最大信息系数（MIC）是一个强大的统计量，可以识别变量之间的依赖性。但是，它可以应用于敏感数据，并且发布可能会泄漏私人信息。作为解决方案，我们提出算法以提供差异隐私的方式近似麦克风。我们表明，经典拉普拉斯机制的自然应用产生的精度不足。因此，我们介绍了MICT统计量，这是一种新的MIC近似值，与差异隐私更加兼容。我们证明MICS是麦克风的一致估计器，我们提供了两个差异性私有版本。我们对各种真实和合成数据集进行实验。结果表明，私人微统计数据极大地超过了拉普拉斯机制的直接应用。此外，对现实世界数据集的实验显示出准确性，当样本量至少适中时可用。

Practitioners use Hidden Markov Models (HMMs) in different problems for about sixty years. Besides, Conditional Random Fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propose two contributions. First, we show that basic Linear-Chain CRFs (LC-CRFs), considered as different from the HMMs, are in fact equivalent to them in the sense that for each LC-CRF there exists a HMM - that we specify - whom posterior distribution is identical to the given LC-CRF. Second, we show that it is possible to reformulate the generative Bayesian classifiers Maximum Posterior Mode (MPM) and Maximum a Posteriori (MAP) used in HMMs, as discriminative ones. The last point is of importance in many fields, especially in Natural Language Processing (NLP), as it shows that in some situations dropping HMMs in favor of CRFs was not necessary.

Using a comprehensive sample of 2,585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly traded U.S. firms. We find that gradient boosted trees outperform other models in one-year-ahead forecasts. Variable permutation tests show that excess stock returns, idiosyncratic risk, and relative size are the more important variables for predictions. Textual features derived from corporate filings do not improve performance materially. In a credit competition model that accounts for the asymmetric cost of default misclassification, the survival random forest is able to capture large dollar profits.

The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks. Typical examples are novel computer chips designed to mimic the architecture of a biological brain, or sensors that get inspiration from, e.g., the visual or olfactory systems in insects and mammals to acquire information about the environment. This approach is not without ambition as it promises to enable engineered devices able to reproduce the level of performance observed in biological organisms -- the main immediate advantage being the efficient use of scarce resources, which translates into low power requirements. The emphasis on low power and energy efficiency of neuromorphic devices is a perfect match for space applications. Spacecraft -- especially miniaturized ones -- have strict energy constraints as they need to operate in an environment which is scarce with resources and extremely hostile. In this work we present an overview of early attempts made to study a neuromorphic approach in a space context at the European Space Agency's (ESA) Advanced Concepts Team (ACT).

When searching for policies, reward-sparse environments often lack sufficient information about which behaviors to improve upon or avoid. In such environments, the policy search process is bound to blindly search for reward-yielding transitions and no early reward can bias this search in one direction or another. A way to overcome this is to use intrinsic motivation in order to explore new transitions until a reward is found. In this work, we use a recently proposed definition of intrinsic motivation, Curiosity, in an evolutionary policy search method. We propose Curiosity-ES, an evolutionary strategy adapted to use Curiosity as a fitness metric. We compare Curiosity with Novelty, a commonly used diversity metric, and find that Curiosity can generate higher diversity over full episodes without the need for an explicit diversity criterion and lead to multiple policies which find reward.

Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications. Despite their computational advantages, reduced order models (ROMs) often fail to accurately reproduce complex dynamics encountered in real life applications. To address this challenge, we leverage NeuralODEs to propose a novel ROM correction approach based on a time-continuous memory formulation. Finally, experimental results show that our proposed method provides a high level of accuracy while retaining the low computational costs inherent to reduced models.

生成流动网络（GFLOWNETS）是一种算法家族，用于训练在非均衡目标密度下离散对象的顺序采样器，并已成功用于各种概率建模任务。现有的Gflownets培训目标是国家本地的，或者是过渡的本地，或者在整个采样轨迹上传播奖励信号。我们认为，这些替代方案代表了梯度偏见变化权衡的相反目的，并提出了一种利用这种权衡以减轻其有害影响的方法。受到强化学习的TD（$ \ lambda $）算法的启发，我们介绍了一个subtrajectory Balance或subtb（$ \ lambda $），这是一个GFLOWNET培训目标，可以从不同长度的部分动作子序列中学习。我们表明，SubTB（$ \ lambda $）会在先前研究和新环境中加速采样器的收敛，并在具有更长的动作序列和比以前的可能性更长的环境中培训Gflownets。我们还对随机梯度动力学进行了比较分析，阐明了GFLOWNET训练中的偏差变化权衡以及亚条件平衡的优势。

来自光场的大量空间和角度信息允许开发多种差异估计方法。但是，对光场的获取需要高存储和处理成本，从而限制了该技术在实际应用中的使用。为了克服这些缺点，压缩感应（CS）理论使光学体系结构的开发能够获得单个编码的光场测量。该测量是使用需要高计算成本的优化算法或深神经网络来解码的。从压缩光场进行的传统差异估计方法需要首先恢复整个光场，然后再恢复后处理步骤，从而需要长时间。相比之下，这项工作提出了通过省略传统方法所需的恢复步骤来从单个压缩测量中进行快速差异估计。具体而言，我们建议共同优化用于获取单个编码光场快照和卷积神经网络（CNN）的光学体系结构，以估计差异图。在实验上，提出的方法估计了与使用深度学习方法重建的光场相当的差异图。此外，所提出的方法在训练和推理方面的速度比估计重建光场差异的最佳方法要快20倍。

深厚的强化学习政策尽管在模拟的视觉控制任务中出色地效率，但表现出令人失望的能力，可以在输入培训图像中跨越跨干扰。图像统计或分散背景元素的变化是防止这种控制策略的概括和现实世界中适用性的陷阱。我们阐述了这样的直觉，即良好的视觉政策应该能够确定哪些像素对其决策很重要，并保留对图像跨图像的重要信息来源的识别。这意味着对具有较小概括差距的政策进行培训应集中在如此重要的像素上，而忽略其他像素。这导致引入显着引导的Q-Networks（SGQN），这是一种视觉增强学习的通用方法，与任何值函数学习方法兼容。 SGQN极大地提高了软演员 - 批评者的概括能力，并且在DeepMind Control Generalization基准上胜过现有的现有方法，为训练效率，概括性差距和政策解释性提供了新的参考。

该文档代表2022 XCSP3竞赛的程序。这场约束求解者的竞争结果在2022年7月31日至2022年8月7日在以色列海法举行的2022年奥运会（Federated Logic Conference）展出。