The ubiquity of camera-embedded devices and the advances in deep learning have stimulated various intelligent mobile video applications. These applications often demand on-device processing of video streams to deliver real-time, high-quality services for privacy and robustness concerns. However, the performance of these applications is constrained by the raw video streams, which tend to be taken with small-aperture cameras of ubiquitous mobile platforms in dim light. Despite extensive low-light video enhancement solutions, they are unfit for deployment to mobile devices due to their complex models and and ignorance of system dynamics like energy budgets. In this paper, we propose AdaEnlight, an energy-aware low-light video stream enhancement system on mobile devices. It achieves real-time video enhancement with competitive visual quality while allowing runtime behavior adaptation to the platform-imposed dynamic energy budgets. We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.
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Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.
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双链DNA断裂（DSB）是一种DNA损伤的形式，可导致异常染色体重排。基于高吞吐量实验的最近技术具有明显的高成本和技术挑战。因此，我们设计了一种基于图形的神经网络的方法来预测DSB（GraphDSB），使用DNA序列特征和染色体结构信息。为了提高模型的表达能力，我们引入跳跃知识架构和几种有效的结构编码方法。结构信息对DSB预测的贡献是通过来自正常人体表皮角蛋白细胞（NHEK）和慢性髓性白血病细胞系（K562）的数据集的实验验证，并且消融研究进一步证明了所提出的设计部件的有效性GraphDSB框架。最后，我们使用GNNExplainer分析节点特征和拓扑到DSB预测的贡献，并证明了5-MER DNA序列特征和两种染色质相互作用模式的高贡献。
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很少有图像分类是一个具有挑战性的问题，旨在仅基于少量培训图像来达到人类的识别水平。少数图像分类的一种主要解决方案是深度度量学习。这些方法是，通过将看不见的样本根据距离的距离进行分类，可在强大的深神经网络中学到的嵌入空间中看到的样品，可以避免以少数图像分类的少数训练图像过度拟合，并实现了最新的图像表现。在本文中，我们提供了对深度度量学习方法的最新审查，以进行2018年至2022年的少量图像分类，并根据度量学习的三个阶段将它们分为三组，即学习功能嵌入，学习课堂表示和学习距离措施。通过这种分类法，我们确定了他们面临的不同方法和问题的新颖性。我们通过讨论当前的挑战和未来趋势进行了少量图像分类的讨论。
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机器学习（ML）已广泛用于无线网络中的有效资源分配（RA）。虽然在小型和简单的网络上实现了极好的性能，但是当发生异质性并且网络尺寸扩展时，大多数现有的基于ML的方法都面临困难。在本文中，专注于在异构设备到设备（D2D）网络中的功率控制/波束成形（PC / BF）上，我们提出了一种名为异构干扰图神经网络（HIGNN）的新型无监督的学习框架来处理这些挑战。首先，我们将多样化的链接特征和干扰关系与异构图形。然后，建议在与相邻链路的有限信息交换之后授权每个链接以获得其各个传输方案。值得注意的是，HIGNN在小型网络上培训后，HIGNN可扩展到具有稳健性能的尺寸的无线网络。数值结果表明，与最先进的基准相比，HIGNN在提供了强大的性能时实现了更高的执行效率。
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Datasets serve as crucial training resources and model performance trackers. However, existing datasets have exposed a plethora of problems, inducing biased models and unreliable evaluation results. In this paper, we propose a model-agnostic dataset evaluation framework for automatic dataset quality evaluation. We seek the statistical properties of the datasets and address three fundamental dimensions: reliability, difficulty, and validity, following a classical testing theory. Taking the Named Entity Recognition (NER) datasets as a case study, we introduce $9$ statistical metrics for a statistical dataset evaluation framework. Experimental results and human evaluation validate that our evaluation framework effectively assesses various aspects of the dataset quality. Furthermore, we study how the dataset scores on our statistical metrics affect the model performance, and appeal for dataset quality evaluation or targeted dataset improvement before training or testing models.
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Diverse data formats and ontologies of task-oriented dialogue (TOD) datasets hinder us from developing general dialogue models that perform well on many datasets and studying knowledge transfer between datasets. To address this issue, we present ConvLab-3, a flexible dialogue system toolkit based on a unified TOD data format. In ConvLab-3, different datasets are transformed into one unified format and loaded by models in the same way. As a result, the cost of adapting a new model or dataset is significantly reduced. Compared to the previous releases of ConvLab (Lee et al., 2019b; Zhu et al., 2020b), ConvLab-3 allows developing dialogue systems with much more datasets and enhances the utility of the reinforcement learning (RL) toolkit for dialogue policies. To showcase the use of ConvLab-3 and inspire future work, we present a comprehensive study with various settings. We show the benefit of pre-training on other datasets for few-shot fine-tuning and RL, and encourage evaluating policy with diverse user simulators.
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供应链平台（SCP）为下游行业提供了许多原材料。与传统的电子商务平台相比，由于用户兴趣有限，SCP中的数据更为稀疏。为了解决数据稀疏问题，可以应用跨域建议（CDR），从而通过源域信息提高目标域的建议性能。但是，将CDR应用于SCP，直接忽略了SCP中商品的层次结构，从而降低了建议性能。为了利用此功能，在本文中，我们以餐饮平台为例，并提出了图形跨域推荐模型GRES。该模型首先构造了树状图，以表示菜肴和成分不同节点的层次结构，然后应用我们提出的Tree2Vec方法将GCN和BERT模型组合到嵌入图中以嵌入图表以获取建议。商业数据集上的实验结果表明，GRES在供应链平台的跨域建议中明显优于最先进的方法。
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深度神经网络已用于多种成功的应用中。但是，由于包含数百万个参数，它们的高度复杂性质导致在延迟需求低的管道中部署期间有问题。结果，更希望获得在推理期间具有相同性能的轻型神经网络。在这项工作中，我们提出了一种基于重量的修剪方法，其中权重根据以前的迭代势头逐渐修剪。神经网络的每个层都根据其相对稀疏性分配了一个重要性值，然后在先前迭代中的重量幅度分配。我们在Alexnet，VGG16和Resnet50等网络上评估了我们的方法，其中包括图像分类数据集，例如CIFAR-10和CIFAR-100。我们发现，在准确性和压缩比方面，结果优于先前的方法。我们的方法能够在两个数据集上获得同一降解的相同降解的15％压缩。
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资源受限的分类任务在实际应用中很常见，例如为疾病诊断分配测试，填补有限数量的职位时雇用决策以及在有限检查预算下制造环境中的缺陷检测。典型的分类算法将学习过程和资源约束视为两个单独的顺序任务。在这里，我们设计了一种自适应学习方法，该方法通过迭代微调错误分类成本来考虑资源限制和共同学习。通过使用公开可用数据集的结构化实验研究，我们评估了采用建议方法的决策树分类器。自适应学习方法的表现要比替代方法要好得多，尤其是对于困难的分类问题，在这种问题上，普通方法的表现可能不令人满意。我们将适应性学习方法设想为处理资源受限分类问题的技术曲目的重要补充。
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