在这项工作中，我们提出了一个完全可区分的图形神经网络（GNN）的架构，用于用于通道解码和展示各种编码方案的竞争性解码性能，例如低密度奇偶校验检查（LDPC）和BCH代码。这个想法是让神经网络（NN）通过给定图的通用消息传递算法，该算法通过用可训练的函数替换节点和边缘消息更新来代表正向误差校正（FEC）代码结构。与许多其他基于深度学习的解码方法相反，提出的解决方案享有对任意块长度的可扩展性，并且训练不受维数的诅咒的限制。我们在常规渠道解码中对最新的解码以及最近的基于深度学习的结果基准了我们提出的解码器。对于（63,45）BCH代码，我们的解决方案优于加权信念传播（BP）的解码约0.4 dB，而解码迭代率明显较小，甚至对于5G NR LDPC代码，我们观察到与常规BP解码相比，我们观察到竞争性能。对于BCH代码，所得的GNN解码器只能以9640个权重进行完全参数。

非正交多访问（NOMA）是一项有趣的技术，可以根据未来的5G和6G网络的要求实现大规模连通性。尽管纯线性处理已经在NOMA系统中达到了良好的性能，但在某些情况下，非线性处理是必须的，以确保可接受的性能。在本文中，我们提出了一个神经网络体系结构，该架构结合了线性和非线性处理的优势。在图形处理单元（GPU）上的高效实现证明了其实时检测性能。使用实验室环境中的实际测量值，我们显示了方法比常规方法的优越性。

我们提出了一种基于神经网络（NN）的算法，用于用于窄带物理随机访问通道（NB-iot）的窄带物理随机通道（NBRACH）的设备检测和到达时间（TOA）和载体频率偏移（CFO）估计（nprach） 。引入的NN体系结构利用了剩余的卷积网络以及对5G新无线电（5G NR）规格的序言结构的了解。第三代合作伙伴项目（3GPP）城市微电池（UMI）频道模型的基准测试，其随机用户与最先进的基线相对于最先进的基线表明，该提出的方法可在虚假的负率（FNR）中最多8 dB增益（FNR）以及假阳性率（FPR）和TOA和CFO估计精度的显着增长。此外，我们的模拟表明，所提出的算法可以在广泛的通道条件，CFO和传输概率上获得收益。引入的同步方法在基站（BS）运行，因此在用户设备上没有引入其他复杂性。它可能通过降低序列长度或发射功率来延长电池寿命。我们的代码可在以下网址提供：https：//github.com/nvlabs/nprach_synch/。

Adaptive partial linear beamforming meets the need of 5G and future 6G applications for high flexibility and adaptability. Choosing an appropriate tradeoff between conflicting goals opens the recently proposed multiuser (MU) detection method. Due to their high spatial resolution, nonlinear beamforming filters can significantly outperform linear approaches in stationary scenarios with massive connectivity. However, a dramatic decrease in performance can be expected in high mobility scenarios because they are very susceptible to changes in the wireless channel. The robustness of linear filters is required, considering these changes. One way to respond appropriately is to use online machine learning algorithms. The theory of algorithms based on the adaptive projected subgradient method (APSM) is rich, and they promise accurate tracking capabilities in dynamic wireless environments. However, one of the main challenges comes from the real-time implementation of these algorithms, which involve projections on time-varying closed convex sets. While the projection operations are relatively simple, their vast number poses a challenge in ultralow latency (ULL) applications where latency constraints must be satisfied in every radio frame. Taking non-orthogonal multiple access (NOMA) systems as an example, this paper explores the acceleration of APSM-based algorithms through massive parallelization. The result is a GPUaccelerated real-time implementation of an orthogonal frequency-division multiplexing (OFDM)based transceiver that enables detection latency of less than one millisecond and therefore complies with the requirements of 5G and beyond. To meet the stringent physical layer latency requirements, careful co-design of hardware and software is essential, especially in virtualized wireless systems with hardware accelerators.

长期护理（LTC）居民的一半营养不良的住院治疗，死亡率，发病率较低。当前的跟踪方法是主观和耗时的。本文介绍了专为LTC设计的自动食品成像和营养进气跟踪（AFINI-T）技术。我们提出了一种用于食品分类的新型卷积Automencoder，在我们的模拟LTC食物摄入数据集上培训了用于食品分类，并在我们的模拟LTC食物摄入数据集上进行测试（每种餐路;每次最多15级;前1个分类准确度：88.9％;意味着进气错误： - 0.4 ml $ \ PM $ 36.7毫升）。营养摄入量的估计与质量的营养估计与质量（$ ^ 2 $ 0.92至0.99）之间的营养估计与方法之间的良好符合（$ \ sigma $ = -2.7至-0.01;零在协议的每一个限制中， 。 AFINI-T方法是深度学习的动力计算营养传感系统，可以提供更准确地和客观地跟踪LTC驻留食物摄入量的新颖手段，以支持和防止营养不良跟踪策略。

In the past years, deep learning has seen an increase of usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their own uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole-Slide-Images under domain shift using the H\&E stained Camelyon17 breast cancer dataset. Although it is known that histopathological data can be subject to strong domain shift and label noise, to our knowledge this is the first work that compares the most common methods for uncertainty estimation under these aspects. In our experiments, we compare Stochastic Variational Inference, Monte-Carlo Dropout, Deep Ensembles, Test-Time Data Augmentation as well as combinations thereof. We observe that ensembles of methods generally lead to higher accuracies and better calibration and that Test-Time Data Augmentation can be a promising alternative when choosing an appropriate set of augmentations. Across methods, a rejection of the most uncertain tiles leads to a significant increase in classification accuracy on both in-distribution as well as out-of-distribution data. Furthermore, we conduct experiments comparing these methods under varying conditions of label noise. We observe that the border regions of the Camelyon17 dataset are subject to label noise and evaluate the robustness of the included methods against different noise levels. Lastly, we publish our code framework to facilitate further research on uncertainty estimation on histopathological data.

Charisma is considered as one's ability to attract and potentially also influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of charisma, mediating human-to-human conversation, or identifying charismatic individuals in big social data. A number of models exist that base charisma on various dimensions, often following the idea that charisma is given if someone could and would help others. Examples include influence (could help) and affability (would help) in scientific studies or power (could help), presence, and warmth (both would help) as a popular concept. Modelling high levels in these dimensions for humanoid robots or virtual agents, seems accomplishable. Beyond, also automatic measurement appears quite feasible with the recent advances in the related fields of Affective Computing and Social Signal Processing. Here, we, thereforem present a blueprint for building machines that can appear charismatic, but also analyse the charisma of others. To this end, we first provide the psychological perspective including different models of charisma and behavioural cues of it. We then switch to conversational charisma in spoken language as an exemplary modality that is essential for human-human and human-computer conversations. The computational perspective then deals with the recognition and generation of charismatic behaviour by AI. This includes an overview of the state of play in the field and the aforementioned blueprint. We then name exemplary use cases of computational charismatic skills before switching to ethical aspects and concluding this overview and perspective on building charisma-enabled AI.

Deep learning-based 3D human pose estimation performs best when trained on large amounts of labeled data, making combined learning from many datasets an important research direction. One obstacle to this endeavor are the different skeleton formats provided by different datasets, i.e., they do not label the same set of anatomical landmarks. There is little prior research on how to best supervise one model with such discrepant labels. We show that simply using separate output heads for different skeletons results in inconsistent depth estimates and insufficient information sharing across skeletons. As a remedy, we propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. The discovered latent 3D points capture the redundancy among skeletons, enabling enhanced information sharing when used for consistency regularization. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model, which outperforms prior work on a range of benchmarks, including the challenging 3D Poses in the Wild (3DPW) dataset. Our code and models are available for research purposes.

This article concerns Bayesian inference using deep linear networks with output dimension one. In the interpolating (zero noise) regime we show that with Gaussian weight priors and MSE negative log-likelihood loss both the predictive posterior and the Bayesian model evidence can be written in closed form in terms of a class of meromorphic special functions called Meijer-G functions. These results are non-asymptotic and hold for any training dataset, network depth, and hidden layer widths, giving exact solutions to Bayesian interpolation using a deep Gaussian process with a Euclidean covariance at each layer. Through novel asymptotic expansions of Meijer-G functions, a rich new picture of the role of depth emerges. Specifically, we find that the posteriors in deep linear networks with data-independent priors are the same as in shallow networks with evidence maximizing data-dependent priors. In this sense, deep linear networks make provably optimal predictions. We also prove that, starting from data-agnostic priors, Bayesian model evidence in wide networks is only maximized at infinite depth. This gives a principled reason to prefer deeper networks (at least in the linear case). Finally, our results show that with data-agnostic priors a novel notion of effective depth given by \[\#\text{hidden layers}\times\frac{\#\text{training data}}{\text{network width}}\] determines the Bayesian posterior in wide linear networks, giving rigorous new scaling laws for generalization error.

In this paper we study the smooth strongly convex minimization problem $\min_{x}\min_y f(x,y)$. The existing optimal first-order methods require $\mathcal{O}(\sqrt{\max\{\kappa_x,\kappa_y\}} \log 1/\epsilon)$ of computations of both $\nabla_x f(x,y)$ and $\nabla_y f(x,y)$, where $\kappa_x$ and $\kappa_y$ are condition numbers with respect to variable blocks $x$ and $y$. We propose a new algorithm that only requires $\mathcal{O}(\sqrt{\kappa_x} \log 1/\epsilon)$ of computations of $\nabla_x f(x,y)$ and $\mathcal{O}(\sqrt{\kappa_y} \log 1/\epsilon)$ computations of $\nabla_y f(x,y)$. In some applications $\kappa_x \gg \kappa_y$, and computation of $\nabla_y f(x,y)$ is significantly cheaper than computation of $\nabla_x f(x,y)$. In this case, our algorithm substantially outperforms the existing state-of-the-art methods.