In subcellular biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, fluorescence staining is slow, expensive, and harmful to cells. In this paper, we treat it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, the subcellular structures vary considerably in size, which causes the multi-scale issue in SSP. However, traditional solutions can not address SSP well since they organize network parameters inefficiently and inflexibly. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks of SSP. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode outperforms existing methods on ten of twelve prediction tasks of SSP and achieves state-of-the-art overall performance.

The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet. However, it also means that users' private data may be collected by commercial organizations without consent and used to train their models. Therefore, it's important and necessary to develop a method or tool to prevent unauthorized data exploitation. In this paper, we propose ConfounderGAN, a generative adversarial network (GAN) that can make personal image data unlearnable to protect the data privacy of its owners. Specifically, the noise produced by the generator for each image has the confounder property. It can build spurious correlations between images and labels, so that the model cannot learn the correct mapping from images to labels in this noise-added dataset. Meanwhile, the discriminator is used to ensure that the generated noise is small and imperceptible, thereby remaining the normal utility of the encrypted image for humans. The experiments are conducted in six image classification datasets, consisting of three natural object datasets and three medical datasets. The results demonstrate that our method not only outperforms state-of-the-art methods in standard settings, but can also be applied to fast encryption scenarios. Moreover, we show a series of transferability and stability experiments to further illustrate the effectiveness and superiority of our method.

Offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets, which is an important step toward the deployment of multi-agent systems in real-world applications. However, in practice, each individual behavior policy that generates multi-agent joint trajectories usually has a different level of how well it performs. e.g., an agent is a random policy while other agents are medium policies. In the cooperative game with global reward, one agent learned by existing offline MARL often inherits this random policy, jeopardizing the performance of the entire team. In this paper, we investigate offline MARL with explicit consideration on the diversity of agent-wise trajectories and propose a novel framework called Shared Individual Trajectories (SIT) to address this problem. Specifically, an attention-based reward decomposition network assigns the credit to each agent through a differentiable key-value memory mechanism in an offline manner. These decomposed credits are then used to reconstruct the joint offline datasets into prioritized experience replay with individual trajectories, thereafter agents can share their good trajectories and conservatively train their policies with a graph attention network (GAT) based critic. We evaluate our method in both discrete control (i.e., StarCraft II and multi-agent particle environment) and continuous control (i.e, multi-agent mujoco). The results indicate that our method achieves significantly better results in complex and mixed offline multi-agent datasets, especially when the difference of data quality between individual trajectories is large.

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

优化组合结构是许多现实世界中的核心，例如生命科学中遇到的问题。例如，抗体设计中涉及的关键步骤之一是在蛋白质序列中找到氨基酸的排列，以改善其与病原体的结合。由于极大的搜索空间和非线性目标，很难对抗体进行组合优化。即使对于适度的抗体设计问题，蛋白质的序列长度为11，我们也面临着超过2.05 x 10^14结构的搜索。应用传统的增强学习算法，例如Q-学习算法来组合优化，导致性能差。我们提出了结构化Q学习（SQL），这是Q学习的扩展，该Q学习结合了结构性先验，以进行组合优化。使用分子对接模拟器，我们证明了SQL可以找到高结合能序列，并在八个具有挑战性的抗体设计任务上对基准的表现良好，包括设计SARS-COV的抗体。

在存在未衡量的混杂因素的情况下，我们解决了数据融合的治疗效应估计问题，即在不同的治疗分配机制下收集的多个数据集。例如，营销人员可以在不同时间/地点为相同产品分配不同的广告策略。为了处理由未衡量的混杂因素和数据融合引起的偏见，我们建议将观察数据分为多组（每个组具有独立治疗分配机制），然后将组指标显式地模拟为潜在的组仪器变量（LATGIV），将其模拟为实施基于IV的回归。在本文中，我们概念化了这种思想，并开发了一个统一的框架，以（1）估计跨群体观察到的变量的分布差异； （2）对不同治疗分配机制的LATGIV模型； （3）插入latgivs以估计治疗响应函数。经验结果证明了与最新方法相比，LATGIV的优势。

基准数据集在评估自然语言理解（NLU）模型中起重要作用。但是，快捷方式（基准数据集中的不需要的偏差）可能会损害基准数据集在揭示模型的实际功能中的有效性。由于快捷方式在覆盖范围，生产率和语义含义上有所不同，因此NLU专家在创建基准数据集时系统地理解和避免它们是一项挑战。在本文中，我们开发了一个视觉分析系统，即短路，以帮助NLU专家探索NLU基准数据集中的快捷方式。该系统允许用户对快捷方式进行多层次探索。具体而言，统计信息视图可帮助用户掌握统计数据，例如基准数据集中快捷方式的覆盖范围和生产率。模板视图采用层次和可解释的模板来汇总不同类型的快捷方式。实例视图允许用户检查快捷方式涵盖的相应实例。我们进行案例研究和专家访谈，以评估系统的有效性和可用性。结果表明，饭店支持用户通过快捷方式更好地了解基准数据集问题，从而激发他们创建具有挑战性和相关的基准数据集。

在分支机构和结合中得出良好的可变选择策略对于现代混合编程（MIP）求解器的效率至关重要。通过在先前的解决方案过程中收集的MIP分支数据，学习分支方法最近变得比启发式方法更好。由于分支机构自然是一项顺序决策任务，因此应该学会优化整个MIP求解过程的实用性，而不是在每个步骤上都是近视。在这项工作中，我们将学习作为离线增强学习（RL）问题进行分支，并提出了一种长期视线的混合搜索方案来构建离线MIP数据集，该数据集对分支决策的长期实用程序。在政策培训阶段，我们部署了基于排名的奖励分配计划，以将有希望的样本与长期或短期视图区分开，并通过离线政策学习训练名为分支排名的分支模型。合成MIP基准和现实世界任务的实验表明，与广泛使用的启发式方法和基于先进的学习分支模型相比，分支rankink更有效，更健壮，并且可以更好地概括为MIP实例的大型MIP实例。

协作多代理增强学习（MARL）已在许多实际应用中广泛使用，在许多实际应用中，每个代理商都根据自己的观察做出决定。大多数主流方法在对分散的局部实用程序函数进行建模时，将每个局部观察结果视为完整的。但是，他们忽略了这样一个事实，即可以将局部观察信息进一步分为几个实体，只有一部分实体有助于建模推理。此外，不同实体的重要性可能会随着时间而变化。为了提高分散政策的性能，使用注意机制用于捕获本地信息的特征。然而，现有的注意模型依赖于密集的完全连接的图，并且无法更好地感知重要状态。为此，我们提出了一个稀疏的状态MARL（S2RL）框架，该框架利用稀疏的注意机制将无关的信息丢弃在局部观察中。通过自我注意力和稀疏注意机制估算局部效用函数，然后将其合并为标准的关节价值函数和中央评论家的辅助关节价值函数。我们将S2RL框架设计为即插即用的模块，使其足够一般，可以应用于各种方法。关于Starcraft II的广泛实验表明，S2RL可以显着提高许多最新方法的性能。

最近的研究表明，引入代理商之间的沟通可以显着提高合作多智能体增强学习（MARL）的整体性能。在许多现实世界的情景中，通信可能是昂贵的，多代理系统的带宽受到某些约束。占据通信资源的冗余消息可以阻止信息性消息的传输，从而危及性能。在本文中，我们的目标是学习最小的足够的通信信息。首先，我们通过完整的图表启动代理之间的通信。然后我们将图形信息瓶颈（GIB）原则介绍到这个完整的图表中，并从图形结构上获得优化。基于优化，提出了一种名为CommGIB的新型多代理通信模块，其有效地压缩了通信图中的结构信息和节点信息来处理带宽约束的设置。进行了交通管制和斯坦径II的广泛实验。结果表明，与最先进的算法相比，所提出的方法可以在带宽限制的环境中实现更好的性能，具有尤其是大型多功能机构任务中的尤其是大的边距。