高维领域的数据经常在许多学科中自然地或由于初步处理而产生并且可以具有需要理解的复杂依赖结构。我们开发了对预计的正态分布的探索性因子分析，以解释使用少数容易解释的潜在因子来解释这些数据的可变性。我们的方法通过新颖的快速交替期望简档条件最大化算法提供了最大似然估计。结果仿真实验在各种环境中均匀优异。我们的方法在2018年12月初使用$ \＃Metoo $哈希特方式时，提供可解释和富有洞察力的结果，以时间课程的平均青少年大脑的时间函数磁共振图像在休息，表征手写的数字和基因来自癌症基因组地图集中癌细胞的表达数据。

具有许多多变量反应和协变量的拟合回归模型可能具有挑战性，但这种反应和协变量有时会具有张于变化的结构。我们扩展了经典多元回归模型以以两种方式利用此类结构：首先，我们对回归系数施加四种类型的低级张量格式。其次，我们使用Tensor-Variate正常分布模拟错误，该误差在协方差矩阵上强加了Kronecker可分离格式。我们通过块放松算法获得最大似然估计，并得出其计算复杂性和渐近分布。我们的回归框架使我们能够制定句子 - 变异的方差（Tanova）方法。当以单向Tanova布局应用时，使我们能够识别与功能磁共振成像研究中的自杀企图或非追求竞技器的相互作用显着相关的脑区。另一个应用程序在野生图像数据集中标记的面孔上使用三向Tanova，以区分与族裔血统，年龄组和性别相关的面部特征。 R包$ Totr $实施方法。

In this paper, we propose SceNDD: a scenario-based naturalistic driving dataset that is built upon data collected from an instrumented vehicle in downtown Indianapolis. The data collection was completed in 68 driving sessions with different drivers, where each session lasted about 20--40 minutes. The main goal of creating this dataset is to provide the research community with real driving scenarios that have diverse trajectories and driving behaviors. The dataset contains ego-vehicle's waypoints, velocity, yaw angle, as well as non-ego actor's waypoints, velocity, yaw angle, entry-time, and exit-time. Certain flexibility is provided to users so that actors, sensors, lanes, roads, and obstacles can be added to the existing scenarios. We used a Joint Probabilistic Data Association (JPDA) tracker to detect non-ego vehicles on the road. We present some preliminary results of the proposed dataset and a few applications associated with it. The complete dataset is expected to be released by early 2023.

For low-level computer vision and image processing ML tasks, training on large datasets is critical for generalization. However, the standard practice of relying on real-world images primarily from the Internet comes with image quality, scalability, and privacy issues, especially in commercial contexts. To address this, we have developed a procedural synthetic data generation pipeline and dataset tailored to low-level vision tasks. Our Unreal engine-based synthetic data pipeline populates large scenes algorithmically with a combination of random 3D objects, materials, and geometric transformations. Then, we calibrate the camera noise profiles to synthesize the noisy images. From this pipeline, we generated a fully synthetic image denoising dataset (FSID) which consists of 175,000 noisy/clean image pairs. We then trained and validated a CNN-based denoising model, and demonstrated that the model trained on this synthetic data alone can achieve competitive denoising results when evaluated on real-world noisy images captured with smartphone cameras.

Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of learning steps has limited its applications to many 3D real-world. To address this limitation, we propose Point Straight Flow (PSF), a model that exhibits impressive performance using one step. Our idea is based on the reformulation of the standard diffusion model, which optimizes the curvy learning trajectory into a straight path. Further, we develop a distillation strategy to shorten the straight path into one step without a performance loss, enabling applications to 3D real-world with latency constraints. We perform evaluations on multiple 3D tasks and find that our PSF performs comparably to the standard diffusion model, outperforming other efficient 3D point cloud generation methods. On real-world applications such as point cloud completion and training-free text-guided generation in a low-latency setup, PSF performs favorably.

Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HUMSET provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HUMSET also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at https://blog.thedeep.io/humset/.

医疗图像分类是图像识别领域中最关键的问题之一。该领域的主要挑战之一是缺乏标记的培训数据。此外，数据集通常会出现类不平衡，因为某些情况很少发生。结果，分类任务的准确性通常很低。特别是深度学习模型，在图像细分和分类问题上显示出令人鼓舞的结果，但它们需要很大的数据集进行培训。因此，需要从相同分布中生成更多的合成样品。先前的工作表明，特征生成更有效，并且比相应的图像生成更高。我们将此想法应用于医学成像领域。我们使用转移学习来训练针对金标准班级注释的小数据集的细分模型。我们提取了学习的功能，并使用它们使用辅助分类器GAN（ACGAN）来生成在类标签上进行调节的合成特征。我们根据其严重程度测试了下游分类任务中生成特征的质量。实验结果表明，这些生成特征的有效性及其对平衡数据和提高分类类别的准确性的总体贡献的结果有希望的结果。

在数据驱动的社会的时代，物联网（IoT）设备的无处不在，存储在不同地方的大量数据，分布式学习已获得了很多吸引力，但是，假设具有独立和相同分布的数据（IID）跨设备。在放松这种假设的同时，由于设备的异质性质，无论如何都无法实现现实，但Federated Learnation（FL）已成为一种保护隐私的解决方案，可以训练与大量设备分布的非IID数据进行协作模型。但是，由于不受限制的参与，打算破坏FL模型的恶意设备（攻击者）的出现是不可避免的。在这项工作中，我们旨在确定此类攻击者并减轻对模型的影响，从本质上讲，在双向标签与勾结的翻转攻击的情况下。我们通过利用本地模型之间的相关性来提出两种基于最小生成树和k-densest图的理论算法。即使攻击者最多占所有客户的70％，我们的FL模型也会消除攻击者的影响力，而先前的作品不能负担超过50％的客户作为攻击者。通过在两个基准数据集（即Mnist和Fashion-Mnist）的实验中确定我们算法的有效性，并具有压倒性的攻击者。我们使用准确性，攻击成功率和早期检测回合建立了算法优于现有算法的优势。

在本文中，我们引入了一种半居中的控制技术，用于在不确定的遮挡环境中运送脆弱物体到目的地的一群机器人。建议的方法已分为两部分。初始部分（第1阶段）包括一种集中的控制策略，用于在代理之间创建特定的形成，以便可以将要运输的对象正确放在系统顶部。我们提出了一种与基于圆形区域的形状控制方法融合在一起的新型三角填料方案，用于在机器人之间创建刚性配置。在后面的部分（第2阶段），需要群体系统以采用基于区域的形状控制方法的分散方式将对象传达到目的地。模拟结果以及比较研究证明了我们提出的方案的有效性。

该报告涵盖了我们对Chaplot等人的“使用变压器的可区分空间计划”的复制工作。。在本文中，考虑了以可不同方式进行空间路径计划的问题。他们表明，他们提出的使用空间规划变压器的方法优于先前数据驱动的模型，并利用可不同的结构来学习映射而无需同时地面真相图。我们通过重现其实验并在新数据上测试其方法来验证这些主张。我们还通过地图提高了障碍物复杂性，研究了计划准确性的稳定性。努力调查和验证映射模块的学习的努力是由于缺乏计算资源和无法到达的作者而导致的失败。