Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when the probability is small. Exploiting low-fidelity models can make this process more feasible, but merging information from multiple low-fidelity and high-fidelity models poses several challenges. This paper presents a robust multi-fidelity surrogate modeling strategy in which the multi-fidelity surrogate is assembled using an active learning strategy using an on-the-fly model adequacy assessment set within a subset simulation framework for efficient reliability analysis. The multi-fidelity surrogate is assembled by first applying a Gaussian process correction to each low-fidelity model and assigning a model probability based on the model's local predictive accuracy and cost. Three strategies are proposed to fuse these individual surrogates into an overall surrogate model based on model averaging and deterministic/stochastic model selection. The strategies also dictate which model evaluations are necessary. No assumptions are made about the relationships between low-fidelity models, while the high-fidelity model is assumed to be the most accurate and most computationally expensive model. Through two analytical and two numerical case studies, including a case study evaluating the failure probability of Tristructural isotropic-coated (TRISO) nuclear fuels, the algorithm is shown to be highly accurate while drastically reducing the number of high-fidelity model calls (and hence computational cost).

尽管No-U-Turn采样器（螺母）是执行贝叶斯推断的广泛采用方法，但它需要许多后梯度，在实践中计算可能很昂贵。最近，人们对基于物理的动力学（或哈密顿）系统和哈密顿神经网络（HNNS）的机器学习引起了重大兴趣。但是，这些类型的体系结构尚未应用于有效地解决贝叶斯推论问题。我们建议使用HNN有效地进行贝叶斯推断，而无需大量的后梯度。我们向HNNS（L-HNN）引入潜在变量输出，以提高表达性和减少的集成误差。我们将L-HNN集成在坚果中，并进一步提出一种在线错误监控方案，以防止L-HNNS可能几乎没有培训数据的区域中采样堕落。考虑到几种复杂的高维后密度，并将其性能与螺母进行比较，我们证明了在线错误监测中的L-HNN。

当采样贝叶斯推断时，一种流行的方法是使用汉密尔顿蒙特卡洛（HMC），特别是No-U-Turn采样器（NUTS），该采样器（NUTS）自动决定汉密尔顿轨迹的结束时间。但是，HMC和螺母可能需要众多目标密度的数值梯度，并且在实践中可能会缓慢。我们建议使用HMC和坚果解决贝叶斯推理问题的汉密尔顿神经网络（HNNS）。一旦训练，HNN不需要在采样过程中的目标密度的数值梯度。此外，它们满足了重要的特性，例如完美的时间可逆性和哈密顿保护性，使其非常适合在HMC和坚果中使用，因为可以显示平稳性。我们还提出了一个称为潜在HNN（L-HNN）的HNN扩展，该扩展能够预测潜在的可变输出。与HNN相比，L-HNN提供了提高表达性和减少的集成误差。最后，我们在具有在线错误监测方案的螺母中使用L-HNN，以防止低概率密度区域的样本退化。我们证明了在螺母中的L-HNN，并在线错误监视了一些涉及复杂，重尾和高本地狂热概率密度的示例。总体而言，具有在线错误监控的坚果中的L-HNN令人满意地推断了这些概率密度。与传统的螺母相比，在线错误监控的螺母中，L-HNN需要1--2个目标密度的数值梯度，并通过数量级提高了每个梯度的有效样本量（ESS）。

TRISTRUCCUCTIONATIOPIC（TRISO）涂层颗粒燃料是强大的核燃料，并确定其可靠性对于先进的核技术的成功至关重要。然而，Triso失效概率很小，相关的计算模型很昂贵。我们使用耦合的主动学习，多尺度建模和子集模拟来估计使用几个1D和2D模型的Triso燃料的故障概率。通过多尺度建模，我们用来自两个低保真（LF）模型的信息融合，取代了昂贵的高保真（HF）模型评估。对于1D TRISO模型，我们考虑了三种多倍性建模策略：仅克里格，Kriging LF预测加克里格校正，深神经网络（DNN）LF预测加克里格校正。虽然这些多尺度建模策略的结果令人满意地比较了从两个LF模型中使用信息融合的策略，但是通常常常称为HF模型。接下来，对于2D Triso模型，我们考虑了两个多倍性建模策略：DNN LF预测加克里格校正（数据驱动）和1D Triso LF预测加克里格校正（基于物理学）。正如所预期的那样，基于物理的策略一直需要对HF模型的最少的呼叫。然而，由于DNN预测是瞬时的，数据驱动的策略具有较低的整体模拟时间，并且1D Triso模型需要不可忽略的模拟时间。

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

The usage of technologically advanced devices has seen a boom in many domains, including education, automation, and healthcare; with most of the services requiring Internet connectivity. To secure a network, device identification plays key role. In this paper, a device fingerprinting (DFP) model, which is able to distinguish between Internet of Things (IoT) and non-IoT devices, as well as uniquely identify individual devices, has been proposed. Four statistical features have been extracted from the consecutive five device-originated packets, to generate individual device fingerprints. The method has been evaluated using the Random Forest (RF) classifier and different datasets. Experimental results have shown that the proposed method achieves up to 99.8% accuracy in distinguishing between IoT and non-IoT devices and over 97.6% in classifying individual devices. These signify that the proposed method is useful in assisting operators in making their networks more secure and robust to security breaches and unauthorized access.

Through a study of multi-gas mixture datasets, we show that in multi-component spectral analysis, the number of functional or non-functional principal components required to retain the essential information is the same as the number of independent constituents in the mixture set. Due to the mutual in-dependency among different gas molecules, near one-to-one projection from the principal component to the mixture constituent can be established, leading to a significant simplification of spectral quantification. Further, with the knowledge of the molar extinction coefficients of each constituent, a complete principal component set can be extracted from the coefficients directly, and few to none training samples are required for the learning model. Compared to other approaches, the proposed methods provide fast and accurate spectral quantification solutions with a small memory size needed.

Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider, we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under three typical settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.

Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP task commonly applied in the financial and scientific domain. The early research focuses on migrating other QA task methods to HybridQA, while with further research, more and more HybridQA-specific methods have been present. With the rapid development of HybridQA, the systematic survey is still under-explored to summarize the main techniques and advance further research. So we present this work to summarize the current HybridQA benchmarks and methods, then analyze the challenges and future directions of this task. The contributions of this paper can be summarized in three folds: (1) first survey, to our best knowledge, including benchmarks, methods and challenges for HybridQA; (2) systematic investigation with the reasonable comparison of the existing systems to articulate their advantages and shortcomings; (3) detailed analysis of challenges in four important dimensions to shed light on future directions.