游戏理论一直是控制疾病传播并提出个人和地区级别最佳政策的有效工具。在此AMS通知文章中，我们关注Covid-19的干预的决策制定，旨在提供数学模型和有效的机器学习方法，以及对过去实施的相关政策的理由，并如何解释当局如何解释当局从游戏理论的角度来看，决策会影响其邻近地区。

膝关节骨关节炎（OA）是最常见的骨关节炎和伤残原因。软骨缺陷被认为是膝关节OA的主要表现，其通过磁共振成像（MRI）可见。因此，对膝关节软骨缺陷的早期检测和评估对于保护膝关节OA患者来说是重要的。通过这种方式，通过将卷积神经网络（CNNS）应用于膝关节MRI，已经在膝关节软骨缺陷评估中进行了许多尝试。然而，软骨的生理特性可能阻碍这种努力：软骨是薄的弯曲层，这意味着只有膝关节MRI中的一小部分体素可以有助于软骨缺陷评估;异构扫描方案进一步挑战CNN在临床实践中的可行性;基于CNN的膝关节软骨评估结果缺乏解释性。为了解决这些挑战，我们将软骨结构和外观模拟到膝关节MRI进入图表表示，该图表能够处理高度多样化的临床数据。然后，由软骨图表示指导，我们设计了一种具有自我关注机制的非欧几里德深度学习网络，提取本地和全局中的软骨功能，并通过可视化结果导出最终评估。我们的综合实验表明，该方法在膝关节软骨缺陷评估中产生了卓越的性能，以及其方便的可解释性3D可视化。

有很好的参数来支持声明，特征表示最终从一般到深度神经网络（DNN）的特定转换，但这种转变仍然相对缺乏缺陷。在这项工作中，我们向理解特征表示的转换来移动一个微小的步骤。我们首先通过分析中间层中的类分离，然后将类别分离过程作为动态图中的社区演变进行了描述。然后，我们介绍模块化，是图形理论中的常见度量，量化社区的演变。我们发现，随着层更深，而是下降或达到特定层的高原，模块化趋于上升。通过渐近分析，我们表明模块化可以提供对特征表示转换的定量分析。通过了解特征表示，我们表明模块化也可用于识别和定位DNN中的冗余层，这为图层修剪提供了理论指导。基于这种鼓舞人心的发现，我们提出了一种基于模块化的层面修剪方法。进一步的实验表明，我们的方法可以修剪冗余层，对性能的影响最小。该代码可在https://github.com/yaolu-zjut/dynamic-graphs-construction中获得。

了解深度神经网络（DNN）中的黑匣子表示是深度学习的重要问题。在这项工作中，我们提出了基于图形的相似性（GBS）来测量层特征的相似性。与之前的作品相反，在特征映射上直接计算相似度，GBS根据具有隐藏图层输出构造的图形来测量相关性。通过将每个输入样本视为节点和对应的层输出相似度作为边缘，我们构造了每层的DNN表示图。图层之间的相似性识别在不同数据集和初始化中培训的模型的表示之间的对应关系。我们展示并证明了GB的不变性属性，包括与各向同性缩放的正交转换和不变性的不变性，并与CKA进行比较GBS。 GBS显示了最先进的性能，反映了相似性，并提供了关于解释隐藏层空间上的对抗性样本行为的见解。

轻量级模型设计已成为应用深度学习技术的重要方向，修剪是实现模型参数和拖鞋的大量减少的有效均值。现有的神经网络修剪方法主要从参数的重要性开始，以及设计参数评估度量来迭代地执行参数修剪。这些方法不是从模型拓扑的角度研究的，可能是有效但不高效的，并且需要完全不同的不同数据集修剪。在本文中，我们研究了神经网络的图形结构，并提出了常规的基于图的修剪（RGP）来执行单次神经网络修剪。我们生成常规图，将图的节点度值设置为满足修剪比率，并通过将边缘交换以获得最佳边缘分布来降低曲线图的平均最短路径长度。最后，将获得的图形映射到神经网络结构中以实现修剪。实验表明，曲线图的平均最短路径长度与相应神经网络的分类精度负相关，所提出的RGP显示出强的精度保持能力，具有极高的参数减少（超过90％）和拖鞋（更多超过90％）。

难以通过二进制面具手动准确标记含糊不清的和复杂形状的目标。在医学图像分割中突出显示二元掩模下面的弱点，其中模糊是普遍的。在多个注释的情况下，通过二元面具对临床医生达成共识更具挑战性。此外，这些不确定的区域与病变结构有关，可能含有有利于诊断的解剖信息。然而，目前关于不确定性的研究主要关注模型培训和数据标签的不确定性。他们都没有调查病变本身的模糊性质的影响。通过图像消光，透过图像消光，将Alpha Matte作为软片介绍，代表医学场景中不确定的区域，并因此提出了一种新的不确定性量化方法来填补填补差距病变结构的不确定性研究。在这项工作中，我们在多任务框架中引入了一种新的架构，以在多任务框架中生成二进制掩模和alpha掩饰，这优于所有最先进的消光算法。建议的不确定性地图能够突出模糊地区和我们提出的新型多任务损失加权策略可以进一步提高性能并证明其具体的益处。为了充分评估我们提出的方法的有效性，我们首先用alpha哑布标记了三个医疗数据集，以解决医学场景中可用消光数据集的短缺，并证明alpha遮罩是一种比定性的二进制掩模更有效的标签方法和量化方面。

Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this paper, we propose to tackle such forecasting problem with Transformer [1]. Although impressed by its performance in our preliminary study, we found its two major weaknesses: (1) locality-agnostics: the point-wise dotproduct self-attention in canonical Transformer architecture is insensitive to local context, which can make the model prone to anomalies in time series; (2) memory bottleneck: space complexity of canonical Transformer grows quadratically with sequence length L, making directly modeling long time series infeasible. In order to solve these two issues, we first propose convolutional self-attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism. Then, we propose LogSparse Transformer with only O(L(log L) 2 ) memory cost, improving forecasting accuracy for time series with fine granularity and strong long-term dependencies under constrained memory budget. Our experiments on both synthetic data and realworld datasets show that it compares favorably to the state-of-the-art.

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.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.