尽管近年来取得了惊人的进步，但最先进的音乐分离系统会产生具有显着感知缺陷的源估计，例如增加无关噪声或消除谐波。我们提出了一个后处理模型（MAKE听起来不错（MSG）后处理器），以增强音乐源分离系统的输出。我们将我们的后处理模型应用于最新的基于波形和基于频谱图的音乐源分离器，包括在训练过程中未见的分离器。我们对源分离器产生的误差的分析表明，波形模型倾向于引入更多高频噪声，而频谱图模型倾向于丢失瞬变和高频含量。我们引入了客观措施来量化这两种错误并显示味精改善了两种错误的源重建。众包主观评估表明，人类的听众更喜欢由MSG进行后处理的低音和鼓的来源估计。

Social insects such as ants communicate via pheromones which allows them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food. This behaviour was shaped through evolutionary processes. In computational models, self-coordination in swarms has been implemented using probabilistic or action rules to shape the decision of each agent and the collective behaviour. However, manual tuned decision rules may limit the behaviour of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any rule. We evolve a swarm of agents representing an ant colony. We use a genetic algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behaviour of each agent. The goal of the colony is to find optimal ways to forage for food in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide its cohorts. The pheromone usage is not encoded into the network; instead, this behaviour is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication did not emerge. We assess the foraging performance by comparing the SNN based model to a rule based system. Our results show that the SNN based model can complete the foraging task more efficiently in a shorter time. Our approach illustrates that even in the absence of pre-defined rules, self coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.

This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.

Reinforcement Learning (RL) algorithms have been successfully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, etc. These scenarios can be framed as Stackelberg security games (SSGs) where defenders and attackers compete to control target resources. The algorithm's competency is assessed by which agent is controlling the targets. This review investigates modeling of SSGs in RL with a focus on possible improvements of target representations in RL algorithms.

Oxidation states are the charges of atoms after their ionic approximation of their bonds, which have been widely used in charge-neutrality verification, crystal structure determination, and reaction estimation. Currently only heuristic rules exist for guessing the oxidation states of a given compound with many exceptions. Recent work has developed machine learning models based on heuristic structural features for predicting the oxidation states of metal ions. However, composition based oxidation state prediction still remains elusive so far, which is more important in new material discovery for which the structures are not even available. This work proposes a novel deep learning based BERT transformer language model BERTOS for predicting the oxidation states of all elements of inorganic compounds given only their chemical composition. Our model achieves 96.82\% accuracy for all-element oxidation states prediction benchmarked on the cleaned ICSD dataset and achieves 97.61\% accuracy for oxide materials. We also demonstrate how it can be used to conduct large-scale screening of hypothetical material compositions for materials discovery.

现有3D网格模型的新型纹理合成是迈向现有模拟器的照片现实资产产生的重要一步。但是现有方法固有地在2D图像空间中起作用，这是从给定的摄像头的角度来看3D空间的投影。这些方法采用摄像头角度，3D模型信息，照明信息并生成逼真的2D图像。为了从另一个角度或照明产生一个逼真的图像，我们需要每次更改参数时进行计算上昂贵的远程通过。同样，很难为可以满足时间约束的模拟器生成此类图像，图像的序列应相似，但只需要根据需要更改照明的观点。该解决方案不能直接与搅拌机和虚幻引擎等现有工具集成。手动解决方案是昂贵且耗时的。因此，我们提出了一个称为Graph生成对抗网络（GGAN）的新系统，该系统可以生成纹理，可以将其直接集成到给定的3D网格模型中，该模型使用Blender和Unreal Engine之类的工具，可以轻松地从任何角度和照明条件进行模拟。

我们介绍了一系列深度学习架构，用于际际关系提取，即参与者不一定在同一句中的关系。我们将这些架构应用于生物医学领域的重要用例：将生物背景分配给生化事件。在这项工作中，生物学背景被定义为观察到生物化学事件的生物系统的类型。神经架构编码并聚合相同候选上下文提到的多个出现，以确定特定事件是否提及的正确上下文。我们提出了两种广泛类型的架构：第一个类型聚合在发射分类之前关于事件的相同候选上下文的多个实例;第二种类型独立分类每个实例并使用结果投票给最终类，类似于集合方法。我们的实验表明，拟议的神经分类器具有竞争力，一些比以前的艺术传统机器学习方法的表现更好，而无需特征工程。我们的分析表明，与传统的机器学习分类器相比，神经方法特别提高精度，并且还表明了句子间关系的难度如何随着事件与上下文提升的距离而增加。

自然语言处理研究人员已经确定了对生成任务的评估方法的局限性，具有新的问题，提出了自动指标和人群判断的有效性。同时，改善生成模型的努力倾向于专注于简单的n-gram重叠度量（例如，Bleu，Rouge）。我们认为，对模型和指标的新进展应该每个人都更直接受益并告知另一个。因此，我们提出了排行榜，竞争排行榜（广告牌）的概括，同时跟踪语言生成任务和指标的进展。与通过预定度量分类提交系统的传统的单向排行榜不同，广告牌可接受发电机和评估度量作为竞争条目。广告牌会自动创建一个基于跨发电机的全局分析选择和线性地组合一些指标的集合度量。此外，指标基于与人类判断的相关性进行排序。我们释放了用于机器翻译，摘要和图像标题的四个广告牌。我们展示了一些多样化度量的线性集合有时会在隔离中显着优于现有的度量。我们的混合效果模型分析表明，大多数自动度量，尤其是基于参考的机器，对人类发电的重估，展示了更新度量的重要性，将来变得更强大（也许与人类更相似）。

我们建立了一种基于规校的图像标题模型的人类评估协议。我们的得分标准及其定义是基于MSCOCO数据集上的机器和人类生成的标题仔细开发。每个字幕沿着权衡（精确和召回）中的两个主要尺寸以及测量文本质量的其他方面（流利，简洁，包容性语言）。我们的评估表明了当前评估实践的几个关键问题。人生成的标题显示出比机器生成的字块的质量大得多，特别是在突出信息的覆盖范围内（即，召回），而所有自动度量都可以说相反。我们基于规度的标准结果表明，曲线芯片，最近使用图像特征的度量标准，与人类判断更好地相关，因为它对召回更敏感。我们希望这项工作将推动更透明的图像标题和自动指标的评估协议。

TorchXrayVision是一个开源软件库，用于使用胸部X射线数据集和深度学习模型。它为广泛的公共可公共胸部X射线数据集提供了一个通用的接口和通用预处理链。此外，通过库培训具有不同架构的许多分类和表示模型，通过库可获得不同的数据组合，以用作基线或特征提取器。