Sequential recommendation aims to capture users' dynamic preferences, in which data sparsity is a key problem. Most contrastive learning models leverage data augmentation to address this problem, but they amplify noises in original sequences. Contrastive learning has the assumption that two views (positive pairs) obtained from the same user behavior sequence must be similar. However, noises typically disturb the user's main intention, which results in the dissimilarity of two views. To address this problem, in this work, we formalize the denoising problem by selecting the user's main intention, and apply contrastive learning for the first time under this topic, i.e., we propose a novel framework, namely Multi-Intention Oriented Contrastive Learning Recommender (IOCRec). In order to create high-quality views with intent-level, we fuse local and global intentions to unify sequential patterns and intent-level self-supervision signals. Specifically, we design the sequence encoder in IOCRec which includes three modules: local module, global module and disentangled module. The global module can capture users' global preferences, which is independent of the local module. The disentangled module can obtain multi-intention behind global and local representations. From a fine-grained perspective, IOCRec separates different intentions to guide the denoising process. Extensive experiments on four widely-used real datasets demonstrate the effectiveness of our new method for sequential recommendation.

Idiopathic intracranial hypertension (IIH) is a disorder characterized by elevated intracranial pressure (ICP) that predominantly affects young obese women. IIH is a diagnosis of exclusion. That is, if increased ICP is suspected, magnetic resonance imaging and magnetic resonance venography of the brain are recommended to exclude secondary causes. Imaging findings, such as empty sella, orbital findings, meningocele, and encephalocele, are not diagnostic of ICP, nor does their absence exclude ICP either. Therefore, venous manometry is recommended as the gold standard for evaluation, regardless of previous anatomic imaging results. Venous manometry is an invasive examination that is frequently applied to derive physiologic information concerning the nature of the pressure gradient. However, the pathogenesis of IIH has not been fully elucidated. The presence of venous sinus stenosis in a subset of patients has provided some support for the potential mechanisms underlying this condition. Hence, this review provides an up‐to‐date discussion on the potential pathogenic mechanisms of IIH with a special focus on venous sinus stenosis.Level of Evidence: 1Technical Efficacy Stage: 2

Ground object detection, based on remote sensing satellite imagery, provides the groundwork of numerous applications, so the detection accuracy is of vital importance. The background of remote sensing images is complex, the object size is various, and there are many small objects. In view of the above problems, a multi-attention object detection method (MA-FPN) based on multi-scale is proposed in this paper, which can effectively make the network pay attention to the location of the object and reduce the loss of small object information. According to feature pyramid network (FPN), we firstly put forward a global spatial attention module, which extracts spatial location-related information from shallow features and fuses it with deep features to enhance the position expression ability of deep features. Besides, the paper provides a pixel feature attention module: the multi-scale convolution kernel is employed to generate the feature map of the same size as the input, as well as channel attention is used to assign weights to each layer of feature maps to obtain pixel-level attention maps with good details. Experiments on NWPU, RSOD, and DOTA datasets show that the proposed algorithm outperforms state of the art methods.