Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features

Dynamics testing and simulation of inflatable deployable

The inflatable deployablemembrane antenna structures have many advantages such as small folding size, high reliability and low cost. The structure mainly consists of its center hub, thin-plate ribs, inflatable thermo-curing torus, reflected membrane and inflation control system. This paper establishes a deployable system to simulate zero-gravity based on the parabolic membrane antenna with inflatable torus and tests the deployable process. The shell-membranes finite element model of the antenna structuresis modeled to simulateof the dynamics charactersof the structure. After that the effectsof the different inflatable pressure inside its support torus, the temperature of thermos-curing on the dynamic characteristics are also discussed.Finally,the dynamic charactersof the inflatable antenna was tested on the condition of the horizontal suspension system with 12 elastic strings and the fully structural vibrational frequency were given, and the mode of vibration and damping ratio was verified to the correctness of the simulation method. These results provide the reference for the design of inflatable deployment antenna structures

Peptide P5 (residues 628–683), comprising the entire membrane proximal region of HIV-1 gp41 and its calcium-binding site, is a potent inhibitor of HIV-1 infection

The membrane proximal region (MPR) of the transmembrane subunit, gp41, of the HIV envelope glycoprotein plays a critical role in HIV-1 infection of CD4+ target cells and CD4-independent mucosal entry. It contains continuous epitopes recognized by neutralizing IgG antibodies 2F5, 4E10 and Z13, and is therefore considered to be a promising target for vaccine design. Moreover, some MPR-derived peptides, such as T20 (enfuvirtide), are in clinical use as HIV-1 inhibitors. We have shown that an extended MPR peptide, P5, harbouring the lectin-like domain of gp41 and a calcium-binding site, is implicated in the interaction of HIV with its mucosal receptor. We now investigate the potential antiviral activities of P5 and other such long MPR-derived peptides. Structural studies of gp41 MPR-derived peptides using circular dichroism showed that the peptides P5 (a.a.628–683), P1 (a.a.648–683), P5L (a.a.613–683) and P7 (a.a.613–746) displayed a well-defined α-helical structure. Peptides P5 inhibited HIV-1 envelope mediated cell-cell fusion and infection of peripheral blood mononuclear cells by both X4- and R5-tropic HIV-1 strains, whereas peptides P5 mutated in the calcium binding site or P1 lacked antiviral activity, when P5L blocked cell fusion in contrast to P7. Strikingly, P5 inhibited CD4-dependent infection by T20-resistant R5-tropic HIV-1 variants. Cell-cell fusion studies indicated that the anti-HIV-1 activity of P5, unlike T20, could not be abrogated in the presence of the N-terminal leucine zipper domain (LZ). These results suggested that P5 could serve as a potent fusion inhibitor

Galactosyl ceramide expressed on dendritic cells can mediate HIV-1 transfer from monocyte derived dendritic cells to autologous T cells

AbstractMucosa, comprising epithelial and dendritic cells, are the major sites for Human Immunodeficiency Virus type 1 (HIV-1) transmission. There, DCs can capture incoming HIV-1 and in turn transfer virus to CD4+ T lymphocytes in a two-phase process, thereby initiating HIV-1 dissemination. We show that the glycosphingolipid Galactosyl Ceramide (GalCer), acting as mucosal epithelial receptor for HIV-1, was expressed by human monocyte derived immature DCs (iDCs), human primary DCs isolated from blood and mucosal tissue and in situ on mucosal tissue and acts as HIV-1-gp41 receptor. Blocking both GalCer and CD4 with specific mAbs results in a >95% transfer inhibition of HIV-1 from human monocyte-derived iDCs to autologous resting T cells. GalCer interaction with HIV-1 controls the early infection-independent phase of HIV-1 transfer to T cells. Thus, GalCer appears as an initial receptor for HIV-1, common to both mucosal epithelial cells and iDCs

Numerical simulations for gas-structure interaction in inflated deployment of folded membrane boom

AbstractIt is very important for gas-structure interaction between compressible ideal gas and elastic structure of space folded membrane booms during the inflatable deployment. In order to study this gas-structure interaction problem, Arbitrary Lagrangian-Eulerian (ALE) finite element method was employed. Gas-structure interaction equation was built based on equilibrium integration relationship, and solved by operator split method. In addition, numerical analysis of V-shape folded membrane booms inflated by gas was given, the variation of inner pressure as well as deployment velocities of inflatable boom at different stage were simulated. Moreover, these results are consistent with the experiment of the same boom, which shows that both ALE method and operator split method are feasible and reliable methods to study gas-structure interaction problem

Identification of the role of immune-related genes in the diagnosis of bipolar disorder with metabolic syndrome through machine learning and comprehensive bioinformatics analysis

BackgroundBipolar disorder and metabolic syndrome are both associated with the expression of immune disorders. The current study aims to find the effective diagnostic candidate genes for bipolar affective disorder with metabolic syndrome.MethodsA validation data set of bipolar disorder and metabolic syndrome was provided by the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were found utilizing the Limma package, followed by weighted gene co-expression network analysis (WGCNA). Further analyses were performed to identify the key immune-related center genes through function enrichment analysis, followed by machine learning-based techniques for the construction of protein–protein interaction (PPI) network and identification of the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). The receiver operating characteristic (ROC) curve was plotted to diagnose bipolar affective disorder with metabolic syndrome. To investigate the immune cell imbalance in bipolar disorder, the infiltration of the immune cells was developed.ResultsThere were 2,289 DEGs in bipolar disorder, and 691 module genes in metabolic syndrome were identified. The DEGs of bipolar disorder and metabolic syndrome module genes crossed into 129 genes, so a total of 5 candidate genes were finally selected through machine learning. The ROC curve results-based assessment of the diagnostic value was done. These results suggest that these candidate genes have high diagnostic value.ConclusionPotential candidate genes for bipolar disorder with metabolic syndrome were found in 5 candidate genes (AP1G2, C1orf54, DMAC2L, RABEPK and ZFAND5), all of which have diagnostic significance

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

Efficacy of Metformin for Benign Thyroid Nodules in Subjects With Insulin Resistance: A Systematic Review and Meta-Analysis

Background: To evaluate the effect of metformin therapy on decreasing benign thyroid nodule volume in subjects with insulin resistance (IR).Method: Randomized controlled trials (RCTs) and self-controlled trials for the meta-analysis published, before January 31, 2018 were selected from the PubMed, Cochrane Library, Embase, Web of Science, Chinese Biomedical Literature Database, National Knowledge Infrastructure, WANFANG and VIP Database. Pooled standard mean difference with 95% confidence interval was estimated by fixed- or random-effects model depending on heterogeneity. The risk of bias using the Cochrane Collaboration's tool was used to assess the quality of the RCTs contained. The quality of self-controlled studies was evaluated using the Methodological index for non-randomized studies (MINORS) method.Results: 7 studies (3 RCTs and 4 prospective self-controlled studies) with 240 patients were considered to be appropriate for the meta-analysis. The results of the meta-analysis indicated that the volume of thyroid nodule decreased significantly after metformin therapy (SMD −0.62, 95% CI −0.98 ~ −0.27). 6 studies reported the changes of the level of TSH. TSH levels decreased significantly after metformin therapy (SMD −0.27, 95% CI −0.47 ~ −0.07). The pooled data indicated an increase in FT3 level, and an unchanged FT4 level after metformin therapy (FT3, SMD 0.25, 95% CI 0.05 ~ 0.45; FT4, SMD −0.07, 95% CI −0.27 ~ 0.13). HOMA-IR levels decreased significantly after metformin therapy based on the pooled results of 3 RCTs and 3 prospective self-controlled studies (SMD −1.08, 95% CI −1.69 ~ −0.47).Conclusion: The meta-analysis demonstrated that metformin was safe and useful in shrinking benign thyroid nodules volume, improving thyroid function and IR. A large number of high-quality prospective studies still need to be carried out