663 research outputs found
Leveraging Deep-learning and Field Experiment Response Heterogeneity to Enhance Customer Targeting Effectiveness
Firms seek to better understand heterogeneity in the customer response to marketing campaigns, which can boost customer targeting effectiveness. Motivated by the success of modern machine learning techniques, this paper presents a framework that leverages deep-learning algorithms and field experiment response heterogeneity to enhance customer targeting effectiveness. We recommend firms run a pilot randomized experiment and use the data to train various deep-learning models. By incorporating recurrent neural nets and deep perceptron nets, our optimal deep-learning model can capture both temporal and network effects in the purchase history, after addressing the common issues in most predictive models such as imbalanced training, data sparsity, temporality, and scalability. We then apply the learned optimal model to identify customer targets from the large amount of remaining customers with the highest predicted purchase probabilities. Our application with a large department store on a total of 2.8 million customers supports that optimal deep-learning models can identify higher-value customer targets and lead to better sales performance of marketing campaigns, compared to industry common practices of targeting by past purchase frequency or spending amount. We demonstrate that companies may achieve sub-optimal customer targeting not because they offer inferior campaign incentives, but because they leverage worse targeting rules and select low-value customer targets. The results inform managers that beyond gauging the causal impact of marketing interventions, data from field experiments can also be leveraged to identify high-value customer targets. Overall, deep-learning algorithms can be integrated with field experiment response heterogeneity to improve the effectiveness of targeted campaigns
Flavonoid accumulation and identification of flavonoid biosynthesis genes in Dimocarpus longan lour. by transcriptome sequencing
Dimocarpus longan Lour. (D. longan) is widely cultivated and is very popular around the world. Its by-products such as roots and leaves have been used as traditional Chinese medicines due to their content of important secondary metabolites, especially flavonoids. However, the economic value and application of D. longan roots and leaves are limited because they accumulate relatively low levels of flavonoids. Therefore, it is important to find key genes that regulate the accumulation of the predominant flavonoid compounds in D. longan roots and leaves. Here, we have used RNA-sequencing to describe the transcriptome of D. longan. We obtained 75,229,529 raw reads and 15.04 GB of clean data, generating 56,055 unigenes (N50 = 1,583 nt, mean length = 829.61 nt). Next, we annotated these unigenes using the various available bioinformatics databases. By this approach, we identified 6,684 genes differentially expressed between root and leaf tissues, of which thirteen were identified as flavonoid biosynthesis genes. Of these, eight genes were much highly expressed in roots (DlC4H, DlHCT, DlDFR, DlANS, DlANR, DlCHS, DlF3′H, and DlF3H), and two were much highly expressed in leaves (DlLAR and DlFLS). The contents of thirteen flavonoids in D. longan roots and leaves were measured by LC-MS, and epicatechin was found to be the predominant flavonoid in both tissues, which was significantly higher than the other flavonoids measured in the study. Its contents were 213,773.65 ng/g in roots and 22,388.71 ng/g in leaves. Our findings will facilitate efforts to increase the economic value and expand the applications of D. longan roots and leaves by means of genetic engineering
Beam energy distribution influences on density modulation efficiency in seeded free-electron lasers
The beam energy spread at the entrance of undulator system is of paramount
importance for efficient density modulation in high-gain seeded free-electron
lasers (FELs). In this paper, the dependences of high harmonic micro-bunching
in the high-gain harmonic generation (HGHG), echo-enabled harmonic generation
(EEHG) and phase-merging enhanced harmonic generation (PEHG) schemes on the
electron energy spread distribution are studied. Theoretical investigations and
multi-dimensional numerical simulations are applied to the cases of uniform and
saddle beam energy distributions and compared to a traditional Gaussian
distribution. It shows that the uniform and saddle electron energy
distributions significantly enhance the performance of HGHG-FELs, while they
almost have no influence on EEHG and PEHG schemes. A numerical example
demonstrates that, with about 84keV RMS uniform and/or saddle slice energy
spread, the 30th harmonic radiation can be directly generated by a single-stage
seeding scheme for a soft x-ray FEL facility
Formation Control for Moving Target Enclosing via Relative Localization
In this paper, we investigate the problem of controlling multiple unmanned
aerial vehicles (UAVs) to enclose a moving target in a distributed fashion
based on a relative distance and self-displacement measurements. A relative
localization technique is developed based on the recursive least square
estimation (RLSE) technique with a forgetting factor to estimates both the
``UAV-UAV'' and ``UAV-target'' relative positions. The formation enclosing
motion is planned using a coupled oscillator model, which generates desired
motion for UAVs to distribute evenly on a circle. The coupled-oscillator-based
motion can also facilitate the exponential convergence of relative localization
due to its persistent excitation nature. Based on the generation strategy of
desired formation pattern and relative localization estimates, a cooperative
formation tracking control scheme is proposed, which enables the formation
geometric center to asymptotically converge to the moving target. The
asymptotic convergence performance is analyzed theoretically for both the
relative localization technique and the formation control algorithm. Numerical
simulations are provided to show the efficiency of the proposed algorithm.
Experiments with three quadrotors tracking one target are conducted to evaluate
the proposed target enclosing method in real platforms.Comment: 8 Pages, accepted by IEEE CDC 202
SPColor: Semantic Prior Guided Exemplar-based Image Colorization
Exemplar-based image colorization aims to colorize a target grayscale image
based on a color reference image, and the key is to establish accurate
pixel-level semantic correspondence between these two images. Previous methods
search for correspondence across the entire reference image, and this type of
global matching is easy to get mismatch. We summarize the difficulties in two
aspects: (1) When the reference image only contains a part of objects related
to target image, improper correspondence will be established in unrelated
regions. (2) It is prone to get mismatch in regions where the shape or texture
of the object is easily confused. To overcome these issues, we propose SPColor,
a semantic prior guided exemplar-based image colorization framework. Different
from previous methods, SPColor first coarsely classifies pixels of the
reference and target images to several pseudo-classes under the guidance of
semantic prior, then the correspondences are only established locally between
the pixels in the same class via the newly designed semantic prior guided
correspondence network. In this way, improper correspondence between different
semantic classes is explicitly excluded, and the mismatch is obviously
alleviated. Besides, to better reserve the color from reference, a similarity
masked perceptual loss is designed. Noting that the carefully designed SPColor
utilizes the semantic prior provided by an unsupervised segmentation model,
which is free for additional manual semantic annotations. Experiments
demonstrate that our model outperforms recent state-of-the-art methods both
quantitatively and qualitatively on public dataset
Amplification and adaptation of centromeric repeats in polyploid switchgrass species.
Centromeres in most higher eukaryotes are composed of long arrays of satellite repeats from a single satellite repeat family. Why centromeres are dominated by a single satellite repeat and how the satellite repeats originate and evolve are among the most intriguing and long-standing questions in centromere biology. We identified eight satellite repeats in the centromeres of tetraploid switchgrass (Panicum virgatum). Seven repeats showed characteristics associated with classical centromeric repeats with monomeric lengths ranging from 166 to 187Â bp. Interestingly, these repeats share an 80-bp DNA motif. We demonstrate that this 80-bp motif may dictate translational and rotational phasing of the centromeric repeats with the cenH3 nucleosomes. The sequence of the last centromeric repeat, Pv156, is identical to the 5S ribosomal RNA genes. We demonstrate that a 5S ribosomal RNA gene array was recruited to be the functional centromere for one of the switchgrass chromosomes. Our findings reveal that certain types of satellite repeats, which are associated with unique sequence features and are composed of monomers in mono-nucleosomal length, are favorable for centromeres. Centromeric repeats may undergo dynamic amplification and adaptation before the centromeres in the same species become dominated by the best adapted satellite repeat
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