How Can Recommender Systems Benefit from Large Language Models: A Survey

Recommender systems (RS) play important roles to match users' information needs for Internet applications. In natural language processing (NLP) domains, large language model (LLM) has shown astonishing emergent abilities (e.g., instruction following, reasoning), thus giving rise to the promising research direction of adapting LLM to RS for performance enhancements and user experience improvements. In this paper, we conduct a comprehensive survey on this research direction from an application-oriented view. We first summarize existing research works from two orthogonal perspectives: where and how to adapt LLM to RS. For the "WHERE" question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller. For the "HOW" question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLMs or not, and whether to involve conventional recommendation model (CRM) for inference. Detailed analysis and general development trajectories are provided for both questions, respectively. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We also actively maintain a GitHub repository for papers and other related resources in this rising direction: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.Comment: 15 pages; 3 figures; summarization table in appendi

TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency

Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods

Numerical Analysis of the Influence of Deep Excavation on Nearby Pile Foundation Building

In this paper, a numerical simulation is used to establish a three-dimensional model, which considers the height of buildings, the relative position between buildings, and foundation pits. These were studied in detail to investigate the changes in settlement of adjacent buildings and the displacement and internal force of piles caused by deep foundation pit excavation. The results indicate that the number of floors in the building, along with the angle and distance between the building and the excavation pit, have a significant impact on the settlement of the building and the deformation and internal force variation in the piles. For example, when D = 0.1 H, with the increase in the number of floors, the increase in the bending moment of pile 1 at the pile shaft is 62.63 kN·m, and the increase in the bending moment at the pile head is 224.72 kN·m. At this point, the maximum horizontal displacement of the pile shaft occurs at approximately 1.27 H. When θ = 45∘, the maximum difference between the maximum and minimum deformations of the building is 9.71 mm. When D ≤ 1.0 H, the majority of the building is in the primary influence range of surface settlement behind the wall, and the building undergoes a combined deformation of ‘upper convex’ and ‘concave’. When D > 1.0 H, the building predominantly resides in the secondary influence range, and the building undergoes a deformation of ‘upper convex’

Diagnostic Value of Superb Microvascular Imaging in Differentiating Benign and Malignant Breast Tumors: A Systematic Review and Meta-Analysis

Purpose: We performed a systematic review and meta-analysis of studies that investigated the diagnostic performance of Superb Microvascular Imaging (SMI) in differentiating between benign and malignant breast tumors. Methods: Studies published between January 2010 and March 2022 were retrieved by online literature search conducted in PubMed, Embase, Cochrane Library, Web of Science, China Biology Medicine Disc, China National Knowledge Infrastructure, Wanfang, and Vip databases. Pooled sensitivity, specificity, and diagnostic odd ratios were calculated using Stata software 15.0. Heterogeneity among the included studies was assessed using I2 statistic and Q test. Meta-regression and subgroup analyses were conducted to investigate potential sources of heterogeneity. Influence analysis was conducted to determine the robustness of the pooled conclusions. Deeks’ funnel plot asymmetry test was performed to assess publication bias. A summary receiver operating characteristic curve (SROC) was constructed. Results: Twenty-three studies involving 2749 breast lesions were included in our meta-analysis. The pooled sensitivity and specificity were 0.80 (95% confidence interval [CI], 0.77–0.84, inconsistency index [I2] = 28.32%) and 0.84 (95% CI, 0.79–0.88, I2 = 89.36%), respectively. The pooled diagnostic odds ratio was 19.95 (95% CI, 14.84–26.82). The area under the SROC (AUC) was 0.85 (95% CI, 0.81–0.87). Conclusion: SMI has a relatively high sensitivity, specificity, and accuracy for differentiating between benign and malignant breast lesions. It represents a promising supplementary technique for the diagnosis of breast neoplasms

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions

The Role of human papillomavirus infection in breast cancer

Breast cancer is the leading female cancer and the third most common cause of cancer deaths worldwide. Many studies have suggested a possible link between breast cancer pathogenesis and viral infection, particularly mouse mammary tumour virus, simian virus 40, Epstein-Barr virus, and human papillomavirus (HPV). A significant number of recent studies have reported that approximately 29% of human breast cancer tissues were positive for highrisk HPV subtypes, especially HPV subtypes 16, 18, or 33. In contrast, several other investigations did not detect any HPV subtypes in either breast cancer tissue or normal breast tissue from patients diagnosed with breast cancer. Given these conflicting data and the established complexity of the association between HPV with other cancers, a definitive relationship between human breast cancer and HPV infection has not been determined. Recent advances in laboratory methodologies aim to overcome the inherent challenges in detecting HPV in breast cancer tissue. There is an urgent need to obtain additional evidence in order to assess the possibility of breast cancer prevention using HPV vaccines.8 page(s

Comprehensive Analysis of the COBRA-Like (COBL) Gene Family in Gossypium Identifies Two COBLs Potentially Associated with Fiber Quality.

COBRA-Like (COBL) genes, which encode a plant-specific glycosylphosphatidylinositol (GPI) anchored protein, have been proven to be key regulators in the orientation of cell expansion and cellulose crystallinity status. Genome-wide analysis has been performed in A. thaliana, O. sativa, Z. mays and S. lycopersicum, but little in Gossypium. Here we identified 19, 18 and 33 candidate COBL genes from three sequenced cotton species, diploid cotton G. raimondii, G. arboreum and tetraploid cotton G. hirsutum acc. TM-1, respectively. These COBL members were anchored onto 10 chromosomes in G. raimondii and could be divided into two subgroups. Expression patterns of COBL genes showed highly developmental and spatial regulation in G. hirsutum acc. TM-1. Of them, GhCOBL9 and GhCOBL13 were preferentially expressed at the secondary cell wall stage of fiber development and had significantly co-upregulated expression with cellulose synthase genes GhCESA4, GhCESA7 and GhCESA8. Besides, GhCOBL9 Dt and GhCOBL13 Dt were co-localized with previously reported cotton fiber quality quantitative trait loci (QTLs) and the favorable allele types of GhCOBL9 Dt had significantly positive correlations with fiber quality traits, indicating that these two genes might play an important role in fiber development

A Novel Nomogram Based on Imaging Biomarkers of Shear Wave Elastography, Angio Planewave Ultrasensitive Imaging, and Conventional Ultrasound for Preoperative Prediction of Malignancy in Patients with Breast Lesions

Several studies have demonstrated the difficulties in distinguishing malignant lesions of the breast from benign lesions owing to overlapping morphological features on ultrasound. Consequently, we aimed to develop a nomogram based on shear wave elastography (SWE), Angio Planewave Ultrasensitive imaging (Angio PLUS (AP)), and conventional ultrasound imaging biomarkers to predict malignancy in patients with breast lesions. This prospective study included 117 female patients with suspicious lesions of the breast. Features of lesions were extracted from SWE, AP, and conventional ultrasound images. The least absolute shrinkage and selection operator (Lasso) algorithms were used to select breast cancer-related imaging biomarkers, and a nomogram was developed based on six of the 16 imaging biomarkers. This model exhibited good discrimination (area under the receiver operating characteristic curve (AUC): 0.969; 95% confidence interval (CI): 0.928, 0.989) between malignant and benign breast lesions. Moreover, the nomogram also showed demonstrated good calibration and clinical usefulness. In conclusion, our nomogram can be a potentially useful tool for individually-tailored diagnosis of breast tumors in clinical practice

A Novel Nomogram Based on Imaging Biomarkers of Shear Wave Elastography, Angio Planewave Ultrasensitive Imaging, and Conventional Ultrasound for Preoperative Prediction of Malignancy in Patients with Breast Lesions

Several studies have demonstrated the difficulties in distinguishing malignant lesions of the breast from benign lesions owing to overlapping morphological features on ultrasound. Consequently, we aimed to develop a nomogram based on shear wave elastography (SWE), Angio Planewave Ultrasensitive imaging (Angio PLUS (AP)), and conventional ultrasound imaging biomarkers to predict malignancy in patients with breast lesions. This prospective study included 117 female patients with suspicious lesions of the breast. Features of lesions were extracted from SWE, AP, and conventional ultrasound images. The least absolute shrinkage and selection operator (Lasso) algorithms were used to select breast cancer-related imaging biomarkers, and a nomogram was developed based on six of the 16 imaging biomarkers. This model exhibited good discrimination (area under the receiver operating characteristic curve (AUC): 0.969; 95% confidence interval (CI): 0.928, 0.989) between malignant and benign breast lesions. Moreover, the nomogram also showed demonstrated good calibration and clinical usefulness. In conclusion, our nomogram can be a potentially useful tool for individually-tailored diagnosis of breast tumors in clinical practice