In bloodstain examination of collaborative medicine and forensics, the analysis and identification of time since deposition (TSD) plays a significant role. Traditional bloodstain analysis methods can only provide a rough estimate for the TSD of traces, and they are time-consuming. To address this issue, we propose a lightweight framework called Fourier Transform Infrared Network (FTIR-Net) that combines wavelet transform with deep learning. To be specific, we parallelly perform wavelet transform on infrared spectra and compute its second derivative to attain the sequential signal and spectral image. Then, the learning component employs two separate branches to extract features from the one-dimensional (1D) spectra signal and two-dimensional (2D) coefficient images provided by continuous wavelet transform (CWT). To effectively aggregate information from the spectral image, we design a Squeeze-and-Excitation Network (SENet) and combine it with 2D convolution. Finally, the extracted features are concatenated and flattened, followed by two fully connected (FC) layers for retention time analysis. Since the standard bloodstain dataset is lacking, we create a dataset that associates bloodstain with the attenuated total reflectance of Fourier transform infrared (ATR-FTIR). To demonstrate the effectiveness of our model in bloodstain analysis and exploit the properties of the proposed dataset, we present comprehensive experiments and ablation studies.

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer from the limitations of over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper, we propose a simple yet effective framework—GRAPH RANDOM NEURAL NETWORKS (GRAND)—to address these issues. In GRAND, we first design a random propagation strategy to perform graph data augmentation. Then we leverage consistency regularization to optimize the prediction consistency of unlabeled nodes across different data augmentations. Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of- the-art GNN baselines on semi-supervised node classification. Finally, we show that GRAND mitigates the issues of over-smoothing and non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at https://github.com/Grand20/grand.

Network embedding has been extensively studied in recent years. In addition to the works on static networks, some researchers try to propose new models for evolving networks. However, sometimes most of these dynamic network embedding models are still not in line with the actual situation, since these models have a strong assumption that we can achieve all the changes in the whole network, while in fact we cannot do this in some real world networks, such as the web networks and some large social networks. So in this paper, we study a novel and challenging problem, i.e., network embedding under partial monitoring for evolving networks. We propose a model on dynamic networks in which we cannot perceive all the changes of the structure. We analyze our model theoretically, and give a bound to the error between the results of our model and the potential optimal cases. We evaluate the performance of our model from two aspects. The experimental results on real world datasets show that our model outperforms the baseline models by a large margin.

Interactive visualization of streaming points for real-time scatterplots and linear blending of correlation patterns is increasingly becoming the dominant mode of visual analytics for both big data and streaming data from active sensors and broadcasting media. To better visualize and interact with inter-stream patterns, it is generally necessary to smooth out gaps or distortions in the streaming data. Previous approaches either animate the points directly or present a sampled static heat-map. We propose a new approach, called StreamMap, to smoothly blend high-density streaming points and create a visual flow that emphasizes the density pattern distributions. In essence, we present three new contributions for the visualization of high-density streaming points. The first contribution is a density-based method called super kernel density estimation that aggregates streaming points using an adaptive kernel to solve the overlapping problem. The second contribution is a robust density morphing algorithm that generates several smooth intermediate frames for a given pair of frames. The third contribution is a trend representation design that can help convey the flow directions of the streaming points. The experimental results on three datasets demonstrate the effectiveness of StreamMap when dynamic visualization and visual analysis of trend patterns on streaming points are required.

Social instant messaging services (SMS) such as WhatsApp, Snapchat and WeChat, have significantly changed the way people work, live, and communicate, attracting increasing attention from multiple disciplinary including computer science, sociology, psychology, and physics. In SMS, social groups play a very important role in supporting communication among multiple users. An interesting question arises: what are the dynamic mechanisms underlying the group evolution? Or more specifically, in an existing group, who should be invited to join? In this paper, we formalize a novel problem of predicting potential invitees of groups. Employing WeChat, the largest social messaging service in China, as the source for our experimental data, we develop a probabilistic graph model to capture the fundamental factors that determine the probability of a user to be invited to a specific social group. Our results show that the proposed model indeed lead to statistically significant prediction improvements over several state-of-the-art baseline methods.

Image denoising as a part of pre-processing in image analysis is a challenging area of research since noise removal and image detail preservation need a tradeoff. For classical denoising models, the convex total variation (TV) or some nonconvex regularizers are used to achieve the tradeoff. However, the denoising performance of classical models is still inadequate. To overcome this problem, this paper proposes a new variational model for image restoration, where a weighted regularizer is designed to protect more geometric structural details of images from over-smoothing and to remove much noise simultaneously. To solve the model efficiently, a novel algorithm based on Chambolle’s dual projection method and the iteratively reweighting method is presented. Numerical results prove that the proposed denoising method can show better performance than the classical TV-based and the nonconvex regularizer-based denoising methods.

A color design process for fabric images can resort to a solution of a color transfer problem based on given color themes. Usually, the color transfer process contains an image segmentation phase and an image construction phase. In this paper, a novel color transfer method for fabric images is proposed. Compared with classical color transfer methods, the new method has the following three main innovations. First, the new method, in its image segmentation phase, follows an assumption that a fabric image can be decomposed into cartoon and texture components, which means the new color transfer method, in its image segmentation, phase incorporates an image decomposition process. The advantage of the innovation is that the cartoon component is more suitable than the original image to be used to partition the fabric image. Second, the new color transfer method can generate more vivid color transfer results since the above texture component is used to describe yarn texture details in the image construction phase. Third, the total generalized variation (TGV) regularizer is used to further improve the performance of image decomposition. Here, the TGV regularizer is good at estimating the weak lightness variation of the cartoon component with the CIELab color scheme. In addition, by using the augmented Lagrange multiplier method, we derive an efficient algorithm to search for the solutions to the proposed color transfer problem. Numerical results demonstrate that the proposed color transfer method can generate better results for fabric images.

Numerous models have been proposed for modeling social networks to explore their structure or to address application problems, such as community detection and behavior prediction. However, the results are still far from satisfactory. One of the biggest challenges is how to capture all the information of a social network in a unified manner, such as links, communities, user attributes, roles and behaviors.

In this paper, we propose a new fuzzy-based variational model that efficiently computes partitioning of speckled images, such as images obtained from Synthetic Aperture Radar (SAR). The model is derived by using the so-called maximizing a posteriori (MAP) estimation method. The novelties of the model are: (1) the Gamma distribution rather than the classical Gaussian distribution is used to model the gray intensities in each homogeneous region of the images (Gamma distribution function is better suited for speckled images); (2) an adaptive weighted regularization term with respect to a fuzzy membership function is designed to protect the segmentation results from degeneration (being over-smoothed). Compared with the classical total variation (TV) regularizer, the proposed regularization term has a sparser property. In addition, a new alternative direction iteration algorithm is proposed to solve the model. The algorithm is efficient since it integrates the split Bregman method and the Chambolle's projection method. Numerical examples are given to verify the efficiency of our model.

Visualization of high density streaming points has become a challenge in information exploration. In this paper, we present a new pipeline for the interactive visualization of large points set. The pipeline is based on the idea that heat-map can overcome the overlapping problem in visualization of high density streaming points. Thus, we firstly define a regular streaming format for large point set which can be updated or changed continually. Based on streaming points, we use kernel density estimation to estimate the point distribution and visualize the density image. Perceptive and interactive features are also considered in our visualization. To our knowledge, our pipeline is the first work that focuses on perceptive visualization of high density streaming points. The main step of our pipeline is accelerated via GPU rendering in order to make scene of real-time interaction in visualization. We demonstrate the visual effectiveness of our pipeline on a geographical dataset of high-density streaming points.