Snapshot Compressive Imaging (SCI) is a technique for capturing high-dimensional data through snapshot measurements using a two-dimensional (2D) detector. This approach is accomplished via coded aperture compressive temporal imaging (CACTI), which involves applying a temporally variant mask to spatially encode each sequential signal before aggregating the encoded information into a single compressed measurement. The objective of our work is to develop algorithms capable of reconstructing each video frame as a 3D data cube from its 2D measurement. To achieve this goal, we introduce multiple approaches that utilize unconditional and pre-trained 2D score models for video frame reconstruction. Our method involves modeling both the forward perturbation process of the data distribution and its reverse process as stochastic differential equations (SDEs). We also employ score-based deep learning models to estimate the scores of the data distribution across different time steps. Differing from many applications, our sampling process relies on the observed measurement, which directly corresponds to pixel values, rather than class labels. We demonstrate that employing traditional score-based generative methods with 2D score models in SCI, or integrating them into the plug-and-play framework as a deep generative prior, presents challenges. Furthermore, we propose ideas to address these limitations for future research.

The real-time transmission of videos with both high resolution and high frame rate is challenging, due to the limited storage space and significant communication overhead. To meet the real-time requirement, these issues are usually tackled by video quality reduction, which compromises the user experience. While previous methods, such as video compressed sensing, have attempted to address these issues, they often employ a fixed compression rate without considering the varying channel gain and do not adequately address the real-time transmission requirements. To mitigate these shortcomings, we propose an adaptive compressed sensing framework that optimizes the compression rate based on the channel state. This approach equivalently optimizes the video quality while ensuring real-time transmission by reducing communication overhead and thus latency. The feasibility and performance of our method are validated and discussed through extensive experiments on both classic and custom datasets.

Real estate prices have a significant impact on individuals, families, businesses, and governments. The general objective of real estate price prediction is to identify and exploit socioeconomic patterns arising from real estate transactions over multiple aspects, ranging from the property itself to other contributing factors. However, price prediction is a challenging multidimensional problem that involves estimating many characteristics beyond the property itself. In this paper, we use multiple sources of data to evaluate the economic contribution of different socioeconomic characteristics such as surrounding amenities, traffic conditions and social emotions. Our experiments were conducted on 28,550 houses in Beijing, China and we rank each characteristic by its importance. Since the use of multisource information improves the accuracy of predictions, the aforementioned characteristics can be an invaluable resource to assess the economic and social value of real estate. Code and data are available at: https://github.com/IndigoPurple/PATE.

The study of socioeconomic status has been reformed by the availability of digital records containing data on real estate, points of interest, traffic and social media trends such as micro-blogging. In this paper, we describe a heterogeneous, multi-source, multi-modal, multi-view and multi-distributional dataset named "H4M". The mixed dataset contains data on real estate transactions, points of interest, traffic patterns and micro-blogging trends from Beijing, China. The unique composition of H4M makes it an ideal test bed for methodologies and approaches aimed at studying and solving problems related to real estate, traffic, urban mobility planning, social sentiment analysis etc. The dataset is available at: https://indigopurple.github.io/H4M/index.html.

Graph diffusion has quite common phenomenons in our daily life, such as misinformation propagation. As the inverse problem of graph diffusion, the goal of source localization is to identify those nodes of the network from which the information started to spread. Though graph diffusion has been well explored in the literature, the emerging source localization problem is important yet challenging because of its intrinsic ill-posed characteristics. While graph neural networks (GNN) are recently utilized to implement source localization and achieve state-of-the-art performance, a general GNN framework consists of two stages: feature construction and label propagation. Typically, a neural network is pretrained in the feature construction, and then combine with additional functions to jointly perform finetuning for source localization. However, those emerging methods have risks in overfitting the feature construction task, which usually has a gap with the target downstream task of source localization. Such a gap is neglected by previous methods and leads to suboptimal performance. To address this issue, we propose a very simple yet effective method to help better finetune feature construction on the source localization task by adding some noise to the parameters of the feature construction model before finetuning. More specifically, we utilize a matrix-wise perturbing method that adds different uniform noises to different parameter matrices, and design the noise considering the variances and magnitude of network weights. Extensive experiments on six real-world datasets show the proposed method can consistently empower the finetuning of different pretrained feature construction models on the downstream source localization task. Moreover, we conduct an ablation study to investigate the performance with different noise types and intensities. Code is available at: https://github.com/IndigoPurple/PGD.