医疗保健提供者通常会记录给每位患者提供临床，研究和计费目的的临床护理的详细说明。由于这些叙述的非结构性性质，提供者使用专门的员工使用国际疾病（ICD）编码系统为患者的诊断分配诊断代码。此手动过程不仅耗时，而且昂贵且容易出错。先前的工作证明了机器学习（ML）方法在自动化此过程中的潜在效用，但它依靠大量手动标记数据来训练模型。此外，诊断编码系统随着时间的流逝而演变，这使得传统的监督学习策略无法推广到本地应用程序之外。在这项工作中，我们引入了一个普遍的弱监督文本分类框架，该框架仅从类标签描述中学习，而无需使用任何人类标记的文档。它利用预先训练的语言模型中存储的语言领域知识和数据编程框架将代码标签分配给单个文本。我们通过将方法与四个现实世界文本分类数据集中的最先进的弱文本分类器进行比较，除了将ICD代码分配给公开可用的模拟MIMIC-III数据库中的医疗注释外，我们证明了我们的方法的功效和灵活性。

很大一部分临床生理监测警报是错误的。这通常会导致临床人员的警报疲劳，不可避免地会损害患者的安全。为了解决这个问题，研究人员试图构建机器学习（ML）模型，能够准确裁定生命体征（VS）警报在血液动力学监测的患者的床边提出的警报，为真实或人工制品。先前的研究利用了需要大量手工标记数据的监督ML技术。但是，手动收集此类数据可能是昂贵的，耗时的和平凡的，并且是限制医疗保健中ML广泛采用（HC）的关键因素。取而代之的是，我们探索使用多个单独的启发式方法来自动将概率标签分配给使用弱监督的未标记培训数据。我们的弱监督模型在传统的监督技术方面具有竞争力，并且需要较少的领域专家参与，这证明了它们用作ML HC应用中监督学习的高效和实用替代方案。

现实世界中临床干预措施的治疗功效的估计涉及处理诸如死亡时间，重新住院或可能受到检查的复合事件之类的连续结果。在这种情况下，反事实推理需要将混杂的生理特征的影响与正在评估的干预措施的影响中影响基线存活率的影响。在本文中，我们提出了一种潜在变量方法来模拟异质治疗效果，该方法通过提出一个人可以属于具有不同响应特征的潜在簇之一。我们表明，这种潜在结构可以介导基本的生存率，并有助于确定干预的影响。我们证明了我们的方法根据个人对最初进行的多个大型随机临床试验的治疗反应来发现可行的表型的能力，该试验最初是为了评估适当的治疗方法以降低心血管风险。

分析心电图（ECG）是一种廉价而非侵入性，但诊断心脏病的廉价而强大的方式。ECG研究使用机器学习自动检测到到目前为止的异常心跳依赖于大型手动注释的数据集。在收集大量的未标记数据时可以简单地，异常心跳的点点注释是乏味且昂贵的。我们探讨了多种弱监理来源，通过人类设计的启发式学习异常心跳的诊断模型，而无需在各个数据点上使用地面真理标签。我们的作品是第一个直接在时间序列数据上定义薄弱的监督来源。结果表明，随着六个直观的时间序列启发式，我们能够推断出高质量的概率标签估计超过100,000多个心跳，具有很少的人力努力，并使用估计的标签培训对所持测试数据进行评估的竞争分类器。

在这项工作中，我们提出胶水（图偏离网络与局部不确定性估计），在最近提出的图偏差网络（GDN）上建立。胶水不仅自动学习变量之间的复杂依赖性，并使用它们来更好地识别异常行为，而且还量化了其预测性的不确定性，允许我们考虑数据的变化以及具有更高的可解释的异常检测阈值。结果两个真实世界数据集告诉我们，优化负值高斯日志可能性是合理的，因为胶水的预测结果与GDN相提并论而言，实际上比矢量自动投播者基线更好，这对GDN直接优化了MSE损失很重要。总之，我们的实验表明，胶水在异常检测中具有GDN竞争力，具有不确定性估算的额外收益。我们还显示胶水学习有意义的传感器嵌入，将相似的传感器集成在一起。

Multilingual machine translation models can benefit from synergy between different language pairs, but also suffer from interference. While there is a growing number of sophisticated methods that aim to eliminate interference, our understanding of interference as a phenomenon is still limited. This work identifies the main factors that contribute to interference in multilingual machine translation. Through systematic experimentation, we find that interference (or synergy) are primarily determined by model size, data size, and the proportion of each language pair within the total dataset. We observe that substantial interference occurs mainly when the model is very small with respect to the available training data, and that using standard transformer configurations with less than one billion parameters largely alleviates interference and promotes synergy. Moreover, we show that tuning the sampling temperature to control the proportion of each language pair in the data is key to balancing the amount of interference between low and high resource language pairs effectively, and can lead to superior performance overall.

Arbitrary pattern formation (\textsc{Apf}) is well studied problem in swarm robotics. The problem has been considered in two different settings so far; one is in plane and another is in infinite grid. This work deals the problem in infinite rectangular grid setting. The previous works in literature dealing with \textsc{Apf} problem in infinite grid had a fundamental issue. These deterministic algorithms use a lot space of the grid to solve the problem mainly because of maintaining asymmetry of the configuration or to avoid collision. These solution techniques can not be useful if there is a space constrain in the application field. In this work, we consider luminous robots (with one light that can take two colors) in order to avoid symmetry, but we carefully designed a deterministic algorithm which solves the \textsc{Apf} problem using minimal required space in the grid. The robots are autonomous, identical, anonymous and they operate in Look-Compute-Move cycles under a fully asynchronous scheduler. The \textsc{Apf} algorithm proposed in [WALCOM'2019] by Bose et al. can be modified using luminous robots so that it uses minimal space but that algorithm is not move optimal. The algorithm proposed in this paper not only uses minimal space but also asymptotically move optimal. The algorithm proposed in this work is designed for infinite rectangular grid but it can be easily modified to work in a finite grid as well.

The size of an individual cell type, such as a red blood cell, does not vary much among humans. We use this knowledge as a prior for classifying and detecting cells in images with only a few ground truth bounding box annotations, while most of the cells are annotated with points. This setting leads to weakly semi-supervised learning. We propose replacing points with either stochastic (ST) boxes or bounding box predictions during the training process. The proposed "mean-IOU" ST box maximizes the overlap with all the boxes belonging to the sample space with a class-specific approximated prior probability distribution of bounding boxes. Our method trains with both box- and point-labelled images in conjunction, unlike the existing methods, which train first with box- and then point-labelled images. In the most challenging setting, when only 5% images are box-labelled, quantitative experiments on a urine dataset show that our one-stage method outperforms two-stage methods by 5.56 mAP. Furthermore, we suggest an approach that partially answers "how many box-labelled annotations are necessary?" before training a machine learning model.

This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network. While neural network based dynamic predictors perform well with full-state training data, prediction with partial observation during training phase poses a significant challenge. Here a predictor for partial observations is developed using an echo-state network (ESN) and time delay embedding of the partially observed state. The proposed method is theoretically justified with Taken's embedding theorem and strong observability of a nonlinear system. The efficacy of the proposed method is demonstrated on three systems: two synthetic datasets from chaotic dynamical systems and a set of real-time traffic data.

Network-based analyses of dynamical systems have become increasingly popular in climate science. Here we address network construction from a statistical perspective and highlight the often ignored fact that the calculated correlation values are only empirical estimates. To measure spurious behaviour as deviation from a ground truth network, we simulate time-dependent isotropic random fields on the sphere and apply common network construction techniques. We find several ways in which the uncertainty stemming from the estimation procedure has major impact on network characteristics. When the data has locally coherent correlation structure, spurious link bundle teleconnections and spurious high-degree clusters have to be expected. Anisotropic estimation variance can also induce severe biases into empirical networks. We validate our findings with ERA5 reanalysis data. Moreover we explain why commonly applied resampling procedures are inappropriate for significance evaluation and propose a statistically more meaningful ensemble construction framework. By communicating which difficulties arise in estimation from scarce data and by presenting which design decisions increase robustness, we hope to contribute to more reliable climate network construction in the future.