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

Genetic approach to track neural cell fate decisions using human embryonic stem cells

With their capability to undergo unlimited self-renewal and to differentiate into all cell types in the body, human embryonic stem cells (hESCs) hold great promise in human cell therapy. However, there are limited tools for easily identifying and isolating live hESC-derived cells. To track hESC-derived neural progenitor cells (NPCs), we applied homologous recombination to knock-in the mCherry gene into the Nestin locus of hESCs. This facilitated the genetic labeling of Nestin positive neural progenitor cells with mCherry. Our reporter system enables the visualization of neural induction from hESCs both in vitro (embryoid bodies) and in vivo (teratomas). This system also permits the identification of different neural subpopulations based on the intensity of our fluorescent reporter. In this context, a high level of mCherry expression showed enrichment for neural progenitors, while lower mCherry corresponded with more committed neural states. Combination of mCherry high expression with cell surface antigen staining enabled further enrichment of hESC-derived NPCs. These mCherry(+) NPCs could be expanded in culture and their differentiation resulted in a down-regulation of mCherry consistent with the loss of Nestin expression. Therefore, we have developed a fluorescent reporter system that can be used to trace neural differentiation events of hESCs

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

Effect of shock impedance of mesoscale inclusions on the shock-to-detonation transition in liquid nitromethane

Two-dimensional, meso-resolved numerical simulations are performed to investigate the effect of shock impedance of mesoscale inclusions on the shock-to-detonation transition (SDT) in liquid nitromethane (NM). The shock-induced initiation behaviors resulting from the cases with NM mixed with randomly distributed, 100-lm-sized air-filled cavities, polymethyl methacrylate (PMMA), silica, aluminum (Al), and beryllium (Be) particles with various shock impedances are examined. In this paper, hundreds of inclusions are explicitly resolved in the simulation using a diffuse-interface approach to treat two immiscible fluids. Without using any empirically calibrated, phenomenological models, the reaction rate in the simulations only depends on the temperature of liquid NM. The sensitizing effect of different inclusion materials can be rank-ordered from the weakest to the strongest as PMMA ! silica ! air ! Al ! Be in the hot-spot-driven regime of SDT. Air-filled cavities have a more significant sensitizing effect than silica particles, which is in agreement with the experimental finding. For different solid-phase inclusions, hot spots are formed by Mach reflection upon the interaction between the incident shock wave and the particle. The sensitizing effect increases roughly with the shock impedance of the inclusion material. More details of the hot-spot formation process for each solid-phase inclusion material are revealed via zoom-in simulations of a shock passing over a single particle.</p