Effects of Undesired Online Video Advertising Choice on User Behavior and Attitude

Although online video advertising is currently a pervasive medium, its effectiveness is still in great doubt. This study examines the effects of undesired choice on user behavior and attitude in the context of online video advertising. We propose that offering people a choice of video advertisements will motivate them into paying more attention to the chosen advertisement, which in turn leads to better memory of the information contained in the advertisement. Additionally, the choosing behavior will encourage viewers to form a favorable attitude towards the chosen video advertisement and their purchase intention towards the advertised product will also be enhanced. We posit that differentiability of choice-set is able to moderate the choice effect. This work is one of the first to investigate the impact of making an undesired choice regarding video advertisements. It extends our understanding of the impact of choice and presents significant implications for both researchers and practitioners

Designing for User-Generated Contents: An Investigation of Product Tags and Lead User Exposure

Recent advances in the Internet have revolutionized the way people share information and choose products. Various new applications allow users to become an active part in developing content on the Web. This study specifically investigates e-commerce product search websites which allow users to search and evaluate products, share product opinions and interests, as well as communicate with other community members. Despite the increasing number of researchers studying diverse issues in this context, there still lacks a theoretical understanding of how the use of user-generated contents on these websites can actually influence people\u27s decision making and social experience online. This study thus focuses on two prevailing design features on websites based on user-generated information – product tags and lead user exposure. Results from a laboratory experiment using a large-scale, real social-network-based product search website are reported

Asymptotic correlation functions and FFLO signature for the one-dimensional attractive Hubbard model

We study the long-distance asymptotic behavior of various correlation functions for the one-dimensional (1D) attractive Hubbard model in a partially polarized phase through the Bethe ansatz and conformal field theory approaches. We particularly find the oscillating behavior of these correlation functions with spatial power-law decay, of which the pair (spin) correlation function oscillates with a frequency $\Delta k_F$ ($2\Delta k_F$). Here $\Delta k_F=\pi(n_\uparrow-n_\downarrow)$ is the mismatch in the Fermi surfaces of spin-up and spin-down particles. Consequently, the pair correlation function in momentum space has peaks at the mismatch $k=\Delta k_F$, which has been observed in recent numerical work on this model. These singular peaks in momentum space together with the spatial oscillation suggest an analog of the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state in the 1D Hubbard model. The parameter $\beta$ representing the lattice effect becomes prominent in critical exponents which determine the power-law decay of all correlation functions. We point out that the backscattering of unpaired fermions and bound pairs within their own Fermi points gives a microscopic origin of the FFLO pairing in 1D.Comment: 26 pages, 4 figures, published version, a series of study on the 1D attractive Hubbard model, few typos were corrected, references were added, also see arXiv:1708.07784 and arXiv:1708.0777

Turbulence drag modulation by dispersed droplets in Taylor-Couette flow: the effects of the dispersed phase viscosity

The dispersed phase in turbulence can vary from almost inviscid fluid to highly viscous fluid. By changing the viscosity of the dispersed droplet phase, we experimentally investigate how the deformability of dispersed droplets affects the global transport quantity of the turbulent emulsion. Different kinds of silicone oil are employed to result in the viscosity ratio, $\zeta$, ranging from $0.53$ to $8.02$. The droplet volume fraction, $\phi$, is varied from 0\% to 10\% with a spacing of 2\%. The global transport quantity, quantified by the normalized friction coefficient $c_{f,\phi}/c_{f,\phi=0}$, shows a weak dependence on the turbulent intensity due to the vanishing finite-size effect of the droplets. The interesting fact is that, with increasing $\zeta$, the $c_{f,\phi}/c_{f,\phi=0}$ first increases and then saturates to a plateau value which is similar to that of the rigid particle suspension. By performing image analysis, this drag modification is interpreted from the aspect of droplet deformability, which is responsible for the breakup and coalescence effect of the droplets. The statistics of the droplet size distribution show that, with increasing $\zeta$, the stabilizing effect induced by interfacial tension comes to be substantial and the pure inertial breakup process becomes dominant. The measurement of the droplet distribution along the radial direction of the system shows a bulk-clustering effect, which can be attributed to the non-negligible coalescence effect of the droplet. It is found that the droplet coalescence effect could be suppressed as the $\zeta$ increases, thereby affecting the contribution of interfacial tension to the total stress, and accounting for the observed emulsion rheology.Comment: 17 pages, 8 figure

RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning

Automating radiology report generation can significantly alleviate radiologists' workloads. Previous research has primarily focused on realizing highly concise observations while neglecting the precise attributes that determine the severity of diseases (e.g., small pleural effusion). Since incorrect attributes will lead to imprecise radiology reports, strengthening the generation process with precise attribute modeling becomes necessary. Additionally, the temporal information contained in the historical records, which is crucial in evaluating a patient's current condition (e.g., heart size is unchanged), has also been largely disregarded. To address these issues, we propose RECAP, which generates precise and accurate radiology reports via dynamic disease progression reasoning. Specifically, RECAP first predicts the observations and progressions (i.e., spatiotemporal information) given two consecutive radiographs. It then combines the historical records, spatiotemporal information, and radiographs for report generation, where a disease progression graph and dynamic progression reasoning mechanism are devised to accurately select the attributes of each observation and progression. Extensive experiments on two publicly available datasets demonstrate the effectiveness of our model.Comment: Accepted by Findings of EMNLP 202

Truck Traffic and Load Spectra of Indiana Roadways for the Mechanistic-Empirical Pavement Design Guide

The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed for pavement design by the Indiana Department of Transportation (INDOT) since 2009 and has generated efficient pavement designs with a lower cost. It has been demonstrated that the success of MEPDG implementation depends largely on a high level of accuracy associated with the information supplied as design inputs. Vehicular traffic loading is one of the key factors that may cause not only pavement structural failures, such as fatigue cracking and rutting, but also functional surface distresses, including friction and smoothness. In particular, truck load spectra play a critical role in all aspects of the pavement structure design. Inaccurate traffic information will yield an incorrect estimate of pavement thickness, which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness. The primary objective of this study was to update the traffic design input module, and thus to improve the current INDOT pavement design procedures. Efforts were made to reclassify truck traffic categories to accurately account for the specific axle load spectra on two-lane roads with low truck traffic and interstate routes with very high truck traffic. The traffic input module was updated with the most recent data to better reflect the axle load spectra for pavement design. Vehicle platoons were analyzed to better understand the truck traffic characteristics. The unclassified vehicles by traffic recording devices were examined and analyzed to identify possible causes of the inaccurate data collection. Bus traffic in the Indiana urban areas was investigated to provide additional information for highway engineers with respect to city streets as well as highway sections passing through urban areas. New equivalent single axle load (ESAL) values were determined based on the updated traffic data. In addition, a truck traffic data repository and visualization model and a TABLEAU interactive visualization dashboard model were developed for easy access, view, storage, and analysis of MEPDG related traffic data