In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.

In recent years, the development of accurate deep keyword spotting (KWS) models has resulted in KWS technology being embedded in a number of technologies such as voice assistants. Many of these models rely on large amounts of labelled data to achieve good performance. As a result, their use is restricted to applications for which a large labelled speech data set can be obtained. Self-supervised learning seeks to mitigate the need for large labelled data sets by leveraging unlabelled data, which is easier to obtain in large amounts. However, most self-supervised methods have only been investigated for very large models, whereas KWS models are desired to be small. In this paper, we investigate the use of self-supervised pretraining for the smaller KWS models in a label-deficient scenario. We pretrain the Keyword Transformer model using the self-supervised framework Data2Vec and carry out experiments on a label-deficient setup of the Google Speech Commands data set. It is found that the pretrained models greatly outperform the models without pretraining, showing that Data2Vec pretraining can increase the performance of KWS models in label-deficient scenarios. The source code is made publicly available.

环境场景的重建对于自动机器人应用引起了极大的兴趣，因为必须准确表示环境以确保与机器人的安全互动。同样重要的是，确保机器人与其控制器之间的可靠通信也至关重要。大型智能表面（LIS）是一项由于其通信能力而被广泛研究的技术。此外，由于天线元件的数量，这些表面是无线电传感的有力解决方案。本文提出了一种新颖的方法，可以将LIS在其区域散布的散射器建造的室内环境中获得的无线电环境图转换为室内环境的平面图。利用了基于最小二乘（LS）的方法，U-NET（UN）和条件生成对抗网络（CGAN）来执行此任务。我们表明，可以使用本地和全球测量值正确重建平面图。

口语关键字发现（KWS）处理音频流中的关键字的识别，并且由于几年前深度学习引入的范式转换，这已经成为一种快速增长的技术。这使得在无数的小型电子设备中迅速嵌入深度KW，与语音助手的激活一样不同的目的。前景表明这项技术的社会利用方面持续增长。因此，深刻的KW已经成为言语科学家之间的热门研究课题并不令人惊讶，他们不断寻找KWS性能提高和计算复杂性降低。这篇论文激励了本文，我们将文献综述融为深口语KW，以协助对这项技术感兴趣的从业者和研究人员。具体而言，这一概述通过覆盖对深kWs系统的彻底分析（包括语音特征，声学建模和后处理），鲁棒性方法，应用，数据集，评估指标，深kWs系统和视听kws的性能进行全面分析。本文执行的分析允许我们识别未来研究的许多方向，包括从自动语音识别研究和方向上采用的方向，这些研究和对口语问题的问题是独一无二的。

In this short article, we showcase the derivation of the optimal (minimum error variance) estimator, when one part of the stochastic LTI system output is not measured but is able to be predicted from the measured system outputs. Similar derivations have been done before but not using state-space representation.

本文介绍了一种无监督的基于分段的稳健语音活动检测方法（RVAD）。该方法包括两个去噪之后的传递，然后是语音活动检测（VAD）阶段。在第一通道中，通过使用后验信噪比（SNR）加权能量差来检测语音信号中的高能段，并且如果在段内没有检测到间距，则该段被认为是高能量噪声段并设置为零。在第二种通过中，语音信号由语音增强方法进行去噪，探索了几种方法。接下来，具有间距的相邻帧被分组在一起以形成音调段，并且基于语音统计，俯仰段进一步从两端延伸，以便包括浊音和发声声音和可能的非语音部分。最后，将后验SNR加权能量差应用于用于检测语音活动的去噪语音信号的扩展桨距片段。我们使用两个数据库，大鼠和极光-2评估所提出的方法的VAD性能，该方法包含大量噪声条件。在扬声器验证性能方面进一步评估RVAD方法，在Reddots 2016挑战数据库及其噪声损坏版本方面。实验结果表明，RVAD与许多现有方法有利地比较。此外，我们介绍了一种修改版的RVAD，其中通过计算有效的光谱平坦度计算替换计算密集的俯仰提取。修改的版本显着降低了适度较低的VAD性能成本的计算复杂性，这是在处理大量数据并在低资源设备上运行时的优势。 RVAD的源代码被公开可用。

Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source tasks are available for transfer. In this paper, we propose a prior knowledge guided and transferability based framework to select the best source tasks among a collection of brain image segmentation tasks, to improve the transfer learning performance on the given target task. The framework consists of modality analysis, RoI (region of interest) analysis, and transferability estimation, such that the source task selection can be refined step by step. Specifically, we adapt the state-of-the-art analytical transferability estimation metrics to medical image segmentation tasks and further show that their performance can be significantly boosted by filtering candidate source tasks based on modality and RoI characteristics. Our experiments on brain matter, brain tumor, and white matter hyperintensities segmentation datasets reveal that transferring from different tasks under the same modality is often more successful than transferring from the same task under different modalities. Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance. And such similarity can be captured using the Structural Similarity index in the label space.

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.

Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusion model which learns to refine the output spectrogram of an existing TTS model (e.g., FastSpeech 2) by predicting the residual between the model output and the corresponding ground-truth speech. ResGrad has several advantages: 1) Compare with other acceleration methods for DDPM which need to synthesize speech from scratch, ResGrad reduces the complexity of task by changing the generation target from ground-truth mel-spectrogram to the residual, resulting into a more lightweight model and thus a smaller real-time factor. 2) ResGrad is employed in the inference process of the existing TTS model in a plug-and-play way, without re-training this model. We verify ResGrad on the single-speaker dataset LJSpeech and two more challenging datasets with multiple speakers (LibriTTS) and high sampling rate (VCTK). Experimental results show that in comparison with other speed-up methods of DDPMs: 1) ResGrad achieves better sample quality with the same inference speed measured by real-time factor; 2) with similar speech quality, ResGrad synthesizes speech faster than baseline methods by more than 10 times. Audio samples are available at https://resgrad1.github.io/.

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (<50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.