神经网络是高维非线性动力学系统，通过许多相互连接的单元的协调活动来处理信息。了解生物学和机器学习网络的功能和学习如何需要了解这种协调活动的结构，该信息包含在单元之间的跨跨构象中的信息。尽管动态平均场理论（DMFT）阐明了随机神经网络的几个特征，特别是它们可以产生混乱活动，但现有的DMFT方法不支持跨跨化的计算。我们通过通过两点腔法扩展DMFT方法来解决这个长期存在的问题。这首先揭示了活动协调的几个空间和时间特征，包括有效维度，定义为协方差矩阵频谱的参与率。我们的结果提供了一个一般的分析框架，用于研究随机神经网络中集体活动的结构，更广泛地，在具有猝灭障碍的高维非线性动力学系统中。

实验神经科学的进步改变了我们探索神经电路结构和功能的能力。与此同时，机器学习的进步已经释放了人工神经网络（ANNS）的显着计算能力。虽然这两个字段具有不同的工具和应用程序，但它们存在类似的挑战：即，了解如何通过高维表示来嵌入信息并通过高维表示来解决复杂任务。解决这一挑战的一种方法是利用数学和计算工具来分析这些高维表示的几何形状，即神经人口几何形状。我们审查了解生物和人工神经网络功能的几何方法的示例：感知的代表性，在认知系统中的分类能力，解剖和抽象的几何理论，认知地图的拓扑表示，电机系统中的动态不包含一种动态的认知方法。这些发现在一起说明了机器学习，神经科学和几何形状的令人兴奋的趋势，其中神经人口几何形状提供了有用的人口级机械描述符基础任务实现。重要的是，几何描述适用于感官模态，脑区，网络架构和时间尺度。因此，神经人口几何形状有可能统一我们对生物和人工神经网络的结构和功能的理解，弥合单一神经元，人口和行为之间的差距。

The intersection of ground reaction forces in a small, point-like area above the center of mass has been observed in computer simulation models and human walking experiments. This intersection point is often called a virtual pivot point (VPP). With the VPP observed so ubiquitously, it is commonly assumed to provide postural stability for bipedal walking. In this study, we challenge this assumption by questioning if walking without a VPP is possible. Deriving gaits with a neuromuscular reflex model through multi-stage optimization, we found stable walking patterns that show no signs of the VPP-typical intersection of ground reaction forces. We, therefore, conclude that a VPP is not necessary for upright, stable walking. The non-VPP gaits found are stable and successfully rejected step-down perturbations, which indicates that a VPP is not primarily responsible for locomotion robustness or postural stability. However, a collision-based analysis indicates that non-VPP gaits increased the potential for collisions between the vectors of the center of mass velocity and ground reaction forces during walking, suggesting an increased mechanical cost of transport. Although our computer simulation results have yet to be confirmed through experimental studies, they already strongly challenge the existing explanation of the VPP's function and provide an alternative explanation.

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.

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.

The performances of braking control systems for robotic platforms, e.g., assisted and autonomous vehicles, airplanes and drones, are deeply influenced by the road-tire friction experienced during the maneuver. Therefore, the availability of accurate estimation algorithms is of major importance in the development of advanced control schemes. The focus of this paper is on the estimation problem. In particular, a novel estimation algorithm is proposed, based on a multi-layer neural network. The training is based on a synthetic data set, derived from a widely used friction model. The open loop performances of the proposed algorithm are evaluated in a number of simulated scenarios. Moreover, different control schemes are used to test the closed loop scenario, where the estimated optimal slip is used as the set-point. The experimental results and the comparison with a model based baseline show that the proposed approach can provide an effective best slip estimation.

濒危语言的用户努力在数字化介导的世界中蓬勃发展。我们开发了一种自动化方法，用于评估ISO 639认可的每种语言在数字语言支持方面的表现。该评估是基于从143个数字工具的网站上删除支持语言的名称，以代表数字技术可以支持语言的各种方式。该方法使用Mokken量表分析来生成可解释的模型，以量化数字语言支持并在全球范围内监视它。

人类机器人相互作用（HRI）对于在日常生活中广泛使用机器人至关重要。机器人最终将能够通过有效的社会互动来履行人类文明的各种职责。创建直接且易于理解的界面，以与机器人开始在个人工作区中扩散时与机器人互动至关重要。通常，与模拟机器人的交互显示在屏幕上。虚拟现实（VR）是一个更具吸引力的替代方法，它为视觉提示提供了更像现实世界中看到的线索。在这项研究中，我们介绍了Jubileo，这是一种机器人的动画面孔，并使用人类机器人社会互动领域的各种研究和应用开发工具。Jubileo Project不仅提供功能齐全的开源物理机器人。它还提供了一个全面的框架，可以通过VR接口进行操作，从而为HRI应用程序测试带来沉浸式环境，并明显更好地部署速度。

当植物天然产物与药物共容纳时，就会发生药代动力学天然产物 - 药物相互作用（NPDIS）。了解NPDI的机制是防止不良事件的关键。我们构建了一个知识图框架NP-KG，作为迈向药代动力学NPDIS的计算发现的一步。 NP-KG是一个具有生物医学本体论，链接数据和科学文献的全文，由表型知识翻译框架和语义关系提取系统，SEMREP和集成网络和动态推理组成的构建的科学文献的全文。通过路径搜索和元路径发现对药代动力学绿茶和kratom-prug相互作用的案例研究评估NP-KG，以确定与地面真实数据相比的一致性和矛盾信息。完全集成的NP-KG由745,512个节点和7,249,576个边缘组成。 NP-KG的评估导致了一致（绿茶的38.98％，kratom的50％），矛盾（绿茶的15.25％，21.43％，Kratom的21.43％），同等和矛盾的（15.25％）（21.43％，21.43％，21.43％ kratom）信息。几种声称的NPDI的潜在药代动力学机制，包括绿茶 - 茶氧化烯，绿茶 - 纳多洛尔，Kratom-Midazolam，Kratom-Quetiapine和Kratom-Venlafaxine相互作用，与已出版的文献一致。 NP-KG是第一个将生物医学本体论与专注于天然产品的科学文献的全文相结合的公斤。我们证明了NP-KG在鉴定涉及酶，转运蛋白和药物的药代动力学相互作用的应用。我们设想NP-KG将有助于改善人机合作，以指导研究人员将来对药代动力学NPDIS进行研究。 NP-KG框架可在https://doi.org/10.5281/zenodo.6814507和https://github.com/sanyabt/np-kg上公开获得。

创新者是有创造力的人，他们可以唤起代表创新组织主要引擎的开创性思想。过去的研究已广泛调查了谁是创新者以及他们在与工作有关的活动中的行为。在本文中，我们建议有必要分析创新者在其他情况下的行为，例如在非正式沟通空间中，在没有正式结构，规则和工作义务的情况下共享知识。利用通信和网络理论，我们分析了大型跨国公司的Intranet论坛上可用的38,000个帖子。由此，我们解释了创新者在社交网络行为和语言特征方面与其他员工的不同。通过文本挖掘，我们发现创新者编写更多，使用更复杂的语言，介绍新的概念/想法，并使用积极但基于事实的语言。了解创新者的行为和沟通如何支持想要促进创新的经理的决策过程。