A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Boolean \textit{k}-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines.

自主代理需要自定位才能在未知环境中导航。他们可以使用视觉进程（VO）来估计自我运动并使用视觉传感器定位自己。作为惯性传感器或滑板作为轮编码器，这种运动估算策略不会因漂移而受到损害。但是，带有常规摄像机的VO在计算上是要求的，它限制了其在严格的低延迟， - 内存和 - 能量要求的系统中的应用。使用基于事件的相机和神经形态计算硬件为VO问题提供了有希望的低功率解决方案。但是，VO的常规算法不容易转换为神经形态硬件。在这项工作中，我们提出了一种完全由适合神经形态实现的神经元构件构建的VO算法。构建块是代表向量符号体系结构（VSA）计算框架中向量的神经元组，该框架是作为编程神经形态硬件的抽象层提出的。我们提出的VO网络生成并存储了对展示的视觉环境的工作记忆。它更新了此工作内存，同时估计相机的位置和方向的变化。我们证明了如何将VSA作为神经形态机器人技术的计算范式借用。此外，我们的结果代表了使用神经形态计算硬件进行快速和效率的VO以及同时定位和映射（SLAM）的相关任务的重要步骤。我们通过机器人任务和基于事件的数据集对实验进行了实验验证这种方法，并证明了最先进的性能。

在视觉场景理解中，推断对象的位置及其刚性转换仍然是一个开放的问题。在这里，我们提出了一种使用有效的分解网络的神经形态解决方案，该解决方案基于三个关键概念：（1）基于矢量符号体系结构（VSA）的计算框架，带有复杂值值矢量； （2）分层谐振器网络（HRN）的设计，以处理视觉场景中翻译和旋转的非交换性质，而两者都被组合使用； （3）设计多室尖峰拟态神经元模型，用于在神经形态硬件上实现复杂值的矢量结合。 VSA框架使用矢量结合操作来产生生成图像模型，其中绑定充当了几何变换的模棱两可的操作。因此，场景可以描述为向量产物的总和，从而可以通过谐振器网络有效地分解以推断对象及其姿势。 HRN启用了分区体系结构的定义，其中矢量绑定是一个分区内的水平和垂直翻译，以及另一个分区内的旋转和缩放的定义。尖峰神经元模型允许将谐振网络映射到有效且低功耗的神经形态硬件上。在这项工作中，我们使用由简单的2D形状组成的合成场景展示了我们的方法，经历了刚性的几何变换和颜色变化。同伴论文在现实世界的应用程序方案中为机器视觉和机器人技术展示了这种方法。

用于神经形态计算的生物学启发的尖峰神经元是具有动态状态变量的非线性滤波器 - 与深度学习中使用的无状态神经元模型非常不同。 Notel Intel的神经形态研究处理器Loihi 2的下一个版本支持各种具有完全可编程动态的最有状态尖峰神经元模型。在这里，我们展示了先进的尖峰神经元模型，可用于有效地处理仿真Loihi 2硬件的仿真实验中的流数据。在一个示例中，共振和火（RF）神经元用于计算短时间傅里叶变换（STFT），其具有类似的计算复杂度，但是输出带宽的47倍而不是传统的STFT。在另一个例子中，我们描述了一种使用时间率RF神经元的光学流量估计算法，其需要比传统的基于DNN的解决方案超过90倍。我们还展示了有前途的初步结果，使用BackPropagation培训RF神经元进行音频分类任务。最后，我们表明，跳跃的血管谐振器 - RF神经元的变体 - 重复耳蜗的新特性，并激励一种有效的基于尖峰的谱图编码器。

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

In the era of noisy intermediate scale quantum devices, variational quantum circuits (VQCs) are currently one of the main strategies for building quantum machine learning models. These models are made up of a quantum part and a classical part. The quantum part is given by a parametrization $U$, which, in general, is obtained from the product of different quantum gates. By its turn, the classical part corresponds to an optimizer that updates the parameters of $U$ in order to minimize a cost function $C$. However, despite the many applications of VQCs, there are still questions to be answered, such as for example: What is the best sequence of gates to be used? How to optimize their parameters? Which cost function to use? How the architecture of the quantum chips influences the final results? In this article, we focus on answering the last question. We will show that, in general, the cost function will tend to a typical average value the closer the parameterization used is from a $2$-design. Therefore, the closer this parameterization is to a $2$-design, the less the result of the quantum neural network model will depend on its parametrization. As a consequence, we can use the own architecture of the quantum chips to defined the VQC parametrization, avoiding the use of additional swap gates and thus diminishing the VQC depth and the associated errors.