Making with Shenzhen (Characteristics)—Strategy and Everyday Tactics in a City’s Creative Turn

This paper investigates the government-led maker movement in Shenzhen, China by deploying Michel de Certeau’s concepts of “strategy” and “tactics”. While there is a growing body of literature surrounding the maker movement, the discrepancy between the maker movement presented in urban policies and its participants’ actual practices is underexplored. Situating the exploration in the Chinese context, this article looks into how state intervention shapes the maker movement and actors’ participation. This work starts with considerations of political economy to demonstrate how the “Make with Shenzhen” campaign as a strategy fits into the government’s creative city agenda. It then draws upon the findings of a longitudinal ethnographic study to illuminate how discourses, institutions and apparatuses are tactically appropriated by individuals to mobilize symbolic, monetary, social and political resources to serve their interests. We argue that these tactical practices can potentially lead to meaningful changes in the city of Shenzhen and the everyday life of its people. By juxtaposing the strategy of the “Make with Shenzhen” campaign with the tactical practices surrounding it, this study offers insight into the challenges and possibilities brought about by the city-wide learning and making in the Chinese context

Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document

In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document. Previous studies focus more on employing statistical approaches or pre-trained language models (PLMs) to extract sentence embeddings, while ignoring the rich information inherent in the heterogeneous types of interaction between words and sentences. In this paper, we are the first to propose an unsupervised extractive summarizaiton method with heterogeneous graph embeddings (HGEs) for Chinese document. A heterogeneous text graph is constructed to capture different granularities of interactions by incorporating graph structural information. Moreover, our proposed graph is general and flexible where additional nodes such as keywords can be easily integrated. Experimental results demonstrate that our method consistently outperforms the strong baseline in three summarization datasets

Bloom Filter-Based Secure Data Forwarding in Large-Scale Cyber-Physical Systems

Cyber-physical systems (CPSs) connect with the physical world via communication networks, which significantly increases security risks of CPSs. To secure the sensitive data, secure forwarding is an essential component of CPSs. However, CPSs require high dimensional multiattribute and multilevel security requirements due to the significantly increased system scale and diversity, and hence impose high demand on the secure forwarding information query and storage. To tackle these challenges, we propose a practical secure data forwarding scheme for CPSs. Considering the limited storage capability and computational power of entities, we adopt bloom filter to store the secure forwarding information for each entity, which can achieve well balance between the storage consumption and query delay. Furthermore, a novel link-based bloom filter construction method is designed to reduce false positive rate during bloom filter construction. Finally, the effects of false positive rate on the performance of bloom filter-based secure forwarding with different routing policies are discussed

Design and Test of a High QoS Radio Network for CBTC Systems in Subway Tunnels

Communications Based Train Control Systems require high quality radio data communications for train signaling and control. Actually most of these systems use 2.4GHz band with proprietary radio transceivers and leaky feeder as distribution system. All them demand a high QoS radio network to improve the efficiency of railway networks. We present narrow band, broad band and data correlated measurements taken in Madrid underground with a transmission system at 2.4 GHz in a test network of 2 km length in subway tunnels. The architecture proposed has a strong overlap in between cells to improve reliability and QoS. The radio planning of the network is carefully described and modeled with narrow band and broadband measurements and statistics. The result is a network with 99.7% of packets transmitted correctly and average propagation delay of 20ms. These results fulfill the specifications QoS of CBTC systems

A Novel Transformation Electromagnetic Theory-Based Coverage Optimization Method for Wireless Network

Improving the reliability of the radio coverage in shadow area is always an important issue for the wireless communications. The emergence of the transformation electromagnetic (TE) technique provides a new method to control the propagation direction of the radio signal. This paper proposes a coverage optimization method based on the TE technique; a cloak which covers the surface of obstacle is designed to improve the coverage performance in shadow area. The material parameters of cloak are calculated by the transformation electromagnetic method. To solve the calculation problem for the rectangular obstacle, the Fourier series is used to approximately describe the rectangular boundaries. The effectiveness of the proposed cloak for the coverage optimization is validated by the theoretical analysis and simulation results. The simulation results show that the coverage performance can be improved significantly

DiP: Learning Discriminative Implicit Parts for Person Re-Identification

In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods extract part features in an explicit manner, by either using a hand-designed image division or keypoints obtained with external visual systems. In this work, we propose to learn Discriminative implicit Parts (DiPs) which are decoupled from explicit body parts. Therefore, DiPs can learn to extract any discriminative features that can benefit in distinguishing identities, which is beyond predefined body parts (such as accessories). Moreover, we propose a novel implicit position to give a geometric interpretation for each DiP. The implicit position can also serve as a learning signal to encourage DiPs to be more position-equivariant with the identity in the image. Lastly, a set of attributes and auxiliary losses are introduced to further improve the learning of DiPs. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple person ReID benchmarks

The chasm in percutaneous coronary intervention and in-hospital mortality rates among acute myocardial infarction patients in rural and urban hospitals in China: A mediation analysis

Objectives: To determine to what extent the inequality in the ability to provide percutaneous coronary intervention (PCI) translates into outcomes for AMI patients in China. Methods: We identified 82,677 patients who had primary diagnoses of AMI and were hospitalized in Shanxi Province, China, between 2013 and 2017. We applied logistic regressions with inverse probability weighting based on propensity scores and mediation analyses to examine the association of hospital rurality with in-hospital mortality and the potential mediating effects of PCI. Results: In multivariate models where PCI was not adjusted for, rural hospitals were associated with a significantly higher risk of in-hospital mortality (odds ratio [OR]: 1.19, 95% confidence interval [CI]: 1.03–1.37). However, this association was nullified (OR: 0.94, 95% CI: 0.81–1.08) when PCI was included as a covariate. Mediation analyses revealed that PCI significantly mediated 132.3% (95% CI: 104.1–256.6%) of the effect of hospital rurality on in-hospital mortality. The direct effect of hospital rurality on in-hospital mortality was insignificant. Conclusion: The results highlight the need to improve rural hospitals’ infrastructure and address the inequalities of treatments and outcomes in rural and urban hospitals

Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving

A semantic map of the road scene, covering fundamental road elements, is an essential ingredient in autonomous driving systems. It provides important perception foundations for positioning and planning when rendered in the Bird's-Eye-View (BEV). Currently, the prior knowledge of hypothetical depth can guide the learning of translating front perspective views into BEV directly with the help of calibration parameters. However, it suffers from geometric distortions in the representation of distant objects. In addition, another stream of methods without prior knowledge can learn the transformation between front perspective views and BEV implicitly with a global view. Considering that the fusion of different learning methods may bring surprising beneficial effects, we propose a Bi-Mapper framework for top-down road-scene semantic understanding, which incorporates a global view and local prior knowledge. To enhance reliable interaction between them, an asynchronous mutual learning strategy is proposed. At the same time, an Across-Space Loss (ASL) is designed to mitigate the negative impact of geometric distortions. Extensive results on nuScenes and Cam2BEV datasets verify the consistent effectiveness of each module in the proposed Bi-Mapper framework. Compared with exiting road mapping networks, the proposed Bi-Mapper achieves 2.1% higher IoU on the nuScenes dataset. Moreover, we verify the generalization performance of Bi-Mapper in a real-world driving scenario. The source code is publicly available at https://github.com/lynn-yu/Bi-Mapper.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L). The source code is publicly available at https://github.com/lynn-yu/Bi-Mappe

Study of neutron density fluctuation and neutron-proton correlation in Au+Au collisions using PYTHIA8/Angantyr

Utilizing the PYTHIA8 Angantyr model, which incorporates the multiple-parton interactions (MPI) based color reconnection (CR) mechanism, we study the relative neutron density fluctuation and neutron-proton correlation in Au+Au collisions at $\sqrt{s_\text{NN}}$ = 7.7, 11.5, 14.5, 19.6, 27, 39, 62.4, and 200 GeV. In this study, we have not only delved into the dependence of these two remarkable observations on rapidity, centrality, and energy, but also presented an analysis of their interplay with the MPI and CR. Our results have shown that the light nuclei yield ratio of proton, deuteron, and triton, expressed by the elegant expression $N_tN_p/N_d^2$, remains unchanged even as the rapidity coverage and collision centrality increase. Interestingly, we have also revealed that the effect of CR is entirely dependent on the presence of MPI; CR has no impact on the yield ratio if MPI is off. Our findings further demonstrate that the light nuclei yield ratio experiences a slight increase with increasing collision energy as predicted by the PYTHIA8 Angantyr model, but it cannot describe the non-monotonic trend observed by the STAR experiment. Based on the Angantyr model simulation results, it is essential not to overlook the correlation between neutron and proton fluctuations. The Angantyr model is a good baseline for studying collisions in the absence of a Quark-Gluon Plasma (QGP) system, given its lack of flow and jet quenching.Comment: arXiv admin note: text overlap with arXiv:2211.03297 by other author