Distribution shifts between training and testing datasets can significantly affect the effectiveness of machine learning models, as the typical assumption of data homogeneity often does not hold in practical applications. To address this challenge, previous literature explores invariant representation learning methods, including but not limited to domain adversarial learning, feature alignment, and invariant risk minimization. In this paper, we introduce a novel method for generating distribution-free prediction regions, which effectively quantify the uncertainty of predictions in different environments. Our methodology incorporates a weighted conformity score, tailored to the environment of each test sample, to construct adaptive conformal intervals. The proposed method is computationally efficient and can be implemented by subsampling. We demonstrate how these intervals maintain a consistent conditional coverage under some conditions. The numerical experiments, including simulations and real data analysis, show that our method is applicable and robust in dealing with distribution shifts.

Aiming at the existing deep learning radar signal modulation recognition methods are mostly based on time-frequency image (TFI) and consequently result in networks with a large number of parameters due to the significant amount of redundant information contained in TFI, this paper proposes a radar signal intra-pulse modulation recognition method based on point cloud which removes redundant information. Radar signals of different modulation types are mapped into point cloud after Smoothed Pseudo Wigner-Ville Distribution (SPWVD) transformation. Then, PointNet++ is used to classify the point cloud data according to its modulation type and output its corresponding modulation type labels. Simulation results show that the proposed method can effectively recognize radar signals of typical modulation types, and show strong effectiveness and reliability at low signal-to-noise ratio (SNR). Besides, the lightweight characteristics of PointNet++ make the operation of the method more efficient.

Convolutional neural networks (CNNs) have witnessed the flourishing development of remote sensing object detection. For CNN-based detectors, obtaining high-quality deep convolutional features is the foundation for improving the overall detection performance due to the challenging characteristics of remote sensing images, such as large variations in object shape, size, and orientation. In this letter, we present a channel-assisted deformable convolution (CDC) backbone and a bidirectional FPN with concise cross-layer connections (CFPN) for effective feature enhancement, which can significantly improve the detection accuracy with an acceptable complexity increment. In the proposed backbone, CDC modules are embedded into the top two layers of ResNet, where deformable convolution (DC) can guide the network to adapt to the various shapes of objects and channel attention (CA) can address the limitation that DC only focuses on the spatial dimensions while ignores the deep channel information. Our CFPN is a simple yet effective strategy to solve the problem that a complex feature fusion structure leads to a decrement in detection accuracy when cooperating with our enhanced backbone. Qualitative and quantitative experiments on a widely recognized benchmark, namely, DOTA, demonstrate that our method achieves a competitive performance against other advanced models.

Variances in ad impression outcomes across demographic groups are increasingly considered to be potentially indicative of algorithmic bias in personalized ads systems. While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta's ads systems. VRS seeks to achieve a distribution of impressions with respect to selected protected class (PC) attributes that more closely aligns the demographics of an ad's eligible audience (a function of advertiser targeting criteria) with the audience who sees that ad, in a privacy-preserving manner. We first define metrics to quantify fairness gaps in terms of ad impression variances with respect to PC attributes including gender and estimated race. We then present the VRS for re-ranking ads in an impression variance-aware manner. We evaluate VRS via extensive simulations over different parameter choices and study the effect of the VRS on the chosen fairness metric. We finally present online A/B testing results from applying VRS to Meta's ads systems, concluding with a discussion of future work. We have deployed the VRS to all users in the US for housing ads, resulting in significant improvement in our fairness metric. VRS is the first large-scale deployed framework for pursuing fairness for multiple PC attributes in online advertising.