Today, recommender systems have played an increasingly important role in shaping our experiences of digital environments and social interactions. However, as recommender systems become ubiquitous in our society, recent years have also witnessed significant fairness concerns for recommender systems. Specifically, studies have shown that recommender systems may inherit or even amplify biases from historical data, and as a result, provide unfair recommendations. To address fairness risks in recommender systems, most of the previous approaches to date are focused on modifying either the existing training data samples or the deployed recommender algorithms, but unfortunately with limited degrees of success. In this paper, we propose a new approach called fair recommendation with optimized antidote data (FairRoad), which aims to improve the fairness performances of recommender systems through the construction of a small and carefully crafted antidote dataset. Toward this end, we formulate our antidote data generation task as a mathematical optimization problem, which minimizes the unfairness of the targeted recommender systems while not disrupting the deployed recommendation algorithms. Extensive experiments show that our proposed antidote data generation algorithm significantly improve the fairness of recommender systems with a small amounts of antidote data.

如今，学习排名（LTR）技术在信息检索系统中无处不在，尤其是在搜索排名应用程序中。通常用于训练排名模型的查询项目相关性标签通常是对人类行为的嘈杂测量，例如产品搜索的产品评级。粗略的测量使地面真理对单个相关标准进行了非唯一的排名。为了解决歧义，希望使用许多相关标准训练模型，从而产生多标签LTR（MLLTR）。此外，它制定了多个目标，这些目标可能同时优化，例如，在产品搜索中，可以根据产品质量和购买可能性来增加收入来培训排名模型。在这项研究中，我们利用了MLLTR问题的多目标优化（MOO）方面，并采用了最近开发的MOO算法来解决它。具体而言，我们建议一个一般框架，可以通过多种方式将标签的信息组合在一起，以有意义地表征目标之间的权衡。我们的框架允许使用任何基于梯度的MOO算法来解决MLLTR问题。我们在两个公开可用的LTR数据集和一个电子商务数据集上测试了提出的框架，以显示其功效。

We present a lightweight post-processing method to refine the semantic segmentation results of point cloud sequences. Most existing methods usually segment frame by frame and encounter the inherent ambiguity of the problem: based on a measurement in a single frame, labels are sometimes difficult to predict even for humans. To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method. The network, which we call the P2Net, learns the consistency constraints between coincident points from consecutive frames after registration. We evaluate the proposed post-processing method both qualitatively and quantitatively on the SemanticKITTI dataset that consists of real outdoor scenes. The effectiveness of the proposed method is validated by comparing the results predicted by two representative networks with and without the refinement by the post-processing network. Specifically, qualitative visualization validates the key idea that labels of the points that are difficult to predict can be corrected with P2Net. Quantitatively, overall mIoU is improved from 10.5% to 11.7% for PointNet [1] and from 10.8% to 15.9% for PointNet++ [2].

构建复杂三维（3D）塑料部件上的精确微纳米金属图案允许制造用于先进应用的功能装置。但是，这种图案目前是昂贵的，需要具有长制造时间的复杂过程。本作者演示了一种用任意复杂的形状制造微纳米3D金属塑料复合结构的方法。在这种方法中，修饰光固化树脂以制备能够允许随后的化学镀（ELP）的活性前体。新开发了一种多材料数字光处理3D打印机，以使含有由标准树脂或彼此嵌套的标准树脂或有源前体树脂制成的区域的部件的制造。这些部件的选择性3D ELP处理提供了各种金属塑料复合部件，其具有复杂的中空微纳米结构，其尺寸小于40μm的尺寸规模特定的拓扑关系。使用这种技术，可以通过传统方法制造的3D金属拓扑，并且可以在塑料部件内产生金属图案作为进一步小型化电子设备的装置。所提出的方法还可以产生具有改善金属粘附到塑料基材的金属涂层。基于该技术，设计并制造了由不同功能性非金属材料和特定金属图案组成的几种传感器。本结果证明了该方法的可行性，并提出了智能3D微纳米电子，3D可穿戴设备，微/纳米传感器和医疗保健领域的潜在应用。