Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizing all history information, the dialogue state in the last turn is typically adopted as the input for predicting the current state by DST models. However, these models tend to keep the predicted slot values unchanged, which is defined as state momentum in this paper. Specifically, the models struggle to update slot values that need to be changed and correct wrongly predicted slot values in the last turn. To this end, we propose MoNET to tackle state momentum via noise-enhanced training. First, the previous state of each turn in the training data is noised via replacing some of its slot values. Then, the noised previous state is used as the input to learn to predict the current state, improving the model's ability to update and correct slot values. Furthermore, a contrastive context matching framework is designed to narrow the representation distance between a state and its corresponding noised variant, which reduces the impact of noised state and makes the model better understand the dialogue history. Experimental results on MultiWOZ datasets show that MoNET outperforms previous DST methods. Ablations and analysis verify the effectiveness of MoNET in alleviating state momentum and improving anti-noise ability.

真实世界的文本应用程序通常涉及组成广泛的文本控制操作，例如编辑文本W.R.T.属性，操纵关键字和结构，并生成所需属性的新文本。事先的工作通常会学习/芬太尼语言模型（LM）以执行操作的个人或特定子集。最近的研究以插件方式研究了合并操作，通常在复杂序列空间中以昂贵的搜索或优化进行了研究。本文提出了一种新的有效方法，用于在紧凑的文本潜在空间中进行可复合的文本操作。文本潜在矢量的低维度和不同性使我们能够基于给定的任意插入运算符（例如属性分类器）基于普通微分方程（ODE）开发有效的采样器。通过通过有效的适应性将预告片的LMS（例如GPT2）连接到潜在空间，然后我们将采样向量解码为所需的文本序列。灵活的方法允许使用来自不同域中的任何相关数据获取的各种控制操作员（情感，时态，形式，关键字等）。实验表明，在我们的方法中构成这些操作员可以生成或编辑高质量文本，从而在发电质量和效率方面显着改善了以前的方法。

我们报告了激进的量化策略，这些策略极大地加速了复发性神经网络传感器（RNN-T）的推理。我们使用4位整数表示进行权重和激活，并应用量化意识训练（QAT）来重新训练完整模型（声学编码器和语言模型）并实现近乎ISO的准确性。我们表明，根据网络本地属性量身定制的自定义量化方案对于在限制QAT的计算开销的同时，至关重要。密度比语言模型融合已显示出在RNN-T工作负载上的准确性提高，但严重增加了推理的计算成本。我们表明，我们的量化策略可以使用大型宽度宽度进行假设搜索，同时实现与流媒体兼容的运行时间，并且与完整的Precision模型相比，我们可以实现与流相兼容的运行时间和7.6 $ \ times $的完整模型压缩比。通过硬件仿真，我们估计端到端量化的RNN-T（包括LM Fusion）的3.4 $ \ times $从fp16到INT4，导致实时因子（RTF）为0.06。在NIST HUB5 2000，HUB5 2001和RT-03测试集中，我们保留了与LM Fusion相关的大部分收益，将平均WER提高了$ 1.5％。

在图像美学质量评估的任务中，由于美学数据集的正常分布，难以达到高分区域和低得分面积。为了减少标签中的错误并解决正常数据分布的问题，我们提出了一个具有名为AMD-CR的分类和回归的新的美学混合数据集，我们培训了元重传网络以重新重量培训数据的损失不同。此外，我们还提供了一种基于二进制分类任务的伪标签的不同阶段的培训策略，然后我们将其用于审美培训，该课程涉及分类和回归任务的不同阶段。在网络结构的构造中，我们构建一种可以适应输入图像的任何大小的美学自适应块（AAB）结构。此外，我们还使用高效的通道注意力（ECA）来加强每个任务的特征提取能力。实验结果表明，与SROCC中的常规方法相比，我们的方法改善了0.1112。该方法还可以帮助找到无人驾驶飞行器（UAV）和车辆的最佳审美路径规划。

分布式深度学习（DDL）对于大型深度学习（DL）培训至关重要。同步随机梯度下降（SSGD）1是事实上的DDL优化方法。使用足够大的批量大小对于实现DDL运行时加速至关重要。在大量批量设置中，必须增加学习速率以补偿减少的参数更新数量。然而，大型学习率可能会损害SSGD和培训可以很容易地分歧。最近，已经提出了分散的平行SGD（DPSGD）以改善分布式训练速度。在本文中，我们发现DPSGD不仅具有系统明智的运行时效，而且在大批量设置中对SSGD的显着收敛性有益。基于对DPSGD学习动态的详细分析，我们发现DPSGD引入了额外的横向依赖性噪声，可自动调整有效的学习率以提高收敛。此外，我们理论上表明这种噪音平滑了损失景观，因此允许更大的学习率。我们在18个最先进的DL模型/任务中进行广泛的研究，并证明DPSGD通常会收敛于SSGD在大批批量设置中大的学习速率的情况下融合。我们的发现一致地遍布两个不同的应用领域：计算机视觉（CIFAR10和Imagenet-1K）和自动语音识别（SWB300和SWB2000），以及两种不同类型的神经网络模型：卷积神经网络和长短期内存经常性神经网络。

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.

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/.

Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up manually into one which leverages data-driven algorithmic management approaches powered by artificial intelligence (AI) to achieve increasingly sophisticated optimization objectives. In this paper, we provide a survey presenting a unique systematic overview on how AI can empower crowdsourcing - which we refer to as AI-Empowered Crowdsourcing(AIEC). We propose a taxonomy which divides algorithmic crowdsourcing into three major areas: 1) task delegation, 2) motivating workers, and 3) quality control, focusing on the major objectives which need to be accomplished. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.

Fine-grained classification and counting of bone marrow erythroid cells are vital for evaluating the health status and formulating therapeutic schedules for leukemia or hematopathy. Due to the subtle visual differences between different types of erythroid cells, it is challenging to apply existing image-based deep learning models for fine-grained erythroid cell classification. Moreover, there is no large open-source datasets on erythroid cells to support the model training. In this paper, we introduce BMEC (Bone Morrow Erythroid Cells), the first large fine-grained image dataset of erythroid cells, to facilitate more deep learning research on erythroid cells. BMEC contains 5,666 images of individual erythroid cells, each of which is extracted from the bone marrow erythroid cell smears and professionally annotated to one of the four types of erythroid cells. To distinguish the erythroid cells, one key indicator is the cell shape which is closely related to the cell growth and maturation. Therefore, we design a novel shape-aware image classification network for fine-grained erythroid cell classification. The shape feature is extracted from the shape mask image and aggregated to the raw image feature with a shape attention module. With the shape-attended image feature, our network achieved superior classification performance (81.12\% top-1 accuracy) on the BMEC dataset comparing to the baseline methods. Ablation studies also demonstrate the effectiveness of incorporating the shape information for the fine-grained cell classification. To further verify the generalizability of our method, we tested our network on two additional public white blood cells (WBC) datasets and the results show our shape-aware method can generally outperform recent state-of-the-art works on classifying the WBC. The code and BMEC dataset can be found on https://github.com/wangye8899/BMEC.