不平衡的数据是机器学习中经常遇到的问题。尽管有关数据不平衡的抽样技术有大量文献，但仍有有限的研究解决了最佳抽样比率的问题。在本文中，我们试图通过对抽样比对分类准确性的影响进行大规模研究来填补文献中的空白。我们考虑10种流行的抽样方法，并根据20个数据集评估其性能。数值实验的结果表明，最佳采样比在0.7至0.8之间，尽管确切比率取决于数据集。此外，我们发现，尽管原始不平衡比率或功能数量在确定最佳比率方面没有可见作用，但数据集中的样本数量可能会产生切实效果。

深度神经网络（DNN）已经在许多领域实现了最先进的性能。然而，DNN需要高计算时间，并且人们始终期望在较低的计算中进行更好的性能。因此，我们研究人类躯体传感系统并设计神经网络（SPINANNET），以实现更高的计算准确性，计算较少。传统NNS中的隐藏层接收前一层中的输入，应用激活函数，然后将结果传送到下一个图层。在拟议的脊柱植物中，每层分为三个分裂：1）输入分割，2）中间分割，3）输出分割。每个层的输入拆分接收到输入的一部分。每个层的中间分割接收先前层的中间分离的输出和电流层的输入分割的输出。输入权重的数量明显低于传统的DNN。 SPINANNET还可以用作DNN的完全连接或分类层，并支持传统的学习和转移学习。我们在大多数DNN中观察到具有较低计算成本的显着误差。 VGG-5网络上的传统学习具有SPINALNET分类层，为QMNIST，Kuzushiji-Mnist，EMNIST（字母，数字和平衡）数据集提供了最先进的（SOTA）性能。传统学习与Imagenet预训练的初始重量和Spinalnet分类层提供了STL-10，水果360，Bird225和CALTECH-101数据集的SOTA性能。拟议的SPINANNET的脚本可按以下链接提供：https://github.com/dipuk0506/spinalnet

我们研究了数据驱动的深度学习方法的潜力，即从观察它们的混合物中分离两个通信信号。特别是，我们假设一个信号之一的生成过程（称为感兴趣的信号（SOI）），并且对第二个信号的生成过程不了解，称为干扰。单通道源分离问题的这种形式也称为干扰拒绝。我们表明，捕获高分辨率的时间结构（非平稳性），可以准确地同步与SOI和干扰，从而带来了可观的性能增长。有了这个关键的见解，我们提出了一种域信息神经网络（NN）设计，该设计能够改善“现成” NNS和经典检测和干扰拒绝方法，如我们的模拟中所示。我们的发现突出了特定于交流领域知识的关键作用在开发数据驱动的方法方面发挥了作用，这些方法具有前所未有的收益的希望。

我们研究了单通道源分离（SCSS）的问题，并专注于环化信号，这些信号特别适用于各种应用领域。与经典的SCSS方法不同，我们考虑了一个仅可用源的示例而不是模型的设置，从而激发了数据驱动的方法。对于具有基本环化高斯成分的源模型，我们为任何基于模型或数据驱动的分离方法建立了可达到的均方误差（MSE）的下限。我们的分析进一步揭示了最佳分离和相关实施挑战的操作。作为一种计算吸引力的替代方案，我们建议使用U-NET体系结构进行深度学习方法，该方法与最低MSE估计器具有竞争力。我们在模拟中证明，有了合适的域信息架构选择，我们的U-NET方法可以通过大幅减少的计算负担来达到最佳性能。

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.

With the advent of Neural Style Transfer (NST), stylizing an image has become quite popular. A convenient way for extending stylization techniques to videos is by applying them on a per-frame basis. However, such per-frame application usually lacks temporal-consistency expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal-consistency suffers from one or more of the following drawbacks. They (1) are only suitable for a limited range of stylization techniques, (2) can only be applied in an offline fashion requiring the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency-control. Note that existing consistent video-filtering approaches aim to completely remove flickering artifacts and thus do not respect any specific consistency-control aspect. For stylization tasks, however, consistency-control is an essential requirement where a certain amount of flickering can add to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that can stylize video streams while providing interactive consistency-control. Apart from stylization, our approach also supports various other image processing filters. For achieving interactive performance, we develop a lite optical-flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. We show that the final consistent video-output using our flow network is comparable to that being obtained using state-of-the-art optical-flow network. Further, we employ an adaptive combination of local and global consistent features and enable interactive selection between the two. By objective and subjective evaluation, we show that our method is superior to state-of-the-art approaches.

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.

Machine learning is the dominant approach to artificial intelligence, through which computers learn from data and experience. In the framework of supervised learning, for a computer to learn from data accurately and efficiently, some auxiliary information about the data distribution and target function should be provided to it through the learning model. This notion of auxiliary information relates to the concept of regularization in statistical learning theory. A common feature among real-world datasets is that data domains are multiscale and target functions are well-behaved and smooth. In this paper, we propose a learning model that exploits this multiscale data structure and discuss its statistical and computational benefits. The hierarchical learning model is inspired by the logical and progressive easy-to-hard learning mechanism of human beings and has interpretable levels. The model apportions computational resources according to the complexity of data instances and target functions. This property can have multiple benefits, including higher inference speed and computational savings in training a model for many users or when training is interrupted. We provide a statistical analysis of the learning mechanism using multiscale entropies and show that it can yield significantly stronger guarantees than uniform convergence bounds.

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.