在强化学习培训的设置代理神经学可以通过分立令牌相互通信，实现作为一个团队有哪些代理将无法独自做到。然而，使用一个热向量作为离散的通信的当前标准从获取作为零次理解通信这样的更理想的方面令牌防止剂。通过嵌入一词从自然语言处理技术的启发，我们提出了神经代理架构，使他们能够通过从了解到，连续的空间衍生离散令牌进行通信。我们显示了在决策理论框架，我们的技术优化通信在大范围的场景，而一个热令牌是唯一最佳的下严格的假设。在自我发挥的实验，我们验证了我们的培训的工作人员学习集群令牌语义有意义的方式，让他们在其他技术无法嘈杂的环境中交流。最后，我们证明这两种，用我们的方法代理可以有效地应对新的人际交往和人类可以理解未标记的应急代理通信，跑赢使用一个热的沟通。

Random graph models with community structure have been studied extensively in the literature. For both the problems of detecting and recovering community structure, an interesting landscape of statistical and computational phase transitions has emerged. A natural unanswered question is: might it be possible to infer properties of the community structure (for instance, the number and sizes of communities) even in situations where actually finding those communities is believed to be computationally hard? We show the answer is no. In particular, we consider certain hypothesis testing problems between models with different community structures, and we show (in the low-degree polynomial framework) that testing between two options is as hard as finding the communities. In addition, our methods give the first computational lower bounds for testing between two different `planted' distributions, whereas previous results have considered testing between a planted distribution and an i.i.d. `null' distribution.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 hrs) were conducted based on the DARPA Subterranean Challenge. These missions were to deploy a heterogeneous team of robots for a search task to locate and identify artifacts such as climbing rope, drills and mannequins representing human survivors. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items, traversed more distance, covered more unique ground, and had a higher time between safety-related events. Human-Robot Teams were faster at finding the first artifact, but slower to respond to information from the robot team. In routine conditions, scores were comparable for artifacts, distance, and coverage. Reasons for intervention included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. After observing robot autonomy, operators reported increases in robot competency and trust, but that robot behaviour was not always transparent and understandable, even after high mission performance.

Monocular Depth Estimation (MDE) is a fundamental problem in computer vision with numerous applications. Recently, LIDAR-supervised methods have achieved remarkable per-pixel depth accuracy in outdoor scenes. However, significant errors are typically found in the proximity of depth discontinuities, i.e., depth edges, which often hinder the performance of depth-dependent applications that are sensitive to such inaccuracies, e.g., novel view synthesis and augmented reality. Since direct supervision for the location of depth edges is typically unavailable in sparse LIDAR-based scenes, encouraging the MDE model to produce correct depth edges is not straightforward. In this work we propose to learn to detect the location of depth edges from densely-supervised synthetic data, and use it to generate supervision for the depth edges in the MDE training. %Despite the 'domain gap' between synthetic and real data, we show that depth edges that are estimated directly are significantly more accurate than the ones that emerge indirectly from the MDE training. To quantitatively evaluate our approach, and due to the lack of depth edges ground truth in LIDAR-based scenes, we manually annotated subsets of the KITTI and the DDAD datasets with depth edges ground truth. We demonstrate significant gains in the accuracy of the depth edges with comparable per-pixel depth accuracy on several challenging datasets.

Understanding pedestrian behavior patterns is a key component to building autonomous agents that can navigate among humans. We seek a learned dictionary of pedestrian behavior to obtain a semantic description of pedestrian trajectories. Supervised methods for dictionary learning are impractical since pedestrian behaviors may be unknown a priori and the process of manually generating behavior labels is prohibitively time consuming. We instead utilize a novel, unsupervised framework to create a taxonomy of pedestrian behavior observed in a specific space. First, we learn a trajectory latent space that enables unsupervised clustering to create an interpretable pedestrian behavior dictionary. We show the utility of this dictionary for building pedestrian behavior maps to visualize space usage patterns and for computing the distributions of behaviors. We demonstrate a simple but effective trajectory prediction by conditioning on these behavior labels. While many trajectory analysis methods rely on RNNs or transformers, we develop a lightweight, low-parameter approach and show results comparable to SOTA on the ETH and UCY datasets.

Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. This paper applies discrete OT to a swarm of UAVs in a novel way to achieve appropriate task allocation and execution. Drone swarm deployments already operate in multiple domains where sensors are used to gain knowledge of an environment [1]. Use cases such as, chemical and radiation detection, and thermal and RGB imaging create a specific need for an algorithm that considers parameters on both the UAV and waypoint side and allows for updating the matching scheme as the swarm gains information from the environment. Additionally, the need for a centralized planner can be removed by using a distributed algorithm that can dynamically update based on changes in the swarm network or parameters. To this end, we develop a dynamic and distributed OT algorithm that matches a UAV to the optimal waypoint based on one parameter at the UAV and another parameter at the waypoint. We show the convergence and allocation of the algorithm through a case study and test the algorithm's effectiveness against a greedy assignment algorithm in simulation.

We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models. To improve the fidelity of our student, we distill the low-resolution anomaly maps of the teachers by jointly applying standard and adversarial distillation, introducing an adversarial discriminator for each teacher to distinguish between target and generated anomaly maps. We conduct experiments on three benchmarks (Avenue, ShanghaiTech, UCSD Ped2), showing that our method is over 7 times faster than the fastest competing method, and between 28 and 62 times faster than object-centric models, while obtaining comparable results to recent methods. Our evaluation also indicates that our model achieves the best trade-off between speed and accuracy, due to its previously unheard-of speed of 1480 FPS. In addition, we carry out a comprehensive ablation study to justify our architectural design choices.

深度学习已在许多神经影像应用中有效。但是，在许多情况下，捕获与小血管疾病有关的信息的成像序列的数量不足以支持数据驱动的技术。此外，基于队列的研究可能并不总是具有用于准确病变检测的最佳或必需成像序列。因此，有必要确定哪些成像序列对于准确检测至关重要。在这项研究中，我们旨在找到磁共振成像（MRI）序列的最佳组合，以深入基于学习的肿瘤周围空间（EPV）。为此，我们实施了一个有效的轻巧U-NET，适用于EPVS检测，并全面研究了来自易感加权成像（SWI），流体侵入的反转恢复（FLAIR），T1加权（T1W）和T2的不同信息组合 - 加权（T2W）MRI序列。我们得出的结论是，T2W MRI对于准确的EPV检测最为重要，并且在深神经网络中掺入SWI，FLAIR和T1W MRI可能会使精度的提高无关。

运营商网络已成为有希望的深度学习工具，用于近似偏微分方程（PDE）的解决方案。这些网络绘制了描述材料属性，迫使函数和边界数据的输入函数到PDE解决方案。这项工作描述了一种针对操作员网络的新体系结构，该架构模仿了从问题的变异公式或弱公式中获得的数值解决方案的形式。这些想法在通用椭圆的PDE中的应用导致变异模拟操作员网络（Varmion）。像常规的深层操作员网络（DeepOnet）一样，Varmion也由一个子网络组成，该子网络构建了输出的基础函数，另一个构造了这些基础函数系数的基本功能。但是，与deponet相反，在Varmion中，这些网络的体系结构是精确确定的。对Varmion解决方案中误差的分析表明，它包含训练数据中的误差，训练错误，抽样输入中的正交误差和输出功能的贡献，以及测量测试输入功能之间距离的“覆盖错误”以及培训数据集中最近的功能。这也取决于确切网络及其varmion近似的稳定性常数。 Varmion在规范椭圆形PDE中的应用表明，对于大约相同数量的网络参数，平均而言，Varmion的误差比标准DeepOnet较小。此外，其性能对于输入函数的变化，用于采样输入和输出功能的技术，用于构建基本函数的技术以及输入函数的数量更为强大。