As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

数据驱动的预测方法可以有效，准确地将蛋白质序列转化为生物活性结构，对于科学研究和治疗发展非常有价值。使用共同进化信息确定准确的折叠格局是现代蛋白质结构预测方法的成功基础。作为最新的状态，AlphaFold2显着提高了准确性，而无需进行明确的共同进化分析。然而，其性能仍然显示出对可用序列同源物的强烈依赖。我们研究了这种依赖性的原因，并提出了一种元生成模型Evogen，以弥补较差的MSA靶标的Alphafold2的表现不佳。 Evogen使我们能够通过降低搜索的MSA或生成虚拟MSA来操纵折叠景观，并帮助Alphafold2在低数据表方面准确地折叠，甚至通过单序预测来实现令人鼓舞的性能。能够用很少的MSA做出准确的预测，不仅可以更好地概括为孤儿序列的Alphafold2，而且使其在高通量应用程序中的使用民主化。此外，Evogen与AlphaFold2结合产生了一种概率结构生成方法，该方法可以探索蛋白质序列的替代构象，并且序列生成的任务意识可区分算法将使包括蛋白质设计在内的其他相关任务受益。

蛋白质是人类生命的重要组成部分，其结构对于功能和机制分析很重要。最近的工作表明了AI驱动方法对蛋白质结构预测的潜力。但是，新模型的开发受到数据集和基准测试培训程序的限制。据我们所知，现有的开源数据集远不足以满足现代蛋白质序列相关研究的需求。为了解决这个问题，我们介绍了具有高覆盖率和多样性的第一个百万级蛋白质结构预测数据集，称为PSP。该数据集由570K真实结构序列（10TB）和745K互补蒸馏序列（15TB）组成。此外，我们还提供了该数据集上SOTA蛋白结构预测模型的基准测试训练程序。我们通过参与客串比赛验证该数据集的实用程序进行培训，我们的模特赢得了第一名。我们希望我们的PSP数据集以及培训基准能够为AI驱动的蛋白质相关研究提供更广泛的AI/生物学研究人员社区。

本文报告了Chalearn的Autodl挑战系列的结果和后攻击分析，这有助于对自动学习（DL）进行分类，以便在各种环境中引入的深度学习（DL），但缺乏公平的比较。格式化所有输入数据模型（时间序列，图像，视频，文本，表格）作为张量，所有任务都是多标签分类问题。代码提交已在隐藏的任务上执行，具有限制时间和计算资源，推动快速获取结果的解决方案。在此设置中，DL方法占主导地位，但流行的神经结构搜索（NAS）是不切实际的。解决方案依赖于微调预培训的网络，架构匹配数据模块。挑战后测试没有透露超出强加时间限制的改进。虽然没有组件尤其原始或新颖，但是一个高级模块化组织出现了“Meta-Learner”，“数据摄入”，“模型选择器”，“模型/学习者”和“评估员”。这种模块化使得消融研究，揭示了（离坡）元学习，合奏和高效数据管理的重要性。异构模块组合的实验进一步证实了获胜解决方案的（本地）最优性。我们的挑战队遗产包括一个持久的基准（http://utodl.chalearn.org），获胜者的开放源代码，以及免费的“autodl自助服务”。

提供公共产品的公共政策，特别是那些涉及通过限制个人自由来合作，始终会产生对治理合法性的争论。多智能体增强学习（MARL）方法适用于支持以个人利益的成本提供公共政策的公共政策的合法性。在这些政策中，区域间协作大流行控制是一个突出的例子，这对于面临的全球大流行像Covid-19的越来越多的联系方式变得更加重要。在不同的地区系统中，已经观察到不同的协作策略模式，但它缺乏分析过程，以获得这些策略的合法性。在本文中，我们利用大流行控制的区域间合作为例，以展示Marl的必要性，从而合法化执行此类区域间合作的政策。在示例性环境中的实验结果表明，我们的Marl方法能够展示对合作供应公共物品的个人自由的有效性和必要性。我们的Marl代理商在不同的协作水平下学习了不同的最佳政策，这是一种可解释的合作模式，有助于平衡不同类型的地区遭受的损失，从而促进整体福利。与此同时，通过更高的合作水平学习政策会产生更高的全球奖励，这阐述了利益，从而为促进区域间合作的合法性提供了一种新的理由。因此，我们的方法显示了Marl在计算建模和支持同意管理理论中，由诺贝尔奖获奖者J. M. Buchanan开发。

动态状态表示学习是机器人学习中的重要任务。可以捕获动力学信息的潜在空间在加速模型的自由强化学习，缩小模拟到现实差距以及降低运动计划的复杂性等领域中具有广泛的应用。但是，当前的动态状态表示方法在复杂的动态系统（例如可变形对象）上的扩展很差，并且不能将良好定义的仿真函数直接嵌入到训练管道中。我们提出了DIFFSRL，这是一种动态状态表示学习管道，利用可区分的模拟可以将复杂的动力学模型嵌入到端到端训练的一部分。我们还将可区分的动态约束作为管道的一部分集成，这为潜在状态提供了意识到动态约束的激励措施。我们进一步建立了在软体体模拟系统PlastonElab上学习基准的国家表示基准，我们的模型在捕获长期动态和奖励预测方面表现出了卓越的性能。

In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties. Deep generative models are recently proposed to solve inverse problems, but these currently use expensive forward operators and struggle in precisely localizing the exact solutions and fully exploring the parameter spaces without missing solutions. In this work, we propose a novel approach (called iPage) to accelerate the inverse learning process by leveraging probabilistic inference from deep invertible models and deterministic optimization via fast gradient descent. Given a target property, the learned invertible model provides a posterior over the parameter space; we identify these posterior samples as an intelligent prior initialization which enables us to narrow down the search space. We then perform gradient descent to calibrate the inverse solutions within a local region. Meanwhile, a space-filling sampling is imposed on the latent space to better explore and capture all possible solutions. We evaluate our approach on three benchmark tasks and two created datasets with real-world applications from quantum chemistry and additive manufacturing, and find our method achieves superior performance compared to several state-of-the-art baseline methods. The iPage code is available at https://github.com/jxzhangjhu/MatDesINNe.

Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching accuracy comparable to the original models. However, these tickets are proved to be notrobust to adversarial examples, and even worse than their PLM counterparts. To address this problem, we propose a novel method based on learning binary weight masks to identify robust tickets hidden in the original PLMs. Since the loss is not differentiable for the binary mask, we assign the hard concrete distribution to the masks and encourage their sparsity using a smoothing approximation of L0 regularization.Furthermore, we design an adversarial loss objective to guide the search for robust tickets and ensure that the tickets perform well bothin accuracy and robustness. Experimental results show the significant improvement of the proposed method over previous work on adversarial robustness evaluation.

We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable Markov decision process (POMDP), and predictive state representation (PSR) as special cases. Toward finding the minimum assumption that empowers sample efficient learning, we propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation in online interactive decision making. In specific, GEC captures the hardness of exploration by comparing the error of predicting the performance of the updated policy with the in-sample training error evaluated on the historical data. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR, where generalized regular PSR, a new tractable PSR class identified by us, includes nearly all known tractable POMDPs. Furthermore, in terms of algorithm design, we propose a generic posterior sampling algorithm, which can be implemented in both model-free and model-based fashion, under both fully observable and partially observable settings. The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning. We prove that the proposed algorithm is sample efficient by establishing a sublinear regret upper bound in terms of GEC. In summary, we provide a new and unified understanding of both fully observable and partially observable RL.

机器人武器广泛用于自动行业。但是，随着在机器人臂中深入学习的广泛应用，存在新的挑战，例如分配掌握计算能力和对安全性的需求不断增长。在这项工作中，我们提出了一种基于深度学习和边缘云协作的机器人手臂抓握方法。这种方法意识到了机器人组的任意掌握计划，并考虑了掌握效率和信息安全性。此外，由GAN训练的编码器和解码器使图像在压缩时可以加密，从而确保隐私的安全性。该模型在OCID数据集上达到92％的精度，图像压缩比达到0.03％，结构差值高于0.91。