Lenia is a family of cellular automata (CA) generalizing Conway's Game of Life to continuous space, time and states. Lenia has attracted a lot of attention because of the wide diversity of self-organizing patterns it can generate. Among those, some spatially localized patterns (SLPs) resemble life-like artificial creatures. However, those creatures are found in only a small subspace of the Lenia parameter space and are not trivial to discover, necessitating advanced search algorithms. We hypothesize that adding a mass conservation constraint could facilitate the emergence of SLPs. We propose here an extension of the Lenia model, called Flow Lenia, which enables mass conservation. We show a few observations demonstrating its effectiveness in generating SLPs with complex behaviors. Furthermore, we show how Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized. This allows for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules. We argue that this paves the way for the intrinsic evolution of self-organized artificial life forms within continuous CAs.

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging given the potentially large number of individual and environmental variables involved. This paper shows how to address this problem by using fire as the source of external, bursting and wide fluctuations. Fire propagates on a spatial landscape where a group of agents harvest and exploit trees while avoiding the damaging effects of fire spreading. The agents need to solve a conflict to reach a group-level optimal state: while tree harvesting reduces the propagation of fires, it also reduces the availability of resources provided by trees. It is shown that the system displays two major evolutionary innovations that end up in an ecological engineering strategy that favours high biomass along with the suppression of large fires. The implications for potential A.I. management of complex ecosystems are discussed.

人类的文化曲目依赖于创新：我们连续和层次上探讨如何将现有元素组合起来创建新的能力。创新不是孤独的，它依赖于以前解决方案的集体积累和合并。机器学习方法通​​常假定完全连接的多代理网络最适合创新。然而，人类实验室和现场研究表明，动态沟通拓扑可以更强大地实现等级创新。在动态拓扑结构中，人类在单独或小簇中进行创新之间振荡，然后与他人分享结果。据我们所知，在机器学习中，尚未系统地研究多代理拓扑在创新中的作用。目前尚不清楚a）哪些交流拓扑对于哪些创新任务以及b）共享经验的特性改善了多级创新的最佳选择。在这里，我们使用三个不同的创新任务，使用多级分层问题设置（WordCraft）。我们系统地设计了DQNS的网络，分享了他们在不同拓扑（完全连接，小世界，动态，戒指）中共享其重播缓冲区的体验。比较不同任务的不同经验共享拓扑实现的创新水平，这表明，首先，与人类的发现一致，在动态拓扑中共享的经验可以达到整个任务的最高创新水平。其次，当有一条清晰的创新途径时，经验共享并不那么有用。第三，我们提出的两个指标，共享经验的合规性和多样性可以解释不同任务上不同拓扑的成功。这些贡献可以促进我们对最佳AI-AI，人类和人类协作网络的理解，从而激发未来的工具来促进大型组织的集体创新。

自然系统的多样性和质量一直是研究人造生活的社区的难题和灵感。现在广泛承认，使这些特性的适应机制在很大程度上受其居住环境的影响。面临环境变异性的生物具有在不同时间尺度上运行的两种替代适应机制：\ textit {可塑性}，这是表型在不同环境中生存的能力，以及\ textit {EvolDobility}，能够通过突变适应。尽管在环境变异性下至关重要，但两种机制都与假设在稳定环境中不必要的健身成本相关。在这项工作中，我们研究了可塑性和可变性进化模型中环境动力学与适应性之间的相互作用。我们尝试以不同类型的环境为特征，其特征是存在壁ni和确定适应性景观的气候功能。我们从经验上表明，环境动力学对可塑性和可变性的影响有所不同，并且即使在稳定的环境中，各种生态壁ches的存在也支持适应性。我们对选择机制进行消融研究，以分离基于健身的选择和利基限制竞争的作用。从我们的最低模型中获得的结果使我们能够在生物和人工系统的开放性研究中提出有前途的研究方向。

我们对学习协调的互动代理感兴趣，即$ BUILDER $ - 执行操作但忽略任务的目标 - 以及$架构师$指导建造者以朝着任务的目标指导。我们定义和探索正式的设置，其中人工代理配备了允许它们同时学习任务的机制，同时同时演变共享通信协议。实验符号学领域表明，从先验的未知指示中学习的人类熟练程度。因此，我们从中获取灵感并提出了建筑师构建器问题（ABP）：一个不对称的设置，其中建筑师必须学习指导建设者朝构建特定结构。该架构师知道目标结构，但不能在环境中行动，只能向构建器发送任意消息。另一方面的建筑师可以在环境中采取行动，但没有关于手头的任务的知识，必须学会解决它依赖于架构师发送的消息。至关重要的是，消息的含义最初没有在代理商之间定义，而是必须在整个学习中进行协商。在这些约束下，我们建议建筑师构建器迭代（abig），一个解决方案到架构师 - 建筑师的问题，其中建筑师利用Builder的学习模型指导它，同时构建器使用自模仿学习来加强其导游行为。我们分析ABIG的关键学习机制，并在ABP的二维实例化中测试，其中任务涉及抓取立方体，将它们放在给定位置或构建各种形状。在这种环境中，ABIG导致低级，高频，指导通信协议，不仅使建筑师构建器对能够在手头上解决任务，而且还可以概括到未操作任务。

尽管其作为当今机器学习模式的数据效率低下的突出解决方案，但自我监督的学习尚未从纯粹的多功能代理人的角度进行研究。在这项工作中，我们建议对齐内部主观表示，该主观表示自然地出现在多种子体的设置中，其中代理商接受相同的潜在环境国家的部分观察，可以导致更多的数据有效的陈述。我们提出了多种子体环境，其中代理商无法访问其他人的观察，而是可以在有限范围内进行通信，保证可以在个人代表学习中利用的常见上下文。原因是主观观察必然指的是基础环境国家的相同子集，并且关于这些国家的沟通可以自由地提供监督信号。为了突出沟通的重要性，我们将我们的设置称为\ Texit {社会监督的代表学习}。我们提出了一个由AutoEncoders群体组成的最小架构，在那里我们定义了丢失函数，捕获有效通信的不同方面，并检查它们对学习的表示的影响。我们表明我们所提出的架构允许出现对齐的表示。提出具有不同环境状态的不同观点引入的主观性有助于学习卓越的抽象表示，以满足环境状态的相同观点的单一AutoEncoder和AutoEncoders的群体。完全是，我们的结果表明了主观观点的沟通如何导致多助理系统中的更多抽象表现，开放有望的观点，以便在代表学习和紧急沟通的交叉口中进行未来的研究。

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

We consider a model where a signal (discrete or continuous) is observed with an additive Gaussian noise process. The signal is issued from a linear combination of a finite but increasing number of translated features. The features are continuously parameterized by their location and depend on some scale parameter. First, we extend previous prediction results for off-the-grid estimators by taking into account here that the scale parameter may vary. The prediction bounds are analogous, but we improve the minimal distance between two consecutive features locations in order to achieve these bounds. Next, we propose a goodness-of-fit test for the model and give non-asymptotic upper bounds of the testing risk and of the minimax separation rate between two distinguishable signals. In particular, our test encompasses the signal detection framework. We deduce upper bounds on the minimal energy, expressed as the 2-norm of the linear coefficients, to successfully detect a signal in presence of noise. The general model considered in this paper is a non-linear extension of the classical high-dimensional regression model. It turns out that, in this framework, our upper bound on the minimax separation rate matches (up to a logarithmic factor) the lower bound on the minimax separation rate for signal detection in the high dimensional linear model associated to a fixed dictionary of features. We also propose a procedure to test whether the features of the observed signal belong to a given finite collection under the assumption that the linear coefficients may vary, but do not change to opposite signs under the null hypothesis. A non-asymptotic upper bound on the testing risk is given. We illustrate our results on the spikes deconvolution model with Gaussian features on the real line and with the Dirichlet kernel, frequently used in the compressed sensing literature, on the torus.

HTR models development has become a conventional step for digital humanities projects. The performance of these models, often quite high, relies on manual transcription and numerous handwritten documents. Although the method has proven successful for Latin scripts, a similar amount of data is not yet achievable for scripts considered poorly-endowed, like Arabic scripts. In that respect, we are introducing and assessing a new modus operandi for HTR models development and fine-tuning dedicated to the Arabic Maghrib{\=i} scripts. The comparison between several state-of-the-art HTR demonstrates the relevance of a word-based neural approach specialized for Arabic, capable to achieve an error rate below 5% with only 10 pages manually transcribed. These results open new perspectives for Arabic scripts processing and more generally for poorly-endowed languages processing. This research is part of the development of RASAM dataset in partnership with the GIS MOMM and the BULAC.

Accurate diagnosis and prognosis of Alzheimer's disease are crucial to develop new therapies and reduce the associated costs. Recently, with the advances of convolutional neural networks, methods have been proposed to automate these two tasks using structural MRI. However, these methods often suffer from lack of interpretability, generalization, and can be limited in terms of performance. In this paper, we propose a novel deep framework designed to overcome these limitations. Our framework consists of two stages. In the first stage, we propose a deep grading model to extract meaningful features. To enhance the robustness of these features against domain shift, we introduce an innovative collective artificial intelligence strategy for training and evaluating steps. In the second stage, we use a graph convolutional neural network to better capture AD signatures. Our experiments based on 2074 subjects show the competitive performance of our deep framework compared to state-of-the-art methods on different datasets for both AD diagnosis and prognosis.