How does the G20 Summit affect land market? Evidence from China

We employ the difference-in-difference and synthetic control methods to investigate the capitalization effect of hosting the G20 Summit on land market, based on China’s land transaction dataset from 2011 to 2019. We find that hosting the G20 Summit has a significant positive effect on land prices in the host city, increasing land prices by over 22.6% compared to comparable cities. The impact of hosting the G20 Summit on land prices is larger in the post-G20 period than in the preparation period. Further, hosting the G20 Summit has heterogeneous and distributional effects on land prices. The capitalization effects of venue construction and transportation infrastructure upgrading on land prices are the main channels

Distributed Linear Regression with Compositional Covariates

With the availability of extraordinarily huge data sets, solving the problems of distributed statistical methodology and computing for such data sets has become increasingly crucial in the big data area. In this paper, we focus on the distributed sparse penalized linear log-contrast model in massive compositional data. In particular, two distributed optimization techniques under centralized and decentralized topologies are proposed for solving the two different constrained convex optimization problems. Both two proposed algorithms are based on the frameworks of Alternating Direction Method of Multipliers (ADMM) and Coordinate Descent Method of Multipliers(CDMM, Lin et al., 2014, Biometrika). It is worth emphasizing that, in the decentralized topology, we introduce a distributed coordinate-wise descent algorithm based on Group ADMM(GADMM, Elgabli et al., 2020, Journal of Machine Learning Research) for obtaining a communication-efficient regularized estimation. Correspondingly, the convergence theories of the proposed algorithms are rigorously established under some regularity conditions. Numerical experiments on both synthetic and real data are conducted to evaluate our proposed algorithms.Comment: 35 pages,2 figure

Anti-periodic solutions for a class of third-order nonlinear differential equations with a deviating argument

In this paper, we study a class of third-order nonlinear differential equations with a deviating argument and establish some sufficient conditions for the existence and exponential stability of anti-periodic solutions of the equation. These conditions are new and complement to previously known results

Effect of Seed Morph and Light Level on Growth and Reproduction of the Amphicarpic Plant \u3cem\u3eAmphicarpaea edgeworthii\u3c/em\u3e (Fabaceae)

Amphicarpic plants produce aerial and subterranean fruits on an individual plant, and these heteromorphic diaspores give rise to plants that differ in growth and ecology. Amphicarpaea edgeworthii is a summer annual amphicarpic species that grows over a range of light levels. We aimed to compare the response to shading intensity of plants of A. edgeworthii grown throughout their life cycle from aerial seeds (ASP) and from subterranean seeds (SSP). We hypothesized that vegetative and reproductive growth of plants from ASP and SSP respond differently to light. Plants were grown from ASP and SSP under 0, 46, 71 and 90% shading intensities. With plant height as a covariate, vegetative biomass of ASP and SSP did not differ. Leaf area and seed production of SSP were greater and internode length less than they were for ASP in all shading intensities. Aerial and subterranean seed yield, seed mass and number for both ASP and SSP were highest in full light. Aerial seed yield was affected more than subterranean seed yield by shading intensity. The growth and reproductive responses of ASP and SSP of A. edgeworthii may be adaptive to the range of low to high light environments in which this species grows

Divergence in Life History Traits Between Two Populations of a Seed-Dimorphic Halophyte in Response to Soil Salinity

Production of heteromorphic seeds is common in halophytes growing in arid environments with strong spatial and temporal heterogeneity. However, evidence for geographic variation (reflecting local adaptation) is almost nonexistent. Our primary aims were to compare the life history traits of two desert populations of this halophytic summer annual Suaeda corniculata subsp. mongolica and to investigate the phenotypic response of its plant and heteromorphic seeds to different levels of salt stress. Dimorphic seeds (F1) of the halophyte S. corniculata collected from two distant populations (F0) that differ in soil salinity were grown in a common environment under different levels of salinity to minimize the carryover effects from the field environment and tested for variation in plant (F1) and seed (F2) traits. Compared to F1 plants grown in low soil salinity, those grown in high salinity (\u3e0.2 mol⋅L-1) were smaller and produced fewer seeds but had a higher reproductive allocation and a higher non-dormant brown seed: dormant black seed ratio. High salinity during plant growth decreased germination percentage of F2 black seeds but had no effect on F2 brown seeds. Between population differences in life history traits in the common environment corresponded with those in the natural populations. Phenotypic differences between the two populations were retained in F1 plants and in F2 seeds in the common environment, which suggests that the traits are genetically based. Our results indicate that soil salinity plays an ecologically important role in population regeneration of S. corniculata by influencing heteromorphic seed production in the natural habitat

A Survey on Automated Program Repair Techniques

With the rapid development and large-scale popularity of program software, modern society increasingly relies on software systems. However, the problems exposed by software have also come to the fore. Software defect has become an important factor troubling developers. In this context, Automated Program Repair (APR) techniques have emerged, aiming to automatically fix software defect problems and reduce manual debugging work. In particular, benefiting from the advances in deep learning, numerous learning-based APR techniques have emerged in recent years, which also bring new opportunities for APR research. To give researchers a quick overview of APR techniques' complete development and future opportunities, we revisit the evolution of APR techniques and discuss in depth the latest advances in APR research. In this paper, the development of APR techniques is introduced in terms of four different patch generation schemes: search-based, constraint-based, template-based, and learning-based. Moreover, we propose a uniform set of criteria to review and compare each APR tool, summarize the advantages and disadvantages of APR techniques, and discuss the current state of APR development. Furthermore, we introduce the research on the related technical areas of APR that have also provided a strong motivation to advance APR development. Finally, we analyze current challenges and future directions, especially highlighting the critical opportunities that large language models bring to APR research.Comment: This paper's earlier version was submitted to CSUR in August 202

FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and Design

Text-driven fashion synthesis and design is an extremely valuable part of artificial intelligence generative content(AIGC), which has the potential to propel a tremendous revolution in the traditional fashion industry. To advance the research on text-driven fashion synthesis and design, we introduce a new dataset comprising a million high-resolution fashion images with rich structured textual(FIRST) descriptions. In the FIRST, there is a wide range of attire categories and each image-paired textual description is organized at multiple hierarchical levels. Experiments on prevalent generative models trained over FISRT show the necessity of FIRST. We invite the community to further develop more intelligent fashion synthesis and design systems that make fashion design more creative and imaginative based on our dataset. The dataset will be released soon.Comment: 11 pages, 8 figure

The serum matrix metalloproteinase-9 level is an independent predictor of recurrence after ablation of persistent atrial fibrillation

OBJECTIVES: This study investigated whether the serum matrix metalloproteinase-9 level is an independent predictor of recurrence after catheter ablation for persistent atrial fibrillation. METHODS: Fifty-eight consecutive patients with persistent atrial fibrillation were enrolled and underwent catheter ablation. The serum matrix metalloproteinase-9 level was detected before ablation and its relationship with recurrent arrhythmia was analyzed at the end of the follow-up. RESULTS: After a mean follow-up of 12.1±7.2 months, 21 (36.2%) patients had a recurrence of their arrhythmia after catheter ablation. At baseline, the matrix metalloproteinase-9 level was higher in the patients with recurrence than in the non-recurrent group (305.77±88.90 vs 234.41±93.36 ng/ml, respectively, p=0.006). A multivariate analysis showed that the matrix metalloproteinase-9 level was an independent predictor of arrhythmia recurrence, as was a history of atrial fibrillation and the diameter of the left atrium. CONCLUSION: The serum matrix metalloproteinase-9 level is an independent predictor of recurrent arrhythmia after catheter ablation in patients with persistent atrial fibrillation