本文提出了一种基于角度解搜索(MaOEA-CS)的进化多目标优化算法。 MaOEA-CS隐含地包含两个阶段:最重要的边界最优解的探索性搜索 - 第一阶段的角点解,以及基于角度的选择[1]的使用以及探索性搜索第二阶段PF近似的扩展。由于其高效率和对形状的稳健性,它赢得了CEC'2017进化多目标优化竞赛。此外,MaOEA-CS还应用于两个非常不规则PF的实际工程优化问题。实验结果表明,MaOEA-CS优于其他六种最先进的比较算法,这表明它具有处理不规则PF的现实复杂优化问题的能力。
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For the past couple of decades, numerical optimization has played a centralrole in addressing wireless resource management problems such as power controland beamformer design. However, optimization algorithms often entailconsiderable complexity, which creates a serious gap between theoreticaldesign/analysis and real-time processing. To address this challenge, we proposea new learning-based approach. The key idea is to treat the input and output ofa resource allocation algorithm as an unknown non-linear mapping and use a deepneural network (DNN) to approximate it. If the non-linear mapping can belearned accurately by a DNN of moderate size, then resource allocation can bedone in almost real time -- since passing the input through a DNN only requiresa small number of simple operations. In this work, we address both the thereotical and practical aspects ofDNN-based algorithm approximation with applications to wireless resourcemanagement. We first pin down a class of optimization algorithms that are`learnable' in theory by a fully connected DNN. Then, we focus on DNN-basedapproximation to a popular power allocation algorithm named WMMSE (Shi {\it etal} 2011). We show that using a DNN to approximate WMMSE can be fairly accurate-- the approximation error $\epsilon$ depends mildly [in the order of$\log(1/\epsilon)$] on the numbers of neurons and layers of the DNN. On theimplementation side, we use extensive numerical simulations to demonstrate thatDNNs can achieve orders of magnitude speedup in computational time compared tostate-of-the-art power allocation algorithms based on optimization.
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3D点云中的平面检测是关键的预处理步骤,例如点云分割,语义映射和SLAM。与许多最近仅适用于有组织点云的平面检测方法相比,我们的工作针对的是无法进行二维参数化的无组织点云。我们比较了三种有效检测点云平面的方法。一种是本文提出的一种新方法,它通过从一组具有正常法线的点中抽样来产生平面假设。我们将此方法命名为Oriented Point Sampling(OPS)tocontrast,采用更传统的技术,需要对三个取向点进行采样以生成平面假设。我们还实现了基于三个非定向点的局部采样的高效平面检测方法,并将其与OPS和基于文本的3D-KHT算法进行比较,以检测来自SUN RGB-Ddataset的10,000点云的平面。
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视觉注意力预测是一个经典问题,似乎在深度学习时代得到了很好的体现。然而,一个引人注目的问题是随着现有视觉数据集的快速增长的性能得分逐渐增加:现有的深度模型是否真正捕捉到人类视觉注意力的内在机制?为了解决这个问题,本文提出了一个名为VASUN的新数据集,它记录了对太阳图像的自由观察人类注意力。与以前的数据集不同,VASUN中的图像包含许多不规则的视觉模式,现有的深层模型已经隐藏了这些模式。通过对VASUN上的现有模型进行基准测试,我们发现许多最先进的深模型的性能显着下降,而许多经典的浅模型表现令人印象深刻。从这些结果中,我们发现现有深度注意力模型的显着性能提升可能来自于记忆和预测某些特定视觉模式的发生而不是学习人类视觉注意的内在机制。此外,我们还在VASUN上训练了几个基线模型,以展示预测太阳视觉注意力的可行性和关键问题。这些基线模型与建议的数据集一起,可用于从与现有视角互补的新视角重新审视视觉注意力预测的问题。
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太阳眩光是导致交通事故的主要环境危害之一。每年都有许多人因与太阳眩光有关的交通事故而死亡和受伤。提供关于发生太阳光的时间和地点的准确信息将有助于防止太阳眩光造成交通事故并挽救生命。在本研究中,我们建议使用可公开访问的GoogleStreet View(GSV)全景图像来估计和预测sunglare的发生。 GSV图像具有类似于驾驶员的视线,这将使GSVimage适合于估计驾驶员对太阳眩光的可见性。最近开发的卷积神经网络算法用于分割GSV图像并预测太阳眩光上的障碍物。根据给定位置的预测障碍,我们通过估算太阳位置以及这些位置的驾驶员和太阳之间的相对角度,进一步估算了太阳伞的时间窗。我们在美国马萨诸塞州剑桥进行了一个案例研究。结果表明,该方法可以准确预测出眩光的存在。所提出的方法将为驾驶员和交通规划者提供重要的工具,以减轻太阳眩光并减少由太阳眩光引起的潜在交通事故。
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In this paper we present the first large-scale scene attribute database. First, we perform crowdsourced human studies to find a taxonomy of 102 discriminative attributes. We discover attributes related to materials, surface properties , lighting, affordances, and spatial layout. Next, we build the "SUN attribute database" on top of the diverse SUN categorical database. We use crowdsourcing to annotate attributes for 14,340 images from 707 scene categories. We perform numerous experiments to study the interplay between scene attributes and scene categories. We train and evaluate attribute classifiers and then study the feasibility of attributes as an intermediate scene representation for scene classification, zero shot learning, automatic image caption-ing, semantic image search, and parsing natural images. We show that when used as features for these tasks, low dimensional scene attributes can compete with or improve on the state of the art performance. The experiments suggest that scene attributes are an effective low-dimensional feature for capturing high-level context and semantics in scenes.
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In this paper we present the first large-scale scene attribute database. First, we perform crowd-sourced human studies to find a taxonomy of 102 discriminative attributes. Next, we build the "SUN attribute database" on top of the diverse SUN categorical database. Our attribute database spans more than 700 categories and 14,000 images and has potential for use in high-level scene understanding and fine-grained scene recognition. We use our dataset to train attribute classifiers and evaluate how well these relatively simple classifiers can recognize a variety of attributes related to materials, surface properties, lighting, functions and affordances, and spatial envelope properties.
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We propose a definition of saliency by considering what the visual system is trying to optimize when directing attention. The resulting model is a Bayesian framework from which bottom-up saliency emerges naturally as the self-information of visual features, and overall saliency (incorporating top-down information with bottom-up saliency) emerges as the pointwise mutual information between the features and the target when searching for a target. An implementation of our framework demonstrates that our model's bottom-up saliency maps perform as well as or better than existing algorithms in predicting people's fixations in free viewing. Unlike existing saliency measures, which depend on the statistics of the particular image being viewed, our measure of saliency is derived from natural image statistics, obtained in advance from a collection of natural images. For this reason, we call our model SUN (Saliency Using Natural statistics). A measure of saliency based on natural image statistics, rather than based on a single test image, provides a straightforward explanation for many search asymmetries observed in humans; the statistics of a single test image lead to predictions that are not consistent with these asymmetries. In our model, saliency is computed locally, which is consistent with the neuroanatomy of the early visual system and results in an efficient algorithm with few free parameters.
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事实证明,语言模型预训练对于学习通用语言表示非常有用。作为最先进的语言模型预训练模型,BERT(变形金刚的双向编码器表示)在许多语言理解任务中取得了惊人的成果。在本文中,我们进行了详尽的实验,以研究BERT在文本分类任务上的不同微调方法,并为BERTfine调整提供一般解决方案。最后,所提出的解决方案在八个广泛研究的文本分类数据集上获得了新的最新结果。
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在本文中,我们提出了一个新的实体关系提取任务范例。我们将任务转换为多回合问题回答问题,即,实体和关系的提取被转换为从上下文识别答案跨度的任务。这种多转QA形式化有几个关键优势:首先,问题查询编码我们想要识别的实体/关系类的重要信息;其次,QA提供了一种自然的方式来联合建模实体和关系;第三,它允许我们利用完善的机器阅读理解(MRC)模型。在ACE和CoNLL04公司的实验表明,所提出的范例明显优于以前的最佳模型。我们能够获得所有ACE04,ACE05和CoNLL04数据集的最新结果,增加了三个数据集的SOTA结果49.6(+1.2),60.3(+0.7)和69.2(+1.4) , 分别。此外,我们构建了一个新开发的数据集RESUME,它需要多步推理来构造实体依赖关系,而不是先前数据集中三元组提取中的单步依赖提取。提出的多转QA模型也在RESUME数据集上实现了最佳性能。
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