影响重症患者护理的许多基本问题会带来类似的分析挑战：医生无法轻易估计处于危险的医疗状况或治疗的影响，因为医疗状况和药物的因果影响是纠缠的。他们也无法轻易进行研究：没有足够的高质量数据来进行高维观察性因果推断，并且通常无法在道德上进行RCT。但是，机械知识可获得，包括如何吸收人体药物，并且这些知识与有限数据的结合可能就足够了 - 如果我们知道如何结合它们。在这项工作中，我们提出了一个框架，用于在这些复杂条件下对重症患者的因果影响估算：随着时间的流逝，药物与观察之间的相互作用，不大的患者数据集以及可以代替缺乏数据的机械知识。我们将此框架应用于影响重症患者的极其重要的问题，即癫痫发作和大脑中其他潜在有害的电气事件的影响（称为癫痫样活动 - EA）对结局。鉴于涉及的高赌注和数据中的高噪声，可解释性对于解决此类复杂问题的故障排除至关重要。我们匹配的小组的解释性使神经科医生可以执行图表审查，以验证我们的因果分析的质量。例如，我们的工作表明，患者经历了高水平的癫痫发作般的活动（75％的EA负担），并且未经治疗的六个小时的窗口未受治疗，平均而言，这种不良后果的机会增加了16.7％。作为严重的大脑损伤，终生残疾或死亡。我们发现患有轻度但长期EA的患者（平均EA负担> = 50％）患有不良结果的风险增加了11.2％。

We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features. We do this by: (i) the introduction of a perceptual similarity term between generated and real images (ii) incorporating several techniques from the adversarial training literature including multi-scale training and adaptive discriminator augmentation. The combination of these results in not only better pixel reconstruction but also representations which appear to capture better higher-level details within images. More consequentially, we show how our method, Perceptual MAE, leads to better performance when used for downstream tasks outperforming previous methods. We achieve 78.1% top-1 accuracy linear probing on ImageNet-1K and up to 88.1% when fine-tuning, with similar results for other downstream tasks, all without use of additional pre-trained models or data.

Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) has emerged to significantly advance the perception of automated driving. However, current cooperative object detection methods mainly focus on ego-vehicle efficiency without considering the practical issues of system-wide costs. In this paper, we introduce VINet, a unified deep learning-based CP network for scalable, lightweight, and heterogeneous cooperative 3D object detection. VINet is the first CP method designed from the standpoint of large-scale system-level implementation and can be divided into three main phases: 1) Global Pre-Processing and Lightweight Feature Extraction which prepare the data into global style and extract features for cooperation in a lightweight manner; 2) Two-Stream Fusion which fuses the features from scalable and heterogeneous perception nodes; and 3) Central Feature Backbone and 3D Detection Head which further process the fused features and generate cooperative detection results. A cooperative perception platform is designed and developed for CP dataset acquisition and several baselines are compared during the experiments. The experimental analysis shows that VINet can achieve remarkable improvements for pedestrians and cars with 2x less system-wide computational costs and 12x less system-wide communicational costs.

Telework "avatar work," in which people with disabilities can engage in physical work such as customer service, is being implemented in society. In order to enable avatar work in a variety of occupations, we propose a mobile sales system using a mobile frozen drink machine and an avatar robot "OriHime", focusing on mobile customer service like peddling. The effect of the peddling by the system on the customers are examined based on the results of video annotation.

Synthetic data offers the promise of cheap and bountiful training data for settings where lots of labeled real-world data for tasks is unavailable. However, models trained on synthetic data significantly underperform on real-world data. In this paper, we propose Proportional Amplitude Spectrum Training Augmentation (PASTA), a simple and effective augmentation strategy to improve out-of-the-box synthetic-to-real (syn-to-real) generalization performance. PASTA involves perturbing the amplitude spectrums of the synthetic images in the Fourier domain to generate augmented views. We design PASTA to perturb the amplitude spectrums in a structured manner such that high-frequency components are perturbed relatively more than the low-frequency ones. For the tasks of semantic segmentation (GTAV to Real), object detection (Sim10K to Real), and object recognition (VisDA-C Syn to Real), across a total of 5 syn-to-real shifts, we find that PASTA outperforms more complex state-of-the-art generalization methods while being complementary to the same.

Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.

Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limiting their applicability as scientific modeling tools. In this work, we present a machine learning architecture that uses a set of inputs maximally reduced with respect to the full 6-dimensional Lorentz symmetry, and is fully permutation-equivariant throughout. We study the application of this network architecture to the standard task of top quark tagging and show that the resulting network outperforms all existing competitors despite much lower model complexity. In addition, we present a Lorentz-covariant variant of the same network applied to a 4-momentum regression task.

针对目标的对话任务的先前研究缺乏关键观念，该观念在以目标为导向的人工智能代理的背景下进行了深入研究。在这项研究中，我们提出了目标引导的开放域对话计划（TGCP）任务的任务，以评估神经对话代理是否具有目标对话计划的能力。使用TGCP任务，我们研究了现有检索模型和最新强生成模型的对话计划能力。实验结果揭示了当前技术面临的挑战。

尽管变压器已经开始在视力中占主导地位，但将它们应用于大图像仍然很困难。这样做的一个很大的原因是，自我发场的标记数二次缩放，而令牌数量又随图像大小而倍增。在较大的图像（例如1080p）上，网络中总计算的60％以上仅用于创建和应用注意矩阵。我们通过引入Hydra注意来解决这个问题，这是视觉变压器（VITS）的极有效的关注操作。自相矛盾的是，这种效率来自对其极端的多头关注：通过使用尽可能多的注意力头部，Hydra注意力在代币和没有隐藏常数的特征上是线性的，使其比标准自我注意力要快得多。在现成的VIT-B/16中，代币计数的一倍。此外，Hydra注意力保留了ImageNet上的高精度，在某些情况下实际上可以改善它。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。