Kernel operations in resource-constrained edge systems, such as memory swapping and deduplication, use the access frequency (hotness) of memory pages to guide page placement and reclamation. However, these operations suffer from page-hotness skew: a page may contain a mix of highly accessed and infrequently accessed objects, which causes inaccurate page-level classification, wasted DRAM capacity, and expensive I/O. We attribute this skewness to a cross-layer mismatch: the kernel manages memory at page granularity, whereas user-level allocators place objects without considering access hotness.

A fundamental question in immunology is how the adaptive immune system encodes antigen specificity while maintaining repertoire diversity. B cell receptor (BCR) or antibody clonal families, defined by groups of B cells descending from a common ancestor, are key to deciphering this encoding. Although paired heavy and light chains jointly determine antibody specificity, most repertoire analyses have historically relied on heavy-chain-only data due to the loss of native pairing information in bulk BCR sequencing. This reliance introduces potential biases in computational clonal cluster inference, which may complicate efforts to resolve disease-associated immune signatures. Here, we leverage large-scale paired-chain BCR sequencing data to demonstrate that heavy-chain-based clustering may misrepresent true clonal architecture, and identify two major artifacts: chain-mixed clusters, in which similar heavy chains are paired with distinct light chains, and naive-like pseudo-clonal clusters, which are detected in an individual's naive B cell repertoire and exhibit highly similar heavy and light chains without reflecting true clonal expansion. To address these limitations, we present fastBCR-p, an optimized framework that integrates light-chain-informed subclustering, with public sequence aware refinement to improve clonal family inference. By resolving both technical artifacts and biological convergence, fastBCR-p improves the chain concordance and overall clustering quality of clonal inference in real-world datasets. This enables more accurate tracking of immune dynamics in health and disease and facilitates the identification of clinically relevant antibody lineages.

This paper investigates transceiver design optimization strategies for frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar-communication coexistence (RCC) systems, focusing on both radar-centric and communication-centric modes. Specifically, the former formulates the design problem to maximize the signal-to-interference-plus-noise ratio (SINR) in mainlobe deceptive jammer scenarios, whereas the latter aims to maximize the communication rate while simultaneously satisfying a predefined radar SINR constraint. In this framework, practical constraints pertaining to the radar’s transmitted waveform, communication codebook, frequency increment, and receive filter are taken into account. To address the resultant non-convex and NP-hard optimization problems, a maximum block improvement (MBI) approach is employed, where the variables are alternately examined, which are achieved either by leveraging closed-form expressions and hidden convexities or by resorting to the minorization-maximization (MM) approach, while keeping the remaining parameters fixed. The convergence performance of the devised algorithm is thoroughly examined, alongside their computational complexity analyses. Numerical results are provided to validate the efficacy of our approach against mainlobe deceptive jammers, demonstrating superior SINR and communication rate performance compared to existing optimization strategies and benchmark system frameworks.

Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of conditional sequence modeling (CSM), a paradigm that models the action distribution conditioned on both historical trajectories and target returns associated with each state. However, due to the imbalanced return distribution caused by suboptimal datasets, CSM is grappling with a serious distributional shift problem when conditioning on high returns. While recent approaches attempt to empirically tackle this challenge through return rebalancing techniques such as weighted sampling and value-regularized supervision, the relationship between return rebalancing and the performance of CSM methods is not well understood. In this paper, we reveal that both expert-level and full-spectrum return-coverage critically influence the performance and sample efficiency of CSM policies. Building on this finding, we devise a simple yet effective return-coverage rebalancing mechanism that can be seamlessly integrated into common CSM frameworks, including the most widely used one, Decision Transformer (DT). The resulting CSM algorithm, referred to as Return-rebalanced Value-regularized Decision Transformer (RVDT), integrates both implicit and explicit return-coverage rebalancing mechanisms, and achieves state-of-the-art performance in the D4RL experiments.

In recent years, learning-based color and tone enhancement methods for photos have become increasingly popular. However, most learning-based image enhancement methods just learn a mapping from one distribution to another based on one dataset, lacking the ability to adjust images continuously and controllably. It is important to enable the learning-based enhancement models to adjust an image continuously, since in many cases we may want to get a slighter or stronger enhancement effect rather than one fixed adjusted result. In this paper, we propose a quality-guided image enhancement paradigm that enables image enhancement models to learn the distribution of images with various quality ratings. By learning this distribution, image enhancement models can associate image features with their corresponding perceptual qualities, which can be used to adjust images continuously according to different quality scores. To validate the effectiveness of our proposed method, a subjective quality assessment experiment is first conducted, focusing on skin tone adjustment in portrait photography. Guided by the subjective quality ratings obtained from this experiment, our method can adjust the skin tone corresponding to different quality requirements. Furthermore, an experiment conducted on 10 natural raw images corroborates the effectiveness of our model in situations with fewer subjects and fewer shots, and also demonstrates its general applicability to natural images.

Recently, many algorithms have employed image-adaptive lookup tables (LUTs) to achieve real-time image enhancement. Nonetheless, a prevailing trend among existing methods has been the employment of linear combinations of basic LUTs to formulate image-adaptive LUTs, which limits the generalization ability of these methods. To address this limitation, we propose a novel framework named AttentionLut for real-time image enhancement, which utilizes the attention mechanism to generate image-adaptive LUTs. Our proposed framework consists of three lightweight modules. We begin by employing the global image context feature module to extract image-adaptive features. Subsequently, the attention fusion module integrates the image feature with the priori attention feature obtained during training to generate image-adaptive canonical polyadic tensors. Finally, the canonical polyadic reconstruction module is deployed to reconstruct image-adaptive residual 3DLUT, which is subsequently utilized for enhancing input images. Experiments on the benchmark MIT-Adobe FiveK dataset demonstrate that the proposed method achieves better enhancement performance quantitatively and qualitatively than the state-of-the-art methods.

In portrait photography, measuring the perceptual color differences (CDs) of skin tone is significant. Many studies have documented that the perception of skin tone is characteristically different from that of other colors. However, most existing CD measures are proposed based on psychophysical data of uniform color patches or natural images, and do not generalize well to the measurement of skin tone. In this paper, we construct the first large-scale portrait dataset for perceptual skin tone CD assessment and conduct psychophysical experiments to collect 160,000 perceptual CD judgments for 40,000 image triplets. Based on this dataset, we propose a deep skin tone CD measure for portrait photography. Extensive experiments demonstrate that our measure substantially outperforms existing CD measures on the problem of assessing skin tone CDs. The constructed dataset and code will be released to facilitate future research.

Efficient and resilient traffic management relies on accurate prediction of air traffic states. However, the complex spatial-temporal dependencies of air traffic networks make this task challenging. To address this issue, we propose a novel deep learning framework, named Physics-Informed Graph Attention Transformer (), which leverages real-world data and knowledge to predict essential air traffic state parameters. Our approach utilizes fine-grained traffic state detection to extract critical features from aviation databases. The model employs GAT-based spatial learning blocks with temporal Transformers to capture the dynamic spatial-temporal dependencies of data. A dynamic graph generator layer is also utilized to update the airport network’s topological structure adaptively, strengthening the model prediction’s effectiveness. Furthermore, the fluid queuing-theoretic PDEs are incorporated into the loss function, enhancing the model’s interpretability and reliability. Our framework is evaluated on real-world air traffic datasets from 36 major airport hubs within the US. Experimental results demonstrate that our proposed framework efficiently makes accurate predictions and outperforms eight baselines. In conclusion, our proposed framework has the potential to be applied in real-time decision-making systems for air traffic management and provides promising directions for future research. The code for our project is available at: https://github.com/ymlasu/para-atm-collection/tree/master/air-traffic-prediction/PIGAT.