Show Me the Money: Dynamic Recommendations for Revenue Maximization

Recommender Systems (RS) play a vital role in applications such as e-commerce and on-demand content streaming. Research on RS has mainly focused on the customer perspective, i.e., accurate prediction of user preferences and maximization of user utilities. As a result, most existing techniques are not explicitly built for revenue maximization, the primary business goal of enterprises. In this work, we explore and exploit a novel connection between RS and the profitability of a business. As recommendations can be seen as an information channel between a business and its customers, it is interesting and important to investigate how to make strategic dynamic recommendations leading to maximum possible revenue. To this end, we propose a novel \model that takes into account a variety of factors including prices, valuations, saturation effects, and competition amongst products. Under this model, we study the problem of finding revenue-maximizing recommendation strategies over a finite time horizon. We show that this problem is NP-hard, but approximation guarantees can be obtained for a slightly relaxed version, by establishing an elegant connection to matroid theory. Given the prohibitively high complexity of the approximation algorithm, we also design intelligent heuristics for the original problem. Finally, we conduct extensive experiments on two real and synthetic datasets and demonstrate the efficiency, scalability, and effectiveness our algorithms, and that they significantly outperform several intuitive baselines.Comment: Conference version published in PVLDB 7(14). To be presented in the VLDB Conference 2015, in Hawaii. This version gives a detailed submodularity proo

ANALYTIC PROGRAMMING WITH fMRI DATA: A QUICK-START GUIDE FOR STATISTICIANS USING R

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project along with the relevant R code. The goal is to give tatisticians who would like to pursue research in this area a quick start for programming with fMRI data along with the available data visualization tools

POPULATION FUNCTIONAL DATA ANALYSIS OF GROUP ICA-BASED CONNECTIVITY MEASURES FROM fMRI

In this manuscript, we use a two-stage decomposition for the analysis of func- tional magnetic resonance imaging (fMRI). In the first stage, spatial independent component analysis is applied to the group fMRI data to obtain common brain networks (spatial maps) and subject-specific mixing matrices (time courses). In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population- level eigenvectors and subject-specific loadings. Inference is performed using permutation-based exact conditional logistic regression for matched pairs data. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and the major direction of variation in the mixing matrices. The method is applied to a novel fMRI study of Alzheimer\u27s disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity in clinically asymptomatic at risk subjects when compared to controls

CERKL regulates autophagy via the NAD-dependent deacetylase SIRT1

<p>Macroautophagy/autophagy is an important intracellular mechanism for the maintenance of cellular homeostasis. Here we show that the <i>CERKL</i> (ceramide kinase like) gene, a retinal degeneration (RD) pathogenic gene, plays a critical role in regulating autophagy by stabilizing SIRT1. <i>In vitro</i> and <i>in vivo</i>, suppressing CERKL results in impaired autophagy. SIRT1 is one of the main regulators of acetylation/deacetylation in autophagy. In CERKL-depleted retinas and cells, SIRT1 is downregulated. ATG5 and ATG7, 2 essential components of autophagy, show a higher degree of acetylation in CERKL-depleted cells. Overexpression of SIRT1 rescues autophagy in CERKL-depleted cells, whereas CERKL loses its function of regulating autophagy in SIRT1-depleted cells, and overexpression of CERKL upregulates SIRT1. Finally, we show that CERKL directly interacts with SIRT1, and may regulate its phosphorylation at Ser27 to stabilize SIRT1. These results show that CERKL is an important regulator of autophagy and it plays this role by stabilizing the deacetylase SIRT1.</p

Atomic Structure and Dynamics of Single Platinum Atom Interactions with Monolayer MoS

We have studied atomic level interactions between single Pt atoms and the surface of monolayer MoS₂ using aberration-corrected annular dark field scanning transmission electron microscopy at an accelerating voltage of 60 kV. Strong contrast from single Pt atoms on the atomically resolved monolayer MoS₂ lattice enables their exact position to be determined with respect to the MoS₂ lattice, revealing stable binding sites. In regions of MoS₂ free from surface contamination, the Pt atoms are localized in S vacancy sites and exhibit dynamic hopping to nearby vacancy sites driven by the energy supplied by the electron beam. However, in areas of MoS₂ contaminated with carbon surface layers, the Pt atoms appear at various positions with respect to the underlying MoS₂ lattice, including on top of Mo and in off-axis positions. These variations are due to the Pt bonding with the surrounding amorphous carbon layer, which disrupts the intrinsic Pt-MoS₂ interactions, leading to more varied positions. Density functional theory (DFT) calculations reveal that Pt atoms on the surface of MoS₂ have a small barrier for migration and are stabilized when bound to either a single or double sulfur vacancies. DFT calculations have been used to understand how the catalytic activity of the MoS₂ basal plane for hydrogen evolution reaction is influenced by Pt dopants by variation of the hydrogen adsorption free energy. This strong dependence of catalytic effect on interfacial configurations is shown to be common for a series of dopants, which may provide a means to create and optimize reaction centers

Time to First-Line ART Failure and Time to Second-Line ART Switch in the IeDEA Pediatric Cohort

BACKGROUND: Globally, 49% of the estimated 1.8 million children living with HIV are accessing antiretroviral therapy (ART). There are limited data concerning long-term durability of first-line ART regimens and time to transition to second-line. METHODS: Children initiating their first ART regimen between 2 and 14 years of age and enrolled in one of 208 sites in 30 Asia-Pacific and African countries participating in the Pediatric International Epidemiology Databases to Evaluate AIDS consortium were included in this analysis. Outcomes of interest were: first-line ART failure (clinical, immunologic, or virologic), change to second-line, and attrition (death or loss to program ). Cumulative incidence was computed for first-line failure and second-line initiation, with attrition as a competing event. RESULTS: In 27,031 children, median age at ART initiation was 6.7 years. Median baseline CD4% for children ≤5 years of age was 13.2% and CD4 count for those >5 years was 258 cells per microliter. Almost all (94.4%) initiated a nonnucleoside reverse transcriptase inhibitor; 5.3% a protease inhibitor, and 0.3% a triple nucleoside reverse transcriptase inhibitor-based regimen. At 1 year, 7.7% had failed and 14.4% had experienced attrition; by 5 years, the cumulative incidence was 25.9% and 29.4%, respectively. At 1 year after ART failure, 13.7% had transitioned to second-line and 11.2% had experienced attrition; by 5 years, the cumulative incidence was 31.6% and 25.9%, respectively. CONCLUSIONS: High rates of first-line failure and attrition were identified in children within 5 years after ART initiation. Of children meeting failure criteria, only one-third were transitioned to second-line ART within 5 years

Chemical interactions between ship-originated air pollutants and ocean-emitted halogens

Unidad de excelencia María de Maeztu CEX2019-000940-MOcean-going ships supply products from one region to another and contribute to the world's economy. Ship exhaust contains many air pollutants and results in significant changes in marine atmospheric composition. The role of reactive halogen species (RHS) in the troposphere has received increasing recognition and oceans are the largest contributors to their atmospheric burden. However, the impact of shipping emissions on RHS and that of RHS on ship-originated air pollutants have not been studied in detail. Here, an updated Weather Research Forecasting coupled with Chemistry model is utilized to explore the chemical interactions between ship emissions and oceanic RHS over the East Asia seas in summer. The emissions and resulting chemical transformations from shipping activities increase the level of NO and NO at the surface, increase O in the South China Sea, but decrease O in the East China Sea. Such changes in pollutants result in remarkable changes in the levels of RHS (>200% increase of chlorine; ∼30% and ∼5% decrease of bromine and iodine, respectively) as well as in their partitioning. The abundant RHS, in turn, reshape the loadings of air pollutants (∼20% decrease of NO and NO; ∼15% decrease of O) and those of the oxidants (>10% reduction of OH and HO; ∼40% decrease of NO) with marked patterns along the ship tracks. We, therefore, suggest that these important chemical interactions of ship-originated emissions with RHS should be considered in the environmental policy assessments of the role of shipping emissions in air quality and climate

Oxidation resistance of graphene-coated Cu and Cu/Ni alloy

The ability to protect refined metals from reactive environments is vital to many industrial and academic applications. Current solutions, however, typically introduce several negative effects, including increased thickness and changes in the metal physical properties. In this paper, we demonstrate for the first time the ability of graphene films grown by chemical vapor deposition to protect the surface of the metallic growth substrates of Cu and Cu/Ni alloy from air oxidation. SEM, Raman spectroscopy, and XPS studies show that the metal surface is well protected from oxidation even after heating at 200 \degree C in air for up to 4 hours. Our work further shows that graphene provides effective resistance against hydrogen peroxide. This protection method offers significant advantages and can be used on any metal that catalyzes graphene growth