协方差估计在功能数据分析中普遍存在。然而，对多维域的功能观测的情况引入了计算和统计挑战，使标准方法有效地不适用。为了解决这个问题，我们将“协方差网络”（CoVNet）介绍为建模和估算工具。 Covnet模型是“Universal” - 它可用于近似于达到所需精度的任何协方差。此外，该模型可以有效地拟合到数据，其神经网络架构允许我们在实现中采用现代计算工具。 Covnet模型还承认了一个封闭形式的实体分解，可以有效地计算，而不构建协方差本身。这有助于在CoVnet的背景下轻松存储和随后操纵协方差。我们建立了拟议估计者的一致性，得出了汇合速度。通过广泛的仿真研究和休息状态FMRI数据的应用，证明了所提出的方法的有用性。

With the drive to create a decentralized digital economy, Web 3.0 has become a cornerstone of digital transformation, developed on the basis of computing-force networking, distributed data storage, and blockchain. With the rapid realization of quantum devices, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with the advantages of quantum computing and communication. Therefore, in this paper, we introduce a quantum blockchain-driven Web 3.0 framework that provides information-theoretic security for decentralized data transferring and payment transactions. First, we present the framework of quantum blockchain-driven Web 3.0 with future-proof security during the transmission of data and transaction information. Next, we discuss the potential applications and challenges of implementing quantum blockchain in Web 3.0. Finally, we describe a use case for quantum non-fungible tokens (NFTs) and propose a quantum deep learning-based optimal auction for NFT trading to maximize the achievable revenue for sufficient liquidity in Web 3.0. In this way, the proposed framework can achieve proven security and sustainability for the next-generation decentralized digital society.

We introduce an architecture for processing signals supported on hypergraphs via graph neural networks (GNNs), which we call a Hyper-graph Expansion Neural Network (HENN), and provide the first bounds on the stability and transferability error of a hypergraph signal processing model. To do so, we provide a framework for bounding the stability and transferability error of GNNs across arbitrary graphs via spectral similarity. By bounding the difference between two graph shift operators (GSOs) in the positive semi-definite sense via their eigenvalue spectrum, we show that this error depends only on the properties of the GNN and the magnitude of spectral similarity of the GSOs. Moreover, we show that existing transferability results that assume the graphs are small perturbations of one another, or that the graphs are random and drawn from the same distribution or sampled from the same graphon can be recovered using our approach. Thus, both GNNs and our HENNs (trained using normalized Laplacians as graph shift operators) will be increasingly stable and transferable as the graphs become larger. Experimental results illustrate the importance of considering multiple graph representations in HENN, and show its superior performance when transferability is desired.

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.

The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations can transfer across tasks and scale, increasing the impact of modeling research. However, with the emergence of state-of-the-art 100B+ parameters models, large language models are increasingly expensive to accurately design and train. Notably, it can be difficult to evaluate how modeling decisions may impact emergent capabilities, given that these capabilities arise mainly from sheer scale alone. In the process of building BLOOM--the Big Science Large Open-science Open-access Multilingual language model--our goal is to identify an architecture and training setup that makes the best use of our 1,000,000 A100-GPU-hours budget. Specifically, we perform an ablation study at the billion-parameter scale comparing different modeling practices and their impact on zero-shot generalization. In addition, we study the impact of various popular pre-training corpora on zero-shot generalization. We also study the performance of a multilingual model and how it compares to the English-only one. Finally, we consider the scaling behaviour of Transformers to choose the target model size, shape, and training setup. All our models and code are open-sourced at https://huggingface.co/bigscience .

Bayesian optimization (BO) is one of the most effective methods for closed-loop experimental design and black-box optimization. However, a key limitation of BO is that it is an inherently sequential algorithm (one experiment is proposed per round) and thus cannot directly exploit high-throughput (parallel) experiments. Diverse modifications to the BO framework have been proposed in the literature to enable exploitation of parallel experiments but such approaches are limited in the degree of parallelization that they can achieve and can lead to redundant experiments (thus wasting resources and potentially compromising performance). In this work, we present new parallel BO paradigms that exploit the structure of the system to partition the design space. Specifically, we propose an approach that partitions the design space by following the level sets of the performance function and an approach that exploits partially-separable structures of the performance function found. We conduct extensive numerical experiments using a reactor case study to benchmark the effectiveness of these approaches against a variety of state-of-the-art parallel algorithms reported in the literature. Our computational results show that our approaches significantly reduce the required search time and increase the probability of finding a global (rather than local) solution.

在本文中，我们研究了DRL算法在本地导航问题的应用，其中机器人仅配备有限​​量距离的外部感受传感器（例如LIDAR），在未知和混乱的工作区中朝着目标位置移动。基于DRL的碰撞避免政策具有一些优势，但是一旦他们学习合适的动作的能力仅限于传感器范围，它们就非常容易受到本地最小值的影响。由于大多数机器人在非结构化环境中执行任务，因此寻求能够避免本地最小值的广义本地导航政策，尤其是在未经训练的情况下，这是非常兴趣的。为此，我们提出了一种新颖的奖励功能，该功能结合了在训练阶段获得的地图信息，从而提高了代理商故意最佳行动方案的能力。另外，我们使用SAC算法来训练我们的ANN，这表明在最先进的文献中比其他人更有效。一组SIM到SIM和SIM到现实的实验表明，我们提出的奖励与SAC相结合的表现优于比较局部最小值和避免碰撞的方法。

基于学习的控制器，例如神经网络（NN）控制器，可以表现出很高的经验性能，但缺乏正式的安全保证。为了解决此问题，已将控制屏障功能（CBF）应用于安全过滤器，以监视和修改基于学习的控制器的输出，以确保闭环系统的安全性。但是，这种修饰可能是近视的，具有不可预测的长期影响。在这项工作中，我们提出了一个安全的NN控制器，该控制器采用了基于CBF的可区分安全层，并研究了基于学习的控制中安全的NN控制器的性能。具体而言，比较了两个控制器的公式：一个是基于投影的，另一个依赖于我们提出的集合理论参数化。两种方法都证明了在数值实验中使用CBF作为单独的安全滤波器的改进的闭环性能。

现在，可以使用最先进的神经语言模型通过零射门提示来解决临时语言任务，而无需进行监督培训。近年来，这种方法已广受欢迎，研究人员证明了提示在特定的NLP任务上实现强烈准确的提示。但是，找到新任务的提示需要实验。具有不同措辞选择的不同提示模板会导致明显的准确性差异。提示允许用户尝试及时变化，可视化及时性能，并迭代优化提示。我们开发了一个工作流程，该工作流程允许用户首先使用少量数据专注于模型反馈，然后再进入大型数据制度，该数据制度允许使用任务的定量度量来实现有希望的提示的经验基础。然后，该工具可以轻松部署新创建的临时模型。我们使用多种现实世界用例演示了Fackide（http://prompt.vizhub.ai）和我们的工作流程的实用性。

可激发的光电设备代表了在神经形态（脑启发）光子系统中实施人工尖峰神经元的关键构件之一。这项工作介绍并实验研究了用谐振隧穿二极管（RTD）构建的光电 - 光学（O/E/O）人工神经元，该神经元（RTD）耦合到光电探测器作为接收器和垂直腔表面发射激光器作为发射机。我们证明了一个明确定义的兴奋性阈值，在此上面，该神经元在该神经元中产生100 ns的光学尖峰反应，具有特征性的神经样耐受性。我们利用其粉丝功能来执行设备中的重合检测（逻辑和）以及独家逻辑或（XOR）任务。这些结果提供了基于RTD的Spiking光电神经元的确定性触发和任务的首次实验验证，并具有输入和输出光学（I/O）终端。此外，我们还从理论上研究了拟议系统的纳米光子实施的前景，并结合了纳米级RTD元素和纳米剂的整体设计。因此，在未来的神经形态光子硬件中，证明了基于RTD的综合兴奋节点对低足迹，高速光电尖峰神经元的潜力。