Recent research in clustering face embeddings has found that unsupervised, shallow, heuristic-based methods -- including $k$-means and hierarchical agglomerative clustering -- underperform supervised, deep, inductive methods. While the reported improvements are indeed impressive, experiments are mostly limited to face datasets, where the clustered embeddings are highly discriminative or well-separated by class (Recall@1 above 90% and often nearing ceiling), and the experimental methodology seemingly favors the deep methods. We conduct a large-scale empirical study of 17 clustering methods across three datasets and obtain several robust findings. Notably, deep methods are surprisingly fragile for embeddings with more uncertainty, where they match or even perform worse than shallow, heuristic-based methods. When embeddings are highly discriminative, deep methods do outperform the baselines, consistent with past results, but the margin between methods is much smaller than previously reported. We believe our benchmarks broaden the scope of supervised clustering methods beyond the face domain and can serve as a foundation on which these methods could be improved. To enable reproducibility, we include all necessary details in the appendices, and plan to release the code.

Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language. Such observations typically include instructions that, in a reinforcement learning (RL) setting, can directly or indirectly guide a player towards completing reward-worthy tasks. In this work, we study the ability of RL agents to follow such instructions. We conduct experiments that show that the performance of state-of-the-art text-based game agents is largely unaffected by the presence or absence of such instructions, and that these agents are typically unable to execute tasks to completion. To further study and address the task of instruction following, we equip RL agents with an internal structured representation of natural language instructions in the form of Linear Temporal Logic (LTL), a formal language that is increasingly used for temporally extended reward specification in RL. Our framework both supports and highlights the benefit of understanding the temporal semantics of instructions and in measuring progress towards achievement of such a temporally extended behaviour. Experiments with 500+ games in TextWorld demonstrate the superior performance of our approach.

有效地对远程依赖性建模是序列建模的重要目标。最近，使用结构化状态空间序列（S4）层的模型在许多远程任务上实现了最先进的性能。 S4层将线性状态空间模型（SSM）与深度学习技术结合在一起，并利用HIPPO框架进行在线功能近似以实现高性能。但是，该框架导致了架构约束和计算困难，使S4方法变得复杂，可以理解和实施。我们重新审视这样的想法，即遵循河马框架对于高性能是必要的。具体而言，我们替换了许多独立的单输入单输出（SISO）SSM的库S4层与一个多输入的多输出（MIMO）SSM一起使用，并具有降低的潜在尺寸。 MIMO系统的缩小潜在维度允许使用有效的并行扫描，从而简化了将S5层应用于序列到序列转换所需的计算。此外，我们将S5 SSM的状态矩阵初始化，其近似与S4 SSMS使用的河马级矩阵近似，并表明这是MIMO设置的有效初始化。 S5与S4在远程任务上的表现相匹配，包括在远程竞技场基准的套件中平均达到82.46％，而S4的80.48％和最佳的变压器变体的61.41％。

尽管自1970年代以来就已经知道，普通付款游戏中的全球最佳策略概况是纳什均衡，但全球最优性是严格的要求，它限制了结果的适用性。在这项工作中，我们表明任何本地最佳的对称策略概况也是（全局）NASH平衡。此外，我们证明了这一结果对通用收益和本地最佳的扰动是可靠的。应用于机器学习，我们的结果为任何梯度方法提供了全球保证，该方法在对称策略空间中找到了局部最佳。尽管该结果表明单方面偏差的稳定性，但我们仍然确定了广泛的游戏类别，这些游戏混合了当地的最佳选择，在不对称的偏差下是不稳定的。我们通过在一系列对称游戏中运行学习算法来分析不稳定性的普遍性，并通过讨论结果对多代理RL，合作逆RL和分散的POMDP的适用性来得出结论。

扩散张量心脏磁共振（DT-CMR）使我们能够探测体内心肌内心肌细胞的微观结构排列，这是不可侵袭性的，这是其他成像方式不允许的。这种创新的技术可以彻底改变执行心脏临床诊断，风险分层，预后和治疗随访的能力。但是，DT-CMR目前效率低下，获得单个2D静态图像所需的六分钟以上。因此，DT-CMR目前仅限于研究，但在临床上不使用。我们建议减少生产DT-CMR数据集并随后将其降低所需的重复次数，从而减少通过线性因子的采集时间，同时保持可接受的图像质量。我们提出的基于生成的对抗网络，视觉变压器和合奏学习的方法比以前提出的方法表现出色，而且要好得多，从而使单一的呼吸息dt-CMR更接近现实。

顺序蒙特卡洛（SMC）是状态空间模型的推理算法，通过从一系列中间目标分布进行采样来近似后验。目标分布通常被选择为过滤分布，但是这些忽略了未来观察结果的信息，从而导致推理和模型学习的实际和理论局限性。我们介绍了SIXO，这种方法将学习近似平滑分布的目标，并结合了所有观测值的信息。关键思想是使用密度比估计来拟合将过滤分布扭曲到平滑分布中的功能。然后，我们将SMC与这些学习的目标一起使用，以定义模型和建议学习的变异目标。六体的产量可证明更紧密的对数边缘下限，并在各种域中提供了更准确的后验推断和参数估计。

最近的工作据称，利用Softmax跨熵的分类损失不仅可以用于固定设定的分类任务，而且还通过专门为开放式任务开发的优于开销的损失，包括几次射击学习和检索。使用不同的嵌入几何形状研究了软MAX分类器 - 欧几里德，双曲线和球形，并且已经对一个或另一个的优越性进行了索赔，但它们没有得到精心控制的系统。我们对各种固定设定分类和图像检索任务的软MAX损失嵌入几何的实证研究。对于球形损失观察到的一个有趣的财产导致我们提出了一种基于VON MISES-FISHER分配的概率分类器，我们表明它具有最先进的方法竞争，同时生产出完善的盒子校准。我们提供有关亏损之间的权衡以及如何在其中选择的指导。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.