Interaction-aware Factorization Machines for Recommender Systems

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named \emph{Interaction-aware Factorization Machine} (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the \emph{feature aspect} and the \emph{field aspect}, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods

Culture shapes how we look: Comparison between Chinese and African university students

Previous cross-cultural studies find that cultures can shape how we look during scene perception, but don’t mention its condition and limited to the East and West. This study recruited Chinese and African students to testify the cultural effects on two phases. In free-viewing phase: Africans fixated more on the focal objects than Chinese, while Chinese payed more attention to the backgrounds than Africans especially on the first fourth and fifth fixations. In recognition phase, there was no cultural difference on perception, but Chinese recognized more objects than Africans. Based on chosen subjects, we conclude that cultural differences exit on scene perception under conditions of no task and more clearly in its later period, but that differences may be hidden in a deeper way (e.g. memory) in task condition

The impact of different benefit packages of Medical Financial Assistance Scheme on health service utilization of poor population in Rural China

<p>Abstract</p> <p>Background</p> <p>Since 2003 and 2005, National Pilot Medical Financial Assistance Scheme (MFA) has been implemented in rural and urban areas of China to improve the poorest families' accessibility to health services. Local governments of the pilot areas formulated various benefit packages. Comparative evaluation research on the effect of different benefit packages is urgently needed to provide evidence for improving policy-making of MFA. This study was based on a MFA pilot project, which was one component of Health VIII Project conducted in rural China. This article aimed to compare difference in health services utilization of poor families between two benefit package project areas: H8 towns (package covering inpatient service, some designated preventive and curative health services but without out-patient service reimbursement in Health VIII Project,) and H8SP towns (package extending coverage of target population, covering out- patient services and reducing co-payment rate in Health VIII Supportive Project), and to find out major influencing factors on their services utilization.</p> <p>Methods</p> <p>A cross-sectional survey was conducted in 2004, which used stratified cluster sampling method to select poor families who have been enrolled in MFA scheme in rural areas of ChongQing. All family members of the enrolled households were interviewed. 748 and 1129 respondents from two kinds of project towns participated in the survey. Among them, 625 and 869 respondents were included (age≥15) in the analysis of this study. Two-level linear multilevel model and binomial regressions with a log link were used to assess influencing factors on different response variables measuring service utilization.</p> <p>Results</p> <p>In general, there was no statistical significance in physician visits and hospitalizations among all the respondents between the two kinds of benefit package towns. After adjusting for major confounding factors, poor families in H8SP towns had much higher frequency of MFA use (β = 1.17) and less use of hospitalization service (OR = 0.7 (H8SP/H8), 95%CI (0.5, 1.0)) among all the respondents. While calculating use of hospital services among those who needed, there was significant difference (p = 0.032) in percentage of hospitalization use between H8SP towns (46%) and H8 towns (33%). Meanwhile, the non-use but ought-to-use hospitalization ratio of H8SP (54%) was lower than that of H8 (67 %) towns. This indicated that hospitalization utilizations had improved in H8SP towns among those who needed. Awareness of MFA detailed benefit package and presence of physician diagnosed chronic disease had significant association with frequency of MFA use and hospitalizations. There was no significant difference in rate of borrowing money for illness treatment between the two project areas. Large amount of medical debt had strong association with hospitalization utilization.</p> <p>Conclusions</p> <p>The new extended benefit package implemented in pilot towns significantly increased the poor families' accessibility to MFA package in H8SP than that of H8 towns, which reduced poor families' demand of hospitalization services for their chronic diseases, and improved the poor population's utilization of out-patient services to some degree. It can encourage poor people to use more outpatient services thus reduce their hospitalization need. Presence of chronic disease and hospitalization had strong association with the presence of large amount of medical debt, which indicated that: although establishment of MFA had facilitated accessibility of poor families to this new system, and improved service utilization of poor families to some degree, but its role in reducing poor families' medical debt resulted from chronic disease and hospitalization was still very limited. Besides, the following requirements of MFA: co-payment for in-patient services, ceiling and deductibles for reimbursement, limitations on eligibility for diseases reimbursement, also served as most important obstacles for poor families' access to health care.</p> <p>Therefore, there is great need to improve MFA benefit package design in the future, including extending to cover out-patient services, raising ceiling for reimbursement, removing deductibles of MFA, reducing co-payment rate, and integrating MFA with New Rural Cooperative Medical Scheme more closely so as to provide more protection to the poor families.</p

Conformation of methylated GGQ in the Peptidyl Transferase Center during Translation Termination

Abstract The universally conserved Gly-Gly-Gln (GGQ) tripeptide in release factors or release factor-like surveillance proteins is required to catalyze the release of nascent peptide in the ribosome. The glutamine of the GGQ is methylated post-translationally at the N5 position in vivo; this covalent modification is essential for optimal cell growth and efficient translation termination. However, the precise conformation of the methylated-GGQ tripeptide in the ribosome remains unknown. Using cryoEM and X-ray crystallography, we report the conformation of methylated-GGQ in the peptidyl transferase center of the ribosome during canonical translational termination and co-translation quality control. It has been suggested that the GGQ motif arose independently through convergent evolution among otherwise unrelated proteins that catalyze peptide release. The requirement for this tripeptide in the highly conserved peptidyl transferase center suggests that the conformation reported here is likely shared during termination of protein synthesis in all domains of life

Context-aware collaborative topic regression with social matrix factorization for recommender systems

Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we propose a novel context-aware hierarchical Bayesian method. First, we propose the use of spectral clustering for user-item subgrouping, so that users and items in similar contexts are grouped. We then propose a novel hierarchical Bayesian model that can make predictions for each user-item subgroup, our model incorporate not only topic modeling to mine item content but also social matrix factorization to handle ratings and social relationships. Experiments on an Epinions dataset show that our method significantly improves recommendation performance compared with six categories of state-of-the-art recommendation methods in terms of both prediction accuracy and recall. We have also conducted experiments to study the extent to which ratings, contexts, social relationships, and item contents contribute to recommendation performance in terms of prediction accuracy and recall.7 page(s

Capturing semantic correlation for item recommendation in tagging systems

The popularity of tagging systems provides a great opportunity to improve the performance of item recommendation. Although existing approaches use topic modeling to mine the semantic information of items by grouping the tags labelled for items, they overlook an important property that tags link users and items as a bridge. Thus these methods cannot deal with the data sparsity without commonly rated items (DS-WO-CRI) problem, limiting their recommendation performance. Towards solving this challenging problem, we propose a novel tag and rating based collaborative filtering (CF) model for item recommendation, which first uses topic modeling to mine the semantic information of tags for each user and for each item respectively, and then incorporates the semantic information into matrix factorization to factorize rating information and to capture the bridging feature of tags and ratings between users and items. As a result, our model captures the semantic correlation between users and items, and is able to greatly improve recommendation performance, especially in DS-WO-CRI situations. Experiments conducted on two popular real-world datasets demonstrate that our proposed model significantly outperforms the conventional CF approach, the state-of-the-art social relation based CF approach, and the state-of-the-art topic modeling based CF approaches in terms of both precision and recall, and it is an effective approach to the DS-WO-CRI problem.7 page(s

Effects of inert gas jet on the transition from deflagration to detonation in a stoichiometric methane-oxygen mixture

Detonation is an energetic combustion mode augmenting high flow momentum and thermodynamic efficiency, it has been applied in detonation engines, such as pulse detonation engines (PDEs) and rotating detonation engines (RDEs), they have become potential aerospace propulsion equipment. Recently, fluidic jet-in-cross flow (JICF) has been demonstrated experimentally and numerically that can accelerate the deflagration-to-detonation transition (DDT) process. Nonetheless, most of previous studies focused on the jets using combustible mixture or oxygen, which may bring additional risk for turbulence-generated system in detonation engines. In this study, a more safe and controllable inert gas (i.e., Ar) is applied for JICF, experiments are carried out to investigate effects of argon jet as an enhancement method on promoting the DDT in a stoichiometric methane-oxygen mixture. The effects of local argon concentration, turbulence intensity and injection position on the DDT process are systematically examined. Two-dimensional numerical simulations are also performed to elucidate the details of the injection evolution. The experimental results show that turbulence generated by the argon injection can promote flame acceleration and the onset of detonation only in the fast deflagration regime. The enhancing effect is more prominent at higher turbulence intensity by increasing jet injection pressure and shorter injection time. Too long injection duration increases argon local concentration that leads to an adverse effect prohibiting the DDT occurrence. During the initial laminar flame acceleration, referred to as the slow deflagration regime, no enhancement by the argon jet on DDT can be observed. By looking numerically at the flow structure of the argon jet, the vortical features enhance the transport and mixing between reactants and products. The interaction between the reactive travelling wave and the jet structure further induces turbulence and thus accelerates the chemical reaction rate. With the time elapsed, the injected argon entrains largely and dilutes the ambient combustible mixture, and restrains the DDT. Furthermore, a novel dimensionless criterion and a characteristic parameter Turc are proposed, quantitatively analyzing the dominate mechanism in flame propagation and the initial stage of DDT as inert jet is introduced

Diagram of the mechanism of Ca²⁺-inhibited PAH adsorption via blocking of the binding site in plasmid DNA.

<p>A hydrophobic interaction between pyrene and DNA bases occurred via a phosphate backbone structure (A). Ca²⁺-inhibition of the interaction between pyrene and DNA occurred via the capture of PAHs by DNA, thereby blocking the phosphate backbone structure of the DNA periphery (B).</p

XPS analysis of oxygen in DNA, DNA–pyrene, and DNA–Ca.

<p>XPS analysis of oxygen in DNA, DNA–pyrene, and DNA–Ca.</p