深度学习在学习高维数据的低维表示方面取得了巨大的成功。如果在感兴趣的数据中没有隐藏的低维结构，那么这一成功将是不可能的。这种存在是由歧管假设提出的，该假设指出数据在于固有维度低的未知流形。在本文中，我们认为该假设无法正确捕获数据中通常存在的低维结构。假设数据在于单个流形意味着整个数据空间的内在维度相同，并且不允许该空间的子区域具有不同数量的变异因素。为了解决这一缺陷，我们提出了多种假设的结合，该假设适应了非恒定固有维度的存在。我们从经验上验证了在常用图像数据集上的这一假设，发现确实应该允许内在维度变化。我们还表明，具有较高内在维度的类更难分类，以及如何使用这种见解来提高分类精度。然后，我们将注意力转移到该假设的影响下，在深层生成模型（DGM）的背景下。当前的大多数DGM都难以建模具有几个连接组件和/或不同固有维度的数据集建模。为了解决这些缺点，我们提出了群集的DGM，首先将数据聚集，然后在每个群集上训练DGM。我们表明，聚类的DGM可以模拟具有不同固有维度的多个连接组件，并在没有增加计算要求的情况下经验优于其非簇的非群体。

开发有效的自动分类器将真实来源与工件分开，对于宽场光学调查的瞬时随访至关重要。在图像差异过程之后，从减法伪像的瞬态检测鉴定是此类分类器的关键步骤，称为真实 - 博格斯分类问题。我们将自我监督的机器学习模型，深入的自组织地图（DESOM）应用于这个“真实的模拟”分类问题。 DESOM结合了自动编码器和一个自组织图以执行聚类，以根据其维度降低的表示形式来区分真实和虚假的检测。我们使用32x32归一化检测缩略图作为底部的输入。我们展示了不同的模型训练方法，并发现我们的最佳DESOM分类器显示出6.6％的检测率，假阳性率为1.5％。 Desom提供了一种更细微的方法来微调决策边界，以确定与其他类型的分类器（例如在神经网络或决策树上构建的）结合使用时可能进行的实际检测。我们还讨论了DESOM及其局限性的其他潜在用法。

我们概述了新兴机会和挑战，以提高AI对科学发现的效用。AI为行业的独特目标与AI科学的目标创造了识别模式中的识别模式与来自数据的发现模式之间的紧张。如果我们解决了与域驱动的科学模型和数据驱动的AI学习机之间的“弥补差距”相关的根本挑战，那么我们预计这些AI模型可以改变假说发电，科学发现和科学过程本身。

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.

Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.

Cartesian impedance control is a type of motion control strategy for robots that improves safety in partially unknown environments by achieving a compliant behavior of the robot with respect to its external forces. This compliant robot behavior has the added benefit of allowing physical human guidance of the robot. In this paper, we propose a C++ implementation of compliance control valid for any torque-commanded robotic manipulator. The proposed controller implements Cartesian impedance control to track a desired end-effector pose. Additionally, joint impedance is projected in the nullspace of the Cartesian robot motion to track a desired robot joint configuration without perturbing the Cartesian motion of the robot. The proposed implementation also allows the robot to apply desired forces and torques to its environment. Several safety features such as filtering, rate limiting, and saturation are included in the proposed implementation. The core functionalities are in a re-usable base library and a Robot Operating System (ROS) ros_control integration is provided on top of that. The implementation was tested with the KUKA LBR iiwa robot and the Franka Emika Robot (Panda) both in simulation and with the physical robots.

Recently, there has been an interest in improving the resources available in Intrusion Detection System (IDS) techniques. In this sense, several studies related to cybersecurity show that the environment invasions and information kidnapping are increasingly recurrent and complex. The criticality of the business involving operations in an environment using computing resources does not allow the vulnerability of the information. Cybersecurity has taken on a dimension within the universe of indispensable technology in corporations, and the prevention of risks of invasions into the environment is dealt with daily by Security teams. Thus, the main objective of the study was to investigate the Ensemble Learning technique using the Stacking method, supported by the Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) algorithms aiming at an optimization of the results for DDoS attack detection. For this, the Intrusion Detection System concept was used with the application of the Data Mining and Machine Learning Orange tool to obtain better results

The study aims the development of a wearable device to combat the onslaught of covid-19. Likewise, to enhance the regular face shield available in the market. Furthermore, to raise awareness of the health and safety protocols initiated by the government and its affiliates in the enforcement of social distancing with the integration of computer vision algorithms. The wearable device was composed of various hardware and software components such as a transparent polycarbonate face shield, microprocessor, sensors, camera, thin-film transistor on-screen display, jumper wires, power bank, and python programming language. The algorithm incorporated in the study was object detection under computer vision machine learning. The front camera with OpenCV technology determines the distance of a person in front of the user. Utilizing TensorFlow, the target object identifies and detects the image or live feed to get its bounding boxes. The focal length lens requires the determination of the distance from the camera to the target object. To get the focal length, multiply the pixel width by the known distance and divide it by the known width (Rosebrock, 2020). The deployment of unit testing ensures that the parameters are valid in terms of design and specifications.