A5053176592- Transportation and Mobility Innovations
- Transportation Planning and Optimization
- Traffic control and management
- Noise Effects and Management
- Biomedical Text Mining and Ontologies
- Reinforcement Learning in Robotics
- Supramolecular Chemistry and Complexes
- Urban Transport and Accessibility
- Statistical Methods in Clinical Trials
- Smart Grid Security and Resilience
- Age of Information Optimization
- Synthesis and Properties of Aromatic Compounds
- Sharing Economy and Platforms
- Impact of Light on Environment and Health
- DNA and Nucleic Acid Chemistry
Tohoku University
2004-2021
Carnegie Mellon University
2018
Institute for Molecular Science
2004
Kyushu University
2004
Gunma University
2004
Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how best do this: optimal requires optimizing over several sources of uncertainty, including vehicles' travel their dispatched locations, as well coordinating between so that they not attempt pick up same passenger. While prior works have developed models this...
Thephotochemical reaction of [3(3)](1,3,5)cyclophane 2, which is a photoprecursor for the formation propella[3(3)]prismane 18, was studied using sterilizing lamp (254 nm). Upon photolysis in dry and wet CH2Cl2 or MeOH presence 2 mol/L aqueous HCl solution, cyclophane afforded novel cage compounds comprised new skeletons, tetracyclo[6.3.1.0.(2,7)0(4,11)]dodeca-5,9-diene 43, hexacyclo[6.4.0.0.(2,6)0.(4,11)0.(5,10)0(9,12)]dodecane 44, pentacyclo[6.4.0.0.(2,6)0.(4,11)0(5,10)]dodecane 45. All...
Ubiquitous mobile computing have enabled ride-hailing services to collect vast amounts of behavioral data riders and drivers optimize supply demand matching in real time. While these mobility service providers some degree control over the market by assigning vehicles requests, they need deal with uncertainty arising from self-interested driver behavior since workers are usually free drive when not assigned tasks. If a driver's can be accurately replicated on digital twin, more detailed...
Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how best do this: optimal requires optimizing over several sources of uncertainty, including vehicles' travel their dispatched locations, as well coordinating between so that they not attempt pick up same passenger. While prior works have developed models this...
This study aimed to develop a semi-automated process convert legacy data into clinical interchange standards consortium (CDISC) tabulation model (SDTM) format by combining human verification and three methods: normalization; feature extraction distributed representation of dataset names, variable labels; supervised machine learning. Variable labels, values were used as learning features. Because most these are string data, they had been converted make them usable For this purpose, we...
Ubiquitous mobile computing have enabled ride-hailing services to collect vast amounts of behavioral data riders and drivers optimize supply demand matching in real time. While these mobility service providers some degree control over the market by assigning vehicles requests, they need deal with uncertainty arising from self-interested driver behavior since workers are usually free drive when not assigned tasks. In this work, we formulate problem passenger-vehicle a sparsely connected graph...