ID	原文	译文
52557	位置社交网络(LBSN)用户位置数据的分布不均衡，及某些用户出于对隐私安全的考量刻意隐藏自己部分位置信息等因素加剧了兴趣点(POI)推荐难度。	In location-based social networks(LBSNs), the uneven distribution of users' check-in data and the fact that some users deliberately hide some of their location information for privacy and security concerns aggravate the difficulty of point of interest(POI) recommendation.
52558	就此本文提出了基于元学习的时空神经常微分方程(ML-ODE)来进行有效的下一个POI推荐。	Aiming at this issue, a meta-learning based ordinary differential neural network(ML-ODE) is proposed to carry out effective POI recommendation tasks.
52559	该模型主要是将元学习的思想融入到POI推荐过程中，	ML-ODE leverages the meta-learning mechanism to optimize the parameters of recommendation model.
52560	通过不同任务训练优化初始参数，将数据丰富城市中的泛化移动模式迁移到数据匮乏城市，达到优化POI预测任务的目的。该模型将神经常微分方程用于POI推荐领域，	ML-ODE utilizes different tasks to initial parameters, during which the generalized mobility knowledge in the data-rich area is transferred to the datapoor area to achieve the purpose of optimizing the POI recommendation.
52561	定义连续的动态过程，可以接受任意时刻的输入数据，	The network defines a continuous dynamic process that can accept input data that is sporadically-observed.
52562	克服了大多数时序推荐模型静态离散化的时间间隔处理方式，更适用于POI序列推荐任务。	It overcomes the static discretization constraints of most time series prediction models and is more suitable for POI recommendation.
52563	在真实公开数据集Foursqure上的实验结果表明，ML-ODE在POI推荐方面比当前主流的POI预测方法在NDCG@N指标上提升了超过10%。	The experimental results on the Foursqure real public dataset show that ML-ODE has better performance in POI recommendation than the current state-of-the-art POI recommendation method on the index of NDCG@ N which has been increased by more than10%.
52564	在通过传感器采集信源获得观测数据的过程中，估计信源的数量对源信号处理和观测数据分析起着非常重要的作用。	In the processing of acquiring the observed data by using sensors to collect sources, it is very important to estimate the number of sources for signal processing and observed data analysis.
52565	为了确定稀疏信源的数量，本文提出了增强信号线性聚类特性的可视化估计方法。	In order to determine the number of sparse sources, this paper proposes a visual estimation method to enhance the linear clustering characteristics of signals.
52566	首先，利用短时傅里叶变换(STFT)把时域的观测信号变换成频域中的复频谱以增强观测数据的稀疏性;	Firstly, the short time Fourier transform(STFT) is used to transform the observed signal in the time domain into a complex spectrum in the frequency domain to enhance the sparsity of the observed data.