We present a unified probabilistic model that learns a representative set of discrete vehicle actions and predicts the probability of each action given a particular scenario. Our model also enables us to estimate the distribution over continuous trajectories conditioned on a scenario, representing what each discrete action would look like if executed in that scenario. While our primary objective is to learn representative action sets, these capabilities combine to produce accurate multimodal trajectory predictions as a byproduct. Although our learned action representations closely resemble semantically meaningful categories (e.g., "go straight", "turn left", etc.), our method is entirely self-supervised and does not utilize any manually generated labels or categories. Our method builds upon recent advances in variational inference and deep unsupervised clustering, resulting in full distribution estimates based on deterministic model evaluations.

Implicit Neural Representations (INR) have recently shown to be powerful tool for high-quality video compression. However, existing works are limiting as they do not explicitly exploit the temporal redundancy in videos, leading to a long encoding time. Additionally, these methods have fixed architectures which do not scale to longer videos or higher resolutions. To address these issues, we propose NIRVANA, which treats videos as groups of frames and fits separate networks to each group performing patch-wise prediction. This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video. The video representation is modeled autoregressively, with networks fit on a current group initialized using weights from the previous group's model. To further enhance efficiency, we perform quantization of the network parameters during training, requiring no post-hoc pruning or quantization. When compared with previous works on the benchmark UVG dataset, NIRVANA improves encoding quality from 37.36 to 37.70 (in terms of PSNR) and the encoding speed by 12X, while maintaining the same compression rate. In contrast to prior video INR works which struggle with larger resolution and longer videos, we show that our algorithm is highly flexible and scales naturally due to its patch-wise and autoregressive designs. Moreover, our method achieves variable bitrate compression by adapting to videos with varying inter-frame motion. NIRVANA achieves 6X decoding speed and scales well with more GPUs, making it practical for various deployment scenarios.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

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.

The energy sector is facing rapid changes in the transition towards clean renewable sources. However, the growing share of volatile, fluctuating renewable generation such as wind or solar energy has already led to an increase in power grid congestion and network security concerns. Grid operators mitigate these by modifying either generation or demand (redispatching, curtailment, flexible loads). Unfortunately, redispatching of fossil generators leads to excessive grid operation costs and higher emissions, which is in direct opposition to the decarbonization of the energy sector. In this paper, we propose an AlphaZero-based grid topology optimization agent as a non-costly, carbon-free congestion management alternative. Our experimental evaluation confirms the potential of topology optimization for power grid operation, achieves a reduction of the average amount of required redispatching by 60%, and shows the interoperability with traditional congestion management methods. Our approach also ranked 1st in the WCCI 2022 Learning to Run a Power Network (L2RPN) competition. Based on our findings, we identify and discuss open research problems as well as technical challenges for a productive system on a real power grid.

自动情绪识别（ER）最近由于其在许多实际应用中的潜力而引起了很多兴趣。在这种情况下，已经证明多模式方法可以通过结合多样化和互补的信息来源，从而提高性能（超过单峰方法），从而为嘈杂和缺失的方式提供了一些鲁棒性。在本文中，我们根据从视频中提取的面部和声音方式融合的尺寸ER专注于尺寸，其中探索了互补的视听（A-V）关系，以预测个人在价值空间中的情绪状态。大多数最先进的融合技术都依赖于反复的网络或常规的注意机制，这些机制无法有效利用A-V模式的互补性。为了解决这个问题，我们引入了A-V融合的联合跨注意模型，该模型在A-V模态上提取显着特征，从而可以有效利用模式间关系，同时保留模式内关系。特别是，它根据联合特征表示与单个模式的相关性计算交叉意义权重。通过将联合A-V特征表示形式部署到交叉意见模块中，它有助于同时利用内模式和模态关系，从而显着改善系统的性能，而不是香草交叉意见模块。我们提出的方法的有效性是在Recola和AffWild2数据集的挑战性视频中通过实验验证的。结果表明，我们的跨注意A-V融合模型提供了一种具有成本效益的解决方案，即使模式是嘈杂或不存在的，也可以超越最先进的方法。

在本文中，我们为Pavlovian信号传达的多方面的研究 - 一个过程中学到的一个过程，一个代理商通过另一个代理商通知决策的时间扩展预测。信令紧密连接到时间和时间。在生成和接收信号的服务中，已知人类和其他动物代表时间，确定自过去事件以来的时间，预测到未来刺激的时间，并且都识别和生成展开时间的模式。我们调查通过引入部分可观察到的决策域来对学习代理之间的影响和信令在我们称之为霜冻空心的情况下如何影响学习代理之间的影响和信令。在该域中，预测学习代理和加强学习代理被耦合到两部分决策系统，该系统可以在避免时间条件危险时获取稀疏奖励。我们评估了两个域变型：机器代理在七态线性步行中交互，以及虚拟现实环境中的人机交互。我们的结果展示了帕夫洛维亚信号传导的学习速度，对药剂 - 代理协调具有不同时间表示（并且不）的影响，以及颞次锯齿对药剂和人毒剂相互作用的影响方式不同。作为主要贡献，我们将Pavlovian信号传导为固定信号范例与两个代理之间完全自适应通信学习之间的天然桥梁。我们进一步展示了如何从固定的信令过程计算地构建该自适应信令处理，其特征在于，通过快速的连续预测学习和对接收信号的性质的最小限制。因此，我们的结果表明了加固学习代理之间的沟通学习的可行建设者的途径。

对机器学习模型训练的栅极基量子电路的发展越来越兴趣。然而，关于电路设计的参数，噪声和其他测量误差对量子机器学习模型性能的影响很少。在本文中，我们探讨了使用多个标准机器学习数据集和IBM的Qiskit模拟器的关键电路设计参数（Qubits，Deposit等）的实际意义。总的来，我们评估超过6500个独特电路，以$ n \约120700美元。我们发现，一般浅（低深度）宽（更多Qubits）电路拓扑倾向于在没有噪声的情况下更优于更深的内容。我们还探讨了不同噪声概念的影响和影响，并讨论了对分类机学习任务的噪声更多/较低的电路拓扑。基于该研究结果，我们定义了使用基于门的NISQ量子计算机来实现近期承诺的电路拓扑指南。

旨在累积学习的认知架构必须提供必要的信息和控制结构，以允许代理商从他们的经验中逐步学习。这涉及管理代理人的目标，并在其感知 - 认知信息堆栈中连续将感官信息与这些联系起来。学习代理的环境越多，越一般，灵活的必须是这些机制来处理更广泛的相关模式，任务和目标结构。虽然许多研究人员同意不同水平的信息可能在其化妆和结构和处理机制中不同，但对这些差异的详情同意通常不在研究界中共享。已经提出了二进制处理架构（通常称为System-1和系统-2）作为低级别信息的认知处理模型。我们以这种方式不存在认知并不是二进制文件，并且任何抽象级别的知识都涉及我们所指的是神经组织信息的信息，这意味着高水平和低级的数据必须包含符号和亚象的信息。此外，我们认为高和低水平数据抽象的处理之间的主要区分因子可以很大程度上归因于所涉及的注意机制的性质。我们描述了此观点背后的关键论据，并审查文献中的相关证据。

多模式分析最近对情感计算的兴趣很大，因为它可以提高情感识别对孤立的单模态方法的整体准确性。多式联情绪识别最有效的技术有效地利用各种和互补的信息来源，例如面部，声带和生理方式，提供全面的特征表示。在本文中，我们专注于基于视频中提取的面部和声乐方式的融合的尺寸情感识别，其中可以捕获复杂的时空关系。大多数现有的融合技术依赖于经常性网络或传统的注意机制，这些机制没有有效地利用视听（A-V）方式的互补性质。我们介绍了一种跨关注融合方法来提取A-V模式的显着特征，允许准确地预测连续值的价值和唤醒。我们的新的跨关节A-V融合模型有效利用了模态关系。特别地，它计算跨关注权重，以专注于各个模态跨越更贡献的特征，从而组合贡献特征表示，然后将其馈送到完全连接的层以用于预测价和唤醒。所提出的方法的有效性在通过Recolat和疲劳（私人）数据集中的视频上进行了实验验证。结果表明，我们的跨关节A-V融合模型是一种经济高效的方法，优于最先进的融合方法。代码可用：\ url {https://github.com/praveena2j/cross-attentional-av-fusion}