Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.

Motivation: Enhancers are important cis-regulatory elements that regulate a wide range of biological functions and enhance the transcription of target genes. Although many state-of-the-art computational methods have been proposed in order to efficiently identify enhancers, learning globally contextual features is still one of the challenges for computational methods. Regarding the similarities between biological sequences and natural language sentences, the novel BERT-based language techniques have been applied to extracting complex contextual features in various computational biology tasks such as protein function/structure prediction. To speed up the research on enhancer identification, it is urgent to construct a BERT-based enhancer language model. Results: In this paper, we propose a multi-scale enhancer identification method (iEnhancer-ELM) based on enhancer language models, which treat enhancer sequences as natural language sentences that are composed of k-mer nucleotides. iEnhancer-ELM can extract contextual information of multi-scale k-mers with positions from raw enhancer sequences. Benefiting from the complementary information of k-mers in multi-scale, we ensemble four iEnhancer-ELM models for improving enhancer identification. The benchmark comparisons show that our model outperforms state-of-the-art methods. By the interpretable attention mechanism, we finds 30 biological patterns, where 40% (12/30) are verified by a widely used motif tool (STREME) and a popular dataset (JASPAR), demonstrating our model has a potential ability to reveal the biological mechanism of enhancer. Availability: The source code are available at https://github.com/chen-bioinfo/iEnhancer-ELM Contact: junjiechen@hit.edu.cn and junjie.chen.hit@gmail.com; Supplementary information: Supplementary data are available at Bioinformatics online.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy. Yet, existing FL solutions usually rely on a centralized aggregator for model weight aggregation, while assuming clients are honest. Even if data privacy can still be preserved, the problem of single-point failure and data poisoning attack from malicious clients remains unresolved. To tackle this challenge, we propose to use distributed ledger technology (DLT) to achieve FLock, a secure and reliable decentralized Federated Learning system built on blockchain. To guarantee model quality, we design a novel peer-to-peer (P2P) review and reward/slash mechanism to detect and deter malicious clients, powered by on-chain smart contracts. The reward/slash mechanism, in addition, serves as incentives for participants to honestly upload and review model parameters in the FLock system. FLock thus improves the performance and the robustness of FL systems in a fully P2P manner.

Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for link prediction in dynamic interaction graphs: 1) collaborative relationship among users and 2) user personalized interaction patterns. Existing methods often implicitly consider these two factors together, which may lead to noisy user modelling when the two factors diverge. In addition, they usually require time-consuming parameter learning with back-propagation, which is prohibitive for real-time user preference modelling. To this end, this paper proposes FreeGEM, a parameter-free dynamic graph embedding method for link prediction. Firstly, to take advantage of the collaborative relationships, we propose an incremental graph embedding engine to obtain user/item embeddings, which is an Online-Monitor-Offline architecture consisting of an Online module to approximately embed users/items over time, a Monitor module to estimate the approximation error in real time and an Offline module to calibrate the user/item embeddings when the online approximation errors exceed a threshold. Meanwhile, we integrate attribute information into the model, which enables FreeGEM to better model users belonging to some under represented groups. Secondly, we design a personalized dynamic interaction pattern modeller, which combines dynamic time decay with attention mechanism to model user short-term interests. Experimental results on two link prediction tasks show that FreeGEM can outperform the state-of-the-art methods in accuracy while achieving over 36X improvement in efficiency. All code and datasets can be found in https://github.com/FudanCISL/FreeGEM.

高性能的交通流量预测模型设计是一种智能运输系统的核心技术，是工业和学术社区的长期挑战，但仍然具有挑战性。物理原理和数据驱动模型之间缺乏整合是限制该领域发展的重要原因。在文献中，基于物理学的方法通常可以清楚地解释交通流系统的动态过程，但准确性有限，而数据驱动的方法，尤其是使用黑色盒子结构的深度学习，可以提高性能，但不能由于缺乏合理的身体依据，因此要完全信任。为了弥合纯粹数据驱动和物理驱动的方法之间的差距，我们提出了一个物理学引导的深度学习模型，名为时空微分方程网络（STDEN），该模型将交通流动器的物理机理投入到深度神经网络框架中。具体而言，我们假设道路网络上的交通流量是由潜在势能场驱动的（例如水流是由重力场驱动的），并将势能场的时空动态过程作为微分方程网络进行建模。 Stden吸收了数据驱动模型的性能优势和基于物理模型的可解释性，因此被命名为物理指导的预测模型。北京三个现实世界流量数据集的实验表明，我们的模型的表现优于最先进的基线。案例研究进一步验证了stden可以捕获城市交通机制，并具有物理含义的准确预测。提出的微分方程网络建模的框架也可能会阐明其他类似的应用程序。

本文旨在探讨如何合成对其进行训练的现有视频脱毛模型的近距离模糊，可以很好地推广到现实世界中的模糊视频。近年来，基于深度学习的方法已在视频Deblurring任务上取得了希望的成功。但是，对现有合成数据集培训的模型仍然遭受了与现实世界中的模糊场景的概括问题。造成故障的因素仍然未知。因此，我们重新审视经典的模糊综合管道，并找出可能的原因，包括拍摄参数，模糊形成空间和图像信号处理器〜（ISP）。为了分析这些潜在因素的效果，我们首先收集一个超高帧速率（940 fps）原始视频数据集作为数据基础，以综合各种模糊。然后，我们提出了一种新颖的现实模糊合成管道，该管道通过利用模糊形成线索称为原始爆炸。通过大量实验，我们证明了在原始空间中的合成模糊并采用与现实世界测试数据相同的ISP可以有效消除合成数据的负面影响。此外，合成的模糊视频的拍摄参数，例如，曝光时间和框架速率在改善脱毛模型的性能中起着重要作用。令人印象深刻的是，与在现有合成模糊数据集中训练的训练的模型合成的模糊数据训练的模型可以获得超过5DB PSNR的增益。我们认为，新颖的现实合成管道和相应的原始视频数据集可以帮助社区轻松构建自定义的Blur数据集，以改善现实世界的视频DeBlurring性能，而不是费力地收集真实的数据对。

深度学习方法论为高光谱图像（HSI）分析社区的发展做出了很大贡献。但是，这也使HSI分析系统容易受到对抗攻击的影响。为此，我们在本文中提出了一个掩盖的空间光谱自动编码器（MSSA），根据自我监督的学习理论，以增强HSI分析系统的鲁棒性。首先，进行了一个掩盖的序列注意学习模块，以促进沿光谱通道的HSI分析系统的固有鲁棒性。然后，我们开发了一个具有可学习的图形结构的图形卷积网络，以建立全局像素的组合。这样，每种组合中的所有相关像素都可以分散攻击效果，并且在空间方面可以实现更好的防御性能。最后，为了提高防御能力并解决有限标记样品的问题，MSSA采用光谱重建作为借口任务，并以自我监督的方式适合数据集。 - 高光谱分类方法和代表性的对抗防御策略。

多模式情绪分析（MSA）是一种基本复杂的研究问题，因为不同方式与人类情绪表达的模糊性之间的异质性差距。虽然已经成功地建造了MSA的多模式表示，但仍有两个挑战需要解决：1）需要构建更强大的多模式表示来弥合异质性间隙并应对复杂的多模式相互作用和2）必须在整个信息流中有效地建模上下文动态。在这项工作中，我们提出了一种基于相互信息最大化和最小化和身份嵌入（MMMIE）的多模式表示模型。我们将模态对之间的相互信息最大化以及输入数据和相应功能之间的相互信息最小化，以挖掘模态不变和任务相关信息。此外，提出了身份嵌入，以提示下游网络来感知语境信息。两个公共数据集的实验结果证明了所提出的模型的有效性。

联合学习框架通常需要协作者共享共同模型的本地渐变更新，而不是共享培训数据以保留隐私。但是，在梯度泄漏攻击的事先工作表明，可以从梯度揭示私人培训数据。到目前为止，几乎所有相关工程都基于完全连接或卷积神经网络的攻击。鉴于近期适应变压器以解决多种愿景任务的绝大多大浪潮，调查视觉变压器的隐私风险是非常有价值的。在本文中，我们分析了基于自我关注机制的渐变泄漏风险，以理论和实用的方式。特别是，我们提出了4月 - 注意隐私泄漏，这对自我关注的博览会造成了强烈的威胁，如vit。展示视觉变压器如何通过梯度泄露隐私泄漏的风险，我们敦促设计隐私更安全的变压器模型和防守方案的重要性。