肌肉驱动控制是跨越不同领域的兴趣的研究课题，特别是生物力学，机器人和图形。这种类型的控制尤其具有挑战性，因为模型通常是过度的，并且动态被延迟和非线性。然而，这是一个非常良好的测试和调整的致动模型，该模型经历了数百万年的演变，并且涉及有趣的性质利用肌肉肌腱单元的被动力和有效的能量存储和释放。为了促进肌肉致动模拟研究，我们基于Mujoco模拟器释放鸵鸟的3D肌肉骨骼模拟。 Ostriches是地球上最快的搭配之一，因此是研究肌肉驱动的双模运动的优秀模型。该模型基于CT扫描和解剖，用于收集诸如插入位点，长度和钢圈角度的实际肌肉数据。除此之外，我们还提供一组加强学习任务，包括参考运动跟踪和颈部的达到任务。参考运动数据基于我们预处理和适应我们模型的各种行为的运动捕获剪辑。本文介绍了如何使用任务构建和迭代地改进模型。通过将它们与从机车鸟类的实验收集的电拍摄数据进行比较来评估肌肉致动模式的准确性。我们认为，这项工作可以是生物力学，强化学习，图形和机器人社区之间的有用桥梁，通过提供快速且易于使用的模拟。

乳腺癌是女性最常见的恶性肿瘤，每年负责超过50万人死亡。因此，早期和准确的诊断至关重要。人类专业知识是诊断和正确分类乳腺癌并定义适当的治疗，这取决于评价不同生物标志物如跨膜蛋白受体HER2的表达。该评估需要几个步骤，包括免疫组织化学或原位杂交等特殊技术，以评估HER2状态。通过降低诊断中的步骤和人类偏差的次数的目标，赫洛挑战是组织的，作为第16届欧洲数字病理大会的并行事件，旨在自动化仅基于苏木精和曙红染色的HER2地位的评估侵袭性乳腺癌的组织样本。评估HER2状态的方法是在全球21个团队中提出的，并通过一些提议的方法实现了潜在的观点，以推进最先进的。

Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in the last decades, researchers have almost exclusively focused on the single object setting. Multi-object GOT benefits from a wider applicability, rendering it more attractive in real-world applications. We attribute the lack of research interest into this problem to the absence of suitable benchmarks. In this work, we introduce a new large-scale GOT benchmark, LaGOT, containing multiple annotated target objects per sequence. Our benchmark allows researchers to tackle key remaining challenges in GOT, aiming to increase robustness and reduce computation through joint tracking of multiple objects simultaneously. Furthermore, we propose a Transformer-based GOT tracker TaMOS capable of joint processing of multiple objects through shared computation. TaMOs achieves a 4x faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark. Finally, TaMOs achieves highly competitive results on single-object GOT datasets, setting a new state-of-the-art on TrackingNet with a success rate AUC of 84.4%. Our benchmark, code, and trained models will be made publicly available.

Recently, there has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data. We observed two shortcomings: illogical synthetic SQL queries from independent column sampling and arbitrary table joins. To address these issues, we propose a novel synthesis framework that incorporates key relationships from schema, imposes strong typing, and conducts schema-distance-weighted column sampling. We also adopt an intermediate representation (IR) for the SQL-to-text task to further improve the quality of the generated natural language questions. When existing powerful semantic parsers are pre-finetuned on our high-quality synthesized data, our experiments show that these models have significant accuracy boosts on popular benchmarks, including new state-of-the-art performance on Spider.

Recent advances in Federated Learning (FL) have paved the way towards the design of novel strategies for solving multiple learning tasks simultaneously, by leveraging cooperation among networked devices. Multi-Task Learning (MTL) exploits relevant commonalities across tasks to improve efficiency compared with traditional transfer learning approaches. By learning multiple tasks jointly, significant reduction in terms of energy footprints can be obtained. This article provides a first look into the energy costs of MTL processes driven by the Model-Agnostic Meta-Learning (MAML) paradigm and implemented in distributed wireless networks. The paper targets a clustered multi-task network setup where autonomous agents learn different but related tasks. The MTL process is carried out in two stages: the optimization of a meta-model that can be quickly adapted to learn new tasks, and a task-specific model adaptation stage where the learned meta-model is transferred to agents and tailored for a specific task. This work analyzes the main factors that influence the MTL energy balance by considering a multi-task Reinforcement Learning (RL) setup in a robotized environment. Results show that the MAML method can reduce the energy bill by at least 2 times compared with traditional approaches without inductive transfer. Moreover, it is shown that the optimal energy balance in wireless networks depends on uplink/downlink and sidelink communication efficiencies.

Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter. We present a pipelined extractive-abstractive approach where the extractive step filters the content that is passed to the abstractive component. Extremely lengthy input also results in a highly skewed dataset towards negative instances for extractive summarization; we thus adopt a margin ranking loss for extraction to encourage separation between positive and negative examples. Our extraction component operates at the constituent level; our approach to this problem enriches the text with spinal tree information which provides syntactic context (in the form of constituents) to the extraction model. We show an improvement of 3.71 Rouge-1 points over best results reported in prior work on an existing novel chapter dataset.

我们专注于一个典型的物流部门的卸载问题，该问题被建模为顺序的选择任务。在这种类型的任务中，现代的机器学习技术已经显示出比经典系统更好的工作，因为它们更适合随机性，并且能够更好地应对大型不确定性。更具体地说，在这方面，有监督和模仿学习取得了出色的成果，因为需要某种形式的监督，这对于所有设置并不总是可获得的。另一方面，加固学习（RL）需要许多更温和的监督形式，但由于其效率低下仍然不切实际。在本文中，我们提出并理论上激励了一种新颖的无监督奖励构成算法，从专家的观察结果中塑造了算法，该算法放宽了代理商所需的监督水平，并致力于改善我们任务中的RL绩效。

我们研究使用动物视频来提高增强学习（RL）效率和性能的可能性。从理论角度来看，我们激励使用加权策略优化对非政策RL的使用，描述从视频中学习并提出解决方案时面临的主要挑战。我们在离线和在线RL中测试我们的想法，并在一系列2D导航任务上显示令人鼓舞的结果。

在室外和室内环境中的精确定位是一个具有挑战性的问题，目前构成了几种实际应用的重要限制。超宽带（UWB）本地化技术代表了解决该问题的宝贵低成本解决方案。然而，特定无线电环境的非视线（NLOS）条件和复杂性很容易在范围测量中引入正偏见，从而导致高度不准确和不令人满意的位置估计。鉴于此，我们利用了深神网络优化技术的最新进步及其在超低功率微控制器上的实施，以引入有效的范围错误缓解解决方案，该解决方案可在NLOS或LOS条件下提供校正，并具有几兆瓦的功率。我们广泛的实验认可了我们的低成本和力量效率方法的优势和改进。

域的概括（DG）研究了深度学习模型推广到训练分布的能力。在过去的十年中，文献已经大量填充了一系列培训方法，这些方法声称获得了更抽象和强大的数据表示以应对域的转移。最近的研究为DG提供了可再现的基准，指出了天真的经验风险最小化（ERM）对现有算法的有效性。然而，研究人员坚持使用相同过时的特征提取器，并且尚未注意不同骨干的影响。在本文中，我们从骨干开始，提出了对其内在概括能力的全面分析，迄今为止，研究界忽略了。我们评估了各种特征提取器，从标准残差解决方案到基于变压器的架构，发现大规模单域分类精度和DG功能之间的线性相关性。我们广泛的实验表明，通过采用竞争性骨干与有效的数据增强结合使用，普通ERM的表现优于最近的DG解决方案，并实现了最先进的准确性。此外，我们的其他定性研究表明，新型骨架提供了与同类样本更相似的表示，从而将特征空间中的不同域分开。这种概括能力的增强功能使DG算法的边缘空间为调查问题，提出了一个新的范式，将骨干放在聚光灯下，并鼓励在其顶部开发一致的算法。