A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.Comment: Accepted by KDD 201

Leveraging BEV Representation for 360-degree Visual Place Recognition

This paper investigates the advantages of using Bird's Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual cues and spatial awareness. Our method extracts image features using standard convolutional networks and combines the features according to pre-defined 3D grid spatial points. To alleviate the mechanical and time misalignments between cameras, we further introduce deformable attention to learn the compensation. Upon the BEV feature representation, we then employ the polar transform and the Discrete Fourier transform for aggregation, which is shown to be rotation-invariant. In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition. The proposed BEV-based method is evaluated in ablation and comparative studies on two datasets, including on-the-road and off-the-road scenarios. The experimental results verify the hypothesis that BEV can benefit VPR by its superior performance compared to baseline methods. To the best of our knowledge, this is the first trial of employing BEV representation in this task

3D Model-free Visual Localization System from Essential Matrix under Local Planar Motion

Visual localization plays a critical role in the functionality of low-cost autonomous mobile robots. Current state-of-the-art approaches for achieving accurate visual localization are 3D scene-specific, requiring additional computational and storage resources to construct a 3D scene model when facing a new environment. An alternative approach of directly using a database of 2D images for visual localization offers more flexibility. However, such methods currently suffer from limited localization accuracy. In this paper, we propose an accurate and robust multiple checking-based 3D model-free visual localization system to address the aforementioned issues. To ensure high accuracy, our focus is on estimating the pose of a query image relative to the retrieved database images using 2D-2D feature matches. Theoretically, by incorporating the local planar motion constraint into both the estimation of the essential matrix and the triangulation stages, we reduce the minimum required feature matches for absolute pose estimation, thereby enhancing the robustness of outlier rejection. Additionally, we introduce a multiple-checking mechanism to ensure the correctness of the solution throughout the solving process. For validation, qualitative and quantitative experiments are performed on both simulation and two real-world datasets and the experimental results demonstrate a significant enhancement in both accuracy and robustness afforded by the proposed 3D model-free visual localization system

Twist1 enhances hypoxia induced radioresistance in cervical cancer cells by promoting nuclear EGFR localization

Twist1 is a crucial transcription factor that regulates epithelial mesenchymal transition and involves in metastasis. Recent evidence suggests that Twist1 plays important role in hypoxia-induced radioresistance, but the underlying mechanism remains elusive. Here we investigated the change of Twist1 expression in human cervical squamous cancer cell line SiHa after hypoxia treatment. We also explored the role of Twist1 in radioresistance by manipulating the expression level of Twist1.We observed that hypoxia treatment elevated the expression of Twist1 in SiHa cells. Knockdown of Twist1 with siRNA increased the radiosensitivity of SiHa cells under hypoxia condition, accompanied by reduced levels of nuclear Epidermal Growth Factor Receptor (EGFR) and DNA-dependent protein kinase (DNA-PK). Conversely, overexpression of Twist1 led to increased radioresistance of SiHa cells, which in turn increased nuclear EGFR localisation and expression levels of nuclear DNA-PK. Moreover, concomitant high expression of hypoxia-inducible factor-1? (HIF-1?) and Twist1 in primary tumors of cervical cancer patients correlated with the worse prognosis after irradiation treatment. Taken together, these data provide new insights into molecular mechanism underlying hypoxia-induced radio resistance in cervical cancer cells, and suggest that Twist1 is a promising molecular target to improve the efficacy of cancer radiotherapy

PRIMA-1Met suppresses colorectal cancer independent of p53 by targeting MEK

This work was supported by Grant No. 81201779 (Hua Xiong) from the National Natural Science Youth Foundation; Grant No. 81502118 (Yanmei Zou) from the National Natural Science Youth Foundation; Grant No. 2014CFB250 (Yanmei Zou) from the Natural Science Foundation of Hubei Province; Grant No. 81372434 (Huihua Xiong) from the National Natural Science Foundation.PRIMA-1Met is the methylated PRIMA-1 (p53 reactivation and induction of massive apoptosis) and could restore tumor suppressor function of mutant p53 and induce p53 dependent apoptosis in cancer cells harboring mutant p53. However, p53 independent activity of PRIMA-1Met remains elusive. Here we reported that PRIMA-1Met attenuated colorectal cancer cell growth irrespective of p53 status. Kinase profiling revealed that mitogen-activated or extracellular signal-related protein kinase (MEK) might be a potential target of PRIMA-1Met. Pull-down binding and ATP competitive assay showed that PRIMA-1Met directly bound MEK in vitro and in cells. Furthermore, the direct binding sites of PRIMA-1Met were explored by using a computational docking model. Treatment of colorectal cancer cells with PRIMA-1Met inhibited p53-independent phosphorylation of MEK, which in turn impaired anchorage-independent cell growth in vitro. Moreover, PRIMA-1Met suppressed colorectal cancer growth in xenograft mouse model by inhibiting MEK1 activity. Taken together, our findings demonstrate a novel p53-independent activity of PRIMA-1Met to inhibit MEK and suppress colorectal cancer growth.Publisher PDFPeer reviewe