这项研究采用无限脉冲响应（IIR）图神经网络（GNN），有效地对智能网格数据的固有图形网络结构进行建模，以解决网络攻击本地化问题。首先，我们通过数值分析有限脉冲响应（FIR）和IIR图过滤器（GFS）的经验频率响应，以近似理想的光谱响应。我们表明，对于相同的滤波器顺序，IIR GF可以更好地近似所需的光谱响应，并且由于其合理类型的滤镜响应，它们也与较低阶GF的近似值相同。其次，我们提出了一个IIR GNN模型，以有效预测总线上的网络攻击的存在。最后，我们在样本（SW）和BUS（BW）水平的各种网络攻击下评估了模型，并将结果与​​现有架构进行比较。经过实验验证的是，所提出的模型的表现分别优于最先进的FIR GNN模型，分别在SW和BW定位方面分别优于9.2％和14％。

作为一种高度复杂和集成的网络物理系统，现代电网暴露于网络攻击。假数据注入攻击（FDIAS），具体地，通过针对测量数据的完整性来表示对智能电网的主要类别威胁。虽然已经提出了各种解决方案来检测那些网络攻击，但绝大多数作品忽略了电网测量的固有图结构，并仅验证了其检测器，仅针对小于几百辆公共汽车的小型测试系统。为了更好地利用智能电网测量的空间相关性，本文提出了使用Chebyshev Graph卷积网络（CGCN）的大规模交流电网中的网络内人检测深度学习模型。通过降低光谱滤波器的复杂性并使它们本地化，CGCN提供了一种快速高效的卷积操作，以模拟图形结构智能电网数据。我们在数值上验证所提出的CGCN的探测器在7.86以7.86以7.67以带有2848辆总线的大型电网的误报率的7.86以7.86的误报。所值得注意的是，所提出的方法检测为2848辆总线系统的4毫秒下的网络攻击，这使其成为大型系统中的网络内攻击的良好候选者。

由于ICS网络中的错误配置/受损IDS导致的误报可能导致严重的经济和运行损坏。为了解决这个问题，研究专注于利用深度学习技术，有助于减少虚假警报。然而，缺点是这些工作通常需要或隐含地假设要值得信赖的物理和网络传感器。隐含数据的信任是使用人工智能或机器学习进行CPS安全的主要问题，因为在临界攻击检测时，它们更有风险，具有更大的可能性和影响，也受到损害。为了解决这个缺点，对如何在不确定性提供良好决策的情况下重新抑制了问题。然后，决定是检测，并且不确定性包括用于基于ML的ID的数据是否受到损害。因此，该工作提供了一种方法，可以通过在未经警报的先前分配知识的情况下处理不确定性来减少CPS电力系统中的误报的方法。具体而言，提出了一种利用Dempster Shafer组合规则的基于证据的方法，以减少虚假警报。设计了多假设质量函数模型，其利用各种监督学习分类器获得的概率分数。使用该模型，提出了一种基于位置域的基于域的融合框架，并以不同的组合规则评估，该规则融合了来自域间和域内传感器的多个证据。该方法在具有在大型合成电网中的中间攻击仿真测试的网络 - 物理电力系统中进行了证明。为了评估绩效，合理性，信仰，雕刻律等。考虑了决策功能的指标。为了提高性能，提出了一种考虑决策度量作为健身功能的特征选择的多目标基于遗传算法。

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When this subgroup confounds all observed data, neither covariate shift nor label shift assumptions apply. We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target. The identification results are constructive, immediately suggesting an algorithm for estimating the optimal predictor in the target. For continuous observations, when this algorithm becomes impractical, we propose a latent variable model specific to the data generation process at hand. We show how the approach degrades as the size of the shift changes, and verify that it outperforms both covariate and label shift adjustment.

Scholarly text is often laden with jargon, or specialized language that divides disciplines. We extend past work that characterizes science at the level of word types, by using BERT-based word sense induction to find additional words that are widespread but overloaded with different uses across fields. We define scholarly jargon as discipline-specific word types and senses, and estimate its prevalence across hundreds of fields using interpretable, information-theoretic metrics. We demonstrate the utility of our approach for science of science and computational sociolinguistics by highlighting two key social implications. First, we measure audience design, and find that most fields reduce jargon when publishing in general-purpose journals, but some do so more than others. Second, though jargon has varying correlation with articles' citation rates within fields, it nearly always impedes interdisciplinary impact. Broadly, our measurements can inform ways in which language could be revised to serve as a bridge rather than a barrier in science.

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for use with Bayesian quantum state estimation methods. Previously, researchers have looked to Bayesian quantum state tomography due to its unique advantages like natural uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography.

Compact and accurate representations of 3D shapes are central to many perception and robotics tasks. State-of-the-art learning-based methods can reconstruct single objects but scale poorly to large datasets. We present a novel recursive implicit representation to efficiently and accurately encode large datasets of complex 3D shapes by recursively traversing an implicit octree in latent space. Our implicit Recursive Octree Auto-Decoder (ROAD) learns a hierarchically structured latent space enabling state-of-the-art reconstruction results at a compression ratio above 99%. We also propose an efficient curriculum learning scheme that naturally exploits the coarse-to-fine properties of the underlying octree spatial representation. We explore the scaling law relating latent space dimension, dataset size, and reconstruction accuracy, showing that increasing the latent space dimension is enough to scale to large shape datasets. Finally, we show that our learned latent space encodes a coarse-to-fine hierarchical structure yielding reusable latents across different levels of details, and we provide qualitative evidence of generalization to novel shapes outside the training set.

Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science. Recent developments have shown that a modest number of randomized measurements suffices to learn many properties of a quantum many-body system. However, implementing such measurements requires complete control over individual particles, which is unavailable in many experimental platforms. In this work, we present rigorous and efficient algorithms for learning quantum many-body states in systems with any degree of control over individual particles, including when every particle is subject to the same global field and no additional ancilla particles are available. We numerically demonstrate the effectiveness of our algorithms for estimating energy densities in a U(1) lattice gauge theory and classifying topological order using very limited measurement capabilities.

Online media data, in the forms of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost, which not only poses a serious threat to the trustworthiness of digital information but also has severe societal implications. This motivates a growing interest of research in media tampering detection, i.e., using deep learning techniques to examine whether media data have been maliciously manipulated. Depending on the content of the targeted images, media forgery could be divided into image tampering and Deepfake techniques. The former typically moves or erases the visual elements in ordinary images, while the latter manipulates the expressions and even the identity of human faces. Accordingly, the means of defense include image tampering detection and Deepfake detection, which share a wide variety of properties. In this paper, we provide a comprehensive review of the current media tampering detection approaches, and discuss the challenges and trends in this field for future research.