Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCL$_{SED}$, and its variant - UCL-EC$_{SED}$, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012\_100, Events2018\_100, and CrisisLexT\_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.Comment: Accepted by TKDE 202

Liver-targeting MRI contrast agent based on galactose functionalized o-carboxymethyl chitosan

Commercial gadolinium (Gd)-based contrast agents (GBCAs) play important role in clinical diagnostic of hepatocellular carcinoma, but their diagnostic efficacy remained improved. As small molecules, the imaging contrast and window of GBCAs is limited by low liver targeting and retention. Herein, we developed a liver-targeting gadolinium (Ⅲ) chelated macromolecular MRI contrast agent based on galactose functionalized o-carboxymethyl chitosan, namely, CS-Ga-(Gd-DTPA)n, to improve hepatocyte uptake and liver retention. Compared to Gd-DTPA and non-specific macromolecular agent CS-(Gd-DTPA)n, CS-Ga-(Gd-DTPA)n showed higher hepatocyte uptake, excellent cell and blood biocompatibility in vitro. Furthermore, CS-Ga-(Gd-DTPA)n also exhibited higher relaxivity in vitro, prolonged retention and better T1-weighted signal enhancement in liver. At 10 days post-injection of CS-Ga-(Gd-DTPA)n at a dose of 0.03 mM Gd/Kg, Gd had a little accumulation in liver with no liver function damage. The good performance of CS-Ga-(Gd-DTPA)n gives great confidence in developing liver-specifc MRI contrast agents for clinical translation

Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory

In hazardous materials transportation systems, accident causation analysis is important to transportation safety. Complex network theory can be effectively used to understand the causal factors of and their relationships within accidents. In this paper, a higher-order network method is proposed to establish a hazardous materials transportation accident causation network (HMTACN), which considers the sequences and dependences of causal factors. The HMTACN is composed of 125 first- and 118 higher-order nodes that represent causes, and 545 directed edges that denote complex relationships among causes. By analyzing topological properties, the results show that the HMTACN has the characteristics of small-world networks and displays the properties of scale-free networks. Additionally, critical causal factors and key relationships of the HMTACN are discovered. Moreover, unsafe tank or valve states are important causal factors; and leakage, roll-over, collision, and fire are most likely to trigger chain reactions. Important higher-order nodes are discovered, which can represent key relationships in the HMTACN. For example, unsafe distance and improper operation usually lead to collision and roll-over. These results of higher-order nodes cannot be found by the traditional Markov network model. This study provides a practical way to extract and construct an accident causation network from numerous accident investigation reports. It also provides insights into safety management of hazardous materials transportation

Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory

The rising popularity of online social network services has attracted lots of research on mining social media data, especially on mining social events. Social event detection, due to its wide applications, has now become a trivial task. State-of-the-art approaches exploiting Graph Neural Networks (GNNs) usually follow a two-step strategy: 1) constructing text graphs based on various views (\textit{co-user}, \textit{co-entities} and \textit{co-hashtags}); and 2) learning a unified text representation by a specific GNN model. Generally, the results heavily rely on the quality of the constructed graphs and the specific message passing scheme. However, existing methods have deficiencies in both aspects: 1) They fail to recognize the noisy information induced by unreliable views. 2) Temporal information which works as a vital indicator of events is neglected in most works. To this end, we propose ETGNN, a novel Evidential Temporal-aware Graph Neural Network. Specifically, we construct view-specific graphs whose nodes are the texts and edges are determined by several types of shared elements respectively. To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula. Considering the view-specific uncertainty, the representations of all views are converted into mass functions through evidential deep learning (EDL) neural networks, and further combined via Dempster-Shafer theory (DST) to make the final detection. Experimental results on three real-world datasets demonstrate the effectiveness of ETGNN in accuracy, reliability and robustness in social event detection.Comment: Accepted by ICWS202

Calibration and Testing of Discrete Element Modeling Parameters for Fresh Goji Berries

This paper aims at the standard grading of fresh goji berries and develops a variable gap-type fresh goji berry grading machine. To establish a complete simulation model, the discrete element parameters of the model were calibrated by a combination of physical experiments and simulation experiments. The outline of the goji berry was extracted by the SFM-CMVS technique, and a goji berry model was obtained using the multi-spherical particle model filling method in the EDEM software. By designing the free-fall, suspension collision, slope slip, and slope rolling experiments, we obtained the discrete element simulation parameters: the inter-particle collision restitution coefficient was 0.158, the collision restitution coefficient of fresh goji berry–silicone rubber material was 0.195, the static friction coefficient of fresh goji berry–silicone rubber material was 0.377, and the rolling friction coefficient of fresh goji berry–silicone rubber material was 0.063. By designing the steepest ascent search and central composite design experiments with the angle of repose (AoR) value obtained from the physical experiment as the target value (31.27°), we determined the inter-particle static friction coefficient to be 0.454 and the inter-particle rolling friction coefficient to be 0.037. Validation tests were conducted on the calibrated discrete element modeling parameters, and the results showed that the established fresh goji berry model and the optimally calibrated parameter combination are effective for discrete element studies on fresh goji berries

Noninvasive Tracking of Gene Transcript and Neuroprotection after Gene Therapy

Gene therapy holds exceptional potential for translational medicine by improving the products of defective genes in diseases and/or providing necessary biologics from endogenous sources during recovery processes. However, validating methods for the delivery, distribution and expression of the exogenous genes from such therapy can generally not be applicable to monitor effects over the long term because they are invasive. We report here that human granulocyte colony-stimulating factor (hG-CSF) cDNA encoded in scAAV-type 2 adeno-associated virus, as delivered through eye drops at multiple time points after cerebral ischemia using bilateral carotid occlusion for 60 min (BCAO-60) led to significant reduction in mortality rates, cerebral atrophy, and neurological deficits in C57black6 mice. Most importantly, we validated hG-CSF cDNA expression using translatable magnetic resonance imaging (MRI) in living brains. This noninvasive approach for monitoring exogenous gene expression in the brains has potential for great impact in the area of experimental gene therapy in animal models of heart attack, stroke, Alzheimer’s dementia, Parkinson’s disorder and amyotrophic lateral sclerosis, and the translation of such techniques to emergency medicine