在本文中，我们考虑使用Palentir在两个和三个维度中对分段常数对象的恢复和重建，这是相对于当前最新ART的显着增强的参数级别集（PALS）模型。本文的主要贡献是一种新的PALS公式，它仅需要一个单个级别的函数来恢复具有具有多个未知对比度的分段常数对象的场景。我们的模型比当前的多对抗性，多对象问题提供了明显的优势，所有这些问题都需要多个级别集并明确估计对比度大小。给定对比度上的上限和下限，我们的方法能够以任何对比度分布恢复对象，并消除需要知道给定场景中的对比度或其值的需求。我们提供了一个迭代过程，以找到这些空间变化的对比度限制。相对于使用径向基函数（RBF）的大多数PAL方法，我们的模型利用了非异型基函数，从而扩展了给定复杂性的PAL模型可以近似的形状类别。最后，Palentir改善了作为参数识别过程一部分所需的Jacobian矩阵的条件，因此通过控制PALS扩展系数的幅度来加速优化方法，固定基本函数的中心，以及参数映射到图像映射的唯一性，由新参数化提供。我们使用X射线计算机断层扫描，弥漫性光学断层扫描（DOT），Denoising，DeonConvolution问题的2D和3D变体证明了新方法的性能。应用于实验性稀疏CT数据和具有不同类型噪声的模拟数据，以进一步验证所提出的方法。

Advancement in large pretrained language models has significantly improved their performance for conditional language generation tasks including summarization albeit with hallucinations. To reduce hallucinations, conventional methods proposed improving beam search or using a fact checker as a postprocessing step. In this paper, we investigate the use of the Natural Language Inference (NLI) entailment metric to detect and prevent hallucinations in summary generation. We propose an NLI-assisted beam re-ranking mechanism by computing entailment probability scores between the input context and summarization model-generated beams during saliency-enhanced greedy decoding. Moreover, a diversity metric is introduced to compare its effectiveness against vanilla beam search. Our proposed algorithm significantly outperforms vanilla beam decoding on XSum and CNN/DM datasets.

Generative Adversarial Networks (GANs) have received wide acclaim among the machine learning (ML) community for their ability to generate realistic 2D images. ML is being applied more often to complex problems beyond those of computer vision. However, current frameworks often serve as black boxes and lack physics embeddings, leading to poor ability in enforcing constraints and unreliable models. In this work, we develop physics embeddings that can be stringently imposed, referred to as hard constraints, in the neural network architecture. We demonstrate their capability for 3D turbulence by embedding them in GANs, particularly to enforce the mass conservation constraint in incompressible fluid turbulence. In doing so, we also explore and contrast the effects of other methods of imposing physics constraints within the GANs framework, especially penalty-based physics constraints popular in literature. By using physics-informed diagnostics and statistics, we evaluate the strengths and weaknesses of our approach and demonstrate its feasibility.

The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discovery specifically, protein-ligand docking, or chemical docking, is a standard in-silico scoring technique that estimates the binding affinity of molecules with a specific protein target. Recently, however, as the number of virtual molecules available to test has rapidly grown, these classical docking algorithms have created a significant computational bottleneck. We address this problem by introducing Deep Surrogate Docking (DSD), a framework that applies deep learning-based surrogate modeling to accelerate the docking process substantially. DSD can be interpreted as a formalism of several earlier surrogate prefiltering techniques, adding novel metrics and practical training practices. Specifically, we show that graph neural networks (GNNs) can serve as fast and accurate estimators of classical docking algorithms. Additionally, we introduce FiLMv2, a novel GNN architecture which we show outperforms existing state-of-the-art GNN architectures, attaining more accurate and stable performance by allowing the model to filter out irrelevant information from data more efficiently. Through extensive experimentation and analysis, we show that the DSD workflow combined with the FiLMv2 architecture provides a 9.496x speedup in molecule screening with a <3% recall error rate on an example docking task. Our open-source code is available at https://github.com/ryienh/graph-dock.

Dynamic movement primitives are widely used for learning skills which can be demonstrated to a robot by a skilled human or controller. While their generalization capabilities and simple formulation make them very appealing to use, they possess no strong guarantees to satisfy operational safety constraints for a task. In this paper, we present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace. We present a formulation of a non-linear optimization to perturb the DMP forcing weights regressed by locally-weighted regression to admit a Zeroing Barrier Function (ZBF), which certifies workspace constraint satisfaction. We demonstrate the proposed CDMP under different constraints on the end-effector movement such as obstacle avoidance and workspace constraints on a physical robot. A video showing the implementation of the proposed algorithm using different manipulators in different environments could be found here https://youtu.be/hJegJJkJfys.

Duckiebots是低成本的移动机器人，在研究和教育领域广泛使用。尽管Duckietown平台有现有的自动驾驶算法，但它们要么太复杂，要么表现太差，无法导航多车道轨道。此外，必须将内存和计算资源提供给Duckiebot，以便它可以执行其他任务，例如分布式输入检测。为了满足这些约束，我们构建了一种低成本的自主驾驶算法，能够在两车道轨道上驾驶。该算法使用传统的计算机视觉技术来识别轨道上的中央车道并获得相关的转向角度。然后，转向由PID控制器控制，该PID控制器使Duckiebot的运动平滑。将算法的性能与Neurips 2018 AI驾驶奥运会（AIDO）决赛入围者进行了比较，并且除了一名决赛选手以外，它的表现优于所有球员。我们算法的两个主要贡献是其低计算要求和非常快速的设置，并持续努力使其更加可靠。

仇恨言论以贬义的评论以多种形式针对社区，并使人类退后一步。 Hatexplain是最近出版的第一个数据集，用于以理由的形式使用带注释的跨度，以及语音分类类别和有针对性的社区，以使分类更具人性化，可解释，准确和偏见。我们调整BERT以理由和阶级预测的形式执行此任务，并比较我们对跨精度，解释性和偏见的不同指标的性能。我们的新颖性是三倍。首先，我们尝试具有不同重要性值的合并理由类损失。其次，我们对理由的地面真相注意值进行了广泛的实验。随着保守和宽大的关注，我们比较了hatexplain模型的性能并检验我们的假设。第三，为了改善模型中的意外偏见，我们使用目标社区单词的掩盖，并注意偏见和解释性指标的改善。总体而言，我们成功地实现了模型的解释性，偏差删除和对原始BERT实施的几个增量改进。

深度神经网络的过度参数性质导致在低端设备上的部署过程中有很大的障碍，并具有时间和空间限制。使用迭代修剪培训方案稀疏DNN的网络修剪策略通常在计算上很昂贵。结果，在训练之前，在初始化时修剪修剪的技术变得越来越流行。在这项工作中，我们提出了神经元到神经元的跳过连接，这些连接是稀疏的加权跳过连接，以增强修剪的DNN的整体连通性。遵循初步修剪步骤，在修剪网络的单个神经元/通道之间随机添加N2NSKIP连接，同时保持网络的整体稀疏性。我们证明，与没有N2NSKIP连接的修剪的网络相比，在修剪网络中引入N2NSKIP连接可以显着卓越的性能，尤其是在高稀疏度水平上。此外，我们提出了基于热扩散的连接分析，以定量确定修剪网络相对于参考网络的连通性。我们评估方法对两种不同初步修剪方法的疗效，这些方法在初始化时修剪，并通过利用N2NSKIP连接引起的增强连接性来始终获得卓越的性能。

独立组件分析是一种无监督的学习方法，用于从多元信号或数据矩阵计算独立组件（IC）。基于权重矩阵与多元数据矩阵的乘法进行评估。这项研究提出了一个新型的Memristor横杆阵列，用于实施ACY ICA和快速ICA，以用于盲源分离。数据输入以脉冲宽度调制电压的形式应用于横梁阵列，并且已实现的神经网络的重量存储在Memristor中。来自Memristor列的输出电荷用于计算重量更新，该重量更新是通过电压高于Memristor SET/RESET电压执行的。为了证明其潜在应用，采用了基于ICA架构的基于ICA架构的拟议的Memristor横杆阵列用于图像源分离问题。实验结果表明，所提出的方法非常有效地分离图像源，并且与常规ACY的基于软件的ACY实施相比，与结构相似性的百分比相比，结构相似性的百分比为67.27％，图像的对比度得到了改进。 ICA和快速ICA算法。

当机器学习（ML）模型提供其培训分配以外的数据时，他们更有可能做出不准确的预测。在网络物理系统（CPS）中，这可能导致灾难性系统故障。为了减轻这种风险，分布（OOD）检测器可以与ML模型和标志输入并行运行，这可能导致不良结果。尽管OOD探测器在准确性方面进行了很好的研究，但对资源约束CPS的部署的关注较少。在这项研究中，提出了一种设计方法来调整深入OOD检测器，以满足嵌入式应用的准确性和响应时间要求。该方法使用遗传算法来优化检测器的预处理管道，并选择一种平衡鲁棒性和响应时间的量化方法。它还标识了机器人操作系统（ROS）下的几个候选任务图，以部署所选设计。该方法在两个嵌入式平台的文献中的两个基于变异自动编码器的OOD检测器上进行了证明。提供了对设计过程中发生的权衡的洞察力，并表明这种设计方法可以导致相对于不居住的OOD检测器的响应时间急剧减少，同时保持可比较的精度。