姿势图优化是在机器人感知的许多领域遇到的非凸优化问题。它的收敛到准确的解决方案由两个因素来调节：使用成本函数的非线性和姿势变量的初始配置。在本文中，我们提出了Hipe，这是一种用于姿势图初始化的新型分层算法。我们的方法利用了一个粗粒图，该图编码了问题几何形状的抽象表示。我们通过结合来自输入本地区域的最大似然估计来构建此图。通过利用这种表示的稀疏性，我们可以以非线性方式初始化姿势图，而无需与现有方法相比，没有计算开销。最终的初始猜测可以有效地引导用于获得最终解决方案的细粒优化。此外，我们对不同成本函数对最终估计的影响进行了经验分析。我们的实验评估表明，HIPE的使用导致更有效，更健壮的优化过程，与最先进的方法相比。

In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks. We design and implement an effective pipeline for scanning real objects in quantity and effortlessly. Our scan station is built with less than 500$ hardware budget and can collect roughly 4000 images of a scanned object in just 5 minutes. Such a platform is used to build ScanNeRF, a dataset characterized by several train/val/test splits aimed at benchmarking the performance of modern NeRF methods under different conditions. Accordingly, we evaluate three cutting-edge NeRF variants on it to highlight their strengths and weaknesses. The dataset is available on our project page, together with an online benchmark to foster the development of better and better NeRFs.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

在线平台面临着保持社区民用和尊重的压力。因此，从Reddit和Facebook等主流平台上有问题的在线社区的横幅通常会受到热情的公共反应。但是，该策略可以导致用户迁移到具有较低适度标准的替代边缘平台，以及在巨魔和骚扰等反社会行为被广泛接受的地方。由于这些社区的用户经常在主流和边缘平台上保留\ ca，反社会行为可能会溢出到主流平台上。我们通过分析来自迁移到边缘平台的三个被禁止社区的70,000美元的用户来研究这一可能的溢出：r/the \ _donald，r/r/gendericalitical和r/incels。使用差异差异设计，我们将\ CA用户与匹配的对应物进行了对比，以估算边缘平台参与用户对Reddit的反社会行为的因果效应。我们的结果表明，参与边缘社区会增加用户对Reddit的毒性（按照视角API的衡量），并参与了类似于被禁止社区的子雷数 - 这通常也违反了平台规范。效果随着时间的流逝和暴露于边缘平台而加剧。简而言之，我们发现通过共同参与从边缘平台到Reddit的反社会行为溢出的证据。

有效的量子控制对于使用当前技术的实用量子计算实施是必需的。用于确定最佳控制参数的常规算法在计算上是昂贵的，在很大程度上将它们排除在模拟之外。构成作为查找表的现有硬件解决方案不精确且昂贵。通过设计机器学习模型来近似传统工具的结果，可以生成更有效的方法。然后可以将这样的模型合成为硬件加速器以用于量子系统。在这项研究中，我们演示了一种用于预测最佳脉冲参数的机器学习算法。该算法的轻量级足以适合低资源FPGA，并以175 ns的延迟和管道间隔为5 ns，$〜>〜>〜$〜>〜$ 0.99。从长远来看，这种加速器可以在传统计算机无法运行的量子计算硬件附近使用，从而在低潜伏期以合理的成本实现量子控制，而不会在低温环境之外产生大型数据带宽。

嵌入大而冗余的数据，例如图像或文本，在较低维空间的层次结构中是表示方法的关键特征之一，如今，这些特征是一旦相信困难或不可能的问题，这些方法就可以为问题提供最新的解决方案解决。在这项工作中，在具有强大元回味的情节扭转中，我们展示了受过训练的深层模型与它们优化的数据一样多余，因此如何使用深度学习模型来嵌入深度学习模型。特别是，我们表明可以使用表示形式学习来学习经过训练的深层模型的固定大小，低维的嵌入空间，并且可以通过插值或优化来探索此类空间，以实现现成的模型。我们发现，可以学习相同体系结构和多个体系结构的多个实例的嵌入空间。我们解决了信号的图像分类和神经表示，表明如何学习我们的嵌入空间，以分别捕获性能和3D形状的概念。在多架结构的环境中，我们还展示了仅在架构子集中训练的嵌入方式如何才能学会生成已经训练的架构实例，从未在培训时看到实例化。

推理任务（例如答案句子选择（AS2）或事实验证）通常通过将基于变压器的模型作为单个句子对分类器来解决。最近的研究表明，这些任务受益于共同跨多个候选句子的建模依赖性。在本文中，我们首先表明，当用于多转化推理任务进行微调时，流行的预训练的变压器的性能很差。然后，我们提出了一个新的预训练目标，该目标对段落级的语义进行了对多个输入句子进行建模。我们对三个AS2和一个事实验证数据集的评估证明了我们的预训练技术优于传统技术的优势，用于变压器用作多用途推理任务的关节模型，以及用作句子对配方的跨编码器的优势这些任务。我们的代码和预培训模型将在https://github.com/amazon-research/wqa-multi-sentence-inference上发布。

Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data across numerous domains. Their underlying ability to represent nodes as summaries of their vicinities has proven effective for homophilous graphs in particular, in which same-type nodes tend to connect. On heterophilous graphs, in which different-type nodes are likely connected, GNNs perform less consistently, as neighborhood information might be less representative or even misleading. On the other hand, GNN performance is not inferior on all heterophilous graphs, and there is a lack of understanding of what other graph properties affect GNN performance. In this work, we highlight the limitations of the widely used homophily ratio and the recent Cross-Class Neighborhood Similarity (CCNS) metric in estimating GNN performance. To overcome these limitations, we introduce 2-hop Neighbor Class Similarity (2NCS), a new quantitative graph structural property that correlates with GNN performance more strongly and consistently than alternative metrics. 2NCS considers two-hop neighborhoods as a theoretically derived consequence of the two-step label propagation process governing GCN's training-inference process. Experiments on one synthetic and eight real-world graph datasets confirm consistent improvements over existing metrics in estimating the accuracy of GCN- and GAT-based architectures on the node classification task.

Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-2 neuromorphic system. This work represents an improvement over previous efforts, which either focused on the matrix-multiplication mode of BrainScaleS-2 or lacked full automation. Our framework, called hxtorch.snn, enables the hardware-in-the-loop training of spiking neural networks within PyTorch, including support for auto differentiation in a fully-automated hardware experiment workflow. In addition, hxtorch.snn facilitates seamless transitions between emulating on hardware and simulating in software. We demonstrate the capabilities of hxtorch.snn on a classification task using the Yin-Yang dataset employing a gradient-based approach with surrogate gradients and densely sampled membrane observations from the BrainScaleS-2 hardware system.

Generalisation to unseen contexts remains a challenge for embodied navigation agents. In the context of semantic audio-visual navigation (SAVi) tasks, the notion of generalisation should include both generalising to unseen indoor visual scenes as well as generalising to unheard sounding objects. However, previous SAVi task definitions do not include evaluation conditions on truly novel sounding objects, resorting instead to evaluating agents on unheard sound clips of known objects; meanwhile, previous SAVi methods do not include explicit mechanisms for incorporating domain knowledge about object and region semantics. These weaknesses limit the development and assessment of models' abilities to generalise their learned experience. In this work, we introduce the use of knowledge-driven scene priors in the semantic audio-visual embodied navigation task: we combine semantic information from our novel knowledge graph that encodes object-region relations, spatial knowledge from dual Graph Encoder Networks, and background knowledge from a series of pre-training tasks -- all within a reinforcement learning framework for audio-visual navigation. We also define a new audio-visual navigation sub-task, where agents are evaluated on novel sounding objects, as opposed to unheard clips of known objects. We show improvements over strong baselines in generalisation to unseen regions and novel sounding objects, within the Habitat-Matterport3D simulation environment, under the SoundSpaces task.