结构建筑物的坍塌通常被认为是潜在的错失，已经证明了建筑物的损害，导致事故。必须连续监测人类访问受到限制的故障的任何建筑物。通过在计算机视野领域出现的无人机（无人驾驶飞行器），监测任何建筑物并检测这些故障都被视为一种可能性。本文提出了一种新的方法，其中自动无人机遍历目标建筑物，检测建筑物中的任何潜在故障，并定位故障。通过所提供的建筑物的尺寸，产生了建筑物周围的路径。由UAV的板载摄像机捕获的图像通过神经网络系统来确认存在故障。一旦检测到故障，UAV就会向检测到裂缝的相应位置时操纵。使用ROS（机器人操作系统）使用初始化ROS包装器的AIRSIM环境进行仿真，并提供ROS和AIRSIM的集成接口，与UAV一起模拟。

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.

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

Answering complex questions that require making latent decisions is a challenging task, especially when limited supervision is available. Recent works leverage the capabilities of large language models (LMs) to perform complex question answering in a few-shot setting by demonstrating how to output intermediate rationalizations while solving the complex question in a single pass. We introduce ``Successive Prompting'', where we iteratively break down a complex task into a simple task, solve it, and then repeat the process until we get the final solution. Successive prompting decouples the supervision for decomposing complex questions from the supervision for answering simple questions, allowing us to (1) have multiple opportunities to query in-context examples at each reasoning step (2) learn question decomposition separately from question answering, including using synthetic data, and (3) use bespoke (fine-tuned) components for reasoning steps where a large LM does not perform well. The intermediate supervision is typically manually written, which can be expensive to collect. We introduce a way to generate a synthetic dataset which can be used to bootstrap a model's ability to decompose and answer intermediate questions. Our best model (with successive prompting) achieves an improvement of ~5% absolute F1 on a few-shot version of the DROP dataset when compared with a state-of-the-art model with the same supervision.

Test log-likelihood is commonly used to compare different models of the same data and different approximate inference algorithms for fitting the same probabilistic model. We present simple examples demonstrating how comparisons based on test log-likelihood can contradict comparisons according to other objectives. Specifically, our examples show that (i) conclusions about forecast accuracy based on test log-likelihood comparisons may not agree with conclusions based on other distributional quantities like means; and (ii) that approximate Bayesian inference algorithms that attain higher test log-likelihoods need not also yield more accurate posterior approximations.

Default implementations of Bayesian Additive Regression Trees (BART) represent categorical predictors using several binary indicators, one for each level of each categorical predictor. Regression trees built with these indicators partition the levels using a ``remove one a time strategy.'' Unfortunately, the vast majority of partitions of the levels cannot be built with this strategy, severely limiting BART's ability to ``borrow strength'' across groups of levels. We overcome this limitation with a new class of regression tree and a new decision rule prior that can assign multiple levels to both the left and right child of a decision node. Motivated by spatial applications with areal data, we introduce a further decision rule prior that partitions the areas into spatially contiguous regions by deleting edges from random spanning trees of a suitably defined network. We implemented our new regression tree priors in the flexBART package, which, compared to existing implementations, often yields improved out-of-sample predictive performance without much additional computational burden. We demonstrate the efficacy of flexBART using examples from baseball and the spatiotemporal modeling of crime.

Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning (DL) models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different for pediatrics across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to analyze the generalizability of deep adult lung segmentation models to the pediatric population and improve performance through a systematic combinatorial approach consisting of CXR modality-specific weight initializations, stacked generalization, and an ensemble of the stacked generalization models. Novel evaluation metrics consisting of Mean Lung Contour Distance and Average Hash Score are proposed in addition to the Multi-scale Structural Similarity Index Measure, Intersection of Union, and Dice metrics to evaluate segmentation performance. We observed a significant improvement (p < 0.05) in cross-domain generalization through our combinatorial approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.

最近的模型可以产生流利和语法合成评论，同时准确预测用户评分。生成的评论表达了用户对相关产品的估计意见，通常被视为自然语言“理由”，共同预测的评级。但是，先前的研究发现，现有模型通常会产生重复性，普遍适用和通用的解释，从而导致非信息原理。此外，我们的分析表明，以前的模型生成的内容通常包含事实幻觉。这些问题要求采用新颖的解决方案，这些解决方案可以产生信息丰富的和事实扎根的解释。受到最新使用检索内容的启发，除了生成的参数知识外，我们建议用个性化的检索器增强发电机，在该发现者的启发下，猎犬的输出是增强发电机的外部知识。关于Yelp，TripAdvisor和Amazon Movie评论数据集的实验表明，我们的模型可以产生解释，即更可靠地需要进行现有评论，更多样化，并且由人类评估人员评为更有信息。

设备的端到端（E2E）模型已显示出对质量和延迟的英语语音搜索任务的常规模型的改进。 E2E模型还显示了多语言自动语音识别（ASR）的有希望的结果。在本文中，我们将以前的容量解决方案扩展到流应用程序，并提出流媒体多语言E2E ASR系统，该系统在设备上完全运行，质量和延迟与单个单语言模型相当。为了实现这一目标，我们提出了一个编码器端量模型和一个终端（EOU）联合层，以提高质量和延迟权衡。我们的系统以语言不可知论的方式构建，允许它实时支持本条件的代码切换。为了解决大型模型的可行性问题，我们进行了设备分析，并用最近开发的嵌入解码器代替了耗时的LSTM解码器。通过这些更改，我们设法在不到实时的时间内在移动设备上运行了这样的系统。

使用适当的电感偏差，反事实生成网络（CGN）可以从形状，纹理和背景歧管的随机组合中生成新图像。这些图像可以用于训练不变的分类器，避免了深层体系结构学习虚假相关性而不是有意义的问题。结果，改善了室外鲁棒性。但是，CGN体系结构包括多个参数化网络，即BigGan和U2-NET。培训这些网络需要适当的背景知识和广泛的计算。由于一个人并不总是能够访问精确的培训细节，也不总是拥有反事实的必要知识，因此我们的工作解决了以下问题：我们可以使用预先训练的CGN中嵌入的知识来培训低容量的模型，假设对架构组件的黑框访问（即仅访问验证的CGN模型）？在这个方向上，我们提出了一项名为SKDCGN的新颖作品，该作品尝试使用知识蒸馏（KD）尝试知识转移。在我们提出的架构中，每个独立的机制（形状，纹理，背景）都由一个学生“ tinygan”代表，该学生从预验证的老师“ Biggan”中学习。我们通过使用KD和适当的损失函数来证明使用最先进的数据集（例如ImageNet）和MNIST的疗效。此外，作为另一项贡献，我们的论文对CGN的组成机制进行了详尽的研究，以更好地了解每种机制如何影响不变分类器的分类精度。代码可用：https：//github.com/ambekarsameer96/skdcgn