This study delves into multi-variable meteorological spatial-temporal prediction, focusing on the simultaneous forecasting of key meteorological parameters such as temperature, wind speed, and atmospheric pressure. The core challenge of this task lies in identifying commonalities across different variables while capturing their unique features and the interactions among them. To address this, we propose a novel multi-task learning framework tailored for multi-variable meteorological forecasting. Our framework integrates a convolutional variable-specific visual representation module and a variable-interactive spatial-temporal inference module. The former extracts distinct variable information independently for each variable, while the latter employs a tri-level attention mechanism across space, time, and variables to uncover both commonalities and interactions among the variables. An adaptive multi-loss optimization strategy and a local information aggregation module are introduced to balance task optimization complexities and enhance representation stability. Comprehensive experiments across various meteorological prediction tasks confirm the effectiveness of our methods, showcasing superior performance over existing approaches.

Local differential privacy (LDP) has been established as a strong privacy standard for collecting sensitive information from users. Although it has attracted much research attention in recent years, the majority of existing works focus on applying LDP to frequency distribution estimation for each individual value in a discrete domain. This paper concerns the important range queries involving multiple discrete values. Till now, only a few works target this problem. They all rely on the B-ary tree to construct a uniform and hierarchical decomposition, so as to decrease the error when answering large range queries. However, the uniform splitting manner ignores the properties of decomposed sub-domains and processes them equally without preferences, which leads to significant performance penalty.In this paper, we tackle the problem head on: our proposal, privNUD, is a novel domain hierarchical decomposition mechanism. It dynamically decomposes each domain with a tailored granularity into some sub-domains, which sensitively considers the potential chances to answer one range query. The issue of granularity is carefully analyzed for better performance. It also can smartly prune the sub-domains with small frequencies. Besides, an adaptive user allocation technique is designed to dynamically decide the scale of users that are involved in each sub-domain’s frequency estimation. Extensive experiments using real and synthetic datasets demonstrate that privNUD achieves significantly higher result accuracy compared to the up-to-date solutions.

Remote sensing cross-modal image-text retrieval has attracted increasing attention due to its important roles in multiple domains. Existing methods perform salient modeling for the feature that has high relevance between different modalities. However, most works consider the relevance between different modalities but ignore the irrelevance between different modalities, resulting in incomplete modeling of the relevance and irrelevance between different modalities. In this paper, we propose a Relevance and Irrelevance Considered Subspace Mapping Neural Networks (RIR-SMNNs) to simultaneously consider the relevance and irrelevance between different modalities. Specifically, we first utilize Multiscale Image Feature Extraction (MIFE) and Multiscale Text Feature Extraction (MTFE) to extract the multiscale feature of image and text. Then, we perform Local Space Building Module (LSB), which constructs local space that realizes scale alignment. Finally, we perform the Relevance and Irrelevance Local Space Mapping (RIRLSM) to consider the relevance and irrelevance of different modalities in multiple spaces. Experimental results on several remote sensing datasets demonstrate our model outperforms the state-of-the-art approaches.

With the development of deep neural networks, especially generation networks, gray image coloring technology has made great progress. As one of the fields, remote sensing image colorization needs to be solved urgently. This is because remote sensing images cannot obtain clear color images due to the limitations of shooting equipment and transmission equipment. Compared with ordinary images, remote sensing images are characterized by the uneven spatial distribution of objects, therefore, it is a great challenge to ensure the spatial consistency of coloring. To embrace this challenge, we propose a new joint stream DCGAN including a micro stream and a macro stream, in which the latter is set as a prior to constrain the former for colorization. In addition, the Low-level Correlation Feature Extraction (LCFE) module is proposed to obtain the salient shallow detail feature with global correlation, which is used to enhance the global constraints as well as supplement the low-level information to the micro stream. What's more, we propose the Gated Selection (GSM) module by selecting useful information using a gated scheme to fuse features from two streams appropriately. Comprehensive comparison and ablation experiments are implemented and verify the proposed method performs surpasses other methods in both qualitative and quantitative metrics.

In the big data era, distributed computation is becoming a preferred solution for iterative graph analysis. However, graphs are rapidly growing in size and more importantly, there exist a lot of messages across iterations. For better scalability, many distributed systems keep graph data and message data on disk. Now these systems solely employ either pushing or pulling mode to manage data, but neither can always work well during the entire computation. This is mainly because I/O access patterns are dynamic and complex. This article proposes a hybrid solution. It achieves the optimal performance in different scenarios by dynamically and adaptively switching modes between pushing and pulling. Specifically, we first devise a new block-centric pulling technique. It pulls messages much more I/O-efficiently than the existing vertex-centric pulling mode. We then combine pushing and pulling. For general-purpose, we categorize graph algorithms and accordingly present two seamless switching frameworks. We also design performance prediction components specialized to the two frameworks, to decide how and when we can switch modes. Some optimization strategies are also given to further enhance performance, such as priority scheduling and lightweight fault-tolerance. Extensive experiments against state-of-the-art solutions confirm the effectiveness of our proposals.