Array programming provides a powerful, compact, expressive syntax for accessing, manipulating, and operating on data in vectors, matrices, and higher-dimensional arrays [1]. NumPy is the primary array programming library for the Python language [2,3,4,5]. It plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material science, engineering, finance, and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves [6] and the first imaging of a black hole [7].Here we show how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring, and analyzing scientific data. NumPy is the foundation upon which the entire scientific Python universe is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Because of its central position in the ecosystem, NumPy increasingly plays the role of an interoperability layer between these new array computation libraries.

Learning about physical systems from quantum-enhanced experiments, relying on a quantum memory and quantum processing, can outperform learning from experiments in which only classical memory and processing are available. Whereas quantum advantages have been established for a variety of state learning tasks, quantum process learning allows for comparable advantages only with a careful problem formulation and is less understood. We establish an exponential quantum advantage for learning an unknown $n$-qubit quantum process $\mathcal{N}$. We show that a quantum memory allows to efficiently solve the following tasks: (a) learning the Pauli transfer matrix of an arbitrary $\mathcal{N}$, (b) predicting expectation values of bounded Pauli-sparse observables measured on the output of an arbitrary $\mathcal{N}$ upon input of a Pauli-sparse state, and (c) predicting expectation values of arbitrary bounded observables measured on the output of an unknown $\mathcal{N}$ with sparse Pauli transfer matrix upon input of an arbitrary state. With quantum memory, these tasks can be solved using linearly-in-$n$ many copies of the Choi state of $\mathcal{N}$, and even time-efficiently in the case of (b). In contrast, any learner without quantum memory requires exponentially-in-$n$ many queries, even when querying $\mathcal{N}$ on subsystems of adaptively chosen states and performing adaptively chosen measurements. In proving this separation, we extend existing shadow tomography upper and lower bounds from states to channels via the Choi-Jamiolkowski isomorphism. Moreover, we combine Pauli transfer matrix learning with polynomial interpolation techniques to develop a procedure for learning arbitrary Hamiltonians, which may have non-local all-to-all interactions, from short-time dynamics. Our results highlight the power of quantum-enhanced experiments for learning highly complex quantum dynamics.

In this work, we propose a dissipativity-based method for Lipschitz constant estimation of 1D convolutional neural networks (CNNs). In particular, we analyze the dissipativity properties of convolutional, pooling, and fully connected layers making use of incremental quadratic constraints for nonlinear activation functions and pooling operations. The Lipschitz constant of the concatenation of these mappings is then estimated by solving a semidefinite program which we derive from dissipativity theory. To make our method as efficient as possible, we take the structure of convolutional layers into account realizing these finite impulse response filters as causal dynamical systems in state space and carrying out the dissipativity analysis for the state space realizations. The examples we provide show that our Lipschitz bounds are advantageous in terms of accuracy and scalability.

机器学习算法通常用于可能直接影响个人的业务决策，因为信用评分算法拒绝了他们的贷款。从道德（和法律）的角度来看，这是相关的，以确保这些算法不会基于敏感属性（例如性别或种族）来区分这些算法，这可能是操作员和管理人员在不知不觉中和不知不觉中发生的。然后需要统计工具和方法来检测和消除此类潜在偏见。

监督学习方法可以在存在大量标记数据的情况下解决给定的问题。但是，涵盖所有目标类的数据集的采集通常需要昂贵且耗时的手动标签。零击学习模型能够通过利用其语义信息来对看不见的概念进行分类。本研究通过使用非线性声音 - 语义投影介绍了图像嵌入作为有关零击音频分类的附带信息。我们从开放图像数据集中提取语义图像表示形式，并使用不同域中的语义信息在音频集的音频子集上评估模型的性能；图像，音频和文字。我们证明，图像嵌入可以用作语义信息来执行零击音频分类。实验结果表明，图像和文本嵌入式单独和一起显示相似的性能。我们还从测试样品中计算出语义声嵌入，以提供性能的上限。结果表明，分类性能对测试和训练类之间的语义关系以及文本和图像嵌入之间的语义关系高度敏感，当时可见和看不见的类在语义上相似时，可以直至语义声学嵌入。

音频问题回答（AQA）是一项多模式翻译任务，系统分析音频信号和自然语言问题，以产生理想的自然语言答案。在本文中，我们介绍了Clotho-AQA，这是一个用于音频问题的数据集，该数据集由1991年的音频文件组成，分别是从Clotho数据集中选择的15至30秒之间。对于每个音频文件，我们通过使用Amazon Mechanical Turk来收集六个不同的问题和相应的答案。问题和答案由不同的注释者产生。在每个音频的六个问题中，每个问题都被设计为“是”和“否”作为答案，而其余两个问题则具有其他单词答案。对于每个问题，我们都会从三个不同的注释者那里收集答案。我们还提出了两个基线实验，以描述数据集用于AQA任务的使用 - 基于LSTM的多模式二进制分类器，用于“是”或“否”类型答案以及828单字的基于LSTM的多模式多级分类器答案。二进制分类器的准确度为62.7％，多级分类器的前1位准确度为54.2％，前5个精度为93.7％。 Clotho-AQA数据集可在https://zenodo.org/record/6473207上免费在线获取。

本文涉及在Semidefinite限制下培训神经网络（NNS）。这种类型的训练问题最近获得了普及，因为半纤维约束可以用于验证包括例如嘴唇峰常数上限的NN的有趣特性，这与NN的鲁棒性或稳定性有关具有NN控制器的动态系统。使用的SemideFinite约束基于底层激活函数满足的扇区约束。遗憾的是，这些新结果的最大瓶颈之一是将Semidefinite限制纳入NNS的训练所需的计算工作，这限制了它们对大NN的可扩展性。我们通过开发NN培训的内部点方法来解决这一挑战，我们使用屏障函数为SEMIDEFINITE约束实现。为了有效地计算屏障术语的梯度，我们利用了半纤维限制的结构。在实验中，我们展示了我们对先前方法的培训方法的卓越效率，这使我们可以在培训Wassersein生成的对抗网络中使用Semidefinite限制，其中鉴别者必须满足Lipschitz条件。