In recent years, Cross-Modal Retrieval (CMR) has made significant progress in the field of multi-modal analysis. However, since it is time-consuming and labor-intensive to collect large-scale and well-annotated data, the annotation of multi-modal data inevitably contains some noise. This will degrade the retrieval performance of the model. To tackle the problem, numerous robust CMR methods have been developed, including robust learning paradigms, label calibration strategies, and instance selection mechanisms. Unfortunately, they often fail to simultaneously satisfy model performance ceilings, calibration reliability, and data utilization rate. To overcome the limitations, we propose a novel robust cross-modal learning framework, namely Neighbor-aware Instance Refining with Noisy Labels (NIRNL). Specifically, we first propose Cross-modal Margin Preserving (CMP) to adjust the relative distance between positive and negative pairs, thereby enhancing the discrimination between sample pairs. Then, we propose Neighbor-aware Instance Refining (NIR) to identify pure subset, hard subset, and noisy subset through cross-modal neighborhood consensus. Afterward, we construct different tailored optimization strategies for this fine-grained partitioning, thereby maximizing the utilization of all available data while mitigating error propagation. Extensive experiments on three benchmark datasets demonstrate that NIRNL achieves state-of-the-art performance, exhibiting remarkable robustness, especially under high noise rates.

Accurate identification of the epidemic source is crucial for controlling the spread of infectious disease. During the spread of an epidemic, transmission occurs through the edges, which represent the contact relationship between individuals. Source detection, as a reverse problem, should be significantly benefited by leveraging the epidemic state of edges, as it entails both the direction of transmission and the likelihood of disease spreading through the edges. Although Graph Neural Network (GNN)-based methods have been developed for source detection, their performance is limited due to overlooking the epidemic state of edges. Here, we propose a novel GNN-based model, Adaptive Node and Edge Co-evolution Source Detection graph neural network (ANEC-SD), which utilizes the epidemic state of edges and optimizes the node representations individually to enhance prediction performance. In specific, the ANEC-SD consists of two innovative components, where the Co-evolution of Node and Edge Representations Layer is designed to refine the message aggregation mechanism for effectively characterizing the heterogeneous impact from a node to its neighbors by leveraging the epidemic state of edges. While the Propagation Controller determines the optimal number of propagation layers for each node according to node centrality within the infected subgraph, thereby mitigating the issue of over-smoothing. Experiments on six real-world networks demonstrate that our model significantly outperforms existing methods, achieving a 30% improvement on four out of six networks. Further analysis highlights the pivotal role of epidemic state of edges in message passing.

In recent years, GNN-based fake news detection models integrating news content, user characteristics, and propagation structure have gained substantial attention, yet they often face the potential homogenization issues in GNNs, limiting performance in detection. Despite numerous studies focusing on sophisticated models to tackle this issue, many have overlooked the unique structural characteristics of propagation trees. Here, we propose a structure-oriented model named DaFAN, which leverages a dual-attention mechanism to not only address the homogenization issue in message passing but also be able to boost the distinction between true and fake news. In specific, we design a novel Dual Attention Module with the multi-head graph attention mechanism to fuse the multi-modal features by utilizing the inherent characteristics of news propagation trees, and introduce a lightweight feature augmentation module compatible with various GNNs to retain the initial features and optimize the feature selection. Experiments on real datasets demonstrate that our DaFAN model outperforms the state-of-the-art models. Furthermore, the feature augmentation module has notably bolstered our model’s transferability across languages and datasets, fine-tuning on 10% of the target data can significantly surpass the supervised training from scratch.

Forensic person identification is of paramount importance in accidents and criminal investigations. Existing methods based on soft tissue or DNA can be unavailable if the body is badly decomposed, white-ossified, or charred. However, bones last a long time. This raises a natural question: can we learn to identify a person using bone data? We present a novel feature of bones called Neural Boneprint for personal identification. In particular, we exploit the thoracic skeletal data including chest radiographs (CXRs) and computed tomography (CT) images enhanced by the volume rendering technique (VRT) as an example to explore the availability of the neural boneprint. We then represent the neural boneprint as a joint latent embedding of VRT images and CXRs through a bidirectional cross-modality translation and contrastive learning. Preliminary experimental results on real skeletal data demonstrate the effectiveness of the Neural Boneprint for identification. We hope that this approach will provide a promising alternative for challenging forensic cases where conventional methods are limited. The code is available at https://github.com/CheltonNiu/Neural-Boneprint.git.

Monitoring the spread of infectious disease is essential to design and adjust the interventions timely for the prevention of the epidemic outbreak and safeguarding the public health. The governments have generally adopted the incidence-based statistical method to estimate the time-varying effective reproduction number Rt and evaluate the transmission ability of epidemics. However, this method exhibits biases arising from the reported incidence data and assumes the generation interval distribution which is not available at the early stage of epidemic. Recent studies showed that the viral loads characterized by cycle threshold (Ct) of the infected populations evolving throughout the course of epidemic and providing a possibility to infer the epidemic trajectory. In this work, we propose the Cycle Threshold-based Transformer (Ct-Transformer) to estimate Rt. We find the supervised learning of Ct-Transformer outperforms the traditional incidence-based statistic and Ct-based Rt estimating methods, and more importantly Ct-Transformer is robust to the detection resources. Further, we apply the proposed model to self-supervised pre-training tasks and obtain excellent fine-tuned performance, which attains comparable performance with the supervised Ct-Transformer, verified by both the synthetic and real-world datasets. We demonstrate that the Ct-based deep learning method can improve the real-time estimates of Rt, enabling more easily adapted to the track of the newly emerged epidemic.

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Multi-view clustering has received a lot of attentions in data mining recently. Though plenty of works have been investigated on this topic, it is still a severe challenge due to the complex nature of the multiple heterogeneous features. Particularly, existing multi-view clustering algorithms fail to consider the topological structure in the data, which is essential for clustering data on manifold. In this paper, we propose to exploit the implied data manifold by learning the topological relationship between data points. Our method coalesces multiple view-wise graphs with the topological relevance considered, and learns the weights as well as the consensus graph interactively in a unified framework. Furthermore, we manipulate the consensus graph by a connectivity constraint such that the data points from the same cluster are precisely connected into the same component. Substantial experiments on both toy data and real datasets are conducted to validate the effectiveness of the proposed method, compared to the state-of-the-art algorithms over the clustering performance.

The goal of multi-view subspace clustering is to explore a common latent space where the multi-view data points lying on. Myriads of subspace learning algorithms have been investigated to boost the performance of multi-view clustering, but seldom exploiting both the multi-view consistency and multi-view diversity, let alone taking them into consideration simultaneously. To do so, we lodge a novel multi-view subspace clustering via cross-view diversity detection (CDD). CDD is able to exploit these two complementary criteria seamlessly into a holistic design of clustering algorithms. With the consistent part and diverse part being detected, a pure graph can be derived for each view. The consistent pure parts of different views are further fused to a consensus structured graph with exactly k connected components where k is the number of clusters. Thus we can directly obtain the final clustering result without any postprocessing as each connected component precisely corresponds to an individual cluster. We model the above concerns into a unified optimization framework. Our empirical studies validate that the proposed model outperforms several other state-of-the-art methods.

Despite medical advances, the emergence and re-emergence of infectious diseases continue to pose a public health threat. Low-dimensional epidemiological models predict that epidemic transitions are preceded by the phenomenon of critical slowing down (CSD). This has raised the possibility of anticipating disease (re-)emergence using CSD-based early-warning signals (EWS), which are statistical moments estimated from time series data. For EWS to be useful at detecting future (re-)emergence, CSD needs to be a generic (model-independent) feature of epidemiological dynamics irrespective of system complexity. Currently, it is unclear whether the predictions of CSD-derived from simple, low-dimensional systems-pertain to real systems, which are high-dimensional. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models, with increasing structural complexity and dimensionality, for a measles-like infectious disease. Our five models included: i) a nonseasonal homogeneous Susceptible-Exposed-Infectious-Recovered (SEIR) model, ii) a homogeneous SEIR model with seasonality in transmission, iii) an age-structured SEIR model, iv) a multiplex network-based model (Mplex) and v) an agent-based simulator (FRED). All models were parameterised to have a herd-immunity immunization threshold of around 90% coverage, and underwent a linear decrease in vaccine uptake, from 92% to 70% over 15 years. We found evidence of CSD prior to disease re-emergence in all models. We also evaluated the performance of seven EWS: the autocorrelation, coefficient of variation, index of dispersion, kurtosis, mean, skewness, variance. Performance was scored using the Area Under the ROC Curve (AUC) statistic. The best performing EWS were the mean and variance, with AUC > 0.75 one year before the estimated transition time. These two, along with the autocorrelation and index of dispersion, are promising candidate EWS for detecting disease emergence.

In January 2020, a COVID-19 outbreak was detected in Sichuan Province of China. Six weeks later, the outbreak was successfully contained. The aim of this work is to characterize the epidemiology of the Sichuan outbreak and estimate the impact of interventions in limiting SARS-CoV-2 transmission. We analyzed patient records for all laboratory-confirmed cases reported in the province for the period of January 21 to March 16, 2020. To estimate the basic and daily reproduction numbers, we used a Bayesian framework. In addition, we estimated the number of cases averted by the implemented control strategies. The outbreak resulted in 539 confirmed cases, lasted less than two months, and no further local transmission was detected after February 27. The median age of local cases was 8 years older than that of imported cases. We estimated R0 at 2.4 (95% CI: 1.6-3.7). The epidemic was self-sustained for about 3 weeks before going below the epidemic threshold 3 days after the declaration of a public health emergency by Sichuan authorities. Our findings indicate that, were the control measures be adopted four weeks later, the epidemic could have lasted 49 days longer (95% CI: 31-68 days), causing 9,216 more cases (95% CI: 1,317-25,545).

Difficult to be detected in complex network environments, botnets have been huge threats to network security. As the circumscriptions of normal traffics and botnet traffics are blurring, the commonly used botnet detection methods based on traffic analysis often result in high false positive rates. To overcome this issue, we propose an effective botnet detection method based on fuzzy association rules. The proposed method can calculate the features of botnet traffic accurately, which can be used to recognize the normal traffic and botnet. We first collect the data in the laboratory by setting different botnets in the controlled experiment. The botnet traffic features, association rules support, trust and membership are calculated by the proposed method, which are further used to distinguish the type of botnet. When our method is compared with other methods in our data set, we find the former performs better. For the generality, we also test our method on the public data set and also find the higher accuracy rates, which demonstrates the proposed method is effective in detecting the botnets.