最近，我们提供了Wenet，这是一种面向生产的端到端语音识别工具包，它引入了统一的两通道（U2）框架和内置运行时，以解决单个中的流和非流传输模式。模型。为了进一步提高ASR性能并促进各种生产要求，在本文中，我们提出了Wenet 2.0，并提供四个重要的更新。 （1）我们提出了U2 ++，这是一个带有双向注意解码器的统一的两次通行框架，其中包括通过左右注意力解码器的未来上下文信息，以提高共享编码器的代表性和在夺回阶段的表现。 （2）我们将基于N-Gram的语言模型和基于WFST的解码器引入WENET 2.0，从而促进了在生产方案中使用丰富的文本数据。 （3）我们设计了一个统一的上下文偏见框架，该框架利用特定于用户的上下文（例如联系人列表）为生产提供快速适应能力，并提高了使用LM和没有LM场景的ASR准确性。 （4）我们设计了一个统一的IO，以支持大规模数据进行有效的模型培训。总而言之，全新的WENET 2.0可在各种Corpora上的原始WENET上取得高达10 \％的相对识别性能提高，并提供了一些重要的以生产为导向的功能。

在本文中，设计了一种人类的驾驶和决策框架，专为自治车辆（AVS）而设计，其旨在使AVS更好地融入人类驾驶的运输生态，并消除人类驱动程序对自主驾驶的误解和不相容。基于对交互数据集的分析，以模糊推理方法建立驾驶侵略性估计模型。然后，设计了一种人类的驾驶模型，将大脑情绪学习电路模型（BELCM）与两点预览模型集成。在人类的车道改变决策算法中，成本函数全面考虑推动安全性和旅行效率。基于成本函数和多约束，动态游戏算法应用于建模AV和人司机之间的交互和决策。另外，为了保证AVS的车道变化安全，建立了人工潜在的场模型，用于碰撞风险评估。最后，基于驾驶模拟器，利用LOOM-in--in--in--in--in--in--in--in--in-in-in-in in算法进行评估。

当前的密集文本检索模型面临两个典型的挑战。首先，他们采用暹罗双重编码架构来独立编码查询和文档，以快速索引和搜索，同时忽略了较细粒度的术语互动。这导致了次优的召回表现。其次，他们的模型培训高度依赖于负面抽样技术，以在其对比损失中构建负面文档。为了应对这些挑战，我们提出了对抗猎犬速率（AR2），它由双重编码猎犬加上跨编码器等级组成。这两种模型是根据最小群体对手的共同优化的：检索员学会了检索负面文件以欺骗排名者，而排名者学会了对包括地面和检索的候选人进行排名，并提供渐进的直接反馈对双编码器检索器。通过这款对抗性游戏，猎犬逐渐生产出更难的负面文件来训练更好的排名者，而跨编码器排名者提供了渐进式反馈以改善检索器。我们在三个基准测试基准上评估AR2。实验结果表明，AR2始终如一地胜过现有的致密回收者方法，并在所有这些方法上实现了新的最新结果。这包括对自然问题的改进R@5％至77.9％（+2.1％），Triviaqa R@5％至78.2％（+1.4）和MS-Marco MRR@10％至39.5％（+1.3％）。代码和型号可在https://github.com/microsoft/ar2上找到。

本文介绍了WenetsPeech，一个由10000多小时的高质量标记语音组成的多域普通话语料库，2400多小时弱贴言论，大约100万小时的语音，总共22400多小时。我们收集来自YouTube和Podcast的数据，涵盖各种演讲样式，场景，域名，主题和嘈杂的条件。引入了基于光学字符识别（OCR）的方法，以在其对应的视频字幕上为YouTube数据生成音频/文本分段候选，而高质量的ASR转录系统用于为播客数据生成音频/文本对候选。然后我们提出了一种新的端到端标签错误检测方法，可以进一步验证和过滤候选者。我们还提供三个手动标记的高质量测试集，以及WenetsPeech进行评估 - 开发用于训练中的交叉验证目的，从互联网收集的匹配测试，并从真实会议中记录的测试\ _MEETING，以获得更具挑战性的不匹配测试。使用有线exeeEX培训的基线系统，用于三个流行的语音识别工具包，即Kaldi，Espnet和Wenet，以及三个测试集的识别结果也被提供为基准。据我们所知，WenetsPeech是目前最大的开放式普通话语音语料库，其中有利于生产级语音识别的研究。

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.

Despite some successful applications of goal-driven navigation, existing deep reinforcement learning-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-art baselines. Demonstration videos are available at \colorb{https://youtu.be/93LGlGvaN0c.

While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed. This called for a high-performance adaptive bitrate (ABR) algorithm that can maximize the user quality of experience given 5G network characteristics and data rate of UHD contents. Recently, many of the newly proposed ABR techniques were machine-learning based. Among that, Pensieve is one of the state-of-the-art techniques, which utilized reinforcement-learning to generate an ABR algorithm based on observation of past decision performance. By incorporating the context of the 5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New QoE metrics that more accurately represent the QoE of UHD video streaming on the different types of devices were proposed and used to evaluate Pensieve 5G against other ABR techniques including the original Pensieve. The results from the simulation based on the real 5G Standalone (SA) network throughput shows that Pensieve 5G outperforms both conventional algorithms and Pensieve with the average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve 5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC) Network, despite the training being done solely using the data from the 5G Standalone (SA) network.

This paper introduces a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2023. We address specifically three issues, including (1) B-frame coding, (2) YUV 4:2:0 coding, and (3) content-adaptive variable-rate coding with only one single model. Most learned video codecs operate internally in the RGB domain for P-frame coding. B-frame coding for YUV 4:2:0 content is largely under-explored. In addition, while there have been prior works on variable-rate coding with conditional convolution, most of them fail to consider the content information. We build our scheme on conditional augmented normalized flows (CANF). It features conditional motion and inter-frame codecs for efficient B-frame coding. To cope with YUV 4:2:0 content, two conditional inter-frame codecs are used to process the Y and UV components separately, with the coding of the UV components conditioned additionally on the Y component. Moreover, we introduce adaptive feature modulation in every convolutional layer, taking into account both the content information and the coding levels of B-frames to achieve content-adaptive variable-rate coding. Experimental results show that our model outperforms x265 and the winner of last year's challenge on commonly used datasets in terms of PSNR-YUV.