Entry and competition effects in first-price auctions: theory and evidence from procurement auctions

Motivated by several interesting features of the highway mowing auction data from Texas Department of Transportation (TDoT), we propose a two-stage procurement auction model with endogenous entry and uncertain number of actual bidders. Our entry and bidding models pro vide several interesting implications. For the first time, we show that even within an independent private value paradigm, as the number of potential bidders increases, bidders equilibrium bidding behavior may become less aggressive because the entry effect is always positive and may dominate the negative competition effect. We also show that it is possible that the relationship between the expected winning bid and the number of potential bidders is non-monotone decreasing as well. We then develop an empirical model of entry and bidding controlling for unobserved auction heterogeneity to analyze the data. The structural estimates are used to quantify the entry effect and the competition effect with regard to the individual bids and the procurement cost, as well as the savings for the government with regard to the procurement cost when the entry cost is reduced.

Scene-Aware Feature Matching

Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This results in significant performance degradation when handling challenging scenes such as scenes with large viewpoint and illumination changes. To tackle this problem, we propose a novel model named SAM, which applies attentional grouping to guide Scene-Aware feature Matching. SAM handles multi-level features, i.e., image tokens and group tokens, with attention layers, and groups the image tokens with the proposed token grouping module. Our model can be trained by ground-truth matches only and produce reasonable grouping results. With the sense-aware grouping guidance, SAM is not only more accurate and robust but also more interpretable than conventional feature matching models. Sufficient experiments on various applications, including homography estimation, pose estimation, and image matching, demonstrate that our model achieves state-of-the-art performance.Comment: Accepted to ICCV 202

Topology optimization of compliant adaptive wing leading edge with composite materials

AbstractAn approach for designing the compliant adaptive wing leading edge with composite material is proposed based on the topology optimization. Firstly, an equivalent constitutive relationship of laminated glass fiber reinforced epoxy composite plates has been built based on the symmetric laminated plate theory. Then, an optimization objective function of compliant adaptive wing leading edge was used to minimize the least square error (LSE) between deformed curve and desired aerodynamics shape. After that, the topology structures of wing leading edge of different glass fiber ply-orientations were obtained by using the solid isotropic material with penalization (SIMP) model and sensitivity filtering technique. The desired aerodynamics shape of compliant adaptive wing leading edge was obtained based on the proposed approach. The topology structures of wing leading edge depend on the glass fiber ply-orientation. Finally, the corresponding morphing experiment of compliant wing leading edge with composite materials was implemented, which verified the morphing capability of topology structure and illustrated the feasibility for designing compliant wing leading edge. The present paper lays the basis of ply-orientation optimization for compliant adaptive wing leading edge in unmanned aerial vehicle (UAV) field

Autophagy and oxidative stress in cardiovascular diseases

AbstractAutophagy is a highly conserved degradation process by which intracellular components, including soluble macromolecules (e.g. nucleic acids, proteins, carbohydrates, and lipids) and dysfunctional organelles (e.g. mitochondria, ribosomes, peroxisomes, and endoplasmic reticulum) are degraded by the lysosome. Autophagy is orchestrated by the autophagy related protein (Atg) composed protein complexes to form autophagosomes, which fuse with lysosomes to generate autolysosomes where the contents are degraded to provide energy for cell survival in response to environmental and cellular stress. Autophagy is an important player in cardiovascular disease development such as atherosclerosis, cardiac ischemia/reperfusion, cardiomyopathy, heart failure and hypertension. Autophagy in particular contributes to cardiac ischemia, hypertension and diabetes by interaction with reactive oxygen species generated in endoplasmic reticulum and mitochondria. This review highlights the dual role of autophagy in cardiovascular disease development. Full recognition of autophagy as an adaptive or maladaptive response would provide potential new strategies for cardiovascular disease prevention and management. This article is part of a Special Issue entitled: Autophagy and protein quality control in cardiometabolic diseases

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe