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.

许多现实世界中普遍存在的应用程序，例如停车建议和空气污染监测，都能从准确的长期时空预测（LSTF）中受益匪浅。 LSTF利用了空间和时间域，上下文信息和数据中固有模式之间的长期依赖性。最近的研究揭示了多画望神经网络（MGNN）提高预测性能的潜力。但是，由于几个问题，现有的MGNN方法不能直接应用于LSTF：一般性低，不充分使用上下文信息以及不平衡的图形融合方法。为了解决这些问题，我们构建了新的图形模型，以表示每个节点的上下文信息和长期时空数据依赖性结构。为了融合跨多个图形的信息，我们提出了一个新的动态多绘图融合模块，以通过空间注意力和图形注意机制来表征图中节点和跨图的节点的相关性。此外，我们引入了可训练的重量张量，以指示不同图中每个节点的重要性。在两个大规模数据集上进行的广泛实验表明，我们提出的方法显着改善了LSTF预测任务中现有图形神经网络模型的性能。

在狭窄的空间中，基于传统层次自治系统的运动计划可能会导致映射，定位和控制噪声引起碰撞。此外，当无映射时，它将被禁用。为了解决这些问题，我们利用深厚的加强学习，可以证明可以有效地进行自我决策，从而在狭窄的空间中自探索而无需地图，同时避免碰撞。具体而言，基于我们的Ackermann-Steering矩形Zebrat机器人及其凉亭模拟器，我们建议矩形安全区域来表示状态并检测矩形形状的机器人的碰撞，以及无需精心制作的奖励功能，不需要增强功能。目的地信息。然后，我们在模拟的狭窄轨道中基准了五种增强学习算法，包括DDPG，DQN，SAC，PPO和PPO-DISCRETE。经过训练，良好的DDPG和DQN型号可以转移到三个全新的模拟轨道上，然后转移到三个现实世界中。

多亏了机器人技术的快速发展，机器人割草正在兴起，使人类摆脱了繁琐且耗时的景观工作。传统上，机器人割草被认为是“覆盖道路计划”问题，简化了将非凸障碍转换为凸障碍的障碍。此外，机器人的包围通常会扩张转换后的障碍物以避免碰撞。但是，当适用于机器人割草时，草坪上的障碍通常是非凸的，请想象一下草坪上的一个花园，这样上面提到的障碍物处理方法将填补某些凹面区域，以使机器人再也无法访问了它们，因此沿着草坪边缘产生不可避免的未切割区域，从而使景观的优雅降低并激发了返工。为了缩小草坪边缘周围的未切割区域，我们在此将问题重新构架为一个全新的问题，称其为“边缘覆盖路径计划”问题，该问题专门用于路径计划，以覆盖边缘。相应地，我们提出了两种计划方法，即“大小磁盘”和“滑动筷子”计划方法，以通过利用图像形态处理和计算几何技巧来解决问题。通过验证，我们提出的方法可以胜过传统的“逐一扩张”方法。

虽然以完全可差异的模型的端到端学习在自然语言过程（NLP）和机器学习中取得了巨大的成功，但最近的近期兴趣与潜在的离散结构一起学习以改善最新的最终任务性能和更好的归纳偏差更好的解释性。然而，该范例并不直接地适应主流梯度的优化方法。这项工作调查了三个主要的方法来学习此类模型：通过采样，替代梯度，连续放松和边缘似然最大化。我们结束了对这些方法的应用以及检查他们诱导的学习潜在结构的检查。

耐药性是对全球健康的重大威胁，以及整个疾病和药物发育的临床治疗中的重要疑虑。与药物结合有关的蛋白质中的突变是适应性耐药性的常见原因。因此，对突变如何影响药物和靶蛋白之间的相互作用的定量估计对于药物开发和临床实践来说是至关重要的。已经证明，依赖于分子动力学模拟，Rosetta方案以及机器学习方法的计算方法能够预测对蛋白质突变的配体亲和力变化。然而，严重限制的样本量和重质噪声诱导的过烧和泛化问题已经很广泛地采用了用于研究耐药性的机器学习。在本文中，我们提出了一种稳健的机器学习方法，称为Spldextratees，其可以准确地预测蛋白质突变并鉴定引起抗性突变的配体结合亲和力。特别是，所提出的方法按照易于学习的样本开始的特定方案级别，逐渐融入训练中的特定方案，然后在训练中迭代，然后在样本权重再验计算和模型更新之间迭代。此外，我们计算了基于物理的基于物理的结构特征，为机器学习模型提供了对这种数据有限预测任务的蛋白质的有价值的域知识。该实验证实了提出的方法在三种情况下预测激酶抑制剂抗性的方法，并实现了与分子动力学和Rosetta方法相当的预测准确性，具有较少的计算成本。

对于自然语言处理系统，两种证据支持在大型未解除的基层上的神经语言模型中使用文本表示：在应用程序启发基准上的表现（Peters等，2018年，除其他外）以及出现的出现这些陈述中的句法抽象（Tenney等，2019年，尤其）。另一方面，缺乏接地的监督呼吁质疑这些表现如何捕获意义（Bender和Koller，2020）。我们对最近的语言模型应用小说探针 - 特别关注由语义依赖性运作的谓词参数结构（Ivanova等，2012） - 并发现，与语法不同，语义不是通过今天的预磨款模型带到表面上。然后，我们使用卷积图编码器将语义解析明确地将语义解析结合到特定于任务的FineTuning中，为胶水基准测试中的自然语言理解（NLU）任务产生益处。这种方法展示了通用（而不是任务特定的）语言监督的潜力，以上和超越传统的预威胁和芬特。有几个诊断有助于本地化我们方法的好处。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.