On the spatial partitioning of urban transportation networks

It has been recently shown that a macroscopic fundamental diagram (MFD) linking space-mean network flow, density and speed exists in the urban transportation networks under some conditions. An MFD is further well defined if the network is homogeneous with links of similar properties. This collective behavior concept can also be utilized to introduce simple control strategies to improve mobility in homogeneous city centers without the need for details in individual links. However many real urban transportation networks are heterogeneous with different levels of congestion. In order to study the existence of MFD and the feasibility of simple control strategies to improve network performance in heterogeneously congested networks, this paper focuses on the clustering of transportation networks based on the spatial features of congestion during a specific time period. Insights are provided on how to extend this framework in the dynamic case. The objectives of partitioning are to obtain (i) small variance of link densities within a cluster which increases the network flow for the same average density and (ii) spatial compactness of each cluster which makes feasible the application of perimeter control strategies. Therefore, a partitioning mechanism which consists of three consecutive algorithms, is designed to minimize the variance of link densities while maintaining the spatial compactness of the clusters. Firstly, an over segmenting of the network is provided by a sophisticated algorithm (Normalized Cut). Secondly, a merging algorithm is developed based on initial segmenting and a rough partitioning of the network is obtained. Finally, a boundary adjustment algorithm is designed to further improve the quality of partitioning by decreasing the variance of link densities while keeping the spatial compactness of the clusters. In addition, both density variance and shape smoothness metrics are introduced to identify the desired number of clusters and evaluate the partitioning results. These results show that both the objectives of small variance and spatial compactness can be achieved with this partitioning mechanism. A simulation in a real urban transportation network further demonstrates the superiority of the proposed method in effectiveness and robustness compared with other clustering algorithms. (C) 2012 Elsevier Ltd. All rights reserved

Total flavonoids from Ganshanbian (Herba Hyperici Attenuati) effect the expression of CaL-α1C and KATP-Kir6.1 mRNA of the myocardial cell membrane in myocardial ischemia-reperfusion arrhythmia rats

AbstractObjectiveTo observe the impact of total flavonoids from Ganshanbian (Herba Hyperici Attenuati) on the expression of vascular smooth muscle membrane L-type calcium channel alpha1 C subunit (CaL-α1C) and ATP-sensitive K+ channel (KATP)-Kir6.1 mRNA, and explore the mechanisms of the antiarrhythmic effect of Ganshanbian (Herba Hyperici Attenuati) total flavonoids.MethodsThe treatment group was fed total flavonoids from Ganshanbian (Herba Hyperici Attenuati) for 7 days by gavage with 100 mg · kg−1 · d−1. The blank control group and model control group were given the same amount of normal saline for 7 d. Arrhythmias were induced by performing a myocardial ischemia-reperfusion and electrocardiogram was observed. Reverse transcription-polymerase chain reaction was used to detect the expression of CaL-α 1Cand KATP-Kir6.1 mRNA in the myocardial cell membrane of all groups of rats.ResultsTotal flavonoids from Ganshanbian (Herba Hyperici Attenuati) can delay the appearance of myocardial ischemia reperfusion arrhythmias, shorten the duration of myocardial ischemia reperfusion arrhythmias, reduce heart rate, reduce cell membrane expression of CaL-α1C mRNA and enhance the expression of KATP-Kir6.1 mRNA in myocardial ischemia-reperfusion arrhythmic rats.ConclusionTotal flavonoids from Ganshanbian (Herba Hyperici Attenuati) can alleviate arrhythmias by affecting the expression of L-type calcium channels and ATP-sensitive K+ channels

Empirical Observations of Congestion Propagation and Dynamic Partitioning with Probe Data for Large-Scale Systems

Research on congestion propagation in large urban networks has been based mainly on microsimulations of link-level traffic dynamics. However, both the unpredictability of travel behavior and the complexity of accurate physical modeling present challenges, and simulation results may be time-consuming and unrealistic. This paper explores empirical data from large-scale urban networks to identify hidden information in the process of congestion formation. Specifically, the spatiotemporal relation of congested links is studied, congestion propagation is observed from a macroscopic perspective, and critical congestion regimes are identified to aid in the design of peripheral control strategies. To achieve these goals, the maximum connected component of congested links is used to capture congestion propagation in the city. A data set of 20,000 taxis with global positioning system (GPS) data from Shenzhen, China, is used. Empirical macroscopic fundamental diagrams of congested regions observed during propagation are presented, and the critical congestion regimes are quantified. The findings show that the proposed methodology can effectively distinguish congestion pockets from the rest of the network and efficiently track congestion evolution in linear time O(n)

Analysis and Design on Key Updating Policies for Satellite Networks

Satellite networks are becoming increasingly important because of the exciting global communication services they provide. Key management policies have been successfully deployed in terrestrial networks to guarantee the information security. However, long propagation, storage and computation constraints bring new challenges in designing efficient and cost-effective key updating policies for satellite networks. Based on the structure and communication features of satellite networks, a dynamic key management model for satellite networks (DKM-SN) is presented, which includes certificates owned by each satellite, primary keys and session keys both of which are shared between two satellites. Furthermore, a protocol is designed for updating certificates for satellites; different policies for updating primary and session keys are studied and their efficiency and security are analyzed and compared. In addition, simulation environment for satellite networks is built and the key updating processes are implemented in Walker constellation. From the simulation results, further contrasts on key updating time and storage costs between the applications of IBM hybrid key management model (HKMM) and DKM-SN in satellite networks are presented. Finally, important suggestions in designing key updating policies are given

THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval

The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification. This task poses a significant challenge, as verifying hypotheses in the NLI4CT task requires the integration of multiple pieces of evidence from one or two CTR(s) and the application of diverse levels of reasoning, including textual and numerical. To address these problems, we present a multi-granularity system for CTR-based textual entailment and evidence retrieval in this paper. Specifically, we construct a Multi-granularity Inference Network (MGNet) that exploits sentence-level and token-level encoding to handle both textual entailment and evidence retrieval tasks. Moreover, we enhance the numerical inference capability of the system by leveraging a T5-based model, SciFive, which is pre-trained on the medical corpus. Model ensembling and a joint inference method are further utilized in the system to increase the stability and consistency of inference. The system achieves f1-scores of 0.856 and 0.853 on textual entailment and evidence retrieval tasks, resulting in the best performance on both subtasks. The experimental results corroborate the effectiveness of our proposed method. Our code is publicly available at https://github.com/THUMLP/NLI4CT.Comment: Accepted by SemEval202

In-silico Antigenicity Determination and Clustering of Dengue Virus Serotypes

Emerging or re-emerging dengue virus (DENV) causes dengue fever epidemics globally. Current DENV serotypes are defined based on genetic clustering, while discrepancies are frequently observed between the genetic clustering and the antigenicity experiments. Rapid antigenicity determination of DENV mutants in high-throughput way is critical for vaccine selection and epidemic prevention during early outbreaks, where accurate prediction methods are seldom reported for DENV. Here, a highly accurate and efficient in-silico model was set up for DENV based on possible antigenicity-dominant positions (ADPs) of envelope (E) protein. Independent testing showed a high performance of our model with AUC-value of 0.937 and accuracy of 0.896 through quantitative Linear Regression (LR) model. More importantly, our model can successfully detect those cross-reactions between inter-serotype strains, while current genetic clustering failed. Prediction cluster of 1,143 historical strains showed new DENV clusters, and we proposed DENV2 should be further classified into two subgroups. Thus, the DENV serotyping may be re-considered antigenetically rather than genetically. As the first algorithm tailor-made for DENV antigenicity measurement based on mutated sequences, our model may provide fast-responding opportunity for the antigenicity surveillance on DENV variants and potential vaccine study

Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection

Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when generalized to an unseen manipulation. Some recent works show improvement in generalization but rely on features fragile to image distortions such as compression. To this end, we propose Diff-ID, a concise and effective approach that explains and measures the identity loss induced by facial manipulations. When testing on an image of a specific person, Diff-ID utilizes an authentic image of that person as a reference and aligns them to the same identity-insensitive attribute feature space by applying a face-swapping generator. We then visualize the identity loss between the test and the reference image from the image differences of the aligned pairs, and design a custom metric to quantify the identity loss. The metric is then proved to be effective in distinguishing the forgery images from the real ones. Extensive experiments show that our approach achieves high detection performance on DeepFake images and state-of-the-art generalization ability to unknown forgery methods, while also being robust to image distortions