Coarse-to-Fine: Learning Compact Discriminative Representation for Single-Stage Image Retrieval

Image retrieval targets to find images from a database that are visually similar to the query image. Two-stage methods following retrieve-and-rerank paradigm have achieved excellent performance, but their separate local and global modules are inefficient to real-world applications. To better trade-off retrieval efficiency and accuracy, some approaches fuse global and local feature into a joint representation to perform single-stage image retrieval. However, they are still challenging due to various situations to tackle, $e.g.$, background, occlusion and viewpoint. In this work, we design a Coarse-to-Fine framework to learn Compact Discriminative representation (CFCD) for end-to-end single-stage image retrieval-requiring only image-level labels. Specifically, we first design a novel adaptive softmax-based loss which dynamically tunes its scale and margin within each mini-batch and increases them progressively to strengthen supervision during training and intra-class compactness. Furthermore, we propose a mechanism which attentively selects prominent local descriptors and infuse fine-grained semantic relations into the global representation by a hard negative sampling strategy to optimize inter-class distinctiveness at a global scale. Extensive experimental results have demonstrated the effectiveness of our method, which achieves state-of-the-art single-stage image retrieval performance on benchmarks such as Revisited Oxford and Revisited Paris. Code is available at https://github.com/bassyess/CFCD.Comment: Accepted to ICCV 202

Optimal N management affects the fate of urea-15N and improves N uptake and utilization of wheat in different rotation systems

Rice-wheat and maize-wheat rotations are major cropping systems in the middle and lower reaches of Yangtze River in China, where high nitrogen (N) inputs and low N efficiency often exacerbate resource waste and environmental pollution. Due to the changes in factors such as soil properties and moisture content, the N fate and the N utilization characteristics of wheat in different rotations are significantly different. Efficient N management strategies are thus urgently required for promoting maximum wheat yield in different rotation systems while reducing N loss. A 2-year field experiment using isotopic (15N) tracer technique was conducted to evaluate the fate of 15N-labeled urea in wheat fields and the distribution characteristics of N derived from different sources. The wheat yield and N use efficiency under various N rates (180 and 240 kg ha−1, abbreviated as N180 and N240) and preceding crops (rice and maize, abbreviated as R-wheat and M-wheat) were also investigated. The results showed that N240 increased N uptake and grain yield by only 8.77−14.97% and 2.51−4.49% compared with N 180, but decreased N agronomic efficiency (NAE) and N physiological efficiency (NPE) by 14.78−18.79% and 14.06−31.35%. N240 also decreased N recovery in plants by 2.8% on average compared with N180, and increased N residue in soil and N loss to the environment. Compared with that of basal N, the higher proportion of topdressing N was absorbed by wheat rather than lost to the environment. In addition, the accumulation of topdressing N in grain was much higher than that of basal N. Compared with that in R-wheat treatment, plants in M-wheat treatment trended to absorb more 15N and reduce unaccounted N loss, resulting in higher yield potential. Moreover, the M-wheat treatment increased N recovery in 0−20 cm soil but decreased 80−100 cm soil compared with R-wheat treatment, indicating a lower risk of N loss in deeper soil. Collectively, reducing N application rate and increasing the topdressing ratio is an effective way to balance sustainable crop yield for a secure food supply and environmental benefit, which is more urgent in rice-wheat rotation

ChIP-Hub provides an integrative platform for exploring plant regulome

Plant genomes encode a complex and evolutionary diverse regulatory grammar that forms the basis for most life on earth. A wealth of regulome and epigenome data have been generated in various plant species, but no common, standardized resource is available so far for biologists. Here, we present ChIP-Hub, an integrative web-based platform in the ENCODE standards that bundles >10,000 publicly available datasets reanalyzed from >40 plant species, allowing visualization and meta-analysis. We manually curate the datasets through assessing ~540 original publications and comprehensively evaluate their data quality. As a proof of concept, we extensively survey the co-association of different regulators and construct a hierarchical regulatory network under a broad developmental context. Furthermore, we show how our annotation allows to investigate the dynamic activity of tissue-specific regulatory elements (promoters and enhancers) and their underlying sequence grammar. Finally, we analyze the function and conservation of tissue-specific promoters, enhancers and chromatin states using comparative genomics approaches. Taken together, the ChIP-Hub platform and the analysis results provide rich resources for deep exploration of plant ENCODE. ChIP-Hub is available at https://biobigdata.nju.edu.cn/ChIPHub/.Peer Reviewe

Dynamics of retail pricing: a case study of fluid milk

Purpose – The purpose of this paper is to present a new model to empirically analyze retail pricing dynamics led by the competition between retailers, using fluid milk markets of three US metropolitan areas as a case study. The research is important for Chinese public policy makers to find the reasons of retail price fluctuations and provides policy makers with the direction and rationale to intervene in retailing markets. Design/methodology/approach – This paper empirically applies dynamic oligopolistic competition model using Markov switching regression. The dataset used in this study includes 58 four-week-ending observations covering the period from March 1996 to July 2000 for three cities, Boston, Dallas, and Seattle. Findings – The empirical results illustrate the Markov switching regression not only successfully identifies the Markov perfect equilibrium in each market, but decomposes the retail price series into the corresponding equilibrium regimes. The forecasting power of the model is surprising so it can serve as a price monitor of the government. Additionally, the model reveals the different consumer welfare implications given different price regimes. The welfare analysis shows that consumers are most likely to be worse off through price fluctuations, while they are not always better off through a sticky (stable) price series in a market. Research limitations/implications – The first limitation of the paper is the retail price data is four-week ending. If a cycle evolves faster within four weeks, the model would overestimate the duration and underestimate the amplitude of cycles. So the study serves as an upper bound of the reality. The second limitation is the number of regimes. More than three regimes studied in a Markov switching regression may cause a series of empirical issues. However, if we integrate several regimes into one regime, we will lose rich information about the competitiveness of the markets. Practical implications – This paper is the first work to apply the dynamic pricing analysis to food industry by using fluid milk market as a case study. This paper empirically identifies four retail pricing regimes in fluid milk price series and evaluates the characteristics of the regimes. Social implications – This paper assesses the welfare implications of each pricing regime. The results show that the forecasting power of the model is strong in fluid milk retail market. Therefore, the study could serve as a price monitor to the public policymaker. Originality/value – This paper presents the first study to apply dynamic oligopolistic competition model to food marketing research. JEL classification: D24, Q16, O47Food products, Marketing strategy, Prices, Retailing, United States of America

Are China’s grain trade policies effective in the stabilization of domestic food prices? An investigation based on a structural break regime switching model

As the base price, grain prices have played considerably important role in China’s macro-economy and social price level. In this study we investigate the fluctuation characteristics of some main crops of China’s grain during the past two decades by using Structural Break Regime Switching Model. We find that China’s grain price growth has become more stable since 2004 with narrowing low-and high-growth regimes. The implementation of minimum grain purchase price policy, improvement of market structure and diversification of acquisitions which improve farmers’ overall earning expectation and stabilize food price, are the most important motivating factors

Effects of Years of Rice Straw Return on Soil Nitrogen Components from Rice–Wheat Cropped Fields

Straw return is an important farmland management practice that influences the activity of soil nitrogen. Few studies have examined the distribution of soil nitrogen and its components in wheat–rice cropping fields in subtropical China. This study assesses the influence of different years of straw return on the distribution and variation of total soil nitrogen (TN), light fraction nitrogen (LFN), heavy fraction nitrogen (HFN), particulate nitrogen (PN), and mineral-bound nitrogen (MN). We conducted a field experiment with eight years of straw retention treatments in 2017 (no straw retention, NR; 1 year of straw retention, SR1; 2 years of straw retention, SR2; 3 years of straw retention, SR3; 4 years of straw retention, SR4; 5 years of straw retention, SR5; 6 years of straw retention, SR6; 7 years of straw retention, SR7) and one more treatment in 2018 (8 years of straw retention, SR8) in a rice–wheat cropping system at Yangzhou University Experimental Station in China. The results demonstrated that as the number of years of treatment increases, the content of TN, LFN, HFN, PN, and MN at each soil layer gradually increases. Compared with NR, the highest increase in TN, LFN, HFN, PN, and MN under SR1-SR8 in the 0–20 cm soils was 38.10%, 150.73%, 35.61%, 79.97%, and 27.71%, respectively, but increases in TN, HFN, and MN content gradually slowed after six years of straw return. The contents or variation of TN were extremely significantly correlated (p < 0.01) with that of LFN, HFN, PN, and MN, while LFN had the highest variation. In general, straw return could improve the quality of the 0–20 cm nitrogen pool. LFN was the best indicator of changes to the soil nitrogen pool affected by years of straw return

An Improved MbICP Algorithm for Mobile Robot Pose Estimation

This paper presents an improved version of the metric-based iterative closest point algorithm to estimate robot poses by matching 2D laser scans with different overlapping percentages. Because of the greatly varied density distribution of realistic point clouds, a resampling method is used to accelerate the iteration process and protect the calculation of the rejection threshold from being distorted by reducing dense but unhelpful points. A new procedure that combines point-to-point and point-to-line metrics is used to determine the correct correspondence between partially overlapping scans, which maintains both efficiency and accuracy. In addition, a rejection threshold that is based on the MAD-from-median method is utilized to discard correspondences with large distances, which are likely to be incorrect. Experiments show that the improved algorithm is more accurate and robust than the standard algorithm with respect to the existence of non-overlapping areas, and testing demonstrates that it is valid in practice