本文提出了将语音分离和增强（SSE）集成到ESPNET工具包中的最新进展。与以前的ESPNET-SE工作相比，已经添加了许多功能，包括最近的最新语音增强模型，并具有各自的培训和评估食谱。重要的是，已经设计了一个新界面，以灵活地将语音增强前端与其他任务相结合，包括自动语音识别（ASR），语音翻译（ST）和口语理解（SLU）。为了展示这种集成，我们在精心设计的合成数据集上进行了实验，用于嘈杂的多通道ST和SLU任务，可以用作未来研究的基准语料库。除了这些新任务外，我们还使用Chime-4和WSJ0-2MIX进行基准多链和单渠道SE方法。结果表明，即使在ASR以外的任务，尤其是在多频道方案中，SE前端与后端任务的集成也是一个有希望的研究方向。该代码可在https://github.com/espnet/espnet上在线获得。 HuggingFace上发布了这项工作的另一个贡献的多通道ST和SLU数据集。

基于深度的学习模型具有显着提高了语音分离的性能与鸡尾酒会等输入混合物。突出的方法（例如，频域和时域语音分离）通常建立回归模型以使用基于掩蔽的设计和信号级损耗标准（例如，MSE或SI-）来预测来自混合的地面真理语音。（例如，MSE或SI- SNR）。这项研究首次证明了基于综合的方法也可以在这个问题上表现良好，具有很大的灵活性和强大的潜力。具体地，我们提出了一种基于离散符号的识别的新型语音分离/增强模型，并将语音分离/增强相关任务的范例转换为分类。通过利用具有离散符号的输入的合成模型，在预测离散符号序列之后，可以重新合成每个目标语音。基于WSJ0-2MIX和VCTK-Noisy Corpora的评估结果在各种设置中表明，我们所提出的方法可以稳定地用高语音质量且没有任何干扰的分离的语音，这难以避免基于回归的方法。此外，通过忽略的聆听质量损失，通过我们的方法可以轻松实现增强/分离演讲的扬声器转换。

扩散概率模型已经证明了通过配对的扩散和反向过程模拟自然图像和原始音频波形的出色能力。可以利用反向过程的唯一特性（即，从高斯噪声和噪声信号中消除非目标信号）来恢复清洁信号。基于此属性，我们提出了一种基于扩散的基于概率模型的语言增强（漫反射）模型，其旨在从嘈杂的信号中恢复清洁语音信号。所提出的漫射模型的基本架构类似于差异 - 一种具有相对低的计算成本和足迹的高质量音频波形生成模型。为了获得更好的增强性能，我们设计了先进的反向过程，称为支持性反向过程，在每个时间步骤到预测的语音，这会增加噪音。实验结果表明，漫反射率与标准化语音银行语料库SE任务上的相关音频生成模型相当的性能。此外，相对于普遍建议的完整采样时间表，所提出的支持逆过程特别改善了快速采样，采取了几个步骤，从而产生更好的增强，从而通过传统的完整步长推断过程。

以前的研究已经证实了利用明晰度信息达到改善的语音增强（SE）性能的有效性。通过使用铰接特征的地点/方式增强原始声学特征，可以引导SE过程考虑执行增强时输入语音的剖视特性。因此，我们认为关节属性的上下文信息应包括有用的信息，并可以进一步利用不同的语言。在这项研究中，我们提出了一个SE系统，通过优化英语和普通话的增强演讲中的上下文清晰度信息来提高其性能。我们通过联合列车与端到端的自动语音识别（E2E ASR）模型进行联合列车，预测广播序列（BPC）而不是单词序列的序列。同时，开发了两种培训策略，以基于基于BPC的ASR：多任务学习和深度特征培训策略来培训SE系统。 Timit和TMhint DataSet上的实验结果证实了上下文化学信息促进了SE系统，以实现比传统声学模型（AM）更好的结果。此外，与用单声道ASR培训的另一SE系统相比，基于BPC的ASR（提供上下文化学信息）可以在不同的信噪比（SNR）下更有效地改善SE性能。

We revisit the domain of off-policy policy optimization in RL from the perspective of coordinate ascent. One commonly-used approach is to leverage the off-policy policy gradient to optimize a surrogate objective -- the total discounted in expectation return of the target policy with respect to the state distribution of the behavior policy. However, this approach has been shown to suffer from the distribution mismatch issue, and therefore significant efforts are needed for correcting this mismatch either via state distribution correction or a counterfactual method. In this paper, we rethink off-policy learning via Coordinate Ascent Policy Optimization (CAPO), an off-policy actor-critic algorithm that decouples policy improvement from the state distribution of the behavior policy without using the policy gradient. This design obviates the need for distribution correction or importance sampling in the policy improvement step of off-policy policy gradient. We establish the global convergence of CAPO with general coordinate selection and then further quantify the convergence rates of several instances of CAPO with popular coordinate selection rules, including the cyclic and the randomized variants of CAPO. We then extend CAPO to neural policies for a more practical implementation. Through experiments, we demonstrate that CAPO provides a competitive approach to RL in practice.

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.