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。
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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。
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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/。
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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.
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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。
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When data is streaming from multiple sources, conventional training methods update model weights often assuming the same level of reliability for each source; that is: a model does not consider data quality of each source during training. In many applications, sources can have varied levels of noise or corruption that has negative effects on the learning of a robust deep learning model. A key issue is that the quality of data or labels for individual sources is often not available during training and could vary over time. Our solution to this problem is to consider the mistakes made while training on data originating from sources and utilise this to create a perceived data quality for each source. This paper demonstrates a straight-forward and novel technique that can be applied to any gradient descent optimiser: Update model weights as a function of the perceived reliability of data sources within a wider data set. The algorithm controls the plasticity of a given model to weight updates based on the history of losses from individual data sources. We show that applying this technique can significantly improve model performance when trained on a mixture of reliable and unreliable data sources, and maintain performance when models are trained on data sources that are all considered reliable. All code to reproduce this work's experiments and implement the algorithm in the reader's own models is made available.
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Computer vision and machine learning are playing an increasingly important role in computer-assisted diagnosis; however, the application of deep learning to medical imaging has challenges in data availability and data imbalance, and it is especially important that models for medical imaging are built to be trustworthy. Therefore, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which adopts a modular design, leverages self-supervised pre-training, and utilizes a novel surrogate loss function. Experimental evaluations indicate that models generated from the framework are both trustworthy and high-performing. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
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We continue a long line of research aimed at proving convergence of depth 2 neural networks, trained via gradient descent, to a global minimum. Like in many previous works, our model has the following features: regression with quadratic loss function, fully connected feedforward architecture, RelU activations, Gaussian data instances and network initialization, adversarial labels. It is more general in the sense that we allow both layers to be trained simultaneously and at {\em different} rates. Our results improve on state-of-the-art [Oymak Soltanolkotabi 20] (training the first layer only) and [Nguyen 21, Section 3.2] (training both layers with Le Cun's initialization). We also report several simple experiments with synthetic data. They strongly suggest that, at least in our model, the convergence phenomenon extends well beyond the ``NTK regime''.
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Besides the recent impressive results on reinforcement learning (RL), safety is still one of the major research challenges in RL. RL is a machine-learning approach to determine near-optimal policies in Markov decision processes (MDPs). In this paper, we consider the setting where the safety-relevant fragment of the MDP together with a temporal logic safety specification is given and many safety violations can be avoided by planning ahead a short time into the future. We propose an approach for online safety shielding of RL agents. During runtime, the shield analyses the safety of each available action. For any action, the shield computes the maximal probability to not violate the safety specification within the next $k$ steps when executing this action. Based on this probability and a given threshold, the shield decides whether to block an action from the agent. Existing offline shielding approaches compute exhaustively the safety of all state-action combinations ahead of time, resulting in huge computation times and large memory consumption. The intuition behind online shielding is to compute at runtime the set of all states that could be reached in the near future. For each of these states, the safety of all available actions is analysed and used for shielding as soon as one of the considered states is reached. Our approach is well suited for high-level planning problems where the time between decisions can be used for safety computations and it is sustainable for the agent to wait until these computations are finished. For our evaluation, we selected a 2-player version of the classical computer game SNAKE. The game represents a high-level planning problem that requires fast decisions and the multiplayer setting induces a large state space, which is computationally expensive to analyse exhaustively.
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This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants$\unicode{x2014}$what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world$\unicode{x2014}$also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing$\unicode{x2014}$leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first$\unicode{x2014}$and key$\unicode{x2014}$step towards such an ecology.
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