Quantifying the Effect of Mobile Channel Visits on Firm Revenue

The explosive penetration of mobile devices is one of the most prominent trends in e-business. Although the importance of mobile channel has prompted growing literature, little is known about the revenue implications of customer visit toward mobile channel. This study examines (1) the differential effect of mobile visits in affecting firm revenue (i.e. mobile vs. desktop visits), and (2) which type of mobile visits are more effective (i.e., direct vs. search engine and referral traffic; visits for high vs. low involvement products). We collect an unique objective daily data from a leading online travel agency in China. With a vector autoregressive (VAR) method, we find that, compared with desktop channel, mobile channel visits have shorter carryover effect, but larger short-term effect on firm revenues. Further, mobile channel has larger short-term effect on firm revenues for search engine traffic and lower involvement products. Our findings provide important theoretical contributions and notable implications for mobile commerce strategy

Development of a New Fermentation Process for Hardly Degradable Rice Straw Agriculture Waste (Advanced Studies on Sustainable Animal Production: Interrelationships among Human, Animal and Environment, 8th International Symposium of Integrated Field Science)

Oral Sessio

Development of a High-Efficiency Methane Fermentation Process for Hardly Degradable Rice Straw

Symposium Pape

Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations

In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training. In this paper, we study the training robustness of distributional Reinforcement Learning~(RL), a class of state-of-the-art methods that estimate the whole distribution, as opposed to only the expectation, of the total return. Firstly, we validate the contraction of distributional Bellman operators in the State-Noisy Markov Decision Process~(SN-MDP), a typical tabular case that incorporates both random and adversarial state observation noises. In the noisy setting with function approximation, we then analyze the vulnerability of least squared loss in expectation-based RL with either linear or nonlinear function approximation. By contrast, we theoretically characterize the bounded gradient norm of distributional RL loss based on the categorical parameterization equipped with the Kullback-Leibler~(KL) divergence. The resulting stable gradients while the optimization in distributional RL accounts for its better training robustness against state observation noises. Finally, extensive experiments on the suite of environments verified that distributional RL is less vulnerable against both random and adversarial noisy state observations compared with its expectation-based counterpart

Hypermethylated gene ANKDD1A is a candidate tumor suppressor that interacts with FIH1 and decreases HIF1α stability to inhibit cell autophagy in the glioblastoma multiforme hypoxia microenvironment.

Ectopic epigenetic mechanisms play important roles in facilitating tumorigenesis. Here, we first demonstrated that ANKDD1A is a functional tumor suppressor gene, especially in the hypoxia microenvironment. ANKDD1A directly interacts with FIH1 and inhibits the transcriptional activity of HIF1α by upregulating FIH1. In addition, ANKDD1A decreases the half-life of HIF1α by upregulating FIH1, decreases glucose uptake and lactate production, inhibits glioblastoma multiforme (GBM) autophagy, and induces apoptosis in GBM cells under hypoxia. Moreover, ANKDD1A is highly frequently methylated in GBM. The tumor-specific methylation of ANKDD1A indicates that it could be used as a potential epigenetic biomarker as well as a possible therapeutic target

Interpreting Distributional Reinforcement Learning: A Regularization Perspective

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive. In this paper, we attribute the superiority of distributional RL to its regularization effect in terms of the value distribution information regardless of its expectation. Firstly, by leverage of a variant of the gross error model in robust statistics, we decompose the value distribution into its expectation and the remaining distribution part. As such, the extra benefit of distributional RL compared with expectation-based RL is mainly interpreted as the impact of a \textit{risk-sensitive entropy regularization} within the Neural Fitted Z-Iteration framework. Meanwhile, we establish a bridge between the risk-sensitive entropy regularization of distributional RL and the vanilla entropy in maximum entropy RL, focusing specifically on actor-critic algorithms. It reveals that distributional RL induces a corrected reward function and thus promotes a risk-sensitive exploration against the intrinsic uncertainty of the environment. Finally, extensive experiments corroborate the role of the regularization effect of distributional RL and uncover mutual impacts of different entropy regularization. Our research paves a way towards better interpreting the efficacy of distributional RL algorithms, especially through the lens of regularization