Learning with Diversification from Block Sparse Signal

This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data. By allowing diversification on variance and correlation matrix, we effectively address the sensitivity issue of existing block sparse learning methods to pre-defined block information, which enables adaptive block estimation while mitigating the risk of overfitting. Based on this, a diversified block sparse Bayesian learning method (DivSBL) is proposed, utilizing EM algorithm and dual ascent method for hyperparameter estimation. Moreover, we establish the global and local optimality theory of our model. Experiments validate the advantages of DivSBL over existing algorithms.Comment: 12 pages, 12 figures, 3 table

Convergence Rate of Projected Subgradient Method with Time-varying Step-sizes

We establish the optimal ergodic convergence rate for the classical projected subgradient method with a time-varying step-size. This convergence rate remains the same even if we slightly increase the weight of the most recent points, thereby relaxing the ergodic sense.Comment: 4 page

A Unified Encoder-Decoder Framework with Entity Memory

Entities, as important carriers of real-world knowledge, play a key role in many NLP tasks. We focus on incorporating entity knowledge into an encoder-decoder framework for informative text generation. Existing approaches tried to index, retrieve, and read external documents as evidence, but they suffered from a large computational overhead. In this work, we propose an encoder-decoder framework with an entity memory, namely EDMem. The entity knowledge is stored in the memory as latent representations, and the memory is pre-trained on Wikipedia along with encoder-decoder parameters. To precisely generate entity names, we design three decoding methods to constrain entity generation by linking entities in the memory. EDMem is a unified framework that can be used on various entity-intensive question answering and generation tasks. Extensive experimental results show that EDMem outperforms both memory-based auto-encoder models and non-memory encoder-decoder models.Comment: Accepted by the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022

SAR Despeckling via Regional Denoising Diffusion Probabilistic Model

Speckle noise poses a significant challenge in maintaining the quality of synthetic aperture radar (SAR) images, so SAR despeckling techniques have drawn increasing attention. Despite the tremendous advancements of deep learning in fixed-scale SAR image despeckling, these methods still struggle to deal with large-scale SAR images. To address this problem, this paper introduces a novel despeckling approach termed Region Denoising Diffusion Probabilistic Model (R-DDPM) based on generative models. R-DDPM enables versatile despeckling of SAR images across various scales, accomplished within a single training session. Moreover, The artifacts in the fused SAR images can be avoided effectively with the utilization of region-guided inverse sampling. Experiments of our proposed R-DDPM on Sentinel-1 data demonstrates superior performance to existing methods.Comment: 5 pages, 5 figure

Topological Transformation and Free-Space Transport of Photonic Hopfions

Structured light fields embody strong spatial variations of polarisation, phase and amplitude. Understanding, characterization and exploitation of such fields can be achieved through their topological properties. Three-dimensional (3D) topological solitons, such as hopfions, are 3D localized continuous field configurations with nontrivial particle-like structures, that exhibit a host of important topologically protected properties. Here, we propose and demonstrate photonic counterparts of hopfions with exact characteristics of Hopf fibration, Hopf index, and Hopf mapping from real-space vector beams to homotopic hyperspheres representing polarisation states. We experimentally generate photonic hopfions with on-demand high-order Hopf indices and independently controlled topological textures, including N\'eel-, Bloch-, and anti-skyrmionic types. We also demonstrate a robust free-space transport of photonic hopfions, thus, showing potential of hopfions for developing optical topological informatics and communications

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enabled data-driven forecasting models using deep learning techniques. These models have shown promise for diverse Earth system forecasting tasks but either struggle with handling uncertainty or neglect domain-specific prior knowledge, resulting in averaging possible futures to blurred forecasts or generating physically implausible predictions. To address these limitations, we propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1) We develop PreDiff, a conditional latent diffusion model capable of probabilistic forecasts. 2) We incorporate an explicit knowledge control mechanism to align forecasts with domain-specific physical constraints. This is achieved by estimating the deviation from imposed constraints at each denoising step and adjusting the transition distribution accordingly. We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset. Specifically, we impose the law of conservation of energy in N-body MNIST and anticipated precipitation intensity in SEVIR. Experiments demonstrate the effectiveness of PreDiff in handling uncertainty, incorporating domain-specific prior knowledge, and generating forecasts that exhibit high operational utility.Comment: Technical repor

Research on energy sharing between distribution network and multiple systems based on the mixed game strategy and water-electric-gas integrated energy complementation

Introduction: It is significant for energy sharing to study the complementary utilization of multiple energy sources, such as water, electricity and gas, and the interaction among multiple stakeholders.Methods: We propose a research on energy sharing between distribution network and multiple systems based on the mixed game strategy and water-electric-gas integrated energy complementation. Firstly, this paper describes the relationship and functions of all stakeholders under the research framework, and establishes the mathematical model of each unit in the water-electric-gas complementary IES. Secondly, the internal roles are layered based on the relationship between stakeholders in the system. Then a non-cooperative game model for the distribution network operator and multiple subsystems is established according to the theory of Stackelberg game, and a cooperative game model for multiple subsystems is further established based on the theory of Nash bargaining. In the next step, the complexity of the problem is analyzed, followed by the description of the specific algorithm and process of solving the model.Results: Finally, the results of example analysis show that the model proposed in this paper not only balances the interests of stakeholders at the upper and lower layers of the system, but also allocates the interests of multiple subsystems at the lower layer.Discussion: Thus effectively improving the energy utilization of the system

A Large-scale Benchmark for Log Parsing

Log data is pivotal in activities like anomaly detection and failure diagnosis in the automated maintenance of software systems. Due to their unstructured format, log parsing is often required to transform them into a structured format for automated analysis. A variety of log parsers exist, making it vital to benchmark these tools to comprehend their features and performance. However, existing datasets for log parsing are limited in terms of scale and representativeness, posing challenges for studies that aim to evaluate or develop log parsers. This problem becomes more pronounced when these parsers are evaluated for production use. To address these issues, we introduce a new collection of large-scale annotated log datasets, named LogPub, which more accurately mirrors log data observed in real-world software systems. LogPub comprises 14 datasets, each averaging 3.6 million log lines. Utilizing LogPub, we re-evaluate 15 log parsers in a more rigorous and practical setting. We also propose a new evaluation metric to lessen the sensitivity of current metrics to imbalanced data distribution. Furthermore, we are the first to scrutinize the detailed performance of log parsers on logs that represent rare system events and offer comprehensive information for system troubleshooting. Parsing such logs accurately is vital yet challenging. We believe that our work could shed light on the design and evaluation of log parsers in more realistic settings, thereby facilitating their implementation in production systems