Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which propagates information over the whole graph domain by stacking multiple layers. This paradigm suffers from two major limitations, over-squashing and poor long-range dependencies, that can be solved using global attention but significantly increases the computational cost to quadratic complexity. In this work, we propose an alternative approach to overcome these structural limitations by leveraging the ViT/MLP-Mixer architectures introduced in computer vision. We introduce a new class of GNNs, called Graph MLP-Mixer, that holds three key properties. First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on the Long Range Graph Benchmark (LRGB) and the TreeNeighbourMatch datasets. Second, they offer better speed and memory efficiency with a complexity linear to the number of nodes and edges, surpassing the related Graph Transformer and expressive GNN models. Third, they show high expressivity in terms of graph isomorphism as they can distinguish at least 3-WL non-isomorphic graphs. We test our architecture on 4 simulated datasets and 7 real-world benchmarks, and show highly competitive results on all of them.

我们提供了一个理论框架来研究我们称之为单发概括的现象。这种现象是指算法在单个任务中执行转移学习的能力，这意味着它正确地对训练集中具有单个示例的测试点进行了分类。我们提出了一个简单的数据模型，并使用它以两种方式研究这种现象。首先，我们证明了一种非反应基碱线 - 基于最近的邻分类的内核方法无法执行单发概括，而与核的选择无关，并且训练集的大小。其次，我们从经验上表明，我们数据模型最直接的神经网络体系结构几乎完美地执行了单发概括。这种鲜明的差异使我们相信，单发概括机制是对神经网络的经验成功的部分原因。

长期以来，对新概念的强大概括一直是人类智力的独特特征。然而，深层生成模型的最新进展已导致神经体系结构能够从单个训练示例中综合新的视觉概念实例。但是，这些模型与人之间的更精确比较是不可能的，因为生成模型的现有性能指标（即FID，IS，可能性）不适合单次生成场景。在这里，我们提出了一个新框架，以评估沿两个轴的单发生成模型：样本可识别性与多样性（即类内变异性）。使用此框架，我们对Omniglot手写数据集上的代表性单弹性生成模型进行系统评估。我们首先表明类似GAN的模型属于多样性可识别性空间的相对端。对关键模型参数效果的广泛分析进一步表明，空间注意力和上下文集成对多样性可识别性的权衡具有线性贡献。相比之下，解散将模型沿抛物线曲线运输，该模型可用于最大化识别率。使用多样性可识别性框架，我们能够识别紧密近似人类数据的模型和参数。

域泛化涉及从异构地收集培训来源的分类器，以便它推广到从类似的未知目标域中汲取的数据，具有大规模学习和个性化推断的应用。在许多设置中，隐私问题禁止获取培训数据样本的域标签，而是只有汇总培训点集合。利用域标签来创建域不变特征表示的现有方法在此设置中不可应用，需要替代方法来学习概括的分类器。在本文中，我们提出了一个解决这个问题的域 - 自适应方法，它分为两个步骤：（a）我们在仔细选择的特征空间内培训数据来创建伪域，（b）使用这些伪域学习域 - 自适应分类器，该分类器使用有关它所属的输入和伪域的信息进行预测。我们的方法在各种域泛化基准测试中实现了最先进的性能，而无需使用域标签。此外，我们使用群集信息提供关于域泛化的新颖理论保障。我们的方法可以适用于基于集合的方法，即使在大型基准数据集上也可以提供大量的收益。代码可以在：https://github.com/xavierohan/adaclust_domainbed

目的：利用机器学习方法，我们的目标是在患者报告的糖尿病相关的推文中提取明确和隐含的造成关联，并提供一种更好地了解糖尿病在线社区内共享的意见，感受和观察的工具，从而从因果关系角度来。材料和方法：2017年4月至1月2021年间收集了3000多万糖尿病英语糖尿病相关推文。应用深度学习和自然语言处理方法，专注于具有个人和情感内容的推文。将一个原因效果 - Tweet数据集手动标记并用于训练1）微调BERTWEET模型，以检测包含因果关系2）的因果句，其中基于BERT的特征，以提取可能的原因效果关联。以半监督方法聚类原因和效果，并在交互式原因效果网络中可视化。结果：在不平衡数据集中的召回中检测到因果句，召回68％。具有基于BERT的特征的CRF模型表现出用于效果检测的微调伯特模型，具有68％的宏观召回。这导致了96,676个句子与原因效应关联。 “糖尿病”被鉴定为中央簇，然后被“死亡”和“胰岛素”。胰岛素定价相关原因经常与“死亡”相关。结论：开发了一种新颖的方法来检测因果句，并确定与糖尿病相关推文中的显式和隐含，单词和多字原因和相应的效果，利用基于伯伯的架构，并被视为原因效果网络。提取现实生活中的因果关系，患者报告社交媒体数据的结果提供了糖尿病研究中有用的互补信息来源。

变形AutiaceCoder（VAE）是一种强大的深度生成模型，现在广泛地用于通过以无监督方式学习的低维潜在空间来表示高维复杂数据。在原始VAE模型中，输入数据向量独立处理。近年来，一系列论文呈现了VAE的不同扩展来处理顺序数据，这不仅模拟了潜在空间，还可以在数据向量和对应的潜在矢量序列内模拟时间依赖性，依赖于经常性神经网络或状态空间模型。在本文中，我们对这些模型进行了广泛的文献综述。重要的是，我们介绍并讨论了一种称为动态变化自动化器（DVAES）的一般模型，包括这些时间VAE扩展的大的子集。然后我们详细介绍了最近在文献中提出的七种不同的DVAE实例，努力使符号和演示线均匀化，以及将这些模型与现有的经典型号联系起来。我们重新实现了那些七种DVAE模型，我们介绍了在语音分析 - 重新合成任务上进行的实验基准的结果（Pytorch代码被公开可用）。本文得出了广泛讨论了关于DVAE类模型和未来研究指南的重要问题。

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry. But for any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress. This led us in March 2020 to release a benchmark framework that i) comprises of a diverse collection of mathematical and real-world graphs, ii) enables fair model comparison with the same parameter budget to identify key architectures, iii) has an open-source, easy-to-use and reproducible code infrastructure, and iv) is flexible for researchers to experiment with new theoretical ideas. As of December 2022, the GitHub repository has reached 2,000 stars and 380 forks, which demonstrates the utility of the proposed open-source framework through the wide usage by the GNN community. In this paper, we present an updated version of our benchmark with a concise presentation of the aforementioned framework characteristics, an additional medium-sized molecular dataset AQSOL, similar to the popular ZINC, but with a real-world measured chemical target, and discuss how this framework can be leveraged to explore new GNN designs and insights. As a proof of value of our benchmark, we study the case of graph positional encoding (PE) in GNNs, which was introduced with this benchmark and has since spurred interest of exploring more powerful PE for Transformers and GNNs in a robust experimental setting.

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/

Edge computing is changing the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy violation. However, advances in deep learning enabled Internet of Things (IoTs) to take decisions and run cognitive tasks locally. This research introduces a decentralized-control edge model where most computation and decisions are moved to the IoT level. The model aims at decreasing communication to the edge which in return enhances efficiency and decreases latency. The model also avoids data transfer which raises security and privacy risks. To examine the model, we developed SAFEMYRIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current real-time monitoring systems are costly and require continuous network connectivity. The system uses optimized deep learning that run locally on IoTs to detect violations in ridesharing and record violation incidences. The system would enhance safety and security in ridesharing without violating privacy.

Cognitive Computing (COC) aims to build highly cognitive machines with low computational resources that respond in real-time. However, scholarly literature shows varying research areas and various interpretations of COC. This calls for a cohesive architecture that delineates the nature of COC. We argue that if Herbert Simon considered the design science is the science of artificial, cognitive systems are the products of cognitive science or 'the newest science of the artificial'. Therefore, building a conceptual basis for COC is an essential step into prospective cognitive computing-based systems. This paper proposes an architecture of COC through analyzing the literature on COC using a myriad of statistical analysis methods. Then, we compare the statistical analysis results with previous qualitative analysis results to confirm our findings. The study also comprehensively surveys the recent research on COC to identify the state of the art and connect the advances in varied research disciplines in COC. The study found that there are three underlaying computing paradigms, Von-Neuman, Neuromorphic Engineering and Quantum Computing, that comprehensively complement the structure of cognitive computation. The research discuss possible applications and open research directions under the COC umbrella.