传统的神经结构倾向于通过类似数量（例如电流或电压）进行通信，但是，随着CMOS设备收缩和供应电压降低，电压/电流域模拟电路的动态范围变得更窄，可用的边缘变小，噪声免疫力降低。不仅如此，在常规设计中使用操作放大器（运算放大器）和时钟或异步比较器会导致高能量消耗和大型芯片区域，这将不利于构建尖峰神经网络。鉴于此，我们提出了一种神经结构，用于生成和传输时间域信号，包括神经元模块，突触模块和两个重量模块。所提出的神经结构是由晶体管三极区域的泄漏电流驱动的，不使用操作放大器和比较器，因此与常规设计相比，能够提供更高的能量和面积效率。此外，由于内部通信通过时间域信号，该结构提供了更大的噪声免疫力，从而简化了模块之间的接线。提出的神经结构是使用TSMC 65 nm CMOS技术制造的。拟议的神经元和突触分别占据了127 UM2和231 UM2的面积，同时达到了毫秒的时间常数。实际芯片测量表明，所提出的结构成功地用毫秒的时间常数实现了时间信号通信函数，这是迈向人机交互的硬件储层计算的关键步骤。

Deformable registration of two-dimensional/three-dimensional (2D/3D) images of abdominal organs is a complicated task because the abdominal organs deform significantly and their contours are not detected in two-dimensional X-ray images. We propose a supervised deep learning framework that achieves 2D/3D deformable image registration between 3D volumes and single-viewpoint 2D projected images. The proposed method learns the translation from the target 2D projection images and the initial 3D volume to 3D displacement fields. In experiments, we registered 3D-computed tomography (CT) volumes to digitally reconstructed radiographs generated from abdominal 4D-CT volumes. For validation, we used 4D-CT volumes of 35 cases and confirmed that the 3D-CT volumes reflecting the nonlinear and local respiratory organ displacement were reconstructed. The proposed method demonstrate the compatible performance to the conventional methods with a dice similarity coefficient of 91.6 \% for the liver region and 85.9 \% for the stomach region, while estimating a significantly more accurate CT values.

Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use. To that end, feature attribution methods such as SHAP (SHapley Additive exPlanations) are widely used to explain the predictions of black-box machine learning models to customers and developers. However, a parallel trend has been to train machine learning models in collaboration with other data holders without accessing their data. Such models, trained over horizontally or vertically partitioned data, present a challenge for explainable AI because the explaining party may have a biased view of background data or a partial view of the feature space. As a result, explanations obtained from different participants of distributed machine learning might not be consistent with one another, undermining trust in the product. This paper presents an Explainable Data Collaboration Framework based on a model-agnostic additive feature attribution algorithm (KernelSHAP) and Data Collaboration method of privacy-preserving distributed machine learning. In particular, we present three algorithms for different scenarios of explainability in Data Collaboration and verify their consistency with experiments on open-access datasets. Our results demonstrated a significant (by at least a factor of 1.75) decrease in feature attribution discrepancies among the users of distributed machine learning.

We present a lightweight post-processing method to refine the semantic segmentation results of point cloud sequences. Most existing methods usually segment frame by frame and encounter the inherent ambiguity of the problem: based on a measurement in a single frame, labels are sometimes difficult to predict even for humans. To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method. The network, which we call the P2Net, learns the consistency constraints between coincident points from consecutive frames after registration. We evaluate the proposed post-processing method both qualitatively and quantitatively on the SemanticKITTI dataset that consists of real outdoor scenes. The effectiveness of the proposed method is validated by comparing the results predicted by two representative networks with and without the refinement by the post-processing network. Specifically, qualitative visualization validates the key idea that labels of the points that are difficult to predict can be corrected with P2Net. Quantitatively, overall mIoU is improved from 10.5% to 11.7% for PointNet [1] and from 10.8% to 15.9% for PointNet++ [2].

Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and towards "life as it could be", with theoretical, synthetic and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas and contributions from a wide range of subjects. Hybrid Life is an attempt to bring attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. In particular, Hybrid Life focuses on three complementary themes: 1) theories of systems and agents, 2) hybrid augmentation, with augmented architectures combining living and artificial systems, and 3) hybrid interactions among artificial and biological systems. After discussing some of the major sources of inspiration for these themes, we will focus on an overview of the works that appeared in Hybrid Life special sessions, hosted by the annual Artificial Life Conference between 2018 and 2022.

The ability to record high-fidelity videos at high acquisition rates is central to the study of fast moving phenomena. The difficulty of imaging fast moving scenes lies in a trade-off between motion blur and underexposure noise: On the one hand, recordings with long exposure times suffer from motion blur effects caused by movements in the recorded scene. On the other hand, the amount of light reaching camera photosensors decreases with exposure times so that short-exposure recordings suffer from underexposure noise. In this paper, we propose to address this trade-off by treating the problem of high-speed imaging as an underexposed image denoising problem. We combine recent advances on underexposed image denoising using deep learning and adapt these methods to the specificity of the high-speed imaging problem. Leveraging large external datasets with a sensor-specific noise model, our method is able to speedup the acquisition rate of a High-Speed Camera over one order of magnitude while maintaining similar image quality.

我们将知识驱动的程序合成（KDP）作为程序综合任务的变体进行了介绍，该任务需要代理来解决一系列程序合成问题。在KDP中，代理应使用早期问题中的知识来解决后期问题。我们提出了一种基于PushGP的新方法来解决KDPS问题，该问题将子程序作为知识。所提出的方法通过偶数分区（EP）方法从先前解决的问题的解中提取子程序，并使用这些子程序使用自适应替换突变（ARM）来解决即将到来的编程任务。我们称此方法PushGP+EP+ARM。使用PushGP+EP+ARM，在知识提取和利用过程中不需要人类的努力。我们将提出的方法与PushGP进行比较，以及使用人手动提取的子程序的方法。与PushGP相比，我们的PushGP+EP+ARM可以实现更好的火车错误，成功计数和更快的收敛速度。此外，当连续解决六个程序合成问题的序列时，我们证明了PushGP+EP+组的优势。

多源数据融合，共同分析了多个数据源以获得改进的信息，引起了广泛的研究关注。对于多个医疗机构的数据集，数据机密性和跨机构沟通至关重要。在这种情况下，数据协作（DC）分析通过共享维数减少的中间表示，而无需迭代跨机构通信可能是合适的。在分析包括个人信息在内的数据时，共享数据的可识别性至关重要。在这项研究中，研究了DC分析的可识别性。结果表明，共享的中间表示很容易识别为原始数据以进行监督学习。然后，这项研究提出了一个非可读性可识别的直流分析，仅共享多个医疗数据集（包括个人信息）的非可读数据。所提出的方法基于随机样本排列，可解释的直流分析的概念以及无法重建的功能的使用来解决可识别性问题。在医学数据集的数值实验中，提出的方法表现出非可读性可识别性，同时保持了常规DC分析的高识别性能。对于医院的数据集，提出的方法在仅使用本地数据集的本地分析的识别性能方面表现出了9个百分点的改善。

最近，已经开发了数据协作（DC）分析，以跨多个机构跨多个机构提供隐私的综合分析。 DC分析集中了单独构建的维度减少中间表示形式，并通过协作表示实现集成分析，而无需共享原始数据。为了构建协作表示形式，每个机构都会生成并共享一个可共享的锚数据集并集中其中间表示。尽管随机锚数据集对DC分析的功能很好，但使用其分布与RAW数据集的分布接近的锚数据集有望改善识别性能，尤其是对于可解释的DC分析。基于合成少数群体过度采样技术（SMOTE）的扩展，本研究提出了一种锚数据构建技术，以提高识别性能，而不会增加数据泄漏的风险。数值结果证明了所提出的基于SMOTE方法的效率比人工和现实世界数据集的现有锚数据构建体的效率。具体而言，所提出的方法在收入数据集的现有方法上分别实现了9个百分点和38个百分点的性能改进。提出的方法提供了SMOTE的另一种用途，而不是用于不平衡的数据分类，而是用于隐私保护集成分析的关键技术。

近年来，通过分布式数据的隐私保存的因果推断技术的开发引起了人们的关注。为了解决这个问题，我们提出了基于数据协作（DC-QE）的准实验，该实验可以从具有隐私保护的分布式数据中获得因果推断。我们的方法通过仅共享降低维度的中间表示来保留私人数据的隐私，这些中间表示由各方单独构建。此外，我们的方法可以减少随机错误和偏见，而现有方法只能减少治疗效果估计中的随机错误。通过对人工和现实世界数据的数值实验，我们确认我们的方法可以比单个分析得出更好的估计结果。随着我们方法的传播，可以将中间表示形式作为开放数据发布，以帮助研究人员找到因果关系并积累为知识库。