of the servers are controlled by power scaling decisions that are made at a faster time scale. We develop a two-time-scale decision strategy that offers provable power cost and delay guarantees. The performance and robustness of the approach is illustrated through simulations. [ABSTRACT FROM AUTHOR]