胶囊网络（CAPSNET）是图像处理的新兴趋势。与卷积神经网络相反，CAPSNET不容易受到对象变形的影响，因为对象的相对空间信息在整个网络中保存。但是，它们的复杂性主要与胶囊结构和动态路由机制有关，这使得以其原始形式部署封闭式以由小型微控制器（MCU）供电的设备几乎是不合理的。在一个智力从云到边缘迅速转移的时代，这种高复杂性对在边缘的采用capsnets的采用构成了严重的挑战。为了解决此问题，我们提出了一个API，用于执行ARM Cortex-M和RISC-V MCUS中的量化capsnet。我们的软件内核扩展了ARM CMSIS-NN和RISC-V PULP-NN，以用8位整数作为操作数支持胶囊操作。随之而来的是，我们提出了一个框架，以执行CAPSNET的训练后量化。结果显示，记忆足迹的减少近75％，准确性损失范围从0.07％到0.18％。在吞吐量方面，我们的ARM Cortex-M API可以分别在仅119.94和90.60毫秒（MS）的中型胶囊和胶囊层执行（STM32H7555ZIT6U，Cortex-M7 @ 480 MHz）。对于GAP-8 SOC（RISC-V RV32IMCXPULP @ 170 MHz），延迟分别降至7.02和38.03 ms。

Inspired by foundational studies in classical and quantum physics, and by information retrieval studies in quantum information theory, we have recently proved that the notions of 'energy' and 'entropy' can be consistently introduced in human language and, more generally, in human culture. More explicitly, if energy is attributed to words according to their frequency of appearance in a text, then the ensuing energy levels are distributed non-classically, namely, they obey Bose-Einstein, rather than Maxwell-Boltzmann, statistics, as a consequence of the genuinely 'quantum indistinguishability' of the words that appear in the text. Secondly, the 'quantum entanglement' due to the way meaning is carried by a text reduces the (von Neumann) entropy of the words that appear in the text, a behaviour which cannot be explained within classical (thermodynamic or information) entropy. We claim here that this 'quantum-type behaviour is valid in general in human cognition', namely, any text is conceptually more concrete than the words composing it, which entails that the entropy of the overall text decreases. This result can be prolonged to human culture and its collaborative entities having lower entropy than their constituent elements. We use these findings to propose the development of a new 'non-classical thermodynamic theory for human cognition and human culture', which bridges concepts and quantum entities and agrees with some recent findings on the conceptual, not physical, nature of quantum entities.

Neural image classifiers are known to undergo severe performance degradation when exposed to input that exhibits covariate-shift with respect to the training distribution. Successful hand-crafted augmentation pipelines aim at either approximating the expected test domain conditions or to perturb the features that are specific to the training environment. The development of effective pipelines is typically cumbersome, and produce transformations whose impact on the classifier performance are hard to understand and control. In this paper, we show that recent Text-to-Image (T2I) generators' ability to simulate image interventions via natural-language prompts can be leveraged to train more robust models, offering a more interpretable and controllable alternative to traditional augmentation methods. We find that a variety of prompting mechanisms are effective for producing synthetic training data sufficient to achieve state-of-the-art performance in widely-adopted domain-generalization benchmarks and reduce classifiers' dependency on spurious features. Our work suggests that further progress in T2I generation and a tighter integration with other research fields may represent a significant step towards the development of more robust machine learning systems.

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.

Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters. To make this reasoning process more explicit, recent works retrieve a rationalizing LM's internal knowledge by training or prompting it to generate free-text rationales, which can be used to guide task predictions made by either the same LM or a separate reasoning LM. However, rationalizing LMs require expensive rationale annotation and/or computation, without any assurance that their generated rationales improve LM task performance or faithfully reflect LM decision-making. In this paper, we propose PINTO, an LM pipeline that rationalizes via prompt-based learning, and learns to faithfully reason over rationales via counterfactual regularization. First, PINTO maps out a suitable reasoning process for the task input by prompting a frozen rationalizing LM to generate a free-text rationale. Second, PINTO's reasoning LM is fine-tuned to solve the task using the generated rationale as context, while regularized to output less confident predictions when the rationale is perturbed. Across four datasets, we show that PINTO significantly improves the generalization ability of the reasoning LM, yielding higher performance on both in-distribution and out-of-distribution test sets. Also, we find that PINTO's rationales are more faithful to its task predictions than those generated by competitive baselines.

While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging. One critical reason for this is that uncurated robot demonstration data, i.e. play data, collected from non-expert human demonstrators are often noisy, diverse, and distributionally multi-modal. This makes extracting useful, task-centric behaviors from such data a difficult generative modeling problem. In this work, we present Conditional Behavior Transformers (C-BeT), a method that combines the multi-modal generation ability of Behavior Transformer with future-conditioned goal specification. On a suite of simulated benchmark tasks, we find that C-BeT improves upon prior state-of-the-art work in learning from play data by an average of 45.7%. Further, we demonstrate for the first time that useful task-centric behaviors can be learned on a real-world robot purely from play data without any task labels or reward information. Robot videos are best viewed on our project website: https://play-to-policy.github.io

像许多团队运动一样，篮球涉及两组球员，他们从事合作和对抗性活动以赢得比赛。球员和团队正在执行各种复杂的策略，以比对手获得优势。定义，识别和分析不同类型的活动是体育分析中的一项重要任务，因为它可以导致球员和教练人员更好地策略和决策。本文的目的是自动识别篮球小组的活动，从跟踪代表玩家和球的位置的数据。我们在团队运动中提出了一种新颖的深度学习方法，以称为NETS。为了有效地对团队运动中的玩家关系进行建模，我们将基于变压器的体系结构与LSTM嵌入结合在一起，以及一个团队合并层以识别小组活动。培训这样的神经网络通常需要大量注释数据，这会产生高标签成本。为了解决手动标签的稀缺性，我们在自我监督的轨迹预测任务上生成弱标签并预处理神经网络。我们使用了从632个NBA游戏中的大型跟踪数据集来评估我们的方法。结果表明，NET能够以高准确性学习小组活动，并且网络中的自我监督训练对GAR的准确性产生了积极影响。

能够分析和量化人体或行为特征的系统（称为生物识别系统）正在使用和应用变异性增长。由于其从手工制作的功能和传统的机器学习转变为深度学习和自动特征提取，因此生物识别系统的性能增加到了出色的价值。尽管如此，这种快速进步的成本仍然尚不清楚。由于其不透明度，深层神经网络很难理解和分析，因此，由错误动机动机动机的隐藏能力或决定是潜在的风险。研究人员已经开始将注意力集中在理解深度神经网络及其预测的解释上。在本文中，我们根据47篇论文的研究提供了可解释生物识别技术的当前状态，并全面讨论了该领域的发展方向。

以人为本的人工智能考虑了人工智能表现的经验。尽管丰富的研究一直在通过全自动或弱监督学习来帮助AI实现超人类的表现，但较少的努力正在尝试AI如何量身定制人类对人类首选技能水平的限制。在这项工作中，我们指导课程加强学习结果朝着首选的绩效水平，通过从人类的决策过程中学习而不是太困难也不容易。为了实现这一目标，我们开发了一个便携式交互式平台，使用户能够通过操纵任务难度，观察性能并提供课程反馈来在线与代理商进行交互。我们的系统高度可行，使人类可以训练大规模的增强学习应用程序，这些学习应用需要数百万没有服务器的样品。结果证明了互动课程对涉及人类在环的增强学习的有效性。它显示强化学习绩效可以成功地与人类所需的难度水平同步调整。我们认为，这项研究将为实现流动和个性化的适应性困难打开新的大门。

这项工作总结了2022年2022年国际生物识别联合会议（IJCB 2022）的IJCB被遮挡的面部识别竞赛（IJCB-OCFR-2022）。OCFR-2022从学术界吸引了总共3支参与的团队。最终，提交了六个有效的意见书，然后由组织者评估。在严重的面部阻塞面前，举行了竞争是为了应对面部识别的挑战。参与者可以自由使用任何培训数据，并且通过使用众所周知的数据集构成面部图像的部分来构建测试数据。提交的解决方案提出了创新，并以所考虑的基线表现出色。这项竞争的主要输出是具有挑战性，现实，多样化且公开可用的遮挡面部识别基准，并具有明确的评估协议。