565 research outputs found
Modeling Shopping Cart Decisions
The most recent consumer propensity study by SAP indicates that online shopping cart abandonment is high and the associated reasons are complex. In order to examine this phenomenon, we construct online SCA decision as a discrete choice model (DCM) and capture consumer segments by a latent class model (LCM) in this research-in-progress (RIP) paper, grounded on the theories of product involvement, word of mouth, and consumer heterogeneity. We will apply the clickstream dataset from 78,746 consumers at a large Chinese online platform to verify the proposed models in future study. The objective of this research project is to scrutinize the heterogeneous impacts of product involvement and online reviews on shopping cart decision-making in view of individual-level sequential behavior and the associated products in the form of stock-keeping-unit items. We conclude this RIP paper with the discussion of potential theoretical contributions and managerial implications
Electron Density Dependence of in-plane Spin Relaxation Anisotropy in GaAs/AlGaAs Two-Dimensional Electron Gas
We investigated the spin dynamics of two-dimensional electrons in (001)
GaAs/AlGaAs heterostructure using the time resolved Kerr rotation technique
under a transverse magnetic field. The in-plane spin lifetime is found to be
anisotropic below 150k due to the interference of Rashba and Dresselhaus
spin-orbit coupling and D'yakonov-Perel' spin relaxation. The ratio of in-plane
spin lifetimes is measured directly as a function of temperature and pump
power, showing that the electron density in 2DEG channel strongly affects the
Rashba spin-orbit coupling.Comment: 3 pages, 2 figure
Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion
Multi-exposure image fusion (MEF) has emerged as a prominent solution to
address the limitations of digital imaging in representing varied exposure
levels. Despite its advancements, the field grapples with challenges, notably
the reliance on manual designs for network structures and loss functions, and
the constraints of utilizing simulated reference images as ground truths.
Consequently, current methodologies often suffer from color distortions and
exposure artifacts, further complicating the quest for authentic image
representation. In addressing these challenges, this paper presents a
Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which
introduces a bi-level optimization search scheme for automatic design of both
network structures and loss functions. More specifically, we harnesses a unique
dual research mechanism rooted in a novel weighted structure refinement
architecture search. Besides, a hybrid supervised contrast constraint
seamlessly guides and integrates with searching process, facilitating a more
adaptive and comprehensive search for optimal loss functions. We realize the
state-of-the-art performance in comparison to various competitive schemes,
yielding a 10.61% and 4.38% improvement in Visual Information Fidelity (VIF)
for general and no-reference scenarios, respectively, while providing results
with high contrast, rich details and colors
Affine Disjunctive Invariant Generation with Farkas' Lemma
Invariant generation is the classical problem that aims at automated
generation of assertions that over-approximates the set of reachable program
states in a program. We consider the problem of generating affine invariants
over affine while loops (i.e., loops with affine loop guards, conditional
branches and assignment statements), and explore the automated generation of
disjunctive affine invariants. Disjunctive invariants are an important class of
invariants that capture disjunctive features in programs such as multiple
phases, transitions between different modes, etc., and are typically more
precise than conjunctive invariants over programs with these features. To
generate tight affine invariants, existing constraint-solving approaches have
investigated the application of Farkas' Lemma to conjunctive affine invariant
generation, but none of them considers disjunctive affine invariants
Researching Dynamic Brand Competitiveness Based on Consumer Clicking Behavior
Analyzing brand dynamic competition relationship by using consumer sequential online click data, which was collected from JD.com. It is found that the competition intensity of the products across categories is quite different. Owing to the purchasing time of durable-like goods is more flexible, that is, the purchasing probability of such products changes more obviously over time. Therefore, we use the Local Polynomial Regression Model to analyze the relationship between the brand competition of durable-like goods and the purchasing probability of the specific brand. Finding that when brands increase at a half of the total market share for consumers cognition preference, the brands’ competitiveness is peak and makes no significant different from one hundred percent for consumer to complete a transaction. The findings contribute to brand competitiveness for setting up marketing strategy from the dynamic and online consumer behavior’s perspective
Effects of PEGylated lipid nanoparticles on the oral absorption of one BCS II drug: a mechanistic investigation
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