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.

Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this Domain-Adaptive Pre-Training (DAPT; Gururangan et al. (2020)) tends to forget the previous general knowledge acquired by general PLMs, which leads to a catastrophic forgetting phenomenon and sub-optimal performance. To alleviate this problem, we propose a new framework of General Memory Augmented Pre-trained Language Model (G-MAP), which augments the domain-specific PLM by a memory representation built from the frozen general PLM without losing any general knowledge. Specifically, we propose a new memory-augmented layer, and based on it, different augmented strategies are explored to build the memory representation and then adaptively fuse it into the domain-specific PLM. We demonstrate the effectiveness of G-MAP on various domains (biomedical and computer science publications, news, and reviews) and different kinds (text classification, QA, NER) of tasks, and the extensive results show that the proposed G-MAP can achieve SOTA results on all tasks.

图神经网络（GNN）从材料科学家那里引起了越来越多的关注，并证明了建立结构和属性之间的连接的高能力。但是，只有仅提供的未删除结构作为输入，很少有GNN模型可以预测带有可接受的误差水平的放松配置的热力学特性。在这项工作中，我们开发了基于Dimenet ++和混合密度网络的多任务（MT）体系结构，以提高此类任务的性能。将基于CU的单原子合金催化剂的共吸附作为例证，我们表明我们的方法可以可靠地估计CO的吸附能，其平均绝对误差为0.087 eV，从初始CO的吸附结构中，而无需昂贵的第一原则计算。此外，与其他最先进的GNN方法相比，我们的模型在预测具有看不见的底物表面或掺杂物种的催化性能时具有提高的概括能力。我们表明，拟议的GNN策略可以促进催化剂发现。

深度学习表现出巨大的生成任务潜力。生成模型是可以根据某些隐含参数随机生成观测值的模型类。最近，扩散模型由于其发电能力而成为一类生成模型。如今，已经取得了巨大的成就。除了计算机视觉，语音产生，生物信息学和自然语言处理外，还需要在该领域探索更多应用。但是，扩散模型具有缓慢生成过程的自然缺点，从而导致许多增强的作品。该调查总结了扩散模型的领域。我们首先说明了两项具有里程碑意义的作品的主要问题-DDPM和DSM。然后，我们提供各种高级技术，以加快扩散模型 - 训练时间表，无训练采样，混合模型以及得分和扩散统一。关于现有模型，我们还根据特定的NFE提供了FID得分的基准和NLL。此外，引入了带有扩散模型的应用程序，包括计算机视觉，序列建模，音频和科学AI。最后，该领域以及局限性和进一步的方向都进行了摘要。

人工智能通过许多令人印象深刻的应用深刻地彻底改变了药物化学领域，但是这些应用的成功需要大量具有高质量注释的培训样本，这严重限制了数据驱动方法的广泛使用。在本文中，我们专注于反应产量预测问题，该问题有助于化学家仅通过一些实验试验选择新的化学空间中的高收益反应。为了攻击这一挑战，我们首先提出了Metarf，这是一种基于注意力的随机森林模型，该模型专门针对少量产量预测，其中随机森林的注意力重量通过元学习框架自动优化，可以快速地进行优化适合预测新试剂的性能，同时还提供了一些其他样品。为了提高少量学习绩效，我们进一步引入了基于尺寸的采样方法，以确定要进行实验测试然后学习的有价值的样品。我们的方法在三个不同的数据集上进行了评估，并在几乎没有预测上获得了令人满意的性能。在高通量实验（HTE）数据集中，我们方法论的前10个高收益反应的平均产量相对接近理想的产量选择结果。

手术场景细分对于促使机器人手术的认知援助至关重要。但是，以逐帧方式以像素为单位的注释视频是昂贵且耗时的。为了大大减轻标签负担，在这项工作中，我们从机器人手术视频中研究了半监督的场景细分，这实际上是必不可少的，但以前很少探索。我们考虑在等距采样下的临床上适当的注释情况。然后，我们提出了PGV-CL，这是一种新型的伪标签引导的跨视频对比学习方法，以增强场景分割。它有效地利用了未标记的数据来实现可信赖和全球模型的正则化，从而产生更具歧视性的特征表示。具体来说，对于可信赖的表示学习，我们建议合并伪标签以指导对选择，从而获得更可靠的代表对像素对比度。此外，我们将代表学习空间从以前的图像级扩展到交叉视频，该图像可以捕获全球语义以使学习过程受益。我们广泛评估了公共机器人手术数据集Edovis18和公共白内障数据集Cadis的方法。实验结果证明了我们方法的有效性，在不同的标签比下始终超过了最先进的半监督方法，甚至超过了10.1％标签的destovis18上的全面监督培训。

由于难以收集详尽的多标签注释，因此多标签数据集通常包含部分标签。我们考虑了这个弱监督的学习问题的极端，称为单个积极的多标签学习（SPML），其中每个多标签训练图像只有一个正标签。传统上，所有未注释的标签都被认为是SPML中的负标签，它引入了假阴性标签，并导致模型训练被假定的负标签所支配。在这项工作中，我们选择从替代角度来对待所有未经注释的标签，即承认它们是未知的。因此，我们提出熵最大化（EM）损失，以达到提供适当监督信号的特殊梯度制度。此外，我们提出了采用不对称耐受性策略和自定进度程序的不对称伪标记（APL），以与EM损失合作，然后提供更精确的监督。实验表明，我们的方法可显着提高性能，并在所有四个基准测试中实现最先进的结果。代码可从https://github.com/correr-zhou/spml-acktheunknown获得。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

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.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.