随着Covid-19大流行的爆发，急迫有效地识别可能与已经感染Covid-19感染的人密切接触的人。该识别个人的过程，也称为“联系跟踪”，对该病毒传播的遏制和控制具有重大影响。但是，手动跟踪已被证明是无效的呼叫自动接触跟踪方法。因此，本研究提出了一种自动化机器学习系统，用于使用通过手持设备传输的传感器数据来识别可能已经与Covid-19感染的其他人接触的个人。本文介绍了到达最佳解决方案模型的不同方法，其有效地预测使用梯度升压算法和时间序列特征提取的人是否已经密切接近受感染的个体。

相似性是一种比较主观度量，与所考虑的域中变化。在若干NLP应用程序中，例如文档分类，模式识别，聊天问题答案，情绪分析等，识别句子对的准确相似度得分已成为研究的关键领域。在评估相似性的现有模型中，基于上下文比较有效地计算这种相似性的限制，由于居中理论而定位，并且缺乏非语义文本比较已经证明是缺点。因此，本文介绍了基于网络科学，相邻加权关系边缘的原理的多层相似度测量的多层语义相似性网络模型，呈现了基于网络科学的原理和所提出的扩展节点相似性计算公式。评估和测试所提出的多层网络模型，并针对已建立的最先进模型进行测试，并且显示在评估句子相似性时表现出更好的性能分数。

Memory efficiency is crucial in training deep learning networks on resource-restricted devices. During backpropagation, forward tensors are used to calculate gradients. Despite the option of keeping those dependencies in memory until they are reused in backpropagation, some forward tensors can be discarded and recomputed later from saved tensors, so-called checkpoints. This allows, in particular, for resource-constrained heterogeneous environments to make use of all available compute devices. Unfortunately, the definition of these checkpoints is a non-trivial problem and poses a challenge to the programmer - improper or excessive recomputations negate the benefit of checkpointing. In this article, we present XEngine, an approach that schedules network operators to heterogeneous devices in low memory environments by determining checkpoints and recomputations of tensors. Our approach selects suitable resources per timestep and operator and optimizes the end-to-end time for neural networks taking the memory limitation of each device into account. For this, we formulate a mixed-integer quadratic program (MIQP) to schedule operators of deep learning networks on heterogeneous systems. We compare our MIQP solver XEngine against Checkmate, a mixed-integer linear programming (MILP) approach that solves recomputation on a single device. Our solver finds solutions that are up to 22.5 % faster than the fastest Checkmate schedule in which the network is computed exclusively on a single device. We also find valid schedules for networks making use of both central processing units and graphics processing units if memory limitations do not allow scheduling exclusively to the graphics processing unit.

Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology and personalized recommendations, among others. In this work we present an adaptation of the Frank-Wolfe algorithm for learning multidimensional Hawkes processes. Experimental results show that our approach has better or on par accuracy in terms of parameter estimation than other first order methods, while enjoying a significantly faster runtime.

Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone. Using the proposed method, we are the first to optimize such an autofocus-inspired algorithm based on analytical gradients. The algorithm achieves a reduction in MSE by 35.5 % and an improvement in SSIM by 12.6 % over the motion affected reconstruction. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models.

The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a means to ensure that a neural network based classifier bases its predictions solely on human understandable concepts. The concept labels, or rationales as we refer to them, are learned by the concept labeling component of the CBM. Another component learns to predict the target classification label from these predicted concept labels. Unfortunately, these models are heavily reliant on human provided concept labels for each datapoint. To enable CBMs to behave robustly when these labels are not readily available, we show how to equip them with the ability to abstain from predicting concepts when the concept labeling component is uncertain. In other words, our model learns to provide rationales for its predictions, but only whenever it is sure the rationale is correct.

Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint. Then a downstream model uses these predicted concept labels to predict the target label. The predicted concepts act as a rationale for the target prediction. Model trust issues emerge in this paradigm when soft concept labels are used: it has previously been observed that extra information about the data distribution leaks into the concept predictions. In this work we show how Monte-Carlo Dropout can be used to attain soft concept predictions that do not contain leaked information.

在过去的几年中，霍克斯流程的在线学习受到了越来越多的关注，尤其是用于建模演员网络。但是，这些作品通常会模拟事件或参与者的潜在群集之间的丰富相互作用，或者是参与者之间的网络结构。我们建议对参与者网络的潜在结构进行建模，以及在现实世界中的医疗和财务应用环境中进行的丰富互动。合成和现实世界数据的实验结果展示了我们方法的功效。

准确地估算主要山区盆地中的积雪对于水资源经理来说至关重要，以便做出影响当地和全球经济，野生动植物和公共政策的决策。目前，此估计需要多个配备LIDAR的飞机飞行或原位测量值，两者均昂贵，稀疏和对可访问区域有偏见。在本文中，我们证明了来自多个，公开可用的卫星和天气数据源的空间和时间信息的融合，可以估算关键山区的积雪。我们的多源模型的表现优于单源估计值5.0英寸RMSE，并且优于稀疏的原位测量值的估计值1.2英寸RMSE。

Hawkes流程最近从机器学习社区中引起了人们对建模事件序列数据的多功能性的越来越多的关注。尽管它们具有丰富的历史可以追溯到几十年前，但其某些属性（例如用于学习参数的样本复杂性和释放差异化私有版本的样本复杂性）尚未得到彻底的分析。在这项工作中，我们研究了具有背景强度$ \ mu $和激发功能$ \ alpha e^{ - \ beta t} $的标准霍克斯进程。我们提供$ \ mu $和$ \ alpha $的非私人和差异私人估计器，并在两种设置中获得样本复杂性结果以量化隐私成本。我们的分析利用了霍克斯过程的强大混合特性和经典的中央限制定理的结果，结果较弱的随机变量。我们在合成数据集和真实数据集上验证了我们的理论发现。