检测数据分布突然变化的变更点检测（CPD）被认为是时间序列分析中最重要的任务之一。尽管关于离线CPD的文献广泛，但无监督的在线CPD仍面临主要挑战，包括可扩展性，超参数调整和学习限制。为了减轻其中一些挑战，在本文中，我们提出了一种新颖的深度学习方法，用于从多维时间序列中无监督的在线CPD，名为Adaptive LSTM-AUTOENOCODER变更点检测（ALACPD）。 ALACPD利用了基于LSTM-AutoEncoder的神经网络来执行无监督的在线CPD。它连续地适应了传入的样本，而无需保留先前接收的输入，因此没有内存。我们对几个实际时间序列的CPD基准进行了广泛的评估。我们表明，在时间序列细分的质量方面，ALACPD平均在最先进的CPD算法中排名第一，并且就估计更改点的准确性而言，它与表现最好。 ALACPD的实现可在Github \ footNote {\ url {https://github.com/zahraatashgahi/alacpd}}上在线获得。

最近对稀疏神经网络的作品已经证明了独立从头开始训练稀疏子网，以匹配其相应密集网络的性能。然而，识别这种稀疏的子网（获奖票）涉及昂贵的迭代火车 - 培训 - 培训过程（例如，彩票票证假设）或过度扩展的训练时间（例如，动态稀疏训练）。在这项工作中，我们在稀疏神经网络训练和深度合并技术之间汲取了独特的联系，产生了一个名为FreeTickets的新型集合学习框架。 FreeTickets而不是从密集的网络开始，随机初始化稀疏的子网，然后在动态调整其稀疏掩码的同时列举子网，从而在整个训练过程中产生许多不同的稀疏子网。 FreeTickets被定义为这些稀疏子网的集合，在这种单次通过，稀疏稀疏训练中自由获得，其仅使用Vanilla密集培训所需的计算资源的一小部分。此外，尽管是模型的集合，但与单一密集模型相比，FreeTickets的参数和训练拖鞋更少：这种看似反向直观的结果是由于每个子网的高稀疏性。与标准致密基线相比，观察到惯性基因术，以预测准确性，不确定度估计，鲁棒性和效率相比表现出显着的全面改进。 FreeTickets在ImageNet上只使用后者所需的四分之一的培训拖鞋，可以轻松地表达Naive Deep EndleBe。我们的结果提供了对稀疏神经网络的强度的见解，并表明稀疏性的好处超出了通常预期的推理效率。

Sparse neural networks attract increasing interest as they exhibit comparable performance to their dense counterparts while being computationally efficient. Pruning the dense neural networks is among the most widely used methods to obtain a sparse neural network. Driven by the high training cost of such methods that can be unaffordable for a low-resource device, training sparse neural networks sparsely from scratch has recently gained attention. However, existing sparse training algorithms suffer from various issues, including poor performance in high sparsity scenarios, computing dense gradient information during training, or pure random topology search. In this paper, inspired by the evolution of the biological brain and the Hebbian learning theory, we present a new sparse training approach that evolves sparse neural networks according to the behavior of neurons in the network. Concretely, by exploiting the cosine similarity metric to measure the importance of the connections, our proposed method, Cosine similarity-based and Random Topology Exploration (CTRE), evolves the topology of sparse neural networks by adding the most important connections to the network without calculating dense gradient in the backward. We carried out different experiments on eight datasets, including tabular, image, and text datasets, and demonstrate that our proposed method outperforms several state-of-the-art sparse training algorithms in extremely sparse neural networks by a large gap. The implementation code is available on https://github.com/zahraatashgahi/CTRE

Simulation-based falsification is a practical testing method to increase confidence that the system will meet safety requirements. Because full-fidelity simulations can be computationally demanding, we investigate the use of simulators with different levels of fidelity. As a first step, we express the overall safety specification in terms of environmental parameters and structure this safety specification as an optimization problem. We propose a multi-fidelity falsification framework using Bayesian optimization, which is able to determine at which level of fidelity we should conduct a safety evaluation in addition to finding possible instances from the environment that cause the system to fail. This method allows us to automatically switch between inexpensive, inaccurate information from a low-fidelity simulator and expensive, accurate information from a high-fidelity simulator in a cost-effective way. Our experiments on various environments in simulation demonstrate that multi-fidelity Bayesian optimization has falsification performance comparable to single-fidelity Bayesian optimization but with much lower cost.

With the growth of editing and sharing images through the internet, the importance of protecting the images' authorship has increased. Robust watermarking is a known approach to maintaining copyright protection. Robustness and imperceptibility are two factors that are tried to be maximized through watermarking. Usually, there is a trade-off between these two parameters. Increasing the robustness would lessen the imperceptibility of the watermarking. This paper proposes an adaptive method that determines the strength of the watermark embedding in different parts of the cover image regarding its texture and brightness. Adaptive embedding increases the robustness while preserving the quality of the watermarked image. Experimental results also show that the proposed method can effectively reconstruct the embedded payload in different kinds of common watermarking attacks. Our proposed method has shown good performance compared to a recent technique.

In this paper, deep-learning-based approaches namely fine-tuning of pretrained convolutional neural networks (VGG16 and VGG19), and end-to-end training of a developed CNN model, have been used in order to classify X-Ray images into four different classes that include COVID-19, normal, opacity and pneumonia cases. A dataset containing more than 20,000 X-ray scans was retrieved from Kaggle and used in this experiment. A two-stage classification approach was implemented to be compared to the one-shot classification approach. Our hypothesis was that a two-stage model will be able to achieve better performance than a one-shot model. Our results show otherwise as VGG16 achieved 95% accuracy using one-shot approach over 5-fold of training. Future work will focus on a more robust implementation of the two-stage classification model Covid-TSC. The main improvement will be allowing data to flow from the output of stage-1 to the input of stage-2, where stage-1 and stage-2 models are VGG16 models fine-tuned on the Covid-19 dataset.

Chromosome analysis is essential for diagnosing genetic disorders. For hematologic malignancies, identification of somatic clonal aberrations by karyotype analysis remains the standard of care. However, karyotyping is costly and time-consuming because of the largely manual process and the expertise required in identifying and annotating aberrations. Efforts to automate karyotype analysis to date fell short in aberration detection. Using a training set of ~10k patient specimens and ~50k karyograms from over 5 years from the Fred Hutchinson Cancer Center, we created a labeled set of images representing individual chromosomes. These individual chromosomes were used to train and assess deep learning models for classifying the 24 human chromosomes and identifying chromosomal aberrations. The top-accuracy models utilized the recently introduced Topological Vision Transformers (TopViTs) with 2-level-block-Toeplitz masking, to incorporate structural inductive bias. TopViT outperformed CNN (Inception) models with >99.3% accuracy for chromosome identification, and exhibited accuracies >99% for aberration detection in most aberrations. Notably, we were able to show high-quality performance even in "few shot" learning scenarios. Incorporating the definition of clonality substantially improved both precision and recall (sensitivity). When applied to "zero shot" scenarios, the model captured aberrations without training, with perfect precision at >50% recall. Together these results show that modern deep learning models can approach expert-level performance for chromosome aberration detection. To our knowledge, this is the first study demonstrating the downstream effectiveness of TopViTs. These results open up exciting opportunities for not only expediting patient results but providing a scalable technology for early screening of low-abundance chromosomal lesions.

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.

Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery. An increasingly popular strategy in BO is to forgo the sole reliance on high-fidelity data and instead use an ensemble of information sources which provide inexpensive low-fidelity data. The overall premise of this strategy is to reduce the overall sampling costs by querying inexpensive low-fidelity sources whose data are correlated with high-fidelity samples. Here, we propose a multi-fidelity cost-aware BO framework that dramatically outperforms the state-of-the-art technologies in terms of efficiency, consistency, and robustness. We demonstrate the advantages of our framework on analytic and engineering problems and argue that these benefits stem from our two main contributions: (1) we develop a novel acquisition function for multi-fidelity cost-aware BO that safeguards the convergence against the biases of low-fidelity data, and (2) we tailor a newly developed emulator for multi-fidelity BO which enables us to not only simultaneously learn from an ensemble of multi-fidelity datasets, but also identify the severely biased low-fidelity sources that should be excluded from BO.

The BIOSCAN project, led by the International Barcode of Life Consortium, seeks to study changes in biodiversity on a global scale. One component of the project is focused on studying the species interaction and dynamics of all insects. In addition to genetically barcoding insects, over 1.5 million images per year will be collected, each needing taxonomic classification. With the immense volume of incoming images, relying solely on expert taxonomists to label the images would be impossible; however, artificial intelligence and computer vision technology may offer a viable high-throughput solution. Additional tasks including manually weighing individual insects to determine biomass, remain tedious and costly. Here again, computer vision may offer an efficient and compelling alternative. While the use of computer vision methods is appealing for addressing these problems, significant challenges resulting from biological factors present themselves. These challenges are formulated in the context of machine learning in this paper.