社交媒体意见两极分化的大量工作集中在媒体痕迹不同社区的立场（或正交信念）的平坦分类。我们在两个重要方面扩展了这项工作。首先，我们不仅检测到社区之间的分歧点，而且还检测到一致性点。换句话说，我们在存在重叠的情况下估计社区信念。其次，代替平坦的分类，我们考虑了层次的信念估计，在该估计中，社区可能会分层。例如，两个反对党可能在核心问题上不同意，但是在一方，尽管同意基本面，但在进一步的细节上可能会出现分歧。我们称由此产生的组合问题为分层重叠的信念估计问题。为了解决它，本文开发了一类新的无监督的非负矩阵分解（NMF）算法，我们称信仰结构化矩阵分解（BSMF）。我们提出的无监督算法捕获了潜在的信仰交叉点和差异性以及等级结构。我们讨论算法的属性，并在合成数据集和现实世界数据集上进行评估。在合成数据集中，我们的模型将误差降低了40％。在实际的Twitter痕迹中，它的准确性提高了约10％。该模型还可以在理智检查中实现96.08％的自洽性。

Recent studies have revealed the intriguing few-shot learning ability of pretrained language models (PLMs): They can quickly adapt to a new task when fine-tuned on a small amount of labeled data formulated as prompts, without requiring abundant task-specific annotations. Despite their promising performance, most existing few-shot approaches that only learn from the small training set still underperform fully supervised training by nontrivial margins. In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set. To encourage the generator to produce label-discriminative samples, we train it via weighted maximum likelihood where the weight of each token is automatically adjusted based on a discriminative meta-learning objective. A classification PLM can then be fine-tuned on both the few-shot and the synthetic samples with regularization for better generalization and stability. Our approach FewGen achieves an overall better result across seven classification tasks of the GLUE benchmark than existing few-shot learning methods, improving no-augmentation methods by 5+ average points, and outperforming augmentation methods by 3+ average points.

应用于物理工程系统的纯粹数据驱动的深神经网络（DNN）可以推断出违反物理定律的关系，从而导致意外后果。为了应对这一挑战，我们提出了一个基于物理模型的DNN框架，即Phy-Taylor，该框架以物理知识加速了学习合规的表示。 Phy-Taylor框架做出了两个关键的贡献。它引入了一个新的建筑物理兼容神经网络（PHN），并具有新颖的合规机制，我们称{\ em物理学引导的神经网络编辑\/}。 PHN的目的是直接捕获受物质量的启发的非线性，例如动能，势能，电力和空气动力阻力。为此，PHN增强了具有两个关键组成部分的神经网络层：（i）泰勒级数序列扩展的非线性功能捕获物理知识的扩展，以及（ii）缓解噪声影响的抑制器。神经网络编辑机制进一步修改了网络链接和激活功能与物理知识一致。作为扩展，我们还提出了一个自我校正的Phy-Taylor框架，该框架介绍了两个其他功能：（i）基于物理模型的安全关系学习，以及（ii）在违反安全性的情况下自动输出校正。通过实验，我们表明（通过直接表达难以学习的非线性并通过限制依赖性）Phy-Taylor的特征较少的参数和明显加速的训练过程，同时提供增强的模型稳健性和准确性。

最先进的自主车辆（AV）框架中的对象检测依赖于深神经网络。通常，这些网络在整个相机LIDAR帧上均匀地执行对象检测。然而，这种均匀性通过向场景中的所有对象提供相同的优先级而危及AV的安全性，无论其碰撞到AV。在本文中，我们为AV提供了一个新的端到端管道，它稍后引入LIDAR群集的概念和相机推断，以检测和分类对象。我们拟议的框架的好处是双重的。首先，我们的管道优先考虑检测对AV的碰撞风险更高的物体，给予AV的更多时间对不安全的条件作出反应。其次，与流行的深神经网络管道相比，它还提供更快的推理速度。我们使用现实世界数据集设计我们的框架，Waymo Open DataSet，解决LIDAR传感器和物体检测算法的局限性引起的挑战。我们表明我们的新型对象检测管道优先考虑了更高风险物体的检测，同时实现了与相机推断相比的相当精度和25％的平均速度。

This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood (JML) to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course (MOOC). The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative "validation" of the factor model, using auxiliary information about the popularity of items consulted during an open-book exam in the course.

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

Using Structural Health Monitoring (SHM) systems with extensive sensing arrangements on every civil structure can be costly and impractical. Various concepts have been introduced to alleviate such difficulties, such as Population-based SHM (PBSHM). Nevertheless, the studies presented in the literature do not adequately address the challenge of accessing the information on different structural states (conditions) of dissimilar civil structures. The study herein introduces a novel framework named Structural State Translation (SST), which aims to estimate the response data of different civil structures based on the information obtained from a dissimilar structure. SST can be defined as Translating a state of one civil structure to another state after discovering and learning the domain-invariant representation in the source domains of a dissimilar civil structure. SST employs a Domain-Generalized Cycle-Generative (DGCG) model to learn the domain-invariant representation in the acceleration datasets obtained from a numeric bridge structure that is in two different structural conditions. In other words, the model is tested on three dissimilar numeric bridge models to translate their structural conditions. The evaluation results of SST via Mean Magnitude-Squared Coherence (MMSC) and modal identifiers showed that the translated bridge states (synthetic states) are significantly similar to the real ones. As such, the minimum and maximum average MMSC values of real and translated bridge states are 91.2% and 97.1%, the minimum and the maximum difference in natural frequencies are 5.71% and 0%, and the minimum and maximum Modal Assurance Criterion (MAC) values are 0.998 and 0.870. This study is critical for data scarcity and PBSHM, as it demonstrates that it is possible to obtain data from structures while the structure is actually in a different condition or state.

Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA might be argued by variability in its clinical and physical manifestations. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media. Among these data sources, the scientific articles published in the biomedical literature usually make a principled pipeline to study disease. Rapid yet, accurate text mining on large-scale scientific literature may discover novel knowledge and terminology to better understand knee OA and to improve the quality of knee OA diagnosis, prevention, and treatment. The present works aim to utilize artificial neural network strategies to automatically extract vocabularies associated with knee OA diseases. Our finding indicates the feasibility of developing word embedding neural networks for autonomous keyword extraction and abstraction of knee OA.

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based classification to improve bladder tissue classification when annotations are limited in multi-domain data.