356 research outputs found
Transformer Choice Net: A Transformer Neural Network for Choice Prediction
Discrete-choice models, such as Multinomial Logit, Probit, or Mixed-Logit,
are widely used in Marketing, Economics, and Operations Research: given a set
of alternatives, the customer is modeled as choosing one of the alternatives to
maximize a (latent) utility function. However, extending such models to
situations where the customer chooses more than one item (such as in e-commerce
shopping) has proven problematic. While one can construct reasonable models of
the customer's behavior, estimating such models becomes very challenging
because of the combinatorial explosion in the number of possible subsets of
items. In this paper we develop a transformer neural network architecture, the
Transformer Choice Net, that is suitable for predicting multiple choices.
Transformer networks turn out to be especially suitable for this task as they
take into account not only the features of the customer and the items but also
the context, which in this case could be the assortment as well as the
customer's past choices. On a range of benchmark datasets, our architecture
shows uniformly superior out-of-sample prediction performance compared to the
leading models in the literature, without requiring any custom modeling or
tuning for each instance
Phrase Depicting Immoral Behavior Dilates Its Subjective Time Judgment
Intuitive moral emotions play a major role in forming our opinions and moral decisions. However, it is not yet known how we perceive the subjective time of moral-related information. In this study, we compared subjective durations of phrases depicting immoral, disgust, or neutral behaviors in a duration bisection task and found that phrases depicting immoral behavior were perceived as lasting longer than the neutral and disgusting phrases. By contrast, the subjective duration of the disgusting phrase, unlike the immoral phrase, was comparable to the neutral phrase. Moreover, the lengthening effect of the immoral phrase relative to the neutral phrase was significantly correlated to the anonymously prosocial tendency of the observer. Our findings suggest that immoral phrases induce embodied moral reaction, which alters emotional state and subsequently lengthens subjective time
Parallel implementation of 3D global MHD simulations for Earth’s magnetosphere
AbstractThis paper presents a dynamic domain decomposition (D3) technique for implementing the parallelization of the piecewise parabolic method (PPM) for solving the ideal magnetohydrodynamics (MHD) equations. The key point of D3 is distributing the work dynamically among processes during the execution of the PPM algorithm. This parallel code utilizes D3 with a message passing interface (MPI) in order to permit efficient implementation on clusters of distributed memory machines and may also simultaneously exploit threading for multiprocessing shared address space architectures. 3D global MHD simulation results for the Earth’s magnetosphere on the massively parallel supercomputers Deepcomp 1800 and 6800 demonstrate the scalability and efficiency of our parallelization strategy
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to tradeoff between
immediate and future returns are fundamental questions for a typical
ride-hailing platform. We model ride-hailing as a large-scale parallel ranking
problem and study the joint decision-making task of order dispatching and fleet
management in online ride-hailing platforms. This task brings unique challenges
in the following four aspects. First, to facilitate a huge number of vehicles
to act and learn efficiently and robustly, we treat each region cell as an
agent and build a multi-agent reinforcement learning framework. Second, to
coordinate the agents from different regions to achieve long-term benefits, we
leverage the geographical hierarchy of the region grids to perform hierarchical
reinforcement learning. Third, to deal with the heterogeneous and variant
action space for joint order dispatching and fleet management, we design the
action as the ranking weight vector to rank and select the specific order or
the fleet management destination in a unified formulation. Fourth, to achieve
the multi-scale ride-hailing platform, we conduct the decision-making process
in a hierarchical way where a multi-head attention mechanism is utilized to
incorporate the impacts of neighbor agents and capture the key agent in each
scale. The whole novel framework is named as CoRide. Extensive experiments
based on multiple cities real-world data as well as analytic synthetic data
demonstrate that CoRide provides superior performance in terms of platform
revenue and user experience in the task of city-wide hybrid order dispatching
and fleet management over strong baselines.Comment: CIKM 201
- …