与以前的工作不同,这种开放式数据采集包括专为机器学习应用和高锥角人工制品减少而设计的X射线锥束(CB)计算机断层扫描(CT)数据集。用实验室X射线设置扫描42个核桃,不仅提供来自单个物体的数据,而且提供具有自然变化的一类物体的数据。对于每个核桃,获得了三个不同源轨道上的CB投影,提供了具有不同锥角的CB数据,并且能够从可以用于监督学习的组合数据中计算无物质,高质量的地面实况图像。我们提供完整的图像重建管道:原始投影数据,扫描几何描述,使用开放软件的预处理和重建脚本,以及构建的体积。因此,数据集不仅可以用于高角度伪影减少,还可以用于其他任务的算法开发和评估,例如从有限或稀疏角度(低剂量)扫描,超分辨率或分割的图像重建。
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Novel machine learning computational tools open new perspectives for quantum information systems. Here we adopt the open-source programming library Tensorflow to design multi-level quantum gates including a computing reservoir represented by a random unitary matrix. In optics, the reservoir is a disordered medium or a multimodal fiber. We show that trainable operators at the input and the readout enable to realize multi-level gates. We study single and qudit gates, including the scaling properties of the algorithms with the size of the reservoir. The development of multi-level quantum information processing systems has steadily grown over the past few years, with experimental realizations of multi-level, or qudit logic gates for several widely used photonic degrees of freedom such as orbital-angular-momentum and path encoding [1-4]. However, efforts are still needed for increasing the complexity of such systems while still being practical, with the ultimate goal of realizing complex large-scale computing devices that operate in a technologically efficient manner. A key challenge is the development of design techniques that are scalable and versatile. Recent work outlined the relevance of a large class of devices, commonly denoted as "complex" or "multimode." [5, 6] In these systems, many modes, or channels are mixed and controlled at input and readout to realize a target input-output operation. This follows the first experimental demonstrations of assisted light transmission through random media [7-10], which demonstrated many applications including arbitrary linear gates [5], mode conversion, and sorting [11, 12]. The use of complex mode-mixing devices is surprisingly connected to leading paradigms in modern machine learning (ML), as the "reservoir computing" (RC) [13] and the "extreme learning machine" (ELM) [13, 14]. In standard ML, one trains the parameters (weights) of an artificial neural network (ANN) to fit a given function linking input and outputs. In RC, due to the increasing computational effort to train a large number of weights, one internal part of the network is left untrained ("the reservoir") and the weights are optimized only at input and readout. ML concepts such as photonic neuromorphic and reservoir computing [15, 16] are finding many applications in telecommunications [17, 18], multiple scattering [19], image classification [20], biophotonics [10], integrated optics [21], and topological photonics [22]. Various authors have reported the use of ML for augmenting and assisting quantum experiments.[23-25] Here we adopt RC-ML to design complex multi-level gates [2, 3, 26, 27], which form a building block for high-dimensional quantum information processing systems. While low-dimensional examples of such gates have been implemented using bulk and integrated optics, efficiently scaling them up to high dimensions remains a challenge. In quantum key distribution, one uses at least two orthogonal bases to encode information. High-dim
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国际指纹活体检测竞赛(LivDet)是学术界和私人公司的开放和公认的交汇点,它处理区分来自人造材料和图像相对于真实指纹的指纹再现的图像的问题。在本期LivDet中,我们邀请竞争对手提出具有匹配系统的集成算法。目标是调查这种整合对整体绩效的影响程度。提交了12个算法,其中8个在集成系统上运行。
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根据其采集设备对图像进行聚类是多媒体取证中众所周知的问题,其通常通过相机传感器模式噪声(SPN)来面对。这样的问题具有挑战性,因为SPN是类似噪音的信号,难以估计并且易于被许多因素衰减或破坏。此外,SPN的高维度阻碍了大规模应用。现有方法通常基于像素域中的SPN之间的相关性,其可能无法捕获向量子空间的并集中的内部数据结构。在本文中,我们提出了一个精确的聚类框架,它利用了SPN在其内在向量子空间中的线性相关性。这种依赖性在稀疏表示下编码,稀疏表示是通过解决具有非负性约束的Lasso问题而获得的。所提出的框架在聚类估计和指纹关联的数量上是高度准确的。此外,我们的框架可以映射到图像的数量,并且可以抵抗双重JPEG压缩以及异常值的存在,具有实际应用的巨大潜力。德累斯顿和Vision数据库的实验结果表明,我们提出的框架可以很好地适应中等规模和大规模的scalecontexts,并且优于最先进的方法。
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用于对象检测的最新深度学习方法提供了显着的性能,但在机器人应用中使用时具有限制。最相关的问题之一是长训练时间,这是由于相关训练集的大尺寸和平衡,其特征在于少数正面和大量的负面例子(即背景)。提出的方法基于反向传播的端到端学习[22]或使用硬负面挖掘训练的核方法在深层特征之上[8]。这些解决方案是有效的,但对于在线应用来说却非常慢。在本文中,我们提出了一种新的物体检测管道,可以克服这个问题并提供相当的性能,培训速度提高了60倍。我们的管道组合了(i)区域提议网络和[22]的深度特征提取器,以有效地选择候选RoI并将它们编码为强大的代表性,其中包括(ii)FALKON [23]算法,这是一种基于内核的新方法,允许快速训练大规模问题(数百万点)。我们通过利用方法中的随机抽样抽样和一种新颖,快速,自然的方法来解决训练数据的大小和不平衡问题。我们评估了该方法在标准ComputerVision数据集(PASCAL VOC 2007 [5])上的有效性,并通过iCubWorld Transformations [18]数据集证明了其对区域机器人场景的适用性。
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生成对抗网络(GAN)是用于学习来自样本的复杂数据分布的生成模型的创新技术。尽管最近在生成逼真图像方面取得了显着的进步,但是它们的主要缺点之一是,在实践中,即使在对不同数据集进行训练时,它们也倾向于生成具有很小多样性的样本。这种被称为模式崩溃的现象一直是GAN最近几项进展的主要焦点。然而,很少有人理解为什么模式崩溃发生,而且即将出现的方法能够缓解模式崩溃。我们提出了处理模式崩溃的原则方法,我们称之为打包。主要思想是使鉴别器基于来自同一类的多个样本做出决策,无论是真实的还是人工生成的。我们借用二元假设检验的分析工具 - 特别是Blackwell [Bla53]的开创性结果---来证明包装和模式崩溃之间的基本联系。我们证明了包装自然会对模式崩溃的发电机进行处罚,从而减少了发电机的分布。模式在训练过程中崩溃。基准数据集的数值实验表明,包装在实践中也提供了显着的改进。
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灵巧的多指手是非常通用的,并提供了一个通用的通道,可以在以人为中心的环境中执行多项任务。然而,由于其高维度和大量潜在联系,有效控制它们仍然具有挑战性。深度加固学习(DRL)提供了一种模型不可知的方法来控制复杂的动力系统,但尚未证明可以扩展到高维灵巧操作。此外,由于样本效率低下,在物理系统上部署DRL仍然具有挑战性。因此,DRL非机器人的成功迄今仅限于更简单的操纵器和任务。在这项工作中,我们表明无模型DRL可以通过高维24-DoF手有效地扩展到复杂操作任务,并在模拟实验中从头开始解决它们。此外,通过使用少量的人体演示,可以显着降低样本的复杂性,从而可以学习相当于几小时机器人经验的样本量。演示的使用导致表现出非常自然运动的政策,并且令人惊讶地,它们也显着地更强大。
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We report on an extensive study of the benefits and limitations of current deep learning approaches to object recognition in robot vision scenarios, introducing a novel dataset used for our investigation. To avoid the biases in currently available datasets, we consider a natural human-robot interaction setting to design a data-acquisition protocol for visual object recognition on the iCub humanoid robot. Analyzing the performance of off-the-shelf models trained off-line on large-scale image retrieval datasets, we show the necessity for knowledge transfer. We evaluate different ways in which this last step can be done, and identify the major bottlenecks affecting robotic scenarios. By studying both object categorization and identification problems, we highlight key differences between object recognition in robotics applications and in image retrieval tasks, for which the considered deep learning approaches have been originally designed. In a nutshell, our results confirm the remarkable improvements yield by deep learning in this setting, while pointing to specific open challenges that need be addressed for seamless deployment in robotics.
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机器学习算法的性能主要取决于识别一组好的超参数。虽然最近的方法使用贝叶斯优化来自适应地选择配置,但我们专注于通过自适应资源分配和早期停止来加速随机搜索。我们将超参数优化表示为纯探索非随机无限制武装强盗问题,其中将迭代,数据样本或特征等预定义资源分配给随机采样配置。我们为该框架引入了一种新的算法Hyperband,并对其理论属性进行了分析,提供了几种理想的保证。此外,我们在一系列超参数优化问题上将Hyperband与流行的贝叶斯优化方法进行比较。我们观察到Hyperband可以提供超过我们竞争对手的各种深度学习和基于内核的学习问题的数量级加速。
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