Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics. In particular, there has been significant interest in applying deep learning techniques to predicting the mechanical behavior of heterogeneous materials and structures. Researchers have shown that deep learning methods are able to effectively predict mechanical behavior with low error for systems ranging from engineered composites, to geometrically complex metamaterials, to heterogeneous biological tissue. However, there has been comparatively little attention paid to deep learning model calibration, i.e., the match between predicted probabilities of outcomes and the true probabilities of outcomes. In this work, we perform a comprehensive investigation into ML model calibration across seven open access engineering mechanics datasets that cover three distinct types of mechanical problems. Specifically, we evaluate both model and model calibration error for multiple machine learning methods, and investigate the influence of ensemble averaging and post hoc model calibration via temperature scaling. Overall, we find that ensemble averaging of deep neural networks is both an effective and consistent tool for improving model calibration, while temperature scaling has comparatively limited benefits. Looking forward, we anticipate that this investigation will lay the foundation for future work in developing mechanics specific approaches to deep learning model calibration.

研究兴趣大大增加了将数据驱动方法应用于力学问题的问题。尽管传统的机器学习（ML）方法已经实现了许多突破，但它们依赖于以下假设：培训（观察到的）数据和测试（看不见）数据是独立的且分布相同的（i.i.d）。因此，当应用于未知的测试环境和数据分布转移的现实世界力学问题时，传统的ML方法通常会崩溃。相反，分布（OOD）的概括假定测试数据可能会发生变化（即违反I.I.D.假设）。迄今为止，已经提出了多种方法来改善ML方法的OOD概括。但是，由于缺乏针对OOD回归问题的基准数据集，因此这些OOD方法在主导力学领域的回归问题上的效率仍然未知。为了解决这个问题，我们研究了机械回归问题的OOD泛化方法的性能。具体而言，我们确定了三个OOD问题：协变量移位，机制移位和采样偏差。对于每个问题，我们创建了两个基准示例，以扩展机械MNIST数据集收集，并研究了流行的OOD泛化方法在这些机械特定的回归问题上的性能。我们的数值实验表明，在大多数情况下，与传统的ML方法相比，在大多数情况下，在这些OOD问题上的传统ML方法的性能更好，但迫切需要开发更强大的OOD概括方法，这些方法在多个OOD场景中有效。总体而言，我们希望这项研究以及相关的开放访问基准数据集将进一步开发用于机械特定回归问题的OOD泛化方法。

建模生物软组织是由于材料异质性而部分复杂的。微观结构模式在定义这些组织的机械行为方面起着主要作用，既具有挑战性，又难以模拟。最近，基于机器学习的方法来预测异质材料的机械行为，使得更彻底地探索与异质材料块相关的大量输入参数空间。具体而言，我们可以训练机器学习（ML）模型，以近似于计算上昂贵的异质材料模拟，其中ML模型在模拟的数据集上进行了训练，该模拟捕获了感兴趣的材料中存在的空间异质性范围。但是，在更广泛地将这些技术应用于生物组织时，存在一个主要的局限性：相关的微观结构模式既具有挑战性又难以分析。因此，可用于表征正在研究的输入域的有用示例的数量有限。在这项工作中，我们研究了基于ML的生成模型以及程序方法的功效，作为增强有限输入模式数据集的工具。我们发现，具有自适应判别器增强器的基于样式的生成对抗网络能够成功利用1,000个示例模式来创建最真实的生成模式。通常，与真实模式有足够相似之处的不同生成模式可以用作有限元模拟的输入，以有意义地增强训练数据集。为了实现这一方法论贡献，我们创建了一个基于Cahn-Hilliard模式的有限元分析模拟的开放访问数据集。我们预计未来的研究人员将能够利用此数据集并基于此处介绍的工作。

从设计架构材料到跨尺度的机械行为，计算建模是固体力学中的关键工具。最近，人们对使用机器学习来降低基于物理的模拟的计算成本越来越兴趣。值得注意的是，尽管依赖图神经网络（GNN）的机器学习方法在学习机制方面表现出了成功，但GNN的性能尚未针对无数的固体力学问题进行研究。在这项工作中，我们研究了GNN预测机械驱动的紧急行为的基本方面的能力：柱的几何结构与其弯曲方向之间的联系。为此，我们介绍了不对称屈曲柱（ABC）数据集，该数据集由三个不对称和异质列的几个子数据集组成不稳定。由于局部几何形状，实现标准卷积神经网络元模型所需的“图像样”数据表示不是理想的，因此激发了GNN的使用。除了研究GNN模型体系结构外，我们还研究了不同输入数据表示方法，数据增强和将多个模型结合在一起的效果。虽然我们能够获得良好的结果，但我们还表明，预测基于固体力学的新兴行为是非平凡的。因为我们的模型实施和数据集都在开源许可下分配，所以我们希望未来的研究人员可以在我们的工作基础上建立创建增强的机械师特定机器的机器学习管道，以捕获复杂的几何结构的行为。

Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these models are and how well they perform when applied to real-world applications in drastically different domains. While there has been some work investigating how well ODQA models perform when tested for out-of-domain (OOD) generalization, these studies have been conducted only under conservative shifts in data distribution and typically focus on a single component (ie. retrieval) rather than an end-to-end system. In response, we propose a more realistic and challenging domain shift evaluation setting and, through extensive experiments, study end-to-end model performance. We find that not only do models fail to generalize, but high retrieval scores often still yield poor answer prediction accuracy. We then categorize different types of shifts and propose techniques that, when presented with a new dataset, predict if intervention methods are likely to be successful. Finally, using insights from this analysis, we propose and evaluate several intervention methods which improve end-to-end answer F1 score by up to 24 points.

Differentiable Search Indices (DSIs) encode a corpus of documents in the parameters of a model and use the same model to map queries directly to relevant document identifiers. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents (+12\%). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting by a significant margin. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.

Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

The polynomial kernels are widely used in machine learning and they are one of the default choices to develop kernel-based classification and regression models. However, they are rarely used and considered in numerical analysis due to their lack of strict positive definiteness. In particular they do not enjoy the usual property of unisolvency for arbitrary point sets, which is one of the key properties used to build kernel-based interpolation methods. This paper is devoted to establish some initial results for the study of these kernels, and their related interpolation algorithms, in the context of approximation theory. We will first prove necessary and sufficient conditions on point sets which guarantee the existence and uniqueness of an interpolant. We will then study the Reproducing Kernel Hilbert Spaces (or native spaces) of these kernels and their norms, and provide inclusion relations between spaces corresponding to different kernel parameters. With these spaces at hand, it will be further possible to derive generic error estimates which apply to sufficiently smooth functions, thus escaping the native space. Finally, we will show how to employ an efficient stable algorithm to these kernels to obtain accurate interpolants, and we will test them in some numerical experiment. After this analysis several computational and theoretical aspects remain open, and we will outline possible further research directions in a concluding section. This work builds some bridges between kernel and polynomial interpolation, two topics to which the authors, to different extents, have been introduced under the supervision or through the work of Stefano De Marchi. For this reason, they wish to dedicate this work to him in the occasion of his 60th birthday.

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.