到达状态的密度可以帮助理解安全至关重要的系统的风险，尤其是在最坏情况下的情况过于保守的情况下。最近的工作提供了一种数据驱动的方法来计算自主系统在线前进状态的密度分布。在本文中，我们研究了这种方法与模型预测控制在不确定性下的可验证安全路径计划的结合。我们首先使用学习的密度分布来计算在线碰撞的风险。如果这种风险超过可接受的阈值，我们的方法将计划在先前轨迹周围采取新的途径，并在阈值以下碰撞风险。我们的方法非常适合处理具有不确定性和复杂动力学的系统，因为我们的数据驱动方法不需要系统动力学的分析形式，并且可以通过不确定性的任意初始分布来估算正向状态密度。我们设计了两个具有挑战性的场景（自动驾驶和气垫船控制），以在系统不确定性下的障碍物中进行安全运动计划。我们首先表明我们的密度估计方法可以达到与基于蒙特卡洛的方法相似的准确性，同时仅使用0.01倍训练样本。通过利用估计的风险，我们的算法在执行超过0.99的安全速率时达到目标达到最高成功率。

血管分割从图像中提取血管，并作为诊断各种疾病的基础，例如眼科疾病。眼科医生通常需要高分辨率分割结果进行分析，这会导致大多数现有方法的超计算负载。如果基于低分辨率的输入，它们很容易忽略微小的容器或引起分段容器的不连续性。为了解决这些问题，本文提出了一种名为Subersessel的算法，该算法使用低分辨率图像作为输入提供了高分辨率和准确的容器分割。我们首先将超分辨率作为我们的辅助分支，以提供潜在的高分辨率细节特征，可以在测试阶段删除。其次，我们提出了两个模块，以增强感兴趣的分割区域的特征，包括具有特征分解（UFD）模块的上采样和功能相互作用模块（FIM），并限制了损失，以关注感兴趣的功能。与其他最先进的算法相比，在三个公开数据集上进行了广泛的实验表明，我们提出的Supersess可以将更高的细分精度分割为6％以上的细分精度。此外，Supercessel的稳定性也比其他算法更强。发表论文后，我们将发布代码。

特征选择是机器学习的重要过程。它通过选择对预测目标贡献最大的功能来构建一个可解释且健壮的模型。但是，大多数成熟的特征选择算法，包括受监督和半监督，无法完全利用特征之间的复杂潜在结构。我们认为，这些结构对于特征选择过程非常重要，尤其是在缺乏标签并且数据嘈杂的情况下。为此，我们创新地向特征选择问题（即基于批量注意的自我划分特征选择（A-SFS））进行了创新的深入的自我监督机制。首先，多任务自我监督的自动编码器旨在在两个借口任务的支持下揭示功能之间的隐藏结构。在来自多自制的学习模型的集成信息的指导下，批处理注意机制旨在根据基于批处理的特征选择模式产生特征权重，以减轻少数嘈杂数据引入的影响。将此方法与14个主要强大基准进行了比较，包括LightGBM和XGBoost。实验结果表明，A-SFS在大多数数据集中达到了最高的精度。此外，这种设计大大降低了对标签的依赖，仅需1/10个标记的数据即可达到与那些先进的基线相同的性能。结果表明，A-SFS对于嘈杂和缺少数据也是最强大的。

旨在用自然语言和谐地与人类交流的智能对话体系对于促进人工智能时代的人机互动的发展非常出色。有了逐渐复杂的人类计算机交互要求（例如，多模式输入，时间敏感性），传统的基于文本的对话系统很难满足对更加生动和方便的交互的需求。因此，视觉背景增强对话系统（VAD）有可能通过感知和理解多模式信息（即图像或视频中的视觉上下文，文本对话历史记录）与人类进行交流，已成为主要的研究范式。 VAD受益于视觉和文本上下文之间的一致性和互补性，具有产生引人入胜和背景感知响应的潜力。为了描述VAD的开发，我们首先表征VAD的概念和独特功能，然后介绍其通用系统体系结构以说明系统工作流程。随后，对一些研究挑战和代表性作品进行了详细研究，然后进行了权威基准摘要。我们通过提出一些开放问题和有前途的VAD研究趋势来结束本文，例如，在跨模式对话环境下，人机对话的认知机制以及知识增强的跨模式语义互动。

深度学习模型已广泛用于监控视频中的异常检测。典型模型配备了重建普通视频的能力，并评估异常视频的重建错误以指示异常的程度。然而，现有方法遭受了两个缺点。首先，它们只能独立地编码每个身份的运动，而不考虑身份之间的相互作用，这也可以指示异常。其次，他们利用了结构在不同场景下固定的粘合模型，这种配置禁止了对场景的理解。在本文中，我们提出了一个分层时空图卷积神经网络（HSTGCNN）来解决这些问题，HSTGCNN由对应于不同级别的图形表示的多个分支组成。高级图形表示编码人们的轨迹以及多个身份之间的交互，而低级图表表示编码每个人的本地身体姿势。此外，我们建议加权组合在不同场景中更好的多个分支。以这种方式实现了对单级图形表示的改进。实现了对场景的理解并提供异常检测。在低分辨率视频中为在低分辨率视频中编码低分辨率视频中的人员的移动速度和方向编码高级别的图表表示，而在高分辨率视频中将更高的权重分配更高的权重。实验结果表明，建议的HSTGCNN在四个基准数据集（UCSD Spistrian，Shanghaitech，Cuhk Aveance和IITB-Whent）上的当前最先进的模型显着优于最新的最先进模型。

对比视觉语言预培训（剪辑）最近淹没了其可转让的视觉表现学习的关注。由大规模的图像文本对进行监督，剪辑能够对准配对的图像和文本，从而在开放词汇场景中进行零拍摄识别。然而，特定应用与通常预先训练的知识之间存在语义差距，这使得匹配子最优在下游任务上。在本文中，我们提出了VT-CLIP通过可视导向文本来增强视觉语言建模。具体而言，我们指导文本功能以自适应地探索图像上的信息区域，并通过跨关注的Machanism聚合视觉特征。以这种方式，视觉引导文本与图像变得更加语义相关，这极大地利益匹配过程。在几次拍摄的设置中，我们在11名知名分类数据集中评估我们的VT-CLIP，并进行实验广泛的消融研究，以证明VT-CLIP的有效性。代码将很快发布。

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.

Increasing research interests focus on sequential recommender systems, aiming to model dynamic sequence representation precisely. However, the most commonly used loss function in state-of-the-art sequential recommendation models has essential limitations. To name a few, Bayesian Personalized Ranking (BPR) loss suffers the vanishing gradient problem from numerous negative sampling and predictionbiases; Binary Cross-Entropy (BCE) loss subjects to negative sampling numbers, thereby it is likely to ignore valuable negative examples and reduce the training efficiency; Cross-Entropy (CE) loss only focuses on the last timestamp of the training sequence, which causes low utilization of sequence information and results in inferior user sequence representation. To avoid these limitations, in this paper, we propose to calculate Cumulative Cross-Entropy (CCE) loss over the sequence. CCE is simple and direct, which enjoys the virtues of painless deployment, no negative sampling, and effective and efficient training. We conduct extensive experiments on five benchmark datasets to demonstrate the effectiveness and efficiency of CCE. The results show that employing CCE loss on three state-of-the-art models GRU4Rec, SASRec, and S3-Rec can reach 125.63%, 69.90%, and 33.24% average improvement of full ranking NDCG@5, respectively. Using CCE, the performance curve of the models on the test data increases rapidly with the wall clock time, and is superior to that of other loss functions in almost the whole process of model training.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

Deep neural networks are vulnerable to adversarial attacks. In this paper, we take the role of investigators who want to trace the attack and identify the source, that is, the particular model which the adversarial examples are generated from. Techniques derived would aid forensic investigation of attack incidents and serve as deterrence to potential attacks. We consider the buyers-seller setting where a machine learning model is to be distributed to various buyers and each buyer receives a slightly different copy with same functionality. A malicious buyer generates adversarial examples from a particular copy $\mathcal{M}_i$ and uses them to attack other copies. From these adversarial examples, the investigator wants to identify the source $\mathcal{M}_i$. To address this problem, we propose a two-stage separate-and-trace framework. The model separation stage generates multiple copies of a model for a same classification task. This process injects unique characteristics into each copy so that adversarial examples generated have distinct and traceable features. We give a parallel structure which embeds a ``tracer'' in each copy, and a noise-sensitive training loss to achieve this goal. The tracing stage takes in adversarial examples and a few candidate models, and identifies the likely source. Based on the unique features induced by the noise-sensitive loss function, we could effectively trace the potential adversarial copy by considering the output logits from each tracer. Empirical results show that it is possible to trace the origin of the adversarial example and the mechanism can be applied to a wide range of architectures and datasets.