Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To improve simulation efficiency, several inference methods take a sequential approach and iteratively adapt the proposal distributions from which model simulations are generated. However, many of these sequential methods are difficult to use in practice, both because the resulting optimisation problems can be challenging and efficient diagnostic tools are lacking. To overcome these issues, we present Truncated Sequential Neural Posterior Estimation (TSNPE). TSNPE performs sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches. In addition, TSNPE allows to efficiently perform coverage tests that can scale to complex models with many parameters. We demonstrate that TSNPE performs on par with previous methods on established benchmark tasks. We then apply TSNPE to two challenging problems from neuroscience and show that TSNPE can successfully obtain the posterior distributions, whereas previous methods fail. Overall, our results demonstrate that TSNPE is an efficient, accurate, and robust inference method that can scale to challenging scientific models.

In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical transmission systems. First, we present a realization pipeline showing the conversion of the models from Python libraries to the FPGA chip synthesis and implementation. Then, we review the main alternatives for the hardware implementation of nonlinear activation functions. The main results are divided into three parts: a performance comparison, an analysis of how activation functions are implemented, and a report on the complexity of the hardware. The performance in Q-factor is presented for the cases of bidirectional long-short-term memory coupled with convolutional NN (biLSTM + CNN) equalizer, CNN equalizer, and standard 1-StpS digital back-propagation (DBP) for the simulation and experiment propagation of a single channel dual-polarization (SC-DP) 16QAM at 34 GBd along 17x70km of LEAF. The biLSTM+CNN equalizer provides a similar result to DBP and a 1.7 dB Q-factor gain compared with the chromatic dispersion compensation baseline in the experimental dataset. After that, we assess the Q-factor and the impact of hardware utilization when approximating the activation functions of NN using Taylor series, piecewise linear, and look-up table (LUT) approximations. We also show how to mitigate the approximation errors with extra training and provide some insights into possible gradient problems in the LUT approximation. Finally, to evaluate the complexity of hardware implementation to achieve 400G throughput, fixed-point NN-based equalizers with approximated activation functions are developed and implemented in an FPGA.

To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structure. The latter shows 38\% latency decrease, while impacting the Q-factor by only 0.5dB.

In this paper, a hyperparameter tuning based Bayesian optimization of digital twins is carried out to diagnose various faults in grid connected inverters. As fault detection and diagnosis require very high precision, we channelize our efforts towards an online optimization of the digital twins, which, in turn, allows a flexible implementation with limited amount of data. As a result, the proposed framework not only becomes a practical solution for model versioning and deployment of digital twins design with limited data, but also allows integration of deep learning tools to improve the hyperparameter tuning capabilities. For classification performance assessment, we consider different fault cases in virtual synchronous generator (VSG) controlled grid-forming converters and demonstrate the efficacy of our approach. Our research outcomes reveal the increased accuracy and fidelity levels achieved by our digital twin design, overcoming the shortcomings of traditional hyperparameter tuning methods.

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.

及时，准确地检测功率电子中的异常，对于维持复杂的生产系统而变得越来越重要。强大而可解释的策略有助于减少系统的停机时间，并抢占或减轻基础设施网络攻击。这项工作从解释当前数据集和机器学习算法输出中存在的不确定性类型开始。然后引入和分析三种打击这些不确定性的技术。我们进一步介绍了两种异常检测和分类方法，即矩阵曲线算法和异常变压器，它们是在电源电子转换器数据集的背景下应用的。具体而言，矩阵配置文件算法被证明非常适合作为检测流时间序列数据中实时异常的概括方法。迭代矩阵配置文件的结构python库实现用于创建检测器。创建了一系列自定义过滤器并将其添加到检测器中，以调整其灵敏度，回忆和检测精度。我们的数值结果表明，通过简单的参数调整，检测器在各种故障场景中提供了高精度和性能。

注意层是现代端到端自动语音识别系统不可或缺的一部分，例如作为变压器或构象体体系结构的一部分。注意通常是多头的，每个头部都有一组独立的学习参数，并在相同的输入特征序列上运行。多头注意的输出是单个头部输出的融合。我们经验分析了不同注意力头部产生的表示之间的多样性，并证明在训练过程中头部高度相关。我们研究了一些增加注意力头多样性的方法，包括为每个头部使用不同的注意力机制和辅助训练损失功能来促进头部多样性。我们表明，在训练过程中引入多样性辅助损失功能是一种更有效的方法，并且在Librispeech语料库上获得了多达6％的相对相对的改善。最后，我们在注意力头的多样性与头部参数梯度的相似性之间建立了联系。

在本文中，提出了一种新的方法，该方法允许基于神经网络（NN）均衡器的低复杂性发展，以缓解高速相干光学传输系统中的损伤。在这项工作中，我们提供了已应用于馈电和经常性NN设计的各种深层模型压缩方法的全面描述和比较。此外，我们评估了这些策略对每个NN均衡器的性能的影响。考虑量化，重量聚类，修剪和其他用于模型压缩的尖端策略。在这项工作中，我们提出并评估贝叶斯优化辅助压缩，其中选择了压缩的超参数以同时降低复杂性并提高性能。总之，通过使用模拟和实验数据来评估每种压缩方法的复杂性及其性能之间的权衡，以完成分析。通过利用最佳压缩方法，我们表明可以设计基于NN的均衡器，该均衡器比传统的数字背部传播（DBP）均衡器具有更好的性能，并且只有一个步骤。这是通过减少使用加权聚类和修剪算法后在NN均衡器中使用的乘数数量来完成的。此外，我们证明了基于NN的均衡器也可以实现卓越的性能，同时仍然保持与完整的电子色色散补偿块相同的复杂性。我们通过强调开放问题和现有挑战以及未来的研究方向来结束分析。

在本文中，我们提供了一种系统的方法来评估和比较数字信号处理中神经网络层的计算复杂性。我们提供并链接四个软件到硬件的复杂性度量，定义了不同的复杂度指标与层的超参数的关系。本文解释了如何计算这四个指标以进行馈送和经常性层，并定义在这种情况下，我们应该根据我们是否表征了面向更软件或硬件的应用程序来使用特定的度量。新引入的四个指标之一，称为“添加和位移位数（NAB）”，用于异质量化。 NABS不仅表征了操作中使用的位宽的影响，还表征了算术操作中使用的量化类型。我们打算这项工作作为与神经网络在实时数字信号处理中应用相关的复杂性估计级别（目的）的基线，旨在统一计算复杂性估计。

FPGA中首次实施了针对非线性补偿的经常性和前馈神经网络均衡器，其复杂度与分散均衡器的复杂度相当。我们证明，基于NN的均衡器可以胜过1个速度的DBP。