人们对连续可穿戴生命体征传感器的兴趣越来越大，用于在家中远程监测患者。这些监视器通常与警报系统耦合，当生命体征测量值落在预定义的正常范围之外时，它会触发。生命体征的趋势（例如心率提高）通常表明健康状况恶化，但很少被纳入警报系统中。在这项工作中，我们提出了一种新型的离群检测算法，以识别这种异常生命体征趋势。我们引入了一种基于距离的措施，以比较生命体征轨迹。对于我们数据集中的每个患者，我们将生命体征时间序列分为180分钟的非重叠时期。然后，我们使用动态时间扭曲距离计算了所有时期对之间的距离。每个时期的特征都以其平均成对距离（平均链路距离）到所有其他时期，其距离为较大的距离。我们将此方法应用于1561多个患者小时的飞行员数据集，这些数据集是从最近在Covid-19收缩后出院的8例患者的1561个患者小时。我们表明，离群值时期与后来入院的患者相对应。我们还描述了一个这样的患者如何从正常异常转变为异常。

我们对解决几个自然学习问题的一通流算法所需的记忆量给出了下限。在$ \ {0,1 \}^d $中的示例的环境中，可以使用$ \ kappa $ bits对最佳分类器进行编码，我们表明，使用近距离数量的示例学习的算法，$ \ tilde o（\ kappa）$，必须使用$ \ tilde \ omega（d \ kappa）$空间。我们的空间界限与问题自然参数化的环境空间的维度相匹配，即使在示例和最终分类器的大小上是二次的。例如，在$ d $ -sparse线性分类器的设置中，$ \ kappa = \ theta（d \ log d）$，我们的空间下限是$ \ tilde \ omega（d^^^ 2）$。我们的边界与流长$ n $优雅地降级，通常具有$ \ tilde \ omega \ left（d \ kappa \ cdot \ frac \ frac {\ kappa} {n} {n} \ right）$。 $ \ omega（d \ kappa）$的形式的界限以学习奇偶校验和有限字段定义的其他问题而闻名。在狭窄的样本量范围内适用的边界也以线性回归而闻名。对于最近学习应用程序中常见的类型的问题，我们的第一个范围是适用于各种输入尺寸的问题。

部署的监督机器学习模型使预测与世界相互作用。这种现象称为Perdomo等人的表演预测。 （ICML 2020）。了解这种预测的影响以及设计工具，是一个持续的挑战，以控制这种影响。我们提出了一种理论框架，其中目标群体对部署分类器的响应被建模为分类器的函数和群体的当前状态（分布）。我们向两次再培训算法的平衡点表示必要和充分的条件，重复风险最小化和Lazier变体。此外，收敛在最佳分类器附近。因此，我们概括了Perdomo等人的结果。，其表现框架不承担任何对目标人群状态的依赖。我们的模型捕获的特定现象是在不同速率下获取信息和资源的独特群体能够响应最新的部署分类器。我们理论上和经验研究这种现象。

While skin cancer classification has been a popular and valuable deep learning application for years, there has been little consideration of the context in which testing images are taken. Traditional melanoma classifiers rely on the assumption that their testing environments are analogous to the structured images on which they are trained. This paper combats this notion, arguing that mole size, a vital attribute in professional dermatology, is a red herring in automated melanoma detection. Although malignant melanomas are consistently larger than benign melanomas, this distinction proves unreliable and harmful when images cannot be contextually scaled. This implementation builds a custom model that eliminates size as a training feature to prevent overfitting to incorrect parameters. Additionally, random rotation and contrast augmentations are performed to simulate the real-world use of melanoma detection applications. Several custom models with varying forms of data augmentation are implemented to demonstrate the most significant features of the generalization abilities of mole classifiers. These implementations show that user unpredictability is crucial when utilizing such applications. The caution required when manually modifying data is acknowledged, as data loss and biased conclusions are necessary considerations in this process. Additionally, mole size inconsistency and its significance are discussed in both the dermatology and deep learning communities.

Diffusion generative models have recently been applied to domains where the available data can be seen as a discretization of an underlying function, such as audio signals or time series. However, these models operate directly on the discretized data, and there are no semantics in the modeling process that relate the observed data to the underlying functional forms. We generalize diffusion models to operate directly in function space by developing the foundational theory for such models in terms of Gaussian measures on Hilbert spaces. A significant benefit of our function space point of view is that it allows us to explicitly specify the space of functions we are working in, leading us to develop methods for diffusion generative modeling in Sobolev spaces. Our approach allows us to perform both unconditional and conditional generation of function-valued data. We demonstrate our methods on several synthetic and real-world benchmarks.

The precise control of soft and continuum robots requires knowledge of their shape. The shape of these robots has, in contrast to classical rigid robots, infinite degrees of freedom. To partially reconstruct the shape, proprioceptive techniques use built-in sensors resulting in inaccurate results and increased fabrication complexity. Exteroceptive methods so far rely on placing reflective markers on all tracked components and triangulating their position using multiple motion-tracking cameras. Tracking systems are expensive and infeasible for deformable robots interacting with the environment due to marker occlusion and damage. Here, we present a regression approach for 3D shape estimation using a convolutional neural network. The proposed approach takes advantage of data-driven supervised learning and is capable of real-time marker-less shape estimation during inference. Two images of a robotic system are taken simultaneously at 25 Hz from two different perspectives, and are fed to the network, which returns for each pair the parameterized shape. The proposed approach outperforms marker-less state-of-the-art methods by a maximum of 4.4\% in estimation accuracy while at the same time being more robust and requiring no prior knowledge of the shape. The approach can be easily implemented due to only requiring two color cameras without depth and not needing an explicit calibration of the extrinsic parameters. Evaluations on two types of soft robotic arms and a soft robotic fish demonstrate our method's accuracy and versatility on highly deformable systems in real-time. The robust performance of the approach against different scene modifications (camera alignment and brightness) suggests its generalizability to a wider range of experimental setups, which will benefit downstream tasks such as robotic grasping and manipulation.

We propose a framework for learning a fragment of probabilistic computation tree logic (pCTL) formulae from a set of states that are labeled as safe or unsafe. We work in a relational setting and combine ideas from relational Markov Decision Processes with pCTL model-checking. More specifically, we assume that there is an unknown relational pCTL target formula that is satisfied by only safe states, and has a horizon of maximum $k$ steps and a threshold probability $\alpha$. The task then consists of learning this unknown formula from states that are labeled as safe or unsafe by a domain expert. We apply principles of relational learning to induce a pCTL formula that is satisfied by all safe states and none of the unsafe ones. This formula can then be used as a safety specification for this domain, so that the system can avoid getting into dangerous situations in future. Following relational learning principles, we introduce a candidate formula generation process, as well as a method for deciding which candidate formula is a satisfactory specification for the given labeled states. The cases where the expert knows and does not know the system policy are treated, however, much of the learning process is the same for both cases. We evaluate our approach on a synthetic relational domain.

光学相干断层扫描（OCT）是一种非侵入性技术，可在微米分辨率中捕获视网膜的横截面区域。它已被广泛用作辅助成像参考，以检测与眼睛有关的病理学并预测疾病特征的纵向进展。视网膜层分割是至关重要的特征提取技术之一，其中视网膜层厚度的变化和由于液体的存在而引起的视网膜层变形高度相关，与多种流行性眼部疾病（如糖尿病性视网膜病）和年龄相关的黄斑疾病高度相关。变性（AMD）。但是，这些图像是从具有不同强度分布或换句话说的不同设备中获取的，属于不同的成像域。本文提出了一种分割引导的域适应方法，以将来自多个设备的图像调整为单个图像域，其中可用的最先进的预训练模型可用。它避免了即将推出的新数据集的手动标签的时间消耗以及现有网络的重新培训。网络的语义一致性和全球特征一致性将最大程度地减少许多研究人员报告的幻觉效果，这些效应对周期矛盾的生成对抗网络（Cyclegan）体系结构。

蒙特卡洛树搜索（MCTS）是一种搜索最佳决策的最佳先入点方法。 MCT的成功在很大程度上取决于树木的建造方式，并且选择过程在其中起着基本作用。被证明是可靠的一种特殊选择机制是基于树木（UCT）的上限置信度范围。 UCT试图通过考虑存储在MCT的统计树中的值来平衡探索和剥削。但是，对MCTS UCT的一些调整对于这是必要的。在这项工作中，我们使用进化算法（EAS）以替代UCT公式并在MCT中使用进化的表达式来进化数学表达式。更具体地说，我们通过在MCTS方法（SIEA-MCT）中提出的语义启发的进化算法来发展表达式。这是受遗传编程（GP）语义的启发，其中使用健身案例被视为在GP中采用的要求。健身病例通常用于确定个体的适应性，可用于计算个体的语义相似性（或差异）。但是，MCT中没有健身案例。我们通过使用MCT的多个奖励值来扩展此概念，从而使我们能够确定个人及其语义的适应性。通过这样做，我们展示了SIEA-MCT如何能够成功地发展数学表达式，而数学表达式与UCT相比，无需调整这些演变的表达式而产生更好或竞争的结果。我们比较了提出的SIEA-MCT与MCTS算法，MCTS快速动作值估计算法的性能， *-minimax家族的三种变体，一个随机控制器和另外两种EA方法。我们始终展示SIEA-MCT在挑战性的Carcassonne游戏中如何优于大多数这些智能控制者。

矩阵的完成问题旨在从对其个别元素的观察中恢复低级$ r \ ll d $的$ d \ times d $地面真相矩阵。现实世界中的矩阵完成通常是一个巨大的优化问题，$ d $如此之大，以至于即使是$ O（d）$ o（d）$ o（d）$ o（d）$ o（d）$ o（d）$ o（d）$ o（d）$ o（d）$ o（d）$ o（d）$ o（d）$ o（d）$ d $的昂贵。随机梯度下降（SGD）是少数能够大规模求解矩阵完成的算法之一，也可以自然地通过不断发展的地面真相处理流数据。不幸的是，当底层地面真理不足时，SGD经历了戏剧性的减速。它至少需要$ o（\ kappa \ log（1/\ epsilon））$迭代才能获得$ \ epsilon $ -close $ \ epsilon $ -Close以接地真相矩阵，条件号$ \ kappa $。在本文中，我们提出了一个预处理的SGD版本，该版本保留了SGD的所有有利的实践素质用于大规模的在线优化，同时也使其不可知到$ \ kappa $。对于对称地面真相和根平方错误（RMSE）损失，我们证明预处理的SGD收敛到$ \ epsilon $ -Accuracy in $ o（\ log（1/\ epsilon））$ tererations $迭代，并具有快速的线性线性融合率好像地面真相是完美的条件，$ \ kappa = 1 $。在我们的数值实验中，我们观察到在1位跨透明拷贝损失下进行的不条件矩阵完成的加速度，以及贝叶斯个性化排名（BPR）损失等成对损失。