Smart grids, as a critical application of the Internet of Things (IoT), integrate diverse power generation sources while serving a vast number of energy consumers. Therefore, multivariate time series (MTS) forecasting methods are essential for power forecasting. The existing deep MTS forecasting models have already achieved remarkable performance. They deploy different architectures to analyze MTS features from the temporal, frequency and variable dimensions. However, tight combinations of three types of MTS features have rarely been explored, resulting in imperfect predictions, especially for power systems, wherein the variates are correlated mostly because of the close spatial locations of power plants and clients. To address this problem, a novel MTS forecasting model, SimTFV, that is capable of simultaneously modeling temporal-frequency-variable features is proposed in this work. SimTFV mixes temporal and frequency features via an enhanced instance normalization mechanism based on Heisenberg uncertainty principle. Moreover, SimTFV possesses multiple modified attention modules for simultaneously and efficiently extracting MTS features from the temporal and variable dimensions. Extensive experiments on three energy generation benchmarks and one energy consumption benchmark demonstrate the state-of-the-art performance of SimTFV. The code is released on https://github.com/OrigamiSL/SimTFV.

Audio-visual segmentation (AVS) is a challenging multimodal task that needs to fuse the spatial-temporal audio-visual features to achieve pixel-wise segmentation of sounding objects. This work presents AV2TS, which is a novel spatial-temporal framework for AVS. In contrast to the previous AVS approaches where temporal features are secondarily concerned, we cast AVS as a multivariate time series modeling task in which each frame sequence and its corresponding audio sequence form two time series to highlight the significance of temporal features in AVS. In AV2TS, the perception of each modal sequence is expressed as an intra-series feature extraction process, and the cross-modal fusion task is described as inter-series interactions. Specifically, the intra-series features of the frame and audio sequences are extracted via temporal attention. Moreover, an inter-series cross-modal fusion module is implemented by sharing the shared temporal attention maps of the frame and audio sequences. Additionally, AV2TS contains a one-stream fusion module that unifies the learning and relation modeling processes applied to spatially compact visual features and nontrivial acoustic features. Extensive experiments conducted on three AVSBench datasets demonstrate the state-of-the-art performance of AV2TS in both segmentation and semantic segmentation scenarios. Code is released onhttps://github.com/OrigamiSL/AV2TS.

The past decade has witnessed the success of deep learning-based multivariate time series forecasting in Internet of Things (IoT) systems. However, dynamic variable correlation remains a long-standing problem. The majority of existing multivariate forecasting methods either constantly forbid the interactions of all variables or, conversely, keep extracting the correlations of all variables, which is suboptimal for real-world time series with time-varying variable correlations. In contrast, we introduce a novel variable-dynamic forecasting transformer named VDformer. By leveraging empirical mode decomposition (EMD), VDformer can sparsely identify the dominant periodic ingredients of each variable in an arbitrary multivariate sequence via Fourier spectral analysis of its intrinsic mode functions (IMFs) obtained by the EMD. Thus, a mask matrix, where only the variables with identical dominant periodic ingredients are allowed for interactions, can be generated and used in the cross-variable attention modules of VDformer to dynamically gauge and extract the variable correlations. Additionally, better decoder initialization can be obtained by reconstructing the input sequence with these dominant periodic ingredients and extending the reconstructed results to the prediction duration. Extensive experiments on 11 benchmarks, which cover five IoT-related domains, demonstrate the state-of-the-art forecasting performance of VDformer (10.02% MSE reduction relative to the current best method). Code and Appendix are released on https://github.com/OrigamiSL/VDformer.

Energy forecasting plays a vital role in smart grid technology frameworks for monitoring power systems, including energy generation and consumption systems. As a downstream task of time series forecasting, energy forecasting has been thoroughly studied on the basis of deep learning in recent years. However, the sparsity of multivariate energy data, as well as the sparsity involved in cases with multiple solutions, has received minimal attention. To fill this gap, this work analyzes the intra-series and inter-series sparsity of long-term multivariate energy data in a hierarchical manner. Specifically, hierarchical global time stamps are leveraged to represent intra-series sparsity. Moreover, wavelet theory is applied to identify inter-series sparsity according to the correlations of series at different frequency scales. Building upon the above analysis of hierarchical sparsity, this work presents a novel energy forecasting model, the hierarchically sparse transformer, which uses a novel pyramid architecture to hierarchically extract sparse intra-series and inter-series features for effective and efficient energy forecasting. Extensive experiments on four energy-related benchmarks demonstrate the state-of-the-art performance of the proposed model. The source code is released on https://github.com/OrigamiSL/HST.

Recently, there has been a surge of meta-learning-based approaches for the few-shot hyperspectral image classification (FSHSIC) task. Meta-learning leverages prior knowledge to teach a base-learner how to adapt quickly to a new few-shot task, which hinges on the consistency of the prior and new tasks to guarantee validity. However, hyperspectral image classification (HSIC) is an environment-dependent task, which means that the hyperspectral features of two objects in the same category can be essentially distinctive in different environments. Consequently, whether meta-learning is a feasible solution for FSHSIC is an imperative problem to investigate, notwithstanding the promising performance shown in previous meta-learning-based approaches. To this end, this work proposes a simple multilayer perceptron (MLP)-based model named SimHSIC for FSHSIC. SimHSIC utilizes only a few labeled samples from the target HSI to train the model rapidly. Surprisingly, SimHSIC outperforms existing meta-learning-based approaches, which are built upon complex three-dimensional convolutions or transformers and need heavy training processes, in prevailing public benchmarks. On the basis of extensive experiments, we conclude that the relatively better classification performances of meta-learning-based FSHSIC solutions are due mainly to the patching of each HSI pixel with the large surroundings instead of meta-learning. The code is released on https://github.com/OrigamiSL/SimHSIC.

Traditional statistical time series forecasting models rely on model identification methods to identify the worthiest model variants to investigate; therefore, the model parameters change with the statistical features of rolling windows to reach optimality. Currently, although deep-learning-based methods achieve promising multivariate forecasting performance, their representations of variable correlations are consistent regardless of the observed local time series properties and dynamic cross-variable relations, rendering them prone to overfitting. To bridge this gap, we propose FPPformer-MD, a novel inconsistent time series forecasting transformer. FPPformer-MD leverages multiresolution analysis to transform each univariate series into multiple frequency scales and evaluate the local variable correlations via their variances. Thus, FPPformer-MD receives richer input features, and its inner inconsistent cross-variable attention mechanism enables the adaptive extraction of cross-variable features. To further alleviate the overfitting problem, we apply dynamic mode decomposition to perform cross-variable data augmentation, which reconstructs the sequence outliers with other correlated sequences during the model training process. Extensive experiments conducted on thirteen real-world benchmarks demonstrate the state-of-the-art performance of FPPformer-MD.

With the development of Internet of Things (IoT) systems, precise long-term forecasting method is requisite for decision makers to evaluate current statuses and formulate future policies. Currently, Transformer and MLP are two paradigms for deep time-series forecasting and the former one is more prevailing in virtue of its exquisite attention mechanism and encoder-decoder architecture. However, data scientists seem to be more willing to dive into the research of encoder, leaving decoder unconcerned. Some researchers even adopt linear projections in lieu of the decoder to reduce the complexity. We argue that both extracting the features of input sequence and seeking the relations of input and prediction sequence, which are respective functions of encoder and decoder, are of paramount significance. Motivated from the success of FPN in CV field, we propose FPPformer to utilize bottom-up and top-down architectures respectively in encoder and decoder to build the full and rational hierarchy. The cutting-edge patch-wise attention is exploited and further developed with the combination, whose format is also different in encoder and decoder, of revamped element-wise attention in this work. Extensive experiments with six state-of-the-art baselines on twelve benchmarks verify the promising performances of FPPformer and the importance of elaborately devising decoder in time-series forecasting Transformer. The source code is released in https://github.com/OrigamiSL/FPPformer.

Aiming for efficient coordination mechanisms for the distributed weighted set cover problem, we study from ordinal potential game theoretic learning and propose a Nash equilibrium selection algorithm (NESA). An ordinal potential game model is established, where the local utility function is designed by incorporating a greedy heuristic. To distinguish Nash equilibria of different global fitness, we further classify them into the inferior Nash equilibrium (INE) and the superior Nash equilibrium (SNE), and show that the optimal solution must be an SNE. High-quality SNE solutions are obtained by assigning each player a local stochastic rule based on its category and a finite memory. By demonstrating the existence of a finite improvement path from each INE to an SNE, we prove finite-time convergence of the NESA. Numerical experiments are carried out and comparisons against representative methods are presented, which demonstrate the effectiveness as well as the superiority of our methodology to the state-of-the-art.

Toward better approximation for the minimum-weighted vertex cover (MWVC) problem in multiagent systems, we present a distributed algorithm from the perspective of learning in games. For self-organized coordination and optimization, we see each vertex as a potential game player who makes decisions using local information of its own and the immediate neighbors. The resulting Nash equilibrium is classified into two categories, i.e., the inferior Nash equilibrium (INE) and the dominant Nash equilibrium (DNE). We show that the optimal solution must be a DNE. To achieve better approximation ratios, local rules of perturbation and weighted memory are designed, with the former destroying the stability of an INE and the latter facilitating the refinement of a DNE. By showing the existence of an improvement path from any INE to a DNE, we prove that when the memory length is larger than 1, our algorithm converges in finite time to DNEs, which could not be improved by exchanging the action of a selected node with all its unselected neighbors. Moreover, additional freedom for solution efficiency refinement is provided by increasing the memory length. Finally, intensive comparison experiments demonstrate the superiority of the presented methodology to the state of the art, both in solution efficiency and computation speed.

Area coverage and network lifetime are two contradictory issues to the architecture development of a wireless sensor network (WSN). A satisfactory balance could be achieved by deploying abundant sensor nodes randomly and dividing them into$k$exclusive cover sets. Toward self-organized partition with higher efficiency, we address the problem from the perspective of networked potential games and propose a memorial mixed-response algorithm (MMRA), which is implemented in a distributed and synchronous manner. Being viewed as a game player, each sensor node first updates its memory using a temporary action, which is generated by following a mixed response rule. After this, the coordination evolves into the next iteration by each player randomly drawing an action from its memory with equal probabilities. We prove that our algorithm converges with probability 1 to a convention of Nash equilibria, with the worst approximation ratio strictly larger than 0.5. Moreover, it is also found that a tradeoff between solution efficiency and computation time could be achieved via the adjustment of the amount of randomness introduced via the memory length$m$as well as the probability$p_{m}$, where better partition results are more likely to be generated using a larger$m$and smaller$p_{m}$. Comparisons with existing distributed methods demonstrate the superiority of our method in terms of solution refinement as well as convergence speed.

Toward the global optimality and computation time reduction, we address the minimum weighted vertex cover (MWVC) problem by proposing a population based game theoretic optimizer (PGTO) that combines learning in games with population based optimization. A population of candidate solutions are iterated through the procedures of swarm evolution (SE), learning in games (LIG), and local search (LS). Via strict theoretic analysis, we prove that LIG converges with probability one to Nash equilibria which could be further refined by LS. Numerical simulations show that a larger population size and a proper mutation probability are more likely to provide the best performance. Comparison experiments with typical algorithms demonstrate the superiority of the presented methodology to the state of the art, both in terms of solution efficiency and computation time.