Improving the Balance of Unobserved Covariates From Information Theory in Multi-Arm Randomization with Unequal Allocation Ratio

Multi-arm randomization has increasingly widespread applications recently and it is also crucial to ensure that the distributions of important observed covariates as well as the potential unobserved covariates are similar and comparable among all the treatment. However, the theoretical properties of unobserved covariates imbalance in multi-arm randomization with unequal allocation ratio remains unknown. In this paper, we give a general framework analysing the moments and distributions of unobserved covariates imbalance and apply them into different procedures including complete randomization (CR), stratified permuted block (STR-PB) and covariate-adaptive randomization (CAR). The general procedures of multi-arm STR-PB and CAR with unequal allocation ratio are also proposed. In addition, we introduce the concept of entropy to measure the correlation between discrete covariates and verify that we could utilize the correlation to select observed covariates to help better balance the unobserved covariates.Comment: 60 pages, 3 figure

Featured graphic : GDP, livability, population, and income inequality of world cities

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Generative network models for simulating urban networks, the case of inter-city transport network in Southeast Asia

This paper examines the driving forces of urban network formation through the simulation of inter-city transportation networks in Southeast Asia. We present a generative network model (GNM) considering geographical and topological effects, thus combining factors commonly analysed through traditional spatial simulation models (e.g., gravity models) and topological simulation models (e.g., actor-oriented stochastic models)in a single framework. In our GNM, it is assumed that the probability of connections between cities emerges from competing forces. Stimulating factors are a measure of city size (i.e., population) and a topological rule favouring the formation of connections between cities sharing nearest neighbours (i.e., transitive effects). The hampering factors are physical distance between two cities as well as institutional distance (i.e., border effects). We discuss the model in the context of on-going engagements between urban-geographical research and the network science literature, and validate the credence of the model against empirical data on the transport networks connecting 51 major cities in Southeast Asia. Our results show that (1) the generated networks approximate the observed ones in terms of average path length, clustering, modularity, efficiency and quadratic assignment procedure (QAP) correlation between the observed composite network and the generated one, and that (2) GNM performs best when topographical and topological factors are considered simultaneously. Each factor contributes differently to network formation, with transitive effects playing the most important role

Quantifying Coherence with Untrusted Devices

Device-independent (DI) tests allow to witness and quantify the quantum feature of a system, such as entanglement, without trusting the implementation devices. Although DI test is a powerful tool in many quantum information tasks, it generally requires nonlocal settings. Fundamentally, the superposition property of quantum states, quantified by coherence measures, is a distinct feature to distinguish quantum mechanics from classical theories. In literature, witness and quantification of coherence with trusted devices have been well-studied. However, it remains open whether we can witness and quantify single party coherence with untrusted devices, as it is not clear whether the concept of DI tests exists without a nonlocal setting. In this work, we study DI witness and quantification of coherence with untrusted devices. First, we prove a no-go theorem for a fully DI scenario, as well as a semi DI scenario employing a joint measurement with trusted ancillary states. We then propose a general prepare-and-measure semi DI scheme for witnessing and quantifying the amount of coherence. We show how to quantify the relative entropy and the $l_1$ norm of single party coherence with analytical and numerical methods. As coherence is a fundamental resource for tasks such as quantum random number generation and quantum key distribution, we expect our result may shed light on designing new semi DI quantum cryptographic schemes.Comment: 14 pages, 7 figures, comments are welcome

Spatial inequality in the Southeast Asian Intercity Transport Network

Spatial inequality in transport access is both the driver and outcome of rising economic inequality in Southeast Asia. Unlike many regional disparity studies that focus on national economic indicators, this paper takes an urban network approach to assess the spatial inequality in Southeast Asian intercity transport network. We analyze urban connectivity in intercity road, rail, and air networks for a total of 47 Southeast Asian cities. Spatial inequality at the city and network level is revealed via centrality measures and community detection, respectively. Gini coefficients for individual centrality rankings point to a hierarchical degree distribution, a rather even distribution of closeness centrality, and a highly concentrated distribution of betweenness centrality. Four network communities are identified, reflecting the influences of entrenched uneven development, fragmented geography, and economic and political policies

Featured graphic. visualizing urban gastronomy in China

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MMNet: Multi-Mask Network for Referring Image Segmentation

Referring image segmentation aims to segment an object referred to by natural language expression from an image. However, this task is challenging due to the distinct data properties between text and image, and the randomness introduced by diverse objects and unrestricted language expression. Most of previous work focus on improving cross-modal feature fusion while not fully addressing the inherent uncertainty caused by diverse objects and unrestricted language. To tackle these problems, we propose an end-to-end Multi-Mask Network for referring image segmentation(MMNet). we first combine picture and language and then employ an attention mechanism to generate multiple queries that represent different aspects of the language expression. We then utilize these queries to produce a series of corresponding segmentation masks, assigning a score to each mask that reflects its importance. The final result is obtained through the weighted sum of all masks, which greatly reduces the randomness of the language expression. Our proposed framework demonstrates superior performance compared to state-of-the-art approaches on the two most commonly used datasets, RefCOCO, RefCOCO+ and G-Ref, without the need for any post-processing. This further validates the efficacy of our proposed framework.Comment: 10 pages, 5 figure