我们提出Hypernst；基于超网络和stylegan2体系结构的图像艺术风格的神经风格转移（NST）技术。我们的贡献是一种新颖的方法，用于诱导通过度量空间进行参数化的样式转移，并预先训练基于样式的视觉搜索（SBV）。我们首次证明可以使用此类空间来驱动NST，从而从SBVS系统中启用样式的应用程序和插值。技术贡献是一个超网络，可以预测对型号的stylegan2的重量更新，而在各种各样的艺术内容（肖像）上，可以使用面部区域的语义图在每个区域量身定制样式参数化。我们在保留良好的风格转移性能的同时，在内容保存方面显示了超越最高的内容。

纯视觉变压器体系结构对于简短的视频分类和动作识别任务非常有效。但是，由于自我注意力的二次复杂性和缺乏归纳偏见，变压器是资源密集的，并且遭受了数据效率低下的困扰。长期的视频理解任务扩大了变压器的数据和内存效率问题，使当前方法无法在数据或内存限制域上实施。本文介绍了有效的时空注意网络（Stan），该网络使用两流变压器体系结构来模拟静态图像特征和时间上下文特征之间的依赖性。我们提出的方法可以在单个GPU上进行长达两分钟的视频，这是数据效率的，并且可以在几个长的视频理解任务上实现SOTA性能。

超声心动图参数的准确和一致的预测对于心血管诊断和治疗至关重要。特别是，左心室的分割可用于得出心室体积，射血分数（EF）和其他相关测量值。在本文中，我们提出了一种新的自动化方法，称为地位谱图，用于通过检测解剖关键来预测射血分数和分割左心室。基于图形卷积网络（GCN）的直接坐标回归模型用于检测关键点。 GCN可以学会根据每个关键点的局部外观以及所有关键点的全局空间和时间结构来表示心脏形状。我们在echonet基准数据集上评估了我们的电子位计模型。与语义分割相比，GCN显示出准确的分割和鲁棒性和推理运行时的改进。 EF是同时计算的与分割的，我们的方法还获得了最新的射血分数估计。源代码可在线获得：https：//github.com/guybenyosef/echographs。

深度神经网络的视频活动识别对于许多课程令人印象深刻。然而，它缺乏人类性能，特别是为了挑战歧视活动。人类通过识别明确识别的物体和部件之间的关键时空关系来区分这些复杂的活动，例如输入容器的孔径的物体。深度神经网络可以有效地努力学习这些关键关系。因此，我们提出了一种更有人类的识别方法，其解释了顺序时间阶段的视频，并在这些阶段中提取物体和手中的特定关系。随机森林分类器从这些提取的关系中学到。我们将该方法应用于某种东西的具有挑战性的数据集，并对挑战活动的神经网络基线实现更强大的性能。

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.

We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.

The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the enormous success of data augmentation currently remains limited to single-modality tasks like image classification. Indeed, it is particularly difficult to augment each modality while preserving the overall semantic structure of the data; for example, a caption may no longer be a good description of an image after standard augmentations have been applied, such as translation. Moreover, it is challenging to specify reasonable transformations that are not tailored to a particular modality. In this paper, we introduce LeMDA, Learning Multimodal Data Augmentation, an easy-to-use method that automatically learns to jointly augment multimodal data in feature space, with no constraints on the identities of the modalities or the relationship between modalities. We show that LeMDA can (1) profoundly improve the performance of multimodal deep learning architectures, (2) apply to combinations of modalities that have not been previously considered, and (3) achieve state-of-the-art results on a wide range of applications comprised of image, text, and tabular data.

The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations.