Measurement-induced-nonlocality for Dirac particles in Garfinkle–Horowitz–Strominger dilation space–time

AbstractWe investigate the quantum correlation via measurement-induced-nonlocality (MIN) for Dirac particles in Garfinkle–Horowitz–Strominger (GHS) dilation space–time. It is shown that the physical accessible quantum correlation decreases as the dilation parameter increases monotonically. Unlike the case of scalar fields, the physical accessible correlation is not zero when the Hawking temperature is infinite owing to the Pauli exclusion principle and the differences between Fermi–Dirac and Bose–Einstein statistics. Meanwhile, the boundary of MIN related to Bell-violation is derived, which indicates that MIN is more general than quantum nonlocality captured by the violation of Bell-inequality. As a by-product, a tenable quantitative relation about MIN redistribution is obtained whatever the dilation parameter is. In addition, it is worth emphasizing that the underlying reason why the physical accessible correlation and mutual information decrease is that they are redistributed to the physical inaccessible regions

Multimodal-Transport Collaborative Evacuation Strategies for Urban Serious Emergency Incidents Based on Multi-Sources Spatiotemporal Data (Short Paper)

When serious emergency events happen in metropolitan cities where pedestrians and vehicles are in high-density, single modal-transport cannot meet the requirements of quick evacuations. Existing mixed modes of transportation lacks spatiotemporal collaborative ability, which cannot work together to accomplish evacuation tasks in a safe and efficient way. It is of great scientific significance and application value for emergency response to adopt multimodal-transport evacuations and improve their spatial-temporal collaboration ability. However, multimodal-transport evacuation strategies for urban serious emergency event are great challenge to be solved. The reasons lie in that: (1) large-scale urban emergency environment are extremely complicated involving many geographical elements (e.g., road, buildings, over-pass, square, hydrographic net, etc.); (2) Evacuated objects are dynamic and hard to be predicted. (3) the distributions of pedestrians and vehicles are unknown. To such issues, this paper reveals both collaborative and competitive mechanisms of multimodal-transport, and further makes global optimal evacuation strategies from the macro-optimization perspective. Considering detailed geographical environment, pedestrian, vehicle and urban rail transit, a multi-objective multi-dynamic-constraints optimization model for multimodal-transport collaborative emergency evacuation is constructed. Take crowd incidents in Shenzhen as example, empirical experiments with real-world data are conducted to evaluate the evacuation strategies and path planning. It is expected to obtain innovative research achievements on theory and method of urban emergency evacuation in serious emergency events. Moreover, this research results provide spatial-temporal decision support for urban emergency response, which is benefit to constructing smart and safe cities

Mutually Guided Few-shot Learning for Relational Triple Extraction

Knowledge graphs (KGs), containing many entity-relation-entity triples, provide rich information for downstream applications. Although extracting triples from unstructured texts has been widely explored, most of them require a large number of labeled instances. The performance will drop dramatically when only few labeled data are available. To tackle this problem, we propose the Mutually Guided Few-shot learning framework for Relational Triple Extraction (MG-FTE). Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations. To draw the connection between entity and relation, we design a proto-level fusion module to boost the performance of both entity extraction and relation classification. Moreover, a new cross-domain few-shot triple extraction task is introduced. Extensive experiments show that our method outperforms many state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single-domain) and 20.5 F1 score on FewRel 2.0 (cross-domain).Comment: Accepted by ICASSP 202