通过使用机器学习技术的异常检测已成为一种新型强大的工具，可以在标准模型之外寻找新物理学。从历史上看，与JET可观察物的发展相似，理论一致性并不总是在算法和神经网络体系结构的快速发展中扮演核心角色。在这项工作中，我们通过使用能量加权消息传递来构建基于图神经网络的红外和共线安全自动编码器。我们证明，尽管这种方法具有理论上有利的特性，但它也对非QCD结构表现出强大的敏感性。

眼科图像和衍生物，例如视网膜神经纤维层（RNFL）厚度图对于检测和监测眼科疾病至关重要（例如，青光眼）。对于计算机辅助诊断眼疾病，关键技术是自动从眼科图像中提取有意义的特征，这些特征可以揭示与功能视觉丧失相关的生物标志物（例如RNFL变薄模式）。然而，将结构性视网膜损伤与人类视力丧失联系起来的眼科图像的表示，主要是由于患者之间的解剖学变化很大。在存在图像伪像的情况下，这项任务变得更加具有挑战性，由于图像采集和自动细分，这很常见。在本文中，我们提出了一个耐伪造的无监督的学习框架，该框架称为眼科图像的学习表示。 Eyelearn具有一个伪影校正模块，可以学习可以最好地预测无伪影眼镜图像的表示形式。此外，Eyelearn采用聚类引导的对比度学习策略，以明确捕获内部和间形的亲和力。在训练过程中，图像在簇中动态组织，以形成对比样品，其中鼓励在相同或不同的簇中分别学习相似或不同的表示形式。为了评估包冰者，我们使用青光眼患者的现实世界眼科摄影图数据集使用学习的表示形式进行视野预测和青光眼检测。广泛的实验和与最先进方法的比较验证了眼球从眼科图像中学习最佳特征表示的有效性。

产量估计是葡萄园管理中的强大工具，因为它允许种植者微调实践以优化产量和质量。但是，目前使用手动抽样进行估计，这是耗时和不精确的。这项研究表明，近端成像的应用与深度学习相结合，以进行葡萄园中的产量估计。使用车辆安装的传感套件进行连续数据收集，并使用商业收益率监控器在收获时结合了地面真实收益数据的收集，可以生成一个23,581个收益点和107,933张图像的大数据集。此外，这项研究是在机械管理的商业葡萄园中进行的，代表了一个充满挑战的图像分析环境，但在加利福尼亚中央山谷中的一组常见条件。测试了三个模型架构：对象检测，CNN回归和变压器模型。对象检测模型在手工标记的图像上进行了训练以定位葡萄束，并将束数量或像素区域求和以与葡萄产量相关。相反，回归模型端到端训练，以预测图像数据中的葡萄产量，而无需手动标记。结果表明，在代表性的保留数据集上，具有相当的绝对百分比误差为18％和18.5％的变压器和具有像素区域处理的对象检测模型。使用显着映射来证明CNN模型的注意力位于葡萄束的预测位置附近以及葡萄树冠的顶部。总体而言，该研究表明，近端成像和深度学习对于大规模预测葡萄群的适用性。此外，端到端建模方法能够与对象检测方法相当地执行，同时消除了手工标记的需求。

左心室（LV）功能是心脏病患者的患者管理，结局和长期存活方面的重要因素。最近发表的心力衰竭临床指南认识到，仅依赖一种心脏功能（LV射血分数）作为诊断和治疗分层生物标志物的依赖是次优。基于AI的超声心动图分析的最新进展已在LV体积和LV射血分数的自动估计上显示出良好的结果。但是，从随时间变化的2D超声心动图摄取，可以通过从完整的心脏周期中估算功能性生物标志物来获得对心脏功能的更丰富的描述。在这项工作中，我们首次提出了一种基于全心脏周期分割的2D超声心动图的AI方法，用于从2D超声心动图中得出高级生物标志物。这些生物标志物将允许临床医生获得健康和疾病中心脏的丰富图片。 AI模型基于“ NN-UNET”框架，并使用四个不同的数据库进行了训练和测试。结果表明，手动分析和自动分析之间的一致性很高，并展示了晚期收缩期和舒张期生物标志物在患者分层中的潜力。最后，对于50例病例的子集，我们在超声心动图和CMR的临床生物标志物之间进行了相关分析，我们在两种方式之间表现出了极好的一致性。

新生儿癫痫发作是一种通常遇到的神经系统条件。它们是严重神经障碍的第一个临床迹象。因此，需要快速识别和治疗以防止严重的死亡。在神经学领域中使用脑电图（EEG）允许精确地诊断几种医疗条件。然而，解释EEG信号需要高度专业人员的注意，因为婴儿脑在新生儿期间发育不起。检测癫痫发作可能会妨碍对婴儿的神经认知发展的负面影响。近年来，使用机器学习算法的新生儿癫痫发作检测已经获得牵引力。由于需要在癫痫发作检测的情况下对生物信号进行计算廉价的生物信号，因此本研究提供了一种基于机器学习（ML）的架构，其与以前的模型相当的预测性能，但具有最小级别配置。拟议的分类器在赫尔辛基大学医院录制的尼古尔缉获量的公共数据数据上进行了培训和测试。我们的架构实现了87％的最佳敏感性，比本研究中选择的标准ML型号的6％增加了6％。 ML分类器的模型大小优化为仅为4.84 kB，最小预测时间为182.61毫秒，从而使其部署在可穿戴的超边设备上，以便快速准确，并避免基于云的需求和其他这种穷举计算方法。

心房颤动（AF）是全球最普遍的心律失常，其中2％的人口受影响。它与增加的中风，心力衰竭和其他心脏相关并发症的风险有关。监测风险的个体和检测无症状AF可能导致相当大的公共卫生益处，因为无误的人可以采取预防措施的生活方式改变。随着可穿戴设备的增加，个性化的医疗保健将越来越多。这些个性化医疗保健解决方案需要准确地分类生物信号，同时计算廉价。通过推断设备，我们避免基于云和网络连接依赖性等基于云的系统固有的问题。我们提出了一种有效的管道，用于实时心房颤动检测，精度高精度，可在超边缘设备中部署。本研究中采用的特征工程旨在优化所拟议的管道中使用的资源有效的分类器，该分类器能够以每单纯折衷的内存足迹以10 ^ 5倍型号优惠。分类准确性2％。我们还获得了更高的准确性约为6％，同时消耗403 $ \ times $较小的内存，与以前的最先进的（SOA）嵌入式实现相比为5.2 $ \ times $。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work.

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.