The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Perceiving and manipulating objects in a generalizable way has been actively studied by the computer vision and robotics communities, where cross-category generalizable manipulation skills are highly desired yet underexplored. In this work, we propose to learn such generalizable perception and manipulation via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (e.g. buttons, handles, etc), we show that our part-centric approach allows our method to learn object perception and manipulation skills from seen object categories and directly generalize to unseen categories. Following the GAPart definition, we construct a large-scale part-centric interactive dataset, GAPartNet, where rich, part-level annotations (semantics, poses) are provided for 1166 objects and 8489 part instances. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the large domain gaps between seen and unseen object categories, we propose a strong 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both simulation and real world. The dataset and code will be released.

RGB热点对象检测（SOD）结合了两个光谱，以分段图像中的视觉明显区域。大多数现有方法都使用边界图来学习锋利的边界。这些方法忽略了孤立的边界像素与其他自信像素之间的相互作用，从而导致了次优性能。为了解决这个问题，我们为基于SWIN Transformer的RGB-T SOD提出了一个职位感知关系学习网络（PRLNET）。 PRLNET探索像素之间的距离和方向关系，以增强阶层内的紧凑性和类间的分离，从而产生具有清晰边界和均匀区域的显着对象掩模。具体而言，我们开发了一个新颖的签名距离辅助模块（SDMAM）来改善编码器特征表示，该模块考虑了边界邻域中不同像素的距离关系。然后，我们使用定向字段（FRDF）设计一种功能改进方法，该方法通过利用明显对象内部的功能来纠正边界邻域的特征。 FRDF利用对象像素之间的方向信息有效地增强了显着区域的阶层紧凑性。此外，我们构成了一个纯变压器编码器 - 模块网络，以增强RGB-T SOD的多光谱特征表示。最后，我们对三个公共基准数据集进行了定量和定性实验。结果表明，我们所提出的方法的表现优于最新方法。

作为自动驾驶系统的核心部分，运动计划已受到学术界和行业的广泛关注。但是，由于非体力学动力学，尤其是在存在非结构化的环境和动态障碍的情况下，没有能够有效的轨迹计划解决方案能够为空间周期关节优化。为了弥合差距，我们提出了一种多功能和实时轨迹优化方法，该方法可以在任意约束下使用完整的车辆模型生成高质量的可行轨迹。通过利用类似汽车的机器人的差异平坦性能，我们使用平坦的输出来分析所有可行性约束，以简化轨迹计划问题。此外，通过全尺寸多边形实现避免障碍物，以产生较少的保守轨迹，并具有安全保证，尤其是在紧密约束的空间中。我们通过最先进的方法介绍了全面的基准测试，这证明了所提出的方法在效率和轨迹质量方面的重要性。现实世界实验验证了我们算法的实用性。我们将发布我们的代码作为开源软件包，目的是参考研究社区。

Realizing human-like perception is a challenge in open driving scenarios due to corner cases and visual occlusions. To gather knowledge of rare and occluded instances, federated learning assisted connected autonomous vehicle (FLCAV) has been proposed, which leverages vehicular networks to establish federated deep neural networks (DNNs) from distributed data captured by vehicles and road sensors. Without the need of data aggregation, FLCAV preserves privacy while reducing communication costs compared with conventional centralized learning. However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios. This article presents networking and training frameworks for FLCAV perception. Multi-layer graph resource allocation and vehicle-road contrastive sensor placement are proposed to address the network management and sensor deployment problems, respectively. We also develop CarlaFLCAV, a software platform that implements the above system and methods. Experimental results confirm the superiority of the proposed techniques compared with various benchmarks.

Solving real-world optimal control problems are challenging tasks, as the system dynamics can be highly non-linear or including nonconvex objectives and constraints, while in some cases the dynamics are unknown, making it hard to numerically solve the optimal control actions. To deal with such modeling and computation challenges, in this paper, we integrate Neural Networks with the Pontryagin's Minimum Principle (PMP), and propose a computationally efficient framework NN-PMP. The resulting controller can be implemented for systems with unknown and complex dynamics. It can not only utilize the accurate surrogate models parameterized by neural networks, but also efficiently recover the optimality conditions along with the optimal action sequences via PMP conditions. A toy example on a nonlinear Martian Base operation along with a real-world lossy energy storage arbitrage example demonstrates our proposed NN-PMP is a general and versatile computation tool for finding optimal solutions. Compared with solutions provided by the numerical optimization solver with approximated linear dynamics, NN-PMP achieves more efficient system modeling and higher performance in terms of control objectives.

Humans are skilled in reading the interlocutor's emotion from multimodal signals, including spoken words, simultaneous speech, and facial expressions. It is still a challenge to effectively decode emotions from the complex interactions of multimodal signals. In this paper, we design three kinds of multimodal latent representations to refine the emotion analysis process and capture complex multimodal interactions from different views, including a intact three-modal integrating representation, a modality-shared representation, and three modality-individual representations. Then, a modality-semantic hierarchical fusion is proposed to reasonably incorporate these representations into a comprehensive interaction representation. The experimental results demonstrate that our EffMulti outperforms the state-of-the-art methods. The compelling performance benefits from its well-designed framework with ease of implementation, lower computing complexity, and less trainable parameters.

We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered. Specifically, we first introduce the "extra fields" from the mixed finite element method to reformulate the PDEs so as to equivalently transform the three types of BCs into linear forms. Based on the reformulation, we derive the general solutions of the BCs analytically, which are employed to construct an ansatz that automatically satisfies the BCs. With such a framework, we can train the neural networks without adding extra loss terms and thus efficiently handle geometrically complex PDEs, alleviating the unbalanced competition between the loss terms corresponding to the BCs and PDEs. We theoretically demonstrate that the "extra fields" can stabilize the training process. Experimental results on real-world geometrically complex PDEs showcase the effectiveness of our method compared with state-of-the-art baselines.

在离线增强学习中，加权回归是一种常见方法，可以确保学习的政策与行为策略保持接近并防止选择样本外动作。在这项工作中，我们表明，由于政策模型的分配表达有限，以前的方法可能仍会在培训期间选择看不见的动作，这会偏离其最初动机。为了解决这个问题，我们通过将学习的政策分解为两个部分：表达生成行为模型和动作评估模型，采用生成方法。关键见解是，这种去耦避免学习具有封闭形式表达式的明确参数化的策略模型。直接学习行为策略使我们能够利用生成建模的现有进步，例如基于扩散的方法，以建模各种行为。至于行动评估，我们将方法与样本中的计划技术相结合，以进一步避免选择样本外动作并提高计算效率。 D4RL数据集的实验结果表明，与最先进的离线RL方法相比，我们提出的方法具有竞争性或卓越的性能，尤其是在诸如Antmaze之类的复杂任务中。我们还经验证明，我们的方法可以从包含多个独特但类似成功策略的异质数据集中成功学习，而以前的单峰政策失败了。

基于深度学习的方法，例如物理知识的神经网络（PINN）和DeepOnets已显示出解决PDE受约束优化（PDECO）问题的希望。但是，现有方法不足以处理对优化目标具有复杂或非线性依赖性的PDE约束。在本文中，我们提出了一个新颖的双层优化框架，以通过将目标和约束的优化解耦来解决挑战。对于内部循环优化，我们采用PINN仅解决PDE约束。对于外循环，我们通过基于隐式函数定理（IFT）使用Broyden的方法来设计一种新颖的方法，该方法对于近似高度级别而言是有效且准确的。我们进一步介绍了高度级计算的理论解释和误差分析。在多个大规模和非线性PDE约束优化问题上进行了广泛的实验表明，与强基础相比，我们的方法可实现最新的结果。