In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the non-coherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance.

Federated learning achieves joint training of deep models by connecting decentralized data sources, which can significantly mitigate the risk of privacy leakage. However, in a more general case, the distributions of labels among clients are different, called ``label distribution skew''. Directly applying conventional federated learning without consideration of label distribution skew issue significantly hurts the performance of the global model. To this end, we propose a novel federated learning method, named FedMGD, to alleviate the performance degradation caused by the label distribution skew issue. It introduces a global Generative Adversarial Network to model the global data distribution without access to local datasets, so the global model can be trained using the global information of data distribution without privacy leakage. The experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art on several public benchmarks. Code is available at \url{https://github.com/Sheng-T/FedMGD}.

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.

In dense image segmentation tasks (e.g., semantic, panoptic), existing methods can hardly generalize well to unseen image domains, predefined classes, and image resolution & quality variations. Motivated by these observations, we construct a large-scale entity segmentation dataset to explore fine-grained entity segmentation, with a strong focus on open-world and high-quality dense segmentation. The dataset contains images spanning diverse image domains and resolutions, along with high-quality mask annotations for training and testing. Given the high-quality and -resolution nature of the dataset, we propose CropFormer for high-quality segmentation, which can improve mask prediction using high-res image crops that provide more fine-grained image details than the full image. CropFormer is the first query-based Transformer architecture that can effectively ensemble mask predictions from multiple image crops, by learning queries that can associate the same entities across the full image and its crop. With CropFormer, we achieve a significant AP gain of $1.9$ on the challenging fine-grained entity segmentation task. The dataset and code will be released at http://luqi.info/entityv2.github.io/.

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.

Transformer-based language models have become the standard approach to solving natural language processing tasks. However, industry adoption usually requires the maximum throughput to comply with certain latency constraints that prevents Transformer models from being used in production. To address this gap, model compression techniques such as quantization and pruning may be used to improve inference efficiency. However, these compression techniques require specialized software to apply and deploy at scale. In this work, we propose a new pipeline for creating and running Fast Transformer models on CPUs, utilizing hardware-aware pruning, knowledge distillation, quantization, and our own Transformer inference runtime engine with optimized kernels for sparse and quantized operators. We demonstrate the efficiency of our pipeline by creating a Fast DistilBERT model showing minimal accuracy loss on the question-answering SQuADv1.1 benchmark, and throughput results under typical production constraints and environments. Our results outperform existing state-of-the-art Neural Magic's DeepSparse runtime performance by up to 50% and up to 4.1x performance speedup over ONNX Runtime. Source code is publicly available at https://github.com/intel/intel-extension-for-transformers.

科学文献是高质量的语料库，支持大量自然语言处理（NLP）研究。但是，现有数据集围绕英语，这限制了中国科学NLP的发展。在这项工作中，我们提出了CSL，这是一个大规模的中国科学文献数据集，其中包含396K论文的标题，摘要，关键字和学术领域。据我们所知，CSL是中文中的第一个科学文档数据集。 CSL可以用作中国语料库。同样，该半结构化数据是一种自然注释，可以构成许多监督的NLP任务。基于CSL，我们提出了一个基准，以评估跨科学领域任务的模型的性能，即摘要，关键字生成和文本分类。我们分析了现有文本到文本模型在评估任务上的行为，并揭示了中国科学NLP任务的挑战，该任务为未来的研究提供了宝贵的参考。数据和代码可在https://github.com/ydli-ai/csl上找到

创伤性脑损伤（TBI）患者的脑网络分析对于其意识水平评估和预后评估至关重要，这需要分割某些意识相关的大脑区域。但是，由于很难收集TBI患者的手动注释的MR扫描，因此很难构建TBI分割模型。数据增强技术可用于缓解数据稀缺问题。但是，常规数据增强策略（例如空间和强度转化）无法模仿创伤性大脑中的变形和病变，这限制了后续分割任务的性能。为了解决这些问题，我们提出了一种名为TBIGA的新型医学图像授课模型，以通过配对的脑标签图合成TBI MR扫描。我们的TBIGAN方法的主要优势在于，它可以同时生成TBI图像和相应的标签映射，这在以前的医学图像的先前涂上方法中尚未实现。我们首先按照粗到细节的方式在边缘信息的指导下生成成分的图像，然后将合成强度图像用作标签上填充的先验。此外，我们引入了基于注册的模板增强管道，以增加合成图像对的多样性并增强数据增强能力。实验结果表明，提出的TBIGAN方法可以产生具有高质量和有效标签图的足够合成的TBI图像，这可以大大改善与替代方案相比的2D和3D创伤性脑部分割性能。

步态计划是一种通常应用于地面机器人的过程，例如四足机器人； Tilt-Rotor是一种新型的四型四个输入，不是其中之一。在控制倾斜 - 依赖反馈线性化的倾斜旋转时，预计倾斜角度（输入）将过度改变，这在应用程序中可能不会预期。为了帮助抑制倾斜角度的密集变化，在反馈线性化之前，将步态计划程序引入倾斜度。用户提前时间指定倾斜角度，而不是由控制规则给出。但是，基于这种情况，反馈线性化中的去耦矩阵对于某些态度，滚动角度和螺距角的组合可能是单数的。它阻碍了反馈线性化的进一步应用。因此，建立了两个彩色图定理，以最大程度地提高可接受的态度区域，在该区域中，滚动和音高的组合将产生可逆的去耦矩阵。然而，该定理过度限制了倾斜角度的选择，这可以排除一些可行的健壮步态。本文给出了广义的两个彩色图定理。所有健壮的步态都可以根据这种广义定理找到。分析了满足该广义的两个彩色图定理（违反两个彩色图定理）的三个步态的鲁棒性。结果表明，概括的两个颜色图定理完成了对倾斜旋转的稳健步态的搜索。

Rylls Tilt-Rotor是无人机，有八个输入；可以根据控制规则指定推力的四个大小以及推力的四个倾斜角。尽管取得了模拟的成功，但常规反馈线性化证明了投入的过度变化，同时适用于稳定Rylls倾斜旋转。因此，我们以前的研究将额外的步态计划提交了额外的程序，以抑制倾斜角度的意外变化。伴随两个颜色地图定理，倾斜角度是坚定而连续的。设计的步态对态度的改变是可靠的。但是，在进一步应用跟踪模拟测试之前，这不是一个完整的理论。本文进一步讨论了两个颜色图定理之后的一些步态，并模拟了倾斜旋转的跟踪问题。均匀的圆形移动参考设计为由配备了设计健壮步态和反馈线性化控制器的倾斜旋转器跟踪。满足两个彩色图定理的步态显示了鲁棒性。模拟的结果显示了跟踪倾斜旋转的成功。