Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1082 code generation problems using the pandas data analysis framework in data science notebooks. ARCADE features multiple rounds of NL-to-code problems from the same notebook. It requires a model to understand rich multi-modal contexts, such as existing notebook cells and their execution states as well as previous turns of interaction. To establish a strong baseline on this challenging task, we develop PaChiNCo, a 62B code language model (LM) for Python computational notebooks, which significantly outperforms public code LMs. Finally, we explore few-shot prompting strategies to elicit better code with step-by-step decomposition and NL explanation, showing the potential to improve the diversity and explainability of model predictions.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Learning with noisy labels is a vital topic for practical deep learning as models should be robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning approach JoCoR fails when faced with a large ratio of noisy labels. Moreover, selecting small-loss samples can also cause error accumulation as once the noisy samples are mistakenly selected as small-loss samples, they are more likely to be selected again. In this paper, we try to deal with error accumulation in noisy label learning from both model and data perspectives. We introduce mean point ensemble to utilize a more robust loss function and more information from unselected samples to reduce error accumulation from the model perspective. Furthermore, as the flip images have the same semantic meaning as the original images, we select small-loss samples according to the loss values of flip images instead of the original ones to reduce error accumulation from the data perspective. Extensive experiments on CIFAR-10, CIFAR-100, and large-scale Clothing1M show that our method outperforms state-of-the-art noisy label learning methods with different levels of label noise. Our method can also be seamlessly combined with other noisy label learning methods to further improve their performance and generalize well to other tasks. The code is available in https://github.com/zyh-uaiaaaa/MDA-noisy-label-learning.

The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to meet the asymmetric and heterogeneous traffic demands while alleviating the inter-cell interference. We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) that maximizes the long-term expected sum rate under the users' packet dropping ratio constraints. In order to jointly optimize the global resources in a decentralized manner, we propose a federated reinforcement learning (RL) algorithm named federated Wolpertinger deep deterministic policy gradient (FWDDPG) algorithm. The BSs decide their local time-frequency configurations through RL algorithms and achieve global training via exchanging local RL models with their neighbors under a decentralized federated learning framework. Specifically, to deal with the large-scale discrete action space of each BS, we adopt a DDPG-based algorithm to generate actions in a continuous space, and then utilize Wolpertinger policy to reduce the mapping errors from continuous action space back to discrete action space. Simulation results demonstrate the superiority of our proposed algorithm to benchmark algorithms with respect to system sum rate.

合作感知的想法是从多辆车之间的共同感知数据中受益，并克服单车上车载传感器的局限性。但是，由于本地化不准确，通信带宽和模棱两可的融合，多车信息的融合仍然具有挑战性。过去的实践通过放置精确的GNSS定位系统来简化问题，手动指定连接的车辆数量并确定融合策略。本文提出了一个基于地图的合作感​​知框架，名为MAP容器，以提高合作感的准确性和鲁棒性，最终克服了这个问题。概念“地图容器”表示地图是将所有信息转换为地图坐标空间的平台，并将不同的信息源合并到分布式融合体系结构中。在拟议的MAP容器中，考虑使用GNSS信号和传感器功能和地图功能之间的匹配关系以优化环境状态的估计。对仿真数据集和房地车平台的评估结果验证了所提出的方法的有效性。

近年来，随着新颖的策略和应用，神经网络一直在迅速扩展。然而，尽管不可避免地会针对关键应用程序来解决这些挑战，例如神经网络技术诸如神经网络技术中仍未解决诸如神经网络技术的挑战。已经尝试通过用符号表示来表示和嵌入域知识来克服神经网络计算中的挑战。因此，出现了神经符号学习（Nesyl）概念，其中结合了符号表示的各个方面，并将常识带入神经网络（Nesyl）。在可解释性，推理和解释性至关重要的领域中，例如视频和图像字幕，提问和推理，健康信息学和基因组学，Nesyl表现出了有希望的结果。这篇综述介绍了一项有关最先进的Nesyl方法的全面调查，其原理，机器和深度学习算法的进步，诸如Opthalmology之类的应用以及最重要的是该新兴领域的未来观点。

我们研究了具有一般函数近似的部分可观察的MDP（POMDP）的外部评估（OPE）。现有的方法，例如顺序重要性采样估计器和拟合-Q评估，受POMDP中的地平线的诅咒。为了解决这个问题，我们通过引入将未来代理作为输入的未来依赖性值函数来开发一种新颖的无模型OPE方法。未来依赖性的价值函数在完全可观察的MDP中起着与经典价值函数相似的角色。我们为未来依赖性价值作为条件矩方程提供了一个新的Bellman方程，将历史记录代理用作仪器变量。我们进一步提出了一种最小值学习方法，以使用新的Bellman方程来学习未来依赖的价值函数。我们获得PAC结果，这意味着我们的OPE估计器是一致的，只要期货和历史包含有关潜在状态和Bellman完整性的足够信息。最后，我们将方法扩展到学习动力学，并在POMDP中建立我们的方法与众所周知的光谱学习方法之间的联系。

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。

第三代合作伙伴项目已开始研究2021年的第18版。人工智能（AI）空气界面是第18版的关键特征之一，其中选择了用于渠道状态信息的AI（CSI）反馈增强作为代表性。用例。本文提供了5G助长和6G中CSI反馈增强的AI的全面概述。首先介绍和讨论了5G效率的CSI反馈增强AI的范围，包括高架降低，准确性提高和渠道预测。然后，介绍并比较了三个代表性CSI反馈的代表性框架，包括单方面隐式反馈，基于双面自动编码器的隐式反馈和双面显式反馈。最后，已经确定和讨论了CSI反馈增强的AI标准化考虑因素，尤其是重点是评估，复杂性，协作，概括，信息共享，具有渠道预测的联合设计和互惠性。本文为基于AI的CSI反馈增强的标准化研究提供了指南。

通过大量多输入和多重输出实现的许多性能增长取决于发射机（基站）下链路通道状态信息（CSI）的准确性，这通常是通过在接收器（用户终端）估算并馈入的。到发射器。 CSI反馈的开销占据了大量的上行链路带宽资源，尤其是当传输天线数量较大时。基于深度学习（DL）的CSI反馈是指基于DL的自动编码器的CSI压缩和重建，并且可以大大减少反馈开销。在本文中，提供了有关该主题的最新研究的全面概述，首先是在CSI反馈中广泛使用的基本DL概念，然后对一些现有的基于DL的反馈作品进行分类和描述。重点是新型的神经网络体系结构和沟通专家知识的利用来提高CSI反馈准确性。还介绍了有关CSI反馈和CSI反馈与其他通信模块的联合设计的作品，并讨论了一些实际问题，包括培训数据集收集，在线培训，复杂性，概括和标准化效果。在本文的最后，确定了与未来无线通信系统中基于DL的CSI反馈相关的一些挑战和潜在的研究方向。