In this paper, we address matrix-valued distributed stochastic optimization with inequality and equality constraints, where the objective function is a sum of multiple matrix-valued functions with stochastic variables and the considered problems are solved in a distributed manner. A penalty method is derived to deal with the constraints, and a selection principle is proposed for choosing feasible penalty functions and penalty gains. A distributed optimization algorithm based on the gossip model is developed for solving the stochastic optimization problem, and its convergence to the optimal solution is analyzed rigorously. Two numerical examples are given to demonstrate the viability of the main results.

In this paper, the optimization problem subject to N nonidentical closed convex set constraints is studied. The aim is to design a corresponding distributed optimization algorithm over the fixed unbalanced graph to solve the considered problem. To this end, with the push-sum framework improved, the distributed optimization algorithm is newly designed, and its strict convergence analysis is given under the assumption that the involved graph is strongly connected. Finally, simulation results support the good performance of the proposed algorithm.

Considering the popularity of electric vehicles and the flexibility of household appliances, it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents. In this paper, a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcement learning method. First, to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles (EVs), an improved mathematical model integrating driver's experience, unexpected events, and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems. Second, a decoupled advantage actor-critic (DA2C) algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks. Furthermore, separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios. Finally, comprehensive experiments are carried out to compare the proposed approach with existing methods, and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.

Next point-of-interest (POI) recommendation is an important personalized task in location-based social networks (LBSNs) and aims to recommend the next POI for users in a specific situation with historical check-in data. State-of-the-art studies linearly discretize the user's spatiotemporal information and then use recurrent neural network (RNN) based models for modeling. However, these studies ignore the nonlinear effects of spatiotemporal information on user preferences and spatiotemporal correlations between user trajectories and candidate POIs. To address these limitations, a spatiotemporal trajectory (STT) model is proposed in this paper. We use the long short-term memory (LSTM) model with an attention mechanism as the basic framework and introduce the user's spatiotemporal information into the model in encoding. In the process of encoding information, an exponential decay factor is applied to reflect the nonlinear drift of user interest over time and distance. In addition, we design a spatiotemporal matching module in the process of recalling the target to select the most relevant POI by measuring the relevance between the user's current trajectory and the candidate set. We evaluate the performance of our STT model with four real-world datasets. Experimental results show that our model outperforms existing state-of-the-art methods.

Accurate long-term power forecasting is important in the decision-making operation of the power grid and power consumption management of customers to ensure the power system's reliable power supply and the grid economy's reliable operation. However, most time-series forecasting models do not perform well in dealing with long-time-series prediction tasks with a large amount of data. To address this challenge, we propose a parallel time-series prediction model called LDformer. First, we combine Informer with long short-term memory (LSTM) to obtain deep representation abilities in the time series. Then, we propose a parallel encoder module to improve the robustness of the model and combine convolutional layers with an attention mechanism to avoid value redundancy in the attention mechanism. Finally, we propose a probabilistic sparse (ProbSparse) self-attention mechanism combined with UniDrop to reduce the computational overhead and mitigate the risk of losing some key connections in the sequence. Experimental results on five datasets show that LDformer outperforms the state-of-the-art methods for most of the cases when handling the different long-time-series prediction tasks.

Since analog systems play an essential role in modern equipment, test strategy optimization for analog systems has attracted extensive attention in both academia and industry. Although many methods exist for the implementation of effective test strategies, diagnosis for analog systems suffers from the impacts of various stresses due to sophisticated mechanism and variable operational conditions. Consequently, the generated solutions are impractical due to the systems' topology and influence of information redundancy. Additionally, independent tests operating sequentially on the generated strategies may increase the time consumption. To overcome the above weaknesses, we propose a novel approach called heuristic programming (HP) to generate a mixture of test strategies. The experimental results prove that HP and Rollout-HP access the strategy with fewer layers and lower cost consumption than state-of-the-art methods. Both HP and Rollout-HP provide more practical strategies than other methods. Additionally, the cost consumption of the strategy based on HP and Rollout-HP is improved compared with those of other methods because of the updating of the test cost and adaptation of mixture OR nodes. Hence, the proposed HP and Rollout-HP methods have high efficiency.

To address the imbalance problem between supply and demand for taxis and passengers, this paper proposes a distributed ensemble empirical mode decomposition with normalization of spatial attention mechanism based bi-directional gated recurrent unit (EEMDN-SABiGRU) model on Spark for accurate passenger hotspot prediction. It focuses on reducing blind cruising costs, improving carrying efficiency, and maximizing incomes. Specifically, the EEMDN method is put forward to process the passenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of prediction accuracy caused by excessive numerical differences, while dealing with the eigenmodal EMD. Next, a spatial attention mechanism is constructed to capture the characteristics of passenger hotspots in each grid, taking passenger boarding and alighting hotspots as weights and emphasizing the spatial regularity of passengers in the grid. Furthermore, the bi-directional GRU algorithm is merged to deal with the problem that GRU can obtain only the forward information but ignores the backward information, to improve the accuracy of feature extraction. Finally, the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using real-world taxi GPS trajectory data in the Spark parallel computing framework. The experimental results demonstrate that based on the four datasets in the 00-grid, compared with LSTM, EMD-LSTM, EEMD-LSTM, GRU, EMD-GRU, EEMD-GRU, EMDN-GRU, CNN, and BP, the mean absolute percentage error, mean absolute error, root mean square error, and maximum error values of EEMDN-SABiGRU decrease by at least 43.18%, 44.91%, 55.04%, and 39.33%, respectively.

Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation. However, existing methods often fall into what we call interactive misunderstanding, the essence of which is the dilemma in trading off short- and long-term interaction information. To better use the interaction information at various timescales, we propose an interactive segmentation framework, called interactive MEdical image segmentation with self-adaptive Confidence CAlibration (MECCA), which combines action-based confidence learning and multi-agent reinforcement learning. A novel confidence network is learned by predicting the alignment level of the action with short-term interaction information. A confidence-based reward-shaping mechanism is then proposed to explicitly incorporate confidence in the policy gradient calculation, thus directly correcting the model's interactive misunderstanding. MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance, respectively. Numerical experiments on different segmentation tasks show that MECCA can significantly improve short- and long-term interaction information utilization efficiency with remarkably fewer labeled samples. The demo video is available at https://bit.ly/mecca-demo-video.

We study the impact of the distance between two hubs on network coherence defined by Laplacian eigenvalues. Network coherence is a measure of the extent of consensus in a linear system with additive noise. To obtain an exact determination of coherence based on the distance, we choose a family of tree networks with two hubs controlled by two parameters. Using the tree's regular structure, we obtain analytical expressions of the coherences with regard to network parameters and the network size. We then demonstrate that a shorter distance and a larger difference in the degrees of the two hubs lead to a higher coherence. With the same network size and distance, the best coherence occurs in the tree with the largest difference in the hub's degrees. Finally, we establish a correlation between network coherence and average path length and find that they behave linearly.