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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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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Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being applied to modern model architectures including innovative layer building blocks, which is caused by two reasons. Firstly, the high flexibility of rule-based XAI methods leads to numerous potential parameterizations. Secondly, many XAI methods break the implementation-invariance axiom because they struggle with certain model components, e.g., BatchNorm layers. The latter can be addressed with model canonization, which is the process of re-structuring the model to disregard problematic components without changing the underlying function. While model canonization is straightforward for simple architectures (e.g., VGG, ResNet), it can be challenging for more complex and highly interconnected models (e.g., DenseNet). Moreover, there is only little quantifiable evidence that model canonization is beneficial for XAI. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures,including VGG, ResNet, EfficientNet, DenseNets, as well as Relation Networks. We further suggest a XAI evaluation framework with which we quantify and compare the effect sof model canonization for various XAI methods in image classification tasks on the Pascal-VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI. Moreover, addressing the former issue outlined above, we demonstrate how our evaluation framework can be applied to perform hyperparameter search for XAI methods to optimize the quality of explanations.
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Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitatively distinct neural network models often predict similar representational geometries of typical stimulus sets. We propose a Bayesian experimental design approach to synthesizing stimulus sets for adjudicating among representational models efficiently. We apply our method to discriminate among candidate neural network models of behavioral face dissimilarity judgments. Our results indicate that a neural network trained to invert a 3D-face-model graphics renderer is more human-aligned than the same architecture trained on identification, classification, or autoencoding. Our proposed stimulus synthesis objective is generally applicable to designing experiments to be analyzed by representational similarity analysis for model comparison.
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Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
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我们提供了奖励黑客的第一个正式定义,即优化不完美的代理奖励功能的现象,$ \ Mathcal {\ tilde {r}} $,根据真实的奖励功能,$ \ MATHCAL {R} $导致性能差。 。我们说,如果增加预期的代理回报率永远无法减少预期的真实回报,则代理是不可接受的。直觉上,可以通过从奖励功能(使其“较窄”)中留出一些术语或忽略大致等效的结果之间的细粒度区分来创建一个不可接受的代理,但是我们表明情况通常不是这样。一个关键的见解是,奖励的线性性(在州行动访问计数中)使得无法实现的状况非常强烈。特别是,对于所有随机策略的集合,只有在其中一个是恒定的,只有两个奖励函数才能是不可接受的。因此,我们将注意力转移到确定性的政策和有限的随机政策集中,在这些策略中,始终存在非平凡的不可动摇的对,并为简化的存在建立必要和充分的条件,这是一个重要的不被限制的特殊情况。我们的结果揭示了使用奖励函数指定狭窄任务和对齐人类价值的AI系统之间的紧张关系。
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我们提出了Zeroeggs,这是一个神经网络框架,用于语音驱动的手势生成,以零拍出样式控制。这意味着即使在训练过程中看不见的运动样式,也只能通过一个简短的运动剪辑来控制样式。我们的模型使用一个变性框架来学习样式嵌入,从而可以通过潜在的空间操纵或样式嵌入方式的混合和缩放来修改样式。我们框架的概率性质进一步使给定输入相同的各种输出的产生,以解决手势运动的随机性质。在一系列实验中,我们首先证明了模型对新的扬声器和样式的灵活性和概括性。然后,在一项用户研究中,我们表明我们的模型在运动,语音适当性和风格刻画方面的自然性,适当性和刻画的表现优于先前的最先进技术。最后,我们释放了包括手指在内的全身手势运动的高质量数据集,语音跨越了19种不同的样式。
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ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列,该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战,这是由于探测器的几何形状,不均匀的散射和冰中光的吸收,并且低于100 GEV的光,每个事件产生的信号光子数量相对较少。为了应对这一挑战,可以将ICECUBE事件表示为点云图形,并将图形神经网络(GNN)作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开,对不同的中微子事件类型进行分类,并重建沉积的能量,方向和相互作用顶点。基于仿真,我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术,包括已知系统不确定性的影响。对于中微子事件分类,与当前的IceCube方法相比,GNN以固定的假阳性速率(FPR)提高了信号效率的18%。另外,GNN在固定信号效率下将FPR的降低超过8(低于半百分比)。对于能源,方向和相互作用顶点的重建,与当前最大似然技术相比,分辨率平均提高了13%-20%。当在GPU上运行时,GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件,这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。
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视觉假冒物越来越多地导致具有神经图像合成方法快速演变的主流介质中的存在难题。尽管对这种伪造的发现一直是图像法医社区中的一个征税问题,但最近的法医探测器(通用探测器)都能够出人意料地发现伪造的图像,无论发电机架构,损失功能,培训数据集和解决方案如何。这种有趣的属性表明,通用检测器中可能存在可转移的法医特征(T-FF)。在这项工作中,我们进行了第一个分析研究,以发现和理解通用探测器中的T-FF。我们的贡献是2倍:1)我们提出了一个新颖的法医功能相关统计量(FF-RS),以量化和发现通用检测器中的T-FF,以及2)我们的定性和定量研究发现了一个意外的发现:颜色是关键的发现:通用检测器中的T-FF。代码和型号可在https://keshik6.github.io/transferable-forensic-features/
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FIB/SEM断层扫描代表了电池研究和许多其他领域中三维纳米结构表征的必不可少的工具。然而,在许多情况下,对比度和3D分类/重建问题出现,这极大地限制了该技术的适用性,尤其是在多孔材料上,例如电池或燃料电池中用于电极材料的材料。区分不同的组件(例如主动LI存储颗粒和碳/粘合剂材料)很困难,并且通常可以防止对图像数据进行可靠的定量分析,甚至可能导致关于结构 - 质地关系的错误结论。在这项贡献中,我们提出了一种新型的数据分类方法,该方法是通过FIB/SEM断层扫描获得的三维图像数据及其在NMC电池电极材料中的应用。我们使用两个不同的图像信号,即Angled SE2腔室检测器和Inlens检测器信号的信号,将信号组合在一起并训练一个随机森林,即特定的机器学习算法。我们证明,这种方法可以克服适合多相测量的现有技术的当前局限性,并且即使在当前的最新技术失败或对大型训练集的需求之后,它也可以进行定量数据重建。这种方法可能会作为使用FIB/SEM断层扫描的未来研究指南。
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