Using TB-Sized Data to Understand Multi-Device Advertising

In this study, we combine the conversion funnel theory with machine learning methods to understand multi-device advertising. We investigate the important question of how the distribution of ads on multiple devices affects the consumer path to purchase. To handle the sheer volume of TB sized impression data, we develop a MapReduce framework to estimate the non-stationary Hidden Markov Model in parallel. To accommodate the iterative nature of the estimation procedure, we leverage the Apache Spark framework and a corporate cloud computing service. We calibrate the model with hundreds of millions of impressions for 100 advertisers. Our preliminary results show increasing the diversity of device for ads delivery can consistently encourage consumers to become more engaged. In addition, advertiser heterogeneity plays an important role in the variety of the conversion process

Internet search data showed increased interest in supplementary online education during the COVID-19 pandemic, with females showing a greater increase

The COVID-19 pandemic has led to tremendous disruptions in people’s everyday activities, including the pursuit of education. Internet search data may provide insights into potential audiences’ interest in online education. Using Internet search data, we examined the impact of COVID-19 on people’s interest in supplementary online education in the US over nine months (10/14/2019–07/19/2020). We found there was increased interest in supplementary online education after WHO announced COVID-19 as a pandemic, with a greater increase among females than males. We found that the increased interest in online education persisted after the stay-at-home orders were lifted; in addition, we identified concerns over unemployment as a key variable that significantly explained the variance in the interest in online education, even after controlling for COVID cases and deaths. Policymakers and online education platforms may take advantage of people’s, especially women’s increased interest in online education when designing policies or marketing mix

Membrane glycomics reveal heterogeneity and quantitative distribution of cell surface sialylation.

Given that unnatural sugar expression is metabolically achieved, the kinetics and disposition of incorporation can lend insight into the temporal and localization preferences of sialylation across the cell surface. However, common detection schemes lack the ability to detail the molecular diversity and distribution of target moieties. Here we employed a mass spectrometric approach to trace the placement of azido sialic acids on membrane glycoconjugates, which revealed substantial variations in incorporation efficiencies between N-/O-glycans, glycosites, and glycosphingolipids. To further explore the propensity for sialylation, we subsequently mapped the native glycome of model epithelial cell surfaces and illustrate that while glycosylation sites span broadly across the extracellular region, a higher number of heterogeneous glycoforms occur on sialylated sites closest to the transmembrane domain. Beyond imaging techniques, this integrative approach provides unprecedented details about the frequency and structure-specific distribution of cell surface sialylation, a critical feature that regulates cellular interactions and homeostatic pathways

GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction

All tables can be represented as grids. Based on this observation, we propose GridFormer, a novel approach for interpreting unconstrained table structures by predicting the vertex and edge of a grid. First, we propose a flexible table representation in the form of an MXN grid. In this representation, the vertexes and edges of the grid store the localization and adjacency information of the table. Then, we introduce a DETR-style table structure recognizer to efficiently predict this multi-objective information of the grid in a single shot. Specifically, given a set of learned row and column queries, the recognizer directly outputs the vertexes and edges information of the corresponding rows and columns. Extensive experiments on five challenging benchmarks which include wired, wireless, multi-merge-cell, oriented, and distorted tables demonstrate the competitive performance of our model over other methods.Comment: ACMMM202

14-3-3ε Boosts Bleomycin-induced DNA Damage Response by Inhibiting the Drug-resistant Activity of MVP

Major vault protein (MVP) is the predominant constituent of the vault particle, the largest known ribonuclear protein complex. Although emerging evidences have been establishing the links between MVP (vault) and multidrug resistance (MDR), little is known regarding exactly how the MDR activity of MVP is modulated during cellular response to drug-induced DNA damage (DDR). Bleomycin (BLM), an anti-cancer drug, induces DNA double-stranded breaks (DSBs) and consequently triggers the cellular DDR. Due to its physiological implications in hepatocellular carcinoma (HCC) and cell fate decision, 14-3-3ε was chosen as the pathway-specific bait protein to identify the critical target(s) responsible for HCC MDR. By using LC-MS/MS-based proteomic approach, MVP was first identified in the BLM-induced 14-3-3ε interactome formed in HCC cells. Biological characterization revealed that MVP possesses specific activity to promote the resistance to the BLM-induced DDR. On the other hand, 14-3-3ε enhances BLM-induced DDR by interacting with MVP. Mechanistic investigation further revealed that 14-3-3ε, in a phosphorylation-dependent manner, binds to the phosphorylated sites at both Thr52 and Ser864 of the monomer of MVP. Consequently, the phosphorylation-dependent binding between 14-3-3ε and MVP inhibits the drug-resistant activity of MVP for an enhanced DDR to BLM treatment. Our findings provide an insight into the mechanism underlying how the BLM-induced interaction between 14-3-3ε and MVP modulates MDR, implicating novel strategy to overcome the chemotherapeutic resistance through interfering specific protein-protein interactions

Learning-based attitude tracking control with high-performance parameter estimation

This paper aims to handle the optimal attitude tracking control tasks for rigid bodies via a reinforcement learning-based control scheme, in which a constrained parameter estimator is designed to compensate system uncertainties accurately. This estimator guarantees the exponential convergence of estimation errors and can strictly keep all instant estimates always within pre-determined bounds. Based on it, a critic-only adaptive dynamic programming (ADP) control strategy is proposed to learn the optimal control policy with respect to a user-defined cost function. The matching condition on reference control signals, which is commonly employed in relevant ADP design, is not required in the proposed control scheme. We prove the uniform ultimate boundedness of the tracking errors and critic weight's estimation errors under finite excitation conditions by Lyapunov-based analysis. Moreover, an easy-to-implement initial control policy is designed to trigger the real-time learning process. The effectiveness and advantages of the proposed method are verified by both numerical simulations and hardware-in-loop experimental tests