Facial Aesthetics Enhancement (FAE) aims to improve facial attractiveness by adjusting the structure and appearance of a facial image while preserving its identity as much as possible. Most existing methods adopted deep feature-based or score-based guidance for generation models to conduct FAE. Although these methods achieved promising results, they potentially produced excessively beautified results with lower identity consistency or insufficiently improved facial attractiveness. To enhance facial aesthetics with less loss of identity, we propose the Nearest Neighbor Structure Guidance based on Diffusion (NNSG-Diffusion), a diffusion-based FAE method that beautifies a 2D facial image with 3D structure guidance. Specifically, we propose to extract FAE guidance from a nearest neighbor reference face. To allow for less change of facial structures in the FAE process, a 3D face model is recovered by referring to both the matched 2D reference face and the 2D input face, so that the depth and contour guidance can be extracted from the 3D face model. Then the depth and contour clues can provide effective guidance to Stable Diffusion with ControlNet for FAE. Extensive experiments demonstrate that our method is superior to previous relevant methods in enhancing facial aesthetics while preserving facial identity.

Issue reports and Pull Requests (PRs) are two important kinds of artifacts of software projects in GitHub. It is common for developers to leave explicit links in issues/PRs that refer to the other issues/PRs during discussions. Existing studies have demonstrated the value of such links in identifying complex bugs and duplicate issue reports. However, there are no broad examinations of why developers leave links within issues/PRs and the potential impact of such links on software development. Without such knowledge, practitioners and researchers may miss various opportunities to develop practical techniques for better solving bug-fixing or feature implementation related tasks. To fill this gap, we conducted the first empirical study to explore the characteristics of a large number of links within 642,281 issues/PRs of 16,584 popular (>50 stars) Python projects in GitHub. Specifically, we first constructed an Issue Unit Network (IUN, we refer to issue reports or PRs as issue units) by making use of the links between issue units. Then, we manually checked a sample of 1,384 links in the IUN and concluded six major kinds of linking relationships between issue units. For each kind of linking relationships, we presented some common patterns that developers usually adopted while linking issue units. By further analyzing as many as 423,503 links that match these common patterns, we found several interesting findings which indicate potential research directions in the future, including detecting cross-project duplicate issue reports, using IUN to help better identify influential projects and core issue reports.

Spatial filtering and subspace projection methods have been proposed for removing interference signals at radio astronomy (RA) imaging arrays. Given the fact that RA signal levels are usually below noise levels, and since high gain antennas significantly reduce the interference to noise level at antenna feeds, it is often difficult to form the accurate interference signal parameter estimates. By adding a few (1-3) low gain "auxiliary" antennas to an imaging array, it is possible to overcome this problem. Using low cost auxiliaries with an existing array can improve interference rejection by tens of decibels. New extensions to subspace projection spatial filtering methods are presented, along with simulated results for performance comparison.