In healthcare and precision medicine, estimating optimal treatment regimes for right-censored data while ensuring fairness across ethnic subgroups is crucial but remains underexplored. The problem presents two key challenges: measuring heterogeneous treatment effects (HTE) under fairness constraints and dealing with censoring mechanisms. We propose a general framework for estimating HTE using nonparametric methods and integrating user-controllable fairness constraints to address these problems. Under mild regularization assumptions, our method is theoretically grounded, demonstrating the double robustness property of the HTE estimator. Using this framework, we demonstrate that optimal treatment strategies balance fairness and utility. Using extensive simulations and real-world data analysis, we uncovered the potential of this method to guide the selection of treatment methods that are equitable and effective.

Efficient image compression is crucial for remote sensing satellite systems, as it determines the performance of machine vision applications analyzing the downlinked image data at ground stations. However, existing conventional or learning-based image compression approaches exhibit limitations in either high complexity or undesirable vision task performance. This letter investigates an efficient neural image compression standard, JPEG-AI-based self-supervised remote sensing image compression approach, SS-JPEG-AI, for semantic segmentation tasks. Our approach maintains the low-complexity advantages of JPEG-AI while incorporating (1) a computationally efficient transformer-based attention mechanism that enhances reconstruction quality without increasing encoder complexity for onboard systems and (2) a contrastive learning strategy that improves feature discriminability and sharpens inter-category decision boundaries for segmentation tasks. Compared to the state-of-the-art image compression methods, SS-JPEG-AI achieves better Bjøntegaard Delta-rate (BD-rate) performance across the mean Intersection over Union (mIoU) and mean F-score (mFscore) while maintaining up to30 timessmaller computational complexity.

In this paper, we extend our prior research named DKIC [1] and propose the perceptual-oriented learned image compression method, PO-DKIC, which is shown in Figure 1 . Specifically, DKIC adopts a dynamic kernel-based dynamic residual block group to enhance the transform coding and an asymmetric space-channel context entropy model to facilitate the estimation of Gaussian parameters. Based on DKIC, PO-DKIC introduces PatchGAN and LPIPS loss to enhance visual quality. Furthermore, to maximize the overall perceptual quality under a rate constraint, we formulate this challenge into a constrained programming problem and use the Linear Integer Programming method for resolution. The experiments demonstrate that our proposed method can generate realistic images with richer textures and finer details when compared to state-of-the-art image compression techniques.

As generative large language models (LLMs) such as ChatGPT gain widespread adoption in various domains, their potential to propagate and amplify social biases, particularly in high-stakes areas such as the labor market, has become a pressing concern. AI algorithms are not only widely used in the selection of job applicants, individual job seekers may also make use of generative LLMs to help develop their job application materials. Against this backdrop, this research builds on a novel experimental design to examine social biases within ChatGPT-generated job applications in response to real job advertisements. By simulating the process of job application creation, we examine the language patterns and biases that emerge when the model is prompted with diverse job postings. Notably, we present a novel bias evaluation framework based on Masked Language Models to quantitatively assess social bias based on validated inventories of social cues/words, enabling a systematic analysis of the language used. Our findings show that the increasing adoption of generative AI, not only by employers but also increasingly by individual job seekers, can reinforce and exacerbate gender and social inequalities in the labor market through the use of biased and gendered language.

Due to the huge data volume of high-resolution remote sensing imagery (RSI) and limited transmission bandwidth, RSIs are typically compressed for efficient transmission and storage. However, most of the existing compression algorithms are developed based on optimizing for the human perceptual that are not suitable for remote sensing image applications where RSIs are usually used for machine interpretation tasks, such as semantic segmentation for ground–object recognition. In this article, we propose an image coding for machines (ICMs) paradigm based on contrastive learning in a fully supervised manner to boost semantic segmentation of compressed RSIs. Specifically, we build an end-to-end compression framework to make full use of the global semantic information by clustering intracategory projected embeddings and spacing intercategory embeddings apart, to compensate for the loss of feature discriminability during the compression process and reconstruct the decision boundaries between different categories. Compared to the state-of-the-art image compression methods, our proposed method significantly improves the performance of semantic segmentation on the remote sensing labeling benchmark datasets.

A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle. Yet, many applications involve continuous-time systems where the time discretization, in principle, can be managed. The impact of time discretization on RL methods has not been fully characterized in existing theory, but a more detailed analysis of its effect could reveal opportunities for improving data-efficiency. We address this gap by analyzing Monte-Carlo policy evaluation for LQR systems and uncover a fundamental trade-off between approximation and statistical error in value estimation. Importantly, these two errors behave differently to time discretization, leading to an optimal choice of temporal resolution for a given data budget. These findings show that managing the temporal resolution can provably improve policy evaluation efficiency in LQR systems with finite data. Empirically, we demonstrate the trade-off in numerical simulations of LQR instances and standard RL benchmarks for non-linear continuous control.