This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our on-going mission of increasing language coverage and translation quality, and also describe on-going work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.

此技术报告介绍了一个图片到图像神经网络“emojich”，它使用俄语语言中的字幕作为条件生成Emojis。我们的目标是在微调阶段保持普雷雷雷达大型Rudall-e Malevich（XL）参数的泛化能力，同时为生成的图像提供特殊风格。这里介绍了一些工程方法，代码实现，所有超参数，用于再现结果和电报机器人，其中每个人都可以创建自己定制的贴纸。此外，证明了“emojich”模型获得的一些新生成的Emojis。

支持II社区的当前趋势，我们提出了一个称为融合大脑的AI Journey 2021挑战，这些挑战是融合大脑，该挑战是使普通架构处理不同的方式（即图像，文本和代码），并解决视觉和语言的多个任务。融合脑挑战https://github.com/sberbank- ai/fusion_brain_aij2021结合了以下特定任务：code2code翻译，手写文本识别，零拍摄对象检测和视觉问题应答。我们为每个任务创建了数据集以测试参与者的提交。此外，我们在俄语和英语中开设了一个新的手写数据集，其中包含94,130对图像和文本。DataSet的俄罗斯部分是世界上最大的俄罗斯手写数据集。我们还提出了基线解决方案和相应的特定于任务特定解决方案以及整体指标。

In this paper, we propose a new neural network architecture based on the H2 matrix. Even though networks with H2-inspired architecture already exist, and our approach is designed to reduce memory costs and improve performance by taking into account the sparsity template of the H2 matrix. In numerical comparison with alternative neural networks, including the known H2-based ones, our architecture showed itself as beneficial in terms of performance, memory, and scalability.

We deal with a challenging scheduling problem on parallel machines with sequence-dependent setup times and release dates from a real-world application of semiconductor work-shop production. There, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. This causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. This achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of Answer-Set Programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. The former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising KRR paradigm for this problem and is competitive with state-of-the-art CP and MIP solvers. Under consideration in Theory and Practice of Logic Programming (TPLP).

Deep Learning (DL) models tend to perform poorly when the data comes from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection helps to identify such data samples, increasing the model's reliability. Recent works have developed DL-based OOD detection that achieves promising results on 2D medical images. However, scaling most of these approaches on 3D images is computationally intractable. Furthermore, the current 3D solutions struggle to achieve acceptable results in detecting even synthetic OOD samples. Such limited performance might indicate that DL often inefficiently embeds large volumetric images. We argue that using the intensity histogram of the original CT or MRI scan as embedding is descriptive enough to run OOD detection. Therefore, we propose a histogram-based method that requires no DL and achieves almost perfect results in this domain. Our proposal is supported two-fold. We evaluate the performance on the publicly available datasets, where our method scores 1.0 AUROC in most setups. And we score second in the Medical Out-of-Distribution challenge without fine-tuning and exploiting task-specific knowledge. Carefully discussing the limitations, we conclude that our method solves the sample-level OOD detection on 3D medical images in the current setting.

Cancer is one of the leading causes of death worldwide. It is caused by a variety of genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages that maximize the beneficial effects of the drugs and minimize their adverse side effects is vital. Deep neural networks automate and improve drug selection. However, they require a lot of data to be trained on. Therefore, there is a need for machine-learning approaches that require less data. Hybrid quantum neural networks were shown to provide a potential advantage in problems where training data availability is limited. We propose a novel hybrid quantum neural network for drug response prediction, based on a combination of convolutional, graph convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset and show that the hybrid quantum model outperforms its classical analog by 15% in predicting IC50 drug effectiveness values. The proposed hybrid quantum machine learning model is a step towards deep quantum data-efficient algorithms with thousands of quantum gates for solving problems in personalized medicine, where data collection is a challenge.

机器人系统的远程操作用于精确而精致的物体抓握需要高保真的触觉反馈，以获取有关抓握的全面实时信息。在这种情况下，最常见的方法是使用动力学反馈。但是，单个接触点信息不足以检测软件的动态变化形状。本文提出了一个新型的远程触发系统，该系统可为用户的手提供动感和皮肤刺激，以通过灵敏地操纵可变形物体（即移液器）来实现准确的液体分配。实验结果表明，为用户提供多模式触觉反馈的建议方法大大提高了用远程移液器的剂量质量。与纯视觉反馈相比，当用户用多模式触觉界面与视觉反馈混合使用多模式触觉接口时，相对给药误差减少了66 \％，任务执行时间减少了18 \％。在CoVID-19，化学实验，有机材料和伸缩性的抗体测试期间，可以在精致的给药程序中实施该提出的技术。

当研究不受限制的行为并允许小鼠离开笼子去驾驶复杂的迷宫时，小鼠在迷宫中表现出觅食行为，以寻求奖励，不时返回他们的家园，例如。喝。令人惊讶的是，当执行这样的``本垒打''时，老鼠不会遵循确切的反向路径，实际上，入口路径和家居路径几乎没有重叠。最近的工作提出了导航的层次主动推理模型，低级别模型对隐藏状态进行了推断，并提出了解释感官输入的姿势，而高级模型则可以推断出在位置之间移动，从而有效地构建环境地图。但是，使用此``MAP''进行计划，只允许代理找到它以前探索的轨迹，这与观察到的小鼠行为相去甚远。在本文中，我们探讨了通过使用低级生成模型来想象潜在的，但未发现的路径，探讨了将前路径纳入计划算法的方法。我们在网格世界环境中演示了概念证明，展示了代理如何使用从基于像素的观测值中学到的生成模型准确地预测地图中的新的，更短的路径。

胎儿镜检查激光​​光凝是一种广泛采用的方法，用于治疗双胞胎输血综合征（TTTS）。该过程涉及光凝病理吻合术以调节双胞胎之间的血液交换。由于观点有限，胎儿镜的可操作性差，可见性差和照明的可变性，因此该程序尤其具有挑战性。这些挑战可能导致手术时间增加和消融不完全。计算机辅助干预措施（CAI）可以通过识别场景中的关键结构并通过视频马赛克来扩展胎儿镜观景领域，从而为外科医生提供决策支持和背景意识。由于缺乏设计，开发和测试CAI算法的高质量数据，该领域的研究受到了阻碍。通过作为MICCAI2021内窥镜视觉挑战组织的胎儿镜胎盘胎盘分割和注册（FETREG2021）挑战，我们发布了第一个Largescale Multencentre TTTS数据集，用于开发广义和可靠的语义分割和视频摩擦质量algorithms。对于这一挑战，我们发布了一个2060张图像的数据集，该数据集是从18个体内TTTS胎儿镜检查程序和18个简短视频剪辑的船只，工具，胎儿和背景类别的像素通道。七个团队参与了这一挑战，他们的模型性能在一个看不见的测试数据集中评估了658个从6个胎儿镜程序和6个短剪辑的图像的图像。这项挑战为创建通用解决方案提供了用于胎儿镜面场景的理解和摩西式解决方案的机会。在本文中，我们介绍了FETREG2021挑战的发现，以及报告TTTS胎儿镜检查中CAI的详细文献综述。通过这一挑战，它的分析和多中心胎儿镜数据的发布，我们为该领域的未来研究提供了基准。