以任务为导向的通信，主要是使用基于学习的联合源通道编码（JSCC），旨在通过将与任务相关的信息传输到接收方来设计通信有效的边缘推理系统。但是，只有在不引入任何冗余的情况下传输与任务相关的信息可能会导致由于渠道变化引起的学习鲁棒性问题，而JSCC将源数据直接映射到连续的通道输入符号中会对现有数字通信系统提出兼容性问题。在本文中，我们通过首先调查编码表示形式的信息性与接收到的信息失真的鲁棒性之间的固有权衡解决这两个问题，然后提出一种具有任务调制的导向的通信方案，名为Inveete Task-定向的JSCC（DT-JSCC），其中发射器将功能编码为离散表示形式，并使用数字调制方案将其传输到接收器。在DT-JSCC方案中，我们开发了一个可靠的编码框架，称为强大的信息瓶颈（rib），以改善对信道变化的稳健性，并使用变量近似来得出肋骨目标的可拖动变异上限，以克服克服相互信息的计算棘手性。实验结果表明，所提出的DT-JSCC比具有低通信延迟的基线方法更好的推理性能更好，并且由于施加的肋骨框架而表现出对通道变化的鲁棒性。

本文研究了一个新的多设备边缘人工智能（AI）系统，该系统共同利用AI模型分配推理和集成感应和通信（ISAC），以在网络边缘启用低延迟智能服务。在此系统中，多个ISAC设备执行雷达传感以获取多视图数据，然后将提取功能的量化版本卸载到集中式边缘服务器，该功能基于级联功能向量进行模型推断。在此设置和考虑分类任务下，我们通过采用近似但可拖动的度量，即判别增益来衡量推理的准确性，该指标定义为在归一化协方差下欧几里得特征空间中两个类别的距离。为了最大化判别增益，我们首先用衍生的封闭形式表达来量化感应，计算和通信过程的影响。然后，通过将这三个过程集成到联合设计中来开发面向任务的端到端资源管理方法。然而，这种集成的感应，计算和通信（ISCC）设计方法然而，由于判别增益的复杂形式和设备异质性在渠道增益，量化水平和生成的功能方面，导致了具有挑战性的非凸优化问题子集。值得注意的是，可以根据比率方法来最佳解决所考虑的非凸问题。这给出了最佳ISCC方案，该方案共同确定多个设备的传输功率和时间分配，以进行传感和通信，以及它们的量化位分配以进行计算失真控制。通过将人类运动识别作为具体的AI推理任务，进行了广泛的实验来验证我们衍生的最佳ISCC方案的性能。

人工智能（AI）应用的兴趣正在推动无线网络的进一步演变。它已经设想，6G将是转型性的，并将彻底改变无线从“连接的东西”到“连接智能”的演变。然而，最先进的深度学习和基于大数据分析的AI系统需要巨大的计算和通信资源，这在训练和推理过程中都会导致显着的延迟，能耗，网络拥塞和隐私泄漏。通过将模型训练和推理能力嵌入到网络边缘中，Edge AI突出了6G的中断技术，以便于无缝集成传感，通信，计算和智能，从而提高6G网络的效率，有效性，隐私和安全性。在本文中，我们将为我们的愿景提供具有无线通信策略和分散机器学习模型的集成设计的可扩展和值得信赖的Edge AI系统。将描述新设计无线网络，服务驱动资源分配优化方法，以及支持边缘AI的整体端到端系统架构。还讨论了标准化，软件和硬件平台和应用场景以促进边缘AI系统的工业化和商业化。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.

As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation CORGI-PM, which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.

Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy. In some cases, there may be unmeasured variables that can confound the action-reward or action-next-state relationships, rendering many existing OPE approaches ineffective. This paper develops an instrumental variable (IV)-based method for consistent OPE in confounded Markov decision processes (MDPs). Similar to single-stage decision making, we show that IV enables us to correctly identify the target policy's value in infinite horizon settings as well. Furthermore, we propose an efficient and robust value estimator and illustrate its effectiveness through extensive simulations and analysis of real data from a world-leading short-video platform.

Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy. It is critical in a number of sequential decision making problems ranging from healthcare to technology industries. Most of the work in existing literature is focused on evaluating the mean outcome of a given policy, and ignores the variability of the outcome. However, in a variety of applications, criteria other than the mean may be more sensible. For example, when the reward distribution is skewed and asymmetric, quantile-based metrics are often preferred for their robustness. In this paper, we propose a doubly-robust inference procedure for quantile OPE in sequential decision making and study its asymptotic properties. In particular, we propose utilizing state-of-the-art deep conditional generative learning methods to handle parameter-dependent nuisance function estimation. We demonstrate the advantages of this proposed estimator through both simulations and a real-world dataset from a short-video platform. In particular, we find that our proposed estimator outperforms classical OPE estimators for the mean in settings with heavy-tailed reward distributions.