Advertising strategy for profit-maximization: a novel practice on Tmall's online ads manager platforms

Ads manager platform gains popularity among numerous e-commercial vendors/advertisers. It helps advertisers to facilitate the process of displaying their ads to target customers. One of the main challenges faced by advertisers, especially small and medium-sized enterprises, is to configure their advertising strategy properly. An ineffective advertising strategy will bring too many ``just looking'' clicks and, eventually, generate high advertising expenditure unproportionally to the growth of sales. In this paper, we present a novel profit-maximization model for online advertising optimization. The optimization problem is constructed to find optimal set of features to maximize the probability that target customers buy advertising products. We further reformulate the optimization problem to a knapsack problem with changeable parameters, and introduce a self-adjusted algorithm for finding the solution to the problem. Numerical experiment based on statistical data from Tmall show that our proposed method can optimize the advertising strategy given expenditure budget effectively.Comment: Online advertising campaign

ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models

We study the domain adaptation problem with label shift in this work. Under the label shift context, the marginal distribution of the label varies across the training and testing datasets, while the conditional distribution of features given the label is the same. Traditional label shift adaptation methods either suffer from large estimation errors or require cumbersome post-prediction calibrations. To address these issues, we first propose a moment-matching framework for adapting the label shift based on the geometry of the influence function. Under such a framework, we propose a novel method named \underline{E}fficient \underline{L}abel \underline{S}hift \underline{A}daptation (ELSA), in which the adaptation weights can be estimated by solving linear systems. Theoretically, the ELSA estimator is $\sqrt{n}$-consistent ($n$ is the sample size of the source data) and asymptotically normal. Empirically, we show that ELSA can achieve state-of-the-art estimation performances without post-prediction calibrations, thus, gaining computational efficiency

Doubly Flexible Estimation under Label Shift

In studies ranging from clinical medicine to policy research, complete data are usually available from a population $\mathscr{P}$, but the quantity of interest is often sought for a related but different population $\mathscr{Q}$ which only has partial data. In this paper, we consider the setting that both outcome $Y$ and covariate ${\bf X}$ are available from $\mathscr{P}$ whereas only ${\bf X}$ is available from $\mathscr{Q}$, under the so-called label shift assumption, i.e., the conditional distribution of ${\bf X}$ given $Y$ remains the same across the two populations. To estimate the parameter of interest in $\mathscr{Q}$ via leveraging the information from $\mathscr{P}$, the following three ingredients are essential: (a) the common conditional distribution of ${\bf X}$ given $Y$, (b) the regression model of $Y$ given ${\bf X}$ in $\mathscr{P}$, and (c) the density ratio of $Y$ between the two populations. We propose an estimation procedure that only needs standard nonparametric technique to approximate the conditional expectations with respect to (a), while by no means needs an estimate or model for (b) or (c); i.e., doubly flexible to the possible model misspecifications of both (b) and (c). This is conceptually different from the well-known doubly robust estimation in that, double robustness allows at most one model to be misspecified whereas our proposal can allow both (b) and (c) to be misspecified. This is of particular interest in our setting because estimating (c) is difficult, if not impossible, by virtue of the absence of the $Y$-data in $\mathscr{Q}$. Furthermore, even though the estimation of (b) is sometimes off-the-shelf, it can face curse of dimensionality or computational challenges. We develop the large sample theory for the proposed estimator, and examine its finite-sample performance through simulation studies as well as an application to the MIMIC-III database

Three-Dimensional Distribution of Turbulent Mixing in the South China Sea*

A three-dimensional distribution of turbulent mixing in the South China Sea (SCS) is obtained for the first time, using the Gregg–Henyey–Polzin parameterization and hydrographic observations from 2005 to 2012. Results indicate that turbulent mixing generally increases with depth in the SCS, reaching the order of 10[superscript −2] m[superscript 2] s[superscript −1] at depth. In the horizontal direction, turbulence is more active in the northern SCS than in the south and is more active in the east than the west. Two mixing “hotspots” are identified in the bottom water of the Luzon Strait and Zhongsha Island Chain area, where diapycnal diffusivity values are around 3 × 10[superscript −2] m[superscript 2] s[superscript −1]. Potential mechanisms responsible for these spatial patterns are discussed, which include internal tide, bottom bathymetry, and near-inertial energy

Usage of Blogging Software for Laboratory Management to Support Weekly Seminars Using Research Activity Reports

AbstractThis paper reports the design and use of blogging software in laboratory management to support weekly seminars, in which activity reports are an important resource for checking participants’ research activity. The software has three basic functions to support seminars: a report editing, comment, and chat. In order to support knowledge management, we added an evaluation function corresponding to each seminar report and a To-Do-List function to support driven objects as sub-goals. The blogging system was installed in a laboratory seminar, in which a teacher, a doctoral student, and seven students pursuing their master's degree participated over the course of five months. Results show that seminars conducted using the blogging software were evaluated more highly than paper-based seminars. However, only a few participants used the comment function, and the chat function was minimally used

A general framework for quantile estimation with incomplete data

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148388/1/rssb12309.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148388/2/rssb12309-sup-0001-TableS1-S4.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148388/3/rssb12309_am.pd

Research to Break Oil Shale with High Pressure Water Jet Based on Bionic Nozzle

AbstractOil shale is a sedimentary rock, it is increasingly attracting widespread attention as petroleum supplement energy. Using borehole hydraulic mining techniques to mine oil shale seams at a certain depth in the underground, first and foremost is the use of high pressure water jet to make the overall oil shale ore broken into small pieces and peel them off parent rock. Based on the bionic theories, to design the bionic nozzles are adding several bionic units in the internal flow channel surface of the nozzle, it makes the original smooth flow channel inside the nozzle has become bionic non-smooth surface structure, to some extent, effectively improved the hydraulic characteristics of the nozzle internal flow channel, and reduced the flow resistance of the water. Based on CFD simulation and analysis, the reasons for upgrading effect of the crushed oil shale by the bionic nozzle high pressure water jet are analyzed. Experiments show that, in the same working conditions, bionic nozzle compared with the normal nozzle of the same structure parameters, the diameter of the erosion and crushing pit on oil shale samples expanded 4mm, and the crushing pit depth deepened 3.8mm using bionic nozzle