大气效应（例如湍流和背景热噪声）抑制了在开关键控自由空间光学通信中使用的相干光的传播。在这里，我们介绍并实验验证了卷积神经网络，以降低后处理中自由空间光学通信的位错误率，而自由空间光学通信的位比基于高级光学器件的现有解决方案明显简单，更便宜。我们的方法由两个神经网络组成，这是第一个确定在热噪声和湍流中存在相干位序列以及第二个解调相干位序列的存在。通过生成连贯的光线，将它们与热灯结合在一起，并通过湍流的水箱将其结合起来，通过生成开关的键入键流，可以通过实验获得我们网络的所有数据，从而获得了模拟的湍流，并将其传递给了最终的光线。高度准确性。我们的卷积神经网络提高了与阈值分类方案相比的检测准确性，并具有与当前解调和误差校正方案集成的能力。

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for use with Bayesian quantum state estimation methods. Previously, researchers have looked to Bayesian quantum state tomography due to its unique advantages like natural uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography.

A biological system is a complex network of heterogeneous molecular entities and their interactions contributing to various biological characteristics of the system. However, current biological networks are noisy, sparse, and incomplete, limiting our ability to create a holistic view of the biological system and understand the biological phenomena. Experimental identification of such interactions is both time-consuming and expensive. With the recent advancements in high-throughput data generation and significant improvement in computational power, various computational methods have been developed to predict novel interactions in the noisy network. Recently, deep learning methods such as graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction. However, graph neural networks-based methods require human expertise and experimentation to design the appropriate complexity of the model and significantly impact the performance of the model. Furthermore, deep graph neural networks face overfitting problems and tend to be poorly calibrated with high confidence on incorrect predictions. To address these challenges, we propose Bayesian model selection for graph convolutional networks to jointly infer the most plausible number of graph convolution layers (depth) warranted by data and perform dropout regularization simultaneously. Experiments on four interaction datasets show that our proposed method achieves accurate and calibrated predictions. Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.

Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary assumptions of these models is the independent and identical distribution, which suggests that the train and test data are sampled from the same distribution. However, this assumption seldom holds in the real world due to distribution shifts. As a result models that rely on this assumption exhibit poor generalization capabilities. Over the recent years, dedicated efforts have been made to improve the generalization capabilities of these models collectively known as -- \textit{domain generalization methods}. The primary idea behind these methods is to identify stable features or mechanisms that remain invariant across the different distributions. Many generalization approaches employ causal theories to describe invariance since causality and invariance are inextricably intertwined. However, current surveys deal with the causality-aware domain generalization methods on a very high-level. Furthermore, we argue that it is possible to categorize the methods based on how causality is leveraged in that method and in which part of the model pipeline is it used. To this end, we categorize the causal domain generalization methods into three categories, namely, (i) Invariance via Causal Data Augmentation methods which are applied during the data pre-processing stage, (ii) Invariance via Causal representation learning methods that are utilized during the representation learning stage, and (iii) Invariance via Transferring Causal mechanisms methods that are applied during the classification stage of the pipeline. Furthermore, this survey includes in-depth insights into benchmark datasets and code repositories for domain generalization methods. We conclude the survey with insights and discussions on future directions.

在线社交网络（OSN）有助于访问各种数据，使研究人员能够分析用户的行为并开发用户行为分析模型。这些模型在很大程度上依赖于观察到的数据，这些数据通常由于参与不平等而产生偏差。这种不平等由三组在线用户组成：潜伏者 - 仅消耗内容的用户，招聘者 - 对内容创建的用户和贡献者很少贡献 - 负责创建大多数在线内容的用户。在解释人口水平的利益或情感的同时，未能考虑所有群体的贡献，可能会产生偏见的结果。为了减少贡献者引起的偏见，在这项工作中，我们专注于强调参与者在观察到的数据中的贡献，因为与潜伏者相比，它们更有可能贡献，与贡献者相比，它们的人口更大。这些用户行为分析的第一步是找到他们接触但没有互动的主题。为此，我们提出了一个新颖的框架，有助于识别这些用户并估算其主题曝光。暴露估计机制是通过合并来自类似贡献者的行为模式以及用户的人口统计学和个人资料信息来建模的。

手机等边缘设备上的微调模型将对敏感数据实现隐私的个性化。但是，在历史上，Edge训练仅限于具有简单体系结构的相对较小的模型，因为训练既是记忆力和能量密集型的。我们提出了诗人，这是一种算法，可以在存储器筛分的边缘设备上训练大型神经网络。诗人共同优化了重新布置和分页的综合搜索搜索空间，这两种算法可减少返回传播的记忆消耗。鉴于记忆预算和运行时间的限制，我们制定了一项混合成员线性计划（MILP），以进行最佳培训。我们的方法使培训能够在嵌入式设备上显着更大的模型，同时减少能源消耗，同时不修改反向传播的数学正确性。我们证明，可以在皮质类嵌入式设备的内存约束中微调RESNET-18和BERT，同时在能源效率方面的当前边缘训练方法的表现。诗人是一个开源项目，网址为https://github.com/shishirpatil/poet

尽管Covid-19疫苗对病毒取得了惊人的成功，但很大一部分人口仍然不愿接受疫苗接种，这破坏了政府控制该病毒的努力。为了解决这个问题，我们需要了解导致这种行为的不同因素，包括社交媒体话语，新闻媒体宣传，政府的回应，人口统计和社会经济地位以及COVID-19统计等等。涵盖所有这些方面，使得在推断疫苗犹豫的问题时很难形成完整的情况。在本文中，我们构建了一个多源，多模式和多功能在线数据存储库Covaxnet。我们提供描述性分析和见解，以说明Covaxnet中的关键模式。此外，我们提出了一种新颖的方法来连接在线和离线数据，以促进利用互补信息源的推理任务。

问题回答模型努力推广到训练模式的新型组成，诸如更长的序列或更复杂的测试结构。目前的端到端模型学习扁平输入嵌入，可以丢失输入语法上下文。先前的方法通过学习置换不变模型来改善泛化，但这些方法不会扩展到更复杂的火车测试分裂。我们提出了接地的图形解码，一种通过与注意机制接地结构化预测来提高语言表示的组成概括的方法。接地使模型能够从输入中保留语法信息，从而显着提高复杂输入的泛化。通过预测包含查询条件的连词的结构化图，我们学习一个组不变的表示，而不会在目标域上做出假设。我们的模型显着优于现有的基础基础上的组成自由BASE问题（CFQ）数据集，这是一个有挑战性的基准，用于有问题的合成概括。此外，我们有效地解决了98％精度的MCD1分体式。

最近的工作通过从上下文重建令牌来了解源代码的上下文表示。对于诸如英语中汇总代码的下游语义理解任务，这些表示应该理想地捕获程序功能。但是，我们表明流行的基于重建的BERT模型对源代码编辑敏感，即使编辑保存语义。我们提出了僵局：一种学习代码功能的对比预训练任务，而不是形成。触发预先训练神经网络，以识别许多不等效的干扰者之间的程序的功能类似的变体。我们使用自动源到源编译器作为数据增强的形式来缩放可扩展这些变体。对比预训练将JavaScript摘要和打字类型推理准确性提高2％至13％。我们还提出了一个新的零拍摄JavaScript代码克隆检测数据集，显示施加均比更强大和语义有意义。就此而言，我们以39％的Auroc在普发的环境中以39％的AUROC倾斜，高达5％的自然代码。