串联连接的机器人是希望在大规模灾害中的搜索和救援等限制空间中执行任务的候选人。这种机器人通常是韧带，我们假设肢体的添加可以改善移动性。然而，在设计和控制这种装置方面的挑战在于以提高移动性的方式协调高维冗余模块。在这里，我们开发了一个控制串联连接的多腿机器人的一般框架。具体地，我们结合了两种方法来构建一般的形状控制方案，其可以为各种机器人形态的有效运动提供自变形（“Gaits”）的基线模式。首先，我们从维度降低和生物步态分类方案中获取灵感，以产生身体变形和脚提升/降低的循环模式，其促进了任意基板接触图案的产生。其次，我们使用几何力学方法来促进识别这些起伏的最佳相位，以最大化速度和/或稳定性。我们的方案允许在扁平摩擦地形上的多腿机器人机车上的有效Gaits开发有多种数量的四肢（4,6,16，甚至0四肢）和身体致动能力（包括在Limbless设备上的侧壁Gaits）。通过适当协调身体波动和腿部放置，我们的框架结合了Limbless机器人（模块化）和腿机器人（移动性）的优势。我们预计我们的框架可以提供一般的控制方案，以便快速部署一般的多腿机器人，铺平往达在现实条件下遍历复杂环境的机器的方式。

多个旅行推销员问题（MTSP）是众多现实世界应用的众所周知的NP硬性问题。特别是，这项工作涉及Minmax MTSP，其目的是最大程度地减少所有代理商之间的最大巡回演出长度。许多机器人部署需要经常重新计算潜在的大型MTSP实例，从而使计算时间和解决方案质量的自然权衡非常重要。但是，由于其计算复杂性，精确和启发式算法随着城市数量的增加而效率低下。在最新的深入学习学习（DRL）方面的鼓励下，这项工作将MTSP作为一项合作任务，并引入了Dan，Dan是一种分散的基于注意力的神经方法，旨在解决这一关键权衡。在丹中，代理商通过预测彼此的未来决策来学习完全分散的政策，以合作构建巡回演出。我们的模型依赖于变压器体系结构，并使用具有参数共享的多代理RL进行了训练，从而为代理和城市的数量提供了自然的可扩展性。我们对小型至大规模MTSP实例的实验结果（$ 50至$ 1000 $的城市，$ 5 $至20美元的代理商）表明，Dan能够匹配或超越最先进的求解器，同时保持计划时间较低。特别是，在相同的计算时间预算的情况下，DAN在大规模实例（超过100个城市，超过5个代理商）上优于所有基于常规和DRL的基线，并展示了增强的代理协作。一段视频解释了我们的方法并介绍了我们的结果，请参见\ url {https://youtu.be/xi3clsdslvs}。

In this paper, we present a framework for learning quadruped navigation by integrating central pattern generators (CPGs), i.e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework. Through both exteroceptive and proprioceptive sensing, the agent learns to modulate the intrinsic oscillator setpoints (amplitude and frequency) and coordinate rhythmic behavior among different oscillators to track velocity commands while avoiding collisions with the environment. We compare different neural network architectures (i.e. memory-free and memory-enabled) which learn implicit interoscillator couplings, as well as varying the strength of the explicit coupling weights in the oscillator dynamics equations. We train our policies in simulation and perform a sim-to-real transfer to the Unitree Go1 quadruped, where we observe robust navigation in a variety of scenarios. Our results show that both memory-enabled policy representations and explicit interoscillator couplings are beneficial for a successful sim-to-real transfer for navigation tasks. Video results can be found at https://youtu.be/O_LX1oLZOe0.

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an exact mapping from a network with Rectified Linear Units (ReLUs) to an SNN that fires exactly one spike per neuron. For our constructive proof, we assume that an arbitrary multi-layer ReLU network with or without convolutional layers, batch normalization and max pooling layers was trained to high performance on some training set. Furthermore, we assume that we have access to a representative example of input data used during training and to the exact parameters (weights and biases) of the trained ReLU network. The mapping from deep ReLU networks to SNNs causes zero percent drop in accuracy on CIFAR10, CIFAR100 and the ImageNet-like data sets Places365 and PASS. More generally our work shows that an arbitrary deep ReLU network can be replaced by an energy-efficient single-spike neural network without any loss of performance.

Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous studies utilized quantum interference of photons to achieve collective decision-making without choice conflicts when solving the competitive multi-armed bandit problem, a fundamental example of reinforcement learning. However, the bandit problem deals with a static environment where the agent's action does not influence the reward probabilities. This study aims to extend the conventional approach to a more general multi-agent reinforcement learning targeting the grid world problem. Unlike the conventional approach, the proposed scheme deals with a dynamic environment where the reward changes because of agents' actions. A successful photonic reinforcement learning scheme requires both a photonic system that contributes to the quality of learning and a suitable algorithm. This study proposes a novel learning algorithm, discontinuous bandit Q-learning, in view of a potential photonic implementation. Here, state-action pairs in the environment are regarded as slot machines in the context of the bandit problem and an updated amount of Q-value is regarded as the reward of the bandit problem. We perform numerical simulations to validate the effectiveness of the bandit algorithm. In addition, we propose a multi-agent architecture in which agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state-action pair selections among agents. We demonstrate that multi-agent reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.

Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution). We propose a direct D&A approach based on supervised learning to extract fingerprints that lead to robust predictions under relevant interventions on exogenous variables, i.e., climate drivers other than the target. We employ anchor regression, a distributionally-robust statistical learning method inspired by causal inference that extrapolates well to perturbed data under the interventions considered. The residuals from the prediction achieve either uncorrelatedness or mean independence with the exogenous variables, thus guaranteeing robustness. We define D&A as a unified hypothesis testing framework that relies on the same statistical model but uses different targets and test statistics. In the experiments, we first show that the CO2 forcing can be robustly predicted from temperature spatial patterns under strong interventions on the solar forcing. Second, we illustrate attribution to the greenhouse gases and aerosols while protecting against interventions on the aerosols and CO2 forcing, respectively. Our study shows that incorporating robustness constraints against relevant interventions may significantly benefit detection and attribution of climate change.

We discuss pattern languages for closed pattern mining and learning of interval data and distributional data. We first introduce pattern languages relying on pairs of intersection-based constraints or pairs of inclusion based constraints, or both, applied to intervals. We discuss the encoding of such interval patterns as itemsets thus allowing to use closed itemsets mining and formal concept analysis programs. We experiment these languages on clustering and supervised learning tasks. Then we show how to extend the approach to address distributional data.

The long-distance agreement, evidence for syntactic structure, is increasingly used to assess the syntactic generalization of Neural Language Models. Much work has shown that transformers are capable of high accuracy in varied agreement tasks, but the mechanisms by which the models accomplish this behavior are still not well understood. To better understand transformers' internal working, this work contrasts how they handle two superficially similar but theoretically distinct agreement phenomena: subject-verb and object-past participle agreement in French. Using probing and counterfactual analysis methods, our experiments show that i) the agreement task suffers from several confounders which partially question the conclusions drawn so far and ii) transformers handle subject-verb and object-past participle agreements in a way that is consistent with their modeling in theoretical linguistics.

Predicting the physical interaction of proteins is a cornerstone problem in computational biology. New classes of learning-based algorithms are actively being developed, and are typically trained end-to-end on protein complex structures extracted from the Protein Data Bank. These training datasets tend to be large and difficult to use for prototyping and, unlike image or natural language datasets, they are not easily interpretable by non-experts. We present Dock2D-IP and Dock2D-IF, two "toy" datasets that can be used to select algorithms predicting protein-protein interactions$\unicode{x2014}$or any other type of molecular interactions. Using two-dimensional shapes as input, each example from Dock2D-IP ("interaction pose") describes the interaction pose of two shapes known to interact and each example from Dock2D-IF ("interaction fact") describes whether two shapes form a stable complex or not. We propose a number of baseline solutions to the problem and show that the same underlying energy function can be learned either by solving the interaction pose task (formulated as an energy-minimization "docking" problem) or the fact-of-interaction task (formulated as a binding free energy estimation problem).

We present a way to create small yet difficult model counting instances. Our generator is highly parameterizable: the number of variables of the instances it produces, as well as their number of clauses and the number of literals in each clause, can all be set to any value. Our instances have been tested on state of the art model counters, against other difficult model counting instances, in the Model Counting Competition. The smallest unsolved instances of the competition, both in terms of number of variables and number of clauses, were ours. We also observe a peak of difficulty when fixing the number of variables and varying the number of clauses, in both random instances and instances built by our generator. Using these results, we predict the parameter values for which the hardest to count instances will occur.