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。

已经表明，面部识别系统（FRSS）容易受到变形攻击，但大多数研究侧重于基于地标的变形。生成变形的第二种方法使用生成的对抗性网络，这导致令人信服的真实面部图像，这几乎与基于地标的攻击一样挑战。我们提出了一种创建第三种不同类型的变形的方法，这具有更容易训练的优点。我们介绍了\ Texit的理论概念{最糟糕的情况变形}，这是那些最具挑战性的FRS的变形。对于FRS的潜在空间中的一组图像和相应的嵌入，我们使用将空间倒波到图像空间的映射来生成近似这些最坏情况变形的图像。虽然所产生的图像尚未与其他变形一样挑战，但他们可以在未来的变形攻击检测（Mad）方法以及FRSS的弱点研究中提供有价值的信息。 MAD的方法需要在更多各种变形数据库上验证。我们提出的方法有助于实现这种变异。

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

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field. This setup aggregates 2D scores at multiple camera viewpoints into a 3D score, and repurposes a pretrained 2D model for 3D data generation. We identify a technical challenge of distribution mismatch that arises in this application, and propose a novel estimation mechanism to resolve it. We run our algorithm on several off-the-shelf diffusion image generative models, including the recently released Stable Diffusion trained on the large-scale LAION dataset.

This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data.

Automatically fixing software bugs is a challenging task. While recent work showed that natural language context is useful in guiding bug-fixing models, the approach required prompting developers to provide this context, which was simulated through commit messages written after the bug-fixing code changes were made. We instead propose using bug report discussions, which are available before the task is performed and are also naturally occurring, avoiding the need for any additional information from developers. For this, we augment standard bug-fixing datasets with bug report discussions. Using these newly compiled datasets, we demonstrate that various forms of natural language context derived from such discussions can aid bug-fixing, even leading to improved performance over using commit messages corresponding to the oracle bug-fixing commits.

For the majority of the machine learning community, the expensive nature of collecting high-quality human-annotated data and the inability to efficiently finetune very large state-of-the-art pretrained models on limited compute are major bottlenecks for building models for new tasks. We propose a zero-shot simple approach for one such task, Video Moment Retrieval (VMR), that does not perform any additional finetuning and simply repurposes off-the-shelf models trained on other tasks. Our three-step approach consists of moment proposal, moment-query matching and postprocessing, all using only off-the-shelf models. On the QVHighlights benchmark for VMR, we vastly improve performance of previous zero-shot approaches by at least 2.5x on all metrics and reduce the gap between zero-shot and state-of-the-art supervised by over 74%. Further, we also show that our zero-shot approach beats non-pretrained supervised models on the Recall metrics and comes very close on mAP metrics; and that it also performs better than the best pretrained supervised model on shorter moments. Finally, we ablate and analyze our results and propose interesting future directions.

This paper presents a methodology for combining programming and mathematics to optimize elevator wait times. Based on simulated user data generated according to the canonical three-peak model of elevator traffic, we first develop a naive model from an intuitive understanding of the logic behind elevators. We take into consideration a general array of features including capacity, acceleration, and maximum wait time thresholds to adequately model realistic circumstances. Using the same evaluation framework, we proceed to develop a Deep Q Learning model in an attempt to match the hard-coded naive approach for elevator control. Throughout the majority of the paper, we work under a Markov Decision Process (MDP) schema, but later explore how the assumption fails to characterize the highly stochastic overall Elevator Group Control System (EGCS).

球形图像处理已被广泛应用于许多重要领域，例如自动驾驶汽车，全球气候建模和医学成像的全向视觉。扩展针对平面图像开发的算法的算法是非平凡的。在这项工作中，我们专注于具有基于深度学习的正常化程序的球形图像的具有挑战性的任务。我们采用了快速的方向球形帧转换，而不是对平面图像的现有模型的幼稚应用，并基于framelet变换的稀疏性假设而开发了一种新颖的优化框架。此外，通过采用渐进式编码器架构，经过精心设计的新的，表现出色的CNN Denoiser，可以作为隐式正规化程序进行设计。最后，我们使用插件方法来处理提出的优化模型，可以通过训练CNN Denoiser先验来有效地实现。进行了数值实验，并表明所提出的算法可以极大地恢复损坏的球形图像，并使用深度学习的DeNoiser和Paint-＆play模型实现最佳性能。

使用相对比心脏磁共振成像（PC-CMR）进行的流量分析可以量化用于评估心血管功能的重要参数。该分析的重要部分是鉴定正确的CMR视图和质量控制（QC），以检测可能影响流量定量的伪像。我们提出了一个新型的基于深度学习的框架，用于对完整CMR扫描的流量进行完全自动化的分析，该框架首先使用两个顺序卷积神经网络进行这些视图选择和QC步骤，然后进行自动主动脉和肺动脉分段，以实现对量化的量化。钥匙流参数。对于观察分类和QC，获得了0.958和0.914的精度值。对于细分，骰子分数为$> $ 0.969，而平淡的altman情节表示手动和自动峰流量值之间的一致性很高。此外，我们在外部验证数据集上测试了管道，结果表明管道的鲁棒性。这项工作是使用由986例病例组成的多生临床数据进行的，表明在临床环境中使用该管道的潜力。