最佳定价，即确定最大限度地提高给定产品的利润或收入的价格水平，是零售业的重要任务。要选择这样的数量，请先估计产品需求的价格弹性。由于混淆效果和价格内限性，回归方法通常无法恢复这些弹性。因此，通常需要随机实验。然而，例如，弹性可以是高度异构的，这取决于商店的位置。随着随机化经常发生在市级，标准差异差异方法也可能失败。可能的解决方案是基于根据从人工对照构成的治疗方法测量处理对单个（或仅几个）处理单元的影响的方法。例如，对于治疗组中的每个城市，可以从未处理的位置构成反事实。在本文中，我们应用了一种新的高维统计方法，以衡量价格变化对巴西主要零售商的日常销售的影响。所提出的方法结合了主成分（因子）和稀疏回归，导致一种称为因子调整的正规化方法的方法（\ TextTt {FarmTraTeat}）。数据包括每日五种不同产品的日常销售和价格，超过400多名市。审议的产品属于\ emph {甜蜜和糖果}类别和实验已经在2016年和2017年进行。我们的结果证实了高度异质性的假设，从而产生了与独特的市政当局的不同定价策略。

最近，由于许多用例的性能要求严格的性能要求，基于意图的管理正在受到电信网络的良好关注。文献上的几种方法采用电信域中的传统方法来满足KPI的意图，可以将其定义为封闭环。但是，这些方法考虑了每个闭环相互独立的环路，从而降低了组合的闭环性能。同样，当需要许多闭环时，这些方法不容易扩展。在许多领域，多机构增强学习（MARL）技术在许多领域都表现出了巨大的希望，在许多领域中，传统的闭环控制效果不足，通常用于循环之间的复杂协调和冲突管理。在这项工作中，我们提出了一种基于MARL的方法，以实现基于意图的管理，而无需基础系统模型。此外，当存在相互矛盾的意图时，MARL代理可以通过优先考虑重要的KPI来暗中激励循环，而无需人工互动。已经在网络模拟器上进行了实验，以优化三种服务的KPI，我们观察到拟议的系统的性能良好，并且在资源不足或资源稀缺时能够实现所有现有的意图。

监视自动实时流处理系统的行为已成为现实世界应用中最相关的问题之一。这种系统的复杂性已在很大程度上依赖于高维输入数据和数据饥饿的机器学习（ML）算法。我们提出了一个灵活的系统，功能监视（FM），该系统在此类数据集中检测数据漂移，并具有较小且恒定的内存足迹和流应用程序中的小计算成本。该方法基于多变量统计测试，并且是由设计驱动的数据（从数据中估算了完整的参考分布）。它监视系统使用的所有功能，同时每当发生警报时提供可解释的功能排名（以帮助根本原因分析）。系统的计算和记忆轻度是由于使用指数移动直方图而导致的。在我们的实验研究中，我们用其参数分析了系统的行为，更重要的是显示了它检测到与单个特征无直接相关的问题的示例。这说明了FM如何消除添加自定义信号以检测特定类型问题的需求，并且监视功能可用空间通常足够。

当代人工神经网络（ANN）是经过训练的端到端，共同学习功能和分类器以完成感兴趣的任务。尽管非常有效，但这种范式在组装带注释的特定任务数据集和培训大规模网络方面施加了巨大的成本。我们建议通过引入视觉生物标志物分类的辅助预任务来将特征从下游肺超声任务中学习。我们证明，通过培训模型来预测生物标记标签，可以从超声视频中学习一个内容丰富，简洁和可解释的功能空间。值得注意的是，可以从弱视频尺度监督注释的数据中培训生物标志物功能提取器。这些功能可以由针对各种临床任务的各种下游专家模型（诊断，肺严重程度，S/F比）使用。至关重要的是，特定于任务的专家模型的准确性与直接训练此类目标任务的端到端模型相当，同时训练成本大大降低。

The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or aliased when training or testing images observe scene content at different resolutions. The straightforward solution of supersampling by rendering with multiple rays per pixel is impractical for NeRF, because rendering each ray requires querying a multilayer perceptron hundreds of times. Our solution, which we call "mip-NeRF" (à la "mipmap"), extends NeRF to represent the scene at a continuously-valued scale. By efficiently rendering anti-aliased conical frustums instead of rays, mip-NeRF reduces objectionable aliasing artifacts and significantly improves NeRF's ability to represent fine details, while also being 7% faster than NeRF and half the size. Compared to NeRF, mip-NeRF reduces average error rates by 17% on the dataset presented with NeRF and by 60% on a challenging multiscale variant of that dataset that we present. Mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22× faster.

The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often, we're interested in going a step further, and want to approximate the true joint distribution over observed and latent variables, including the true prior and posterior distributions over latent variables. This is known to be generally impossible due to unidentifiability of the model. We address this issue by showing that for a broad family of deep latentvariable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement. Our result requires a factorized prior distribution over the latent variables that is conditioned on an additionally observed variable, such as a class label or almost any other observation. We build on recent developments in nonlinear ICA, which we extend to the case with noisy or undercomplete observations, integrated in a maximum likelihood framework. The result also trivially contains identifiable flow-based generative models as a special case.

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increases. However, creditworthiness assessment models trained with synthetic data show a reduction of 3\% of AUC and 6\% of KS when compared with models trained with real data. These results have a significant impact since they encourage credit risk investigation from synthetic data, making it possible to maintain borrowers' privacy and to address problems that until now have been hampered by the availability of information.