本文提出了一种用于系统识别（ID）的概率贝叶斯公式，并使用随机动态模型对不可分割的哈密顿系统进行了估计。非分离的哈密顿系统是来自不同科学和工程应用的模型，例如天体物理学，机器人技术，涡流动力学，带电的粒子动力学和量子力学。数值实验表明，与最先进的方法相比，所提出的方法以更高的精度和预测性不确定性降低了动态系统。结果进一步表明，在可能存在稀疏和嘈杂的测量的情况下，准确的预测远远超出了训练时间间隔，这为提出的方法提供了鲁棒性和概括性。定量益处是预测准确性，相对误差少于10％的相对误差超过12倍，比基于基准问题的基于最小二乘的方法长12倍。

仇恨言语分类一直是自然语言处理中的一个长期问题。但是，即使有许多仇恨言论检测方法，它们通常忽略了许多仇恨言论，因为它们在自然界中是隐含的。开发数据集以协助隐性仇恨言语分类的任务伴随着自己的挑战；困难是语言上的细微差别，改变了构成仇恨言论的定义以及劳动密集型的注释过程。这导致了可用于训练和测试此类系统的数据稀缺，当使用基于参数的变压器模型来解决该问题时，这会引起较高的差异问题。在本文中，我们探讨了各种优化和正则化技术，并开发了一种基于罗伯塔的新型模型，可实现最先进的性能。

Relu完全连接的网络普遍存在但无法诠释，因为它们适用于多层结构的分段线性函数和模型重量的复杂相互作用。本文采用了一种新的方法来通过在各个件（零件）上的设定操作来实现分段。这是通过近似规范正常形式并使用所得到的模型来完成的。这提供了特殊的优点（a）对拟合功能的参数的强对应关系（高可解释性）; （b）能够符合连续功能的任何组合作为分段功能（易于设计）; （c）在域的目标区域（有针对性学习）中添加新的非线性的能力; （d）避免分层的等式的简单性。它也可以在分段线性函数的总体Max-min表示中表达，这具有理论上的缓解和可信度。在模拟的回归和分类任务和基准数据集上测试了该架构，包括UCI数据集，MNIST，FMNIST和CIFAR 10。此性能与完全连接的架构相同。它可以找到各种应用，其中必须由可解释层替换完全连接的图层。

在此手稿中，提出了基于图像分析的基于图像分析的深度学习框架，用于风力涡轮刀片表面损伤检测。大约三分之一的涡轮机重量的涡轮刀片容易受到损害，并可能导致网格连接的风能转换系统突然故障。风力涡轮机叶片的表面损伤检测需要一个大数据集，以便在早期阶段检测一种损坏。涡轮刀片图像是通过空中图像捕获的。经检查后，发现图像数据集受到限制，因此应用图像增强以改善刀片图像数据集。该方法被建模为多级监督学习问题，并测试了卷积神经网络（CNN），VGG16-RCNN和Alexnet等深度学习方法，以确定涡轮叶片表面损伤的潜在能力。

Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating similar words in vector space. For instance, Word2Vec is a self-supervised predictive model that captures the context of words using a neural network. Similarly, GLoVe is a popular unsupervised model incorporating corpus-wide word co-occurrence statistics. Such word embedding has significantly boosted important NLP tasks, including sentiment analysis, document classification, and machine translation. However, the embeddings are dense floating-point vectors, making them expensive to compute and difficult to interpret. In this paper, we instead propose to represent the semantics of words with a few defining words that are related using propositional logic. To produce such logical embeddings, we introduce a Tsetlin Machine-based autoencoder that learns logical clauses self-supervised. The clauses consist of contextual words like "black," "cup," and "hot" to define other words like "coffee," thus being human-understandable. We evaluate our embedding approach on several intrinsic and extrinsic benchmarks, outperforming GLoVe on six classification tasks. Furthermore, we investigate the interpretability of our embedding using the logical representations acquired during training. We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.

Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

Large training data and expensive model tweaking are standard features of deep learning for images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which raises privacy concerns. Existing solutions are either too expensive to be practical or do not sufficiently protect the confidentiality of data and models. In this paper, we study and compare novel \emph{image disguising} mechanisms, DisguisedNets and InstaHide, aiming to achieve a better trade-off among the level of protection for outsourced DNN model training, the expenses, and the utility of data. DisguisedNets are novel combinations of image blocktization, block-level random permutation, and two block-level secure transformations: random multidimensional projection (RMT) and AES pixel-level encryption (AES). InstaHide is an image mixup and random pixel flipping technique \cite{huang20}. We have analyzed and evaluated them under a multi-level threat model. RMT provides a better security guarantee than InstaHide, under the Level-1 adversarial knowledge with well-preserved model quality. In contrast, AES provides a security guarantee under the Level-2 adversarial knowledge, but it may affect model quality more. The unique features of image disguising also help us to protect models from model-targeted attacks. We have done an extensive experimental evaluation to understand how these methods work in different settings for different datasets.

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.

Real-world datasets exhibit imbalances of varying types and degrees. Several techniques based on re-weighting and margin adjustment of loss are often used to enhance the performance of neural networks, particularly on minority classes. In this work, we analyze the class-imbalanced learning problem by examining the loss landscape of neural networks trained with re-weighting and margin-based techniques. Specifically, we examine the spectral density of Hessian of class-wise loss, through which we observe that the network weights converge to a saddle point in the loss landscapes of minority classes. Following this observation, we also find that optimization methods designed to escape from saddle points can be effectively used to improve generalization on minority classes. We further theoretically and empirically demonstrate that Sharpness-Aware Minimization (SAM), a recent technique that encourages convergence to a flat minima, can be effectively used to escape saddle points for minority classes. Using SAM results in a 6.2\% increase in accuracy on the minority classes over the state-of-the-art Vector Scaling Loss, leading to an overall average increase of 4\% across imbalanced datasets. The code is available at: https://github.com/val-iisc/Saddle-LongTail.