先前关于安全加强学习的工作（RL）研究了对动态（aleatory）随机性的风险规避，并隔离地模拟了不确定性（认知）。我们提出并分析一个新框架，以共同对有限马和折现的无限马MDP中的认知和差异不确定性相关的风险进行建模。我们称此框架结合了规避风险和软性的方法RASR。我们表明，当使用EVAR或熵风险定义风险规定时，可以使用具有时间依赖性风险水平的新的动态程序公式有效地计算RASR中的最佳策略。结果，即使是在无限 - 亨特折扣环境中，最佳的规避风险政策也是确定性但依赖时间的。我们还表明，具有平均后验过渡概率的特定RASR目标减少到规避风险的RL。我们的经验结果表明，我们的新算法始终减轻EVAR和其他标准风险措施衡量的不确定性。
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科学数据中的关系，例如单变量数据中特征的数值和空间分布关系，多元数据中的标量值组合的关系以及时间变化和整体数据中的体积的关联，是复杂且复杂的。本文介绍了一种新型的无监督表示学习模型Voxel2Vec，该模型用于在低维矢量空间中学习标量值/标量值组合的分布式表示。它的基本假设是，如果两个标量值/标量值组合具有相似的上下文，则它们通常在特征方面具有很高的相似性。通过将标量值/标量值组合表示为符号，voxel2vec在空间分布的背景下了解它们之间的相似性，然后允许我们通过传输预测来探索卷之间的整体关联。我们通过将其与单变量数据的等速度相似性图进行比较，并将学习的分布式表示形式与多变量数据分类以及用于时间变化和集合数据的关联分析来证明voxel2vec的有用性和有效性。
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与传统的刚体机器人相比，软机器人不仅表现出前所未有的适应性和灵活性，而且由于其无限的自由度而在模型和控制中提出了新颖的挑战。大多数现有方法主要依赖于近似模型，因此可以利用发达的有限维控制理论。但是，这可能会导致建模不确定性和性能降解。因此，我们建议对软机器人系统利用无限维度分析。我们的控制设计基于越来越多的Cosserat Rod模型，该模型使用非线性偏微分方程（PDE）描述了软机器人臂的运动学和动力学。我们为Cosserat PDE模型设计了无限维状态反馈控制定律，以实现轨迹跟踪（由位置，旋转，线性和角速度组成）并证明其均匀的跟踪收敛性。我们还仅使用位置测量值来估算所有状态变量（包括位置，旋转，应变，曲率，线性和角速度）的所有状态变量（包括位置，旋转，应变，曲率，线性和角速度）。使用模拟评估所提出的算法。
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接触式轨迹优化提供了一种具有吸引力的方法，可以自动生成用于机器人操纵和运动的复杂和接触型行为。但是，由于确保数值可靠性和物理现实主义的挑战，这种技术的可伸缩性受到限制。在本文中，我们提出了初步结果，表明迭代线性二次调节器（ILQR）算法以及最近提出的基于压力场的水力弹性接触模型可以通过接触实现可靠和物理上现实的轨迹优化。我们使用这种方法来合成富含接触的行为，例如四足动物和全臂操纵。此外，Kinova Gen3机器人臂上的开环播放证明了全臂操纵轨迹的身体精度。代码可在https://bit.ly/ilqr_hc上找到，可以在https://youtu.be/iqxjkbm8_ms上找到视频。
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本文回顾了关于压缩视频质量增强质量的第一个NTIRE挑战，重点是拟议的方法和结果。在此挑战中，采用了新的大型不同视频（LDV）数据集。挑战有三个曲目。Track 1和2的目标是增强HEVC在固定QP上压缩的视频，而Track 3旨在增强X265压缩的视频，以固定的位速率压缩。此外，轨道1和3的质量提高了提高保真度（PSNR）的目标，以及提高感知质量的2个目标。这三个曲目完全吸引了482个注册。在测试阶段，分别提交了12个团队，8支球队和11支球队，分别提交了轨道1、2和3的最终结果。拟议的方法和解决方案衡量视频质量增强的最先进。挑战的首页：https：//github.com/renyang-home/ntire21_venh
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Keyword spotting (KWS) based on deep neural networks (DNNs) has achieved massive success in voice control scenarios. However, training of such DNN-based KWS systems often requires significant data and hardware resources. Manufacturers often entrust this process to a third-party platform. This makes the training process uncontrollable, where attackers can implant backdoors in the model by manipulating third-party training data. An effective backdoor attack can force the model to make specified judgments under certain conditions, i.e., triggers. In this paper, we design a backdoor attack scheme based on Voiceprint Selection and Voice Conversion, abbreviated as VSVC. Experimental results demonstrated that VSVC is feasible to achieve an average attack success rate close to 97% in four victim models when poisoning less than 1% of the training data.
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For solving a broad class of nonconvex programming problems on an unbounded constraint set, we provide a self-adaptive step-size strategy that does not include line-search techniques and establishes the convergence of a generic approach under mild assumptions. Specifically, the objective function may not satisfy the convexity condition. Unlike descent line-search algorithms, it does not need a known Lipschitz constant to figure out how big the first step should be. The crucial feature of this process is the steady reduction of the step size until a certain condition is fulfilled. In particular, it can provide a new gradient projection approach to optimization problems with an unbounded constrained set. The correctness of the proposed method is verified by preliminary results from some computational examples. To demonstrate the effectiveness of the proposed technique for large-scale problems, we apply it to some experiments on machine learning, such as supervised feature selection, multi-variable logistic regressions and neural networks for classification.
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Despite their popularity in deep learning and machine learning in general, the theoretical properties of adaptive optimizers such as Adagrad, RMSProp, Adam or AdamW are not yet fully understood. In this paper, we develop a novel framework to study the stability and generalization of these optimization methods. Based on this framework, we show provable guarantees about such properties that depend heavily on a single parameter $\beta_2$. Our empirical experiments support our claims and provide practical insights into the stability and generalization properties of adaptive optimization methods.
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In this work, we present a dense tracking and mapping system named Vox-Fusion, which seamlessly fuses neural implicit representations with traditional volumetric fusion methods. Our approach is inspired by the recently developed implicit mapping and positioning system and further extends the idea so that it can be freely applied to practical scenarios. Specifically, we leverage a voxel-based neural implicit surface representation to encode and optimize the scene inside each voxel. Furthermore, we adopt an octree-based structure to divide the scene and support dynamic expansion, enabling our system to track and map arbitrary scenes without knowing the environment like in previous works. Moreover, we proposed a high-performance multi-process framework to speed up the method, thus supporting some applications that require real-time performance. The evaluation results show that our methods can achieve better accuracy and completeness than previous methods. We also show that our Vox-Fusion can be used in augmented reality and virtual reality applications. Our source code is publicly available at https://github.com/zju3dv/Vox-Fusion.
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随着电子商务的繁荣，旨在按照预测的有用性分数对产品评论进行分类的多模式审查帮助预测（MRHP）已成为研究热点。此任务的先前工作集中于基于注意力的模态融合，信息集成和关系建模，该模型主要暴露了以下缺点：1）由于其不加区分的注意公式，该模型可能无法捕获真正的基本信息； 2）缺乏适当的建模方法，可以充分利用提供的数据之间的相关性。在本文中，我们提出了SANCL：MRHP的选择性关注和自然对比学习。 SANCL采用基于探测的策略来对更大意义的区域进行高度注意权重。它还基于数据集中的自然匹配属性构建了对比度学习框架。两个基准数据集的实验结果（三个类别）表明，SANCL在记忆消耗较低的情况下实现了最先进的基线性能。
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