在自动机器人群的现有文献中，采用的可见性模型具有一些与实际传感设备实现不符的理想主义假设。本文在更现实的可见性模型中调查了这个问题，称为不透明的脂肪机器人，具有纤细的全向相机。机器人被建模为单位磁盘，每个磁盘都具有全向摄像头，表示为尺寸较小的磁盘。我们假设机器人具有指南针，可以在其局部坐标系统的两个轴方向和方向上达成共识。机器人配备了可见的灯光，这些灯光是通信的媒介，也可以用作记忆的形式。我们为相互可见性问题提供了分布式算法，该算法在半同步设置中被证明是正确的。我们的算法还为领导者选举提供了解决方案，我们将其用作主要算法中的子例程。尽管在完整的可见性模型中，领导者选举在两个轴心协议中是微不足道的，但在我们的案例中，这是具有挑战性的，并且具有独立的利益。

Non-convex AC optimal power flow (AC-OPF) is a fundamental optimization problem in power system analysis. The computational complexity of conventional solvers is typically high and not suitable for large-scale networks in real-time operation. Hence, deep learning based approaches have gained intensive attention to conduct the time-consuming training process offline. Supervised learning methods may yield a feasible AC-OPF solution with a small optimality gap. However, they often need conventional solvers to generate the training dataset. This paper proposes an end-to-end unsupervised learning based framework for AC-OPF. We develop a deep neural network to output a partial set of decision variables while the remaining variables are recovered by solving AC power flow equations. The fast decoupled power flow solver is adopted to further reduce the computational time. In addition, we propose using a modified augmented Lagrangian function as the training loss. The multipliers are adjusted dynamically based on the degree of constraint violation. Extensive numerical test results corroborate the advantages of our proposed approach over some existing methods.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literature documents for textual user query, and COVID-19 question answering task predicts proper text fragments from text corpus as the answer of specific COVID-19 related questions. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions.

Machine learning driven image-based controllers allow robotic systems to take intelligent actions based on the visual feedback from their environment. Understanding when these controllers might lead to system safety violations is important for their integration in safety-critical applications and engineering corrective safety measures for the system. Existing methods leverage simulation-based testing (or falsification) to find the failures of vision-based controllers, i.e., the visual inputs that lead to closed-loop safety violations. However, these techniques do not scale well to the scenarios involving high-dimensional and complex visual inputs, such as RGB images. In this work, we cast the problem of finding closed-loop vision failures as a Hamilton-Jacobi (HJ) reachability problem. Our approach blends simulation-based analysis with HJ reachability methods to compute an approximation of the backward reachable tube (BRT) of the system, i.e., the set of unsafe states for the system under vision-based controllers. Utilizing the BRT, we can tractably and systematically find the system states and corresponding visual inputs that lead to closed-loop failures. These visual inputs can be subsequently analyzed to find the input characteristics that might have caused the failure. Besides its scalability to high-dimensional visual inputs, an explicit computation of BRT allows the proposed approach to capture non-trivial system failures that are difficult to expose via random simulations. We demonstrate our framework on two case studies involving an RGB image-based neural network controller for (a) autonomous indoor navigation, and (b) autonomous aircraft taxiing.

太阳水箱是在太阳的Ca II K摄影观测中观察到的明亮的色球环特征。这些是高磁场浓度的区域，因此是太阳的磁性活性的示踪剂，并且是研究太阳长期可变性的最重要特征之一，因为记录了一个多世纪以来的Ca II K镜头。。但是，从一个世纪的数据库中检测到份额是一项非平凡的任务，需要大量的人力资源来手动进行。因此，在这项研究中，我们提出了一种图像处理算法，该算法可以从CA II K摄影观测中识别出太阳份量。拟议的研究已在Kodaikanal太阳能天文台的档案数据上实施。为了确保算法有效，无论噪声水平，亮度和其他图像属性如何，我们从数据存档中随机绘制图像样本以测试我们的算法。

仇恨语音检测的最先进方法通常在室外设置中表现出较差的性能。通常，这是由于分类器过度强调特定于源的信息，从而对其域的不变性产生负面影响。先前的工作试图使用功能归因方法从手动策划的列表中惩罚与仇恨语音有关的条款，该方法量化了分类器在做出预测时分配给输入术语的重要性。取而代之的是，我们提出了一种域适应方法，该方法会使用域分类器自动提取和惩罚特定于源的术语，该域分类器学会区分域和仇恨语音类别的功能 - 属性分数，从而在交叉域评估中始终如一地改进。

太阳能动力学天文台（SDO）是NASA多光谱十年的长达任务，每天都在日常产生来自Sun的观测数据的trabytes，以证明机器学习方法的潜力并铺路未来深空任务计划的方式。特别是，在最近的几项研究中提出了使用图像到图像翻译实际上产生极端超紫罗兰通道的想法，这是一种增强任务较少通道的提高任务的方法，并且由于低下链接而减轻了挑战。深空的速率。本文通过关注四个通道和基于编码器的建筑的排列来研究这种深度学习方法的潜力和局限性，并特别注意太阳表面的形态特征和亮度如何影响神经网络预测。在这项工作中，我们想回答以下问题：可以将通过图像到图像翻译产生的太阳电晕的合成图像用于太阳的科学研究吗？分析强调，神经网络在计数率（像素强度）上产生高质量的图像，通常可以在1％误差范围内跨通道跨通道重现协方差。但是，模型性能在极高的能量事件（如耀斑）的对应关系中大大减少，我们认为原因与此类事件的稀有性有关，这对模型训练构成了挑战。

扩散模型是图像产生和似然估计的最新方法。在这项工作中，我们将连续的时间扩散模型推广到任意的Riemannian流形，并得出了可能性估计的变异框架。在计算上，我们提出了计算可能性估计中需要的黎曼分歧的新方法。此外，在概括欧几里得案例时，我们证明，最大化该变异的下限等效于Riemannian得分匹配。从经验上讲，我们证明了Riemannian扩散模型在各种光滑的歧管上的表达能力，例如球体，Tori，双曲线和正交组。我们提出的方法在所有基准测试基准上实现了新的最先进的可能性。

Lipschitz Bound估计是使深度神经网络正规化以使其可抵抗对抗性攻击的有效方法。这在从加强学习到自主系统的各种应用中很有用。在本文中，我们强调了卷积神经网络（CNN）获得非平凡的Lipschitz结合证书的显着差距，并通过广泛的图形分析在经验上支持它。我们还表明，可以使用展开的卷积层或Toeplitz矩阵将卷积神经网络（CNN）转换为完全连接的网络。此外，我们提出了一种简单的算法，以显示实际Lipschitz常数和所获得的紧密结合之间的特定数据分布中现有的20x-50x差距。我们还对各种网络体系结构进行了一组彻底的实验，并在MNIST和CIFAR-10等数据集上进行基准测试。所有这些建议都通过广泛的测试，图形，直方图和比较分析来支持。