在涉及互连单元之间相互影响或因果关系的现实现象中，平衡状态通常用图形模型中的循环表示。 \ textIt {关系因果模型}的表达性图形模型可以表示和理由，这些动态系统表现出此类周期或反馈循环。从观察数据中学习因果模型的现有循环因果发现算法假定，数据实例是独立的且分布相同的，这使得它们不适合关系因果模型。同时，关系因果模型的因果发现算法假定循环。在这项工作中，我们研究了基于约束的关系因果发现算法的必要条件，对于\ textit {Cyclic {Cyclicit {Cyclication {Cyclication {Cyclication {Cyclication {Cyclication {Cyclication {Cyclication {cyclication {Cyclication {Cyclication {Cyclication {Cyclication {Cyclication {cyclical otalational otinational Causal Models}}。我们介绍了\ textit {关系clclification}，这是专门为关系模型设计的操作，可实现有关循环关系因果模型的可识别性的推理。我们表明，在关系周期性和$ \ sigma $ - 信仰的假设下，关系因果发现算法RCD（Maier等人，2013年）是合理的，对于环状模型而言是完整的。我们提出了实验结果以支持我们的主张。

独立测试在观察数据中的统计和因果推断中起着核心作用。标准独立测试假定数据样本是独立的，并且分布相同（i.i.d。），但是在以关系系统为中心的许多现实世界数据集和应用中违反了该假设。这项工作通过为影响个人实例的一组观测值定义足够的观察表，研究了从关系系统中估算独立性的问题。具体而言，我们通过将内核平均嵌入为关系变量的灵活聚合函数来定义关系数据的边际和条件独立性测试。我们提出了一个一致的，非参数，可扩展的内核测试，以对非I.I.D的关系独立性测试进行操作。一组结构假设下的观察数据。我们在经验上对各种合成和半合成网络进行了经验评估我们提出的方法，并证明了与基于最新内核的独立性测试相比其有效性。

Off-policy evaluation methods are important in recommendation systems and search engines, where data collected under an existing logging policy is used to estimate the performance of a new proposed policy. A common approach to this problem is weighting, where data is weighted by a density ratio between the probability of actions given contexts in the target and logged policies. In practice, two issues often arise. First, many problems have very large action spaces and we may not observe rewards for most actions, and so in finite samples we may encounter a positivity violation. Second, many recommendation systems are not probabilistic and so having access to logging and target policy densities may not be feasible. To address these issues, we introduce the featurized embedded permutation weighting estimator. The estimator computes the density ratio in an action embedding space, which reduces the possibility of positivity violations. The density ratio is computed leveraging recent advances in normalizing flows and density ratio estimation as a classification problem, in order to obtain estimates which are feasible in practice.

在线强化学习（RL）算法通常难以在复杂的人体面对应用中部署，因为它们可能会缓慢学习并且早期性能差。为了解决这个问题，我们介绍了一种结合人类洞察速度学习的实用算法。我们的算法，约束采样增强学习（CSRL）将现有域知识包含为RL策略的约束/限制。它需要多种潜在的政策限制，以保持稳健性，以便在利用有用的时击败个体限制，以便快速学习。鉴于基础RL学习算法（例如UCRL，DQN，Rainbow），我们提出了对消除方案的上下置信度，该方案利用了限制与其观察性能之间的关系，以便自适应地切换它们。我们将我们的算法用DQN型算法和UCRL作为基础算法，并在四种环境中评估我们的算法，包括基于实际数据的三个模拟器：建议，教育活动排序和HIV处理测序。在所有情况下，CSRL比基线更快地学习良好的政策。

基于中央限制定理（CLT）的置信区间是经典统计的基石。尽管仅渐近地有效，但它们是无处不在的，因为它们允许在非常弱的假设下进行统计推断，即使不可能进行非反应性推断，通常也可以应用于问题。本文引入了这种渐近置信区间的时间均匀类似物。为了详细说明，我们的方法采用置信序列（CS）的形式 - 随着时间的推移均匀有效的置信区间序列。 CSS在任意停止时间时提供有效的推断，与需要预先确定样本量的经典置信区间不同，因此没有受到“窥视”数据的惩罚。文献中现有的CSS是非肿瘤的，因此不享受上述渐近置信区间的广泛适用性。我们的工作通过给出“渐近CSS”的定义来弥合差距，并得出仅需要类似CLT的假设的通用渐近CS。虽然CLT在固定样本量下近似于高斯的样本平均值的分布，但我们使用强大的不变性原理（来自Komlos，Major和Tusnady的1970年代的开创性工作），按照整个样品平均过程均匀地近似于整个样品平均过程。隐性的高斯过程。我们通过在观察性研究中基于双重稳健的估计量来得出非参数渐近级别的CSS来证明它们的实用性，即使在固定的时间方案中，也可能不存在非催化方法（由于混淆偏见）。这些使双重强大的因果推断可以连续监测并自适应地停止。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

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.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.