532 research outputs found
Geo-Social Group Queries with Minimum Acquaintance Constraint
The prosperity of location-based social networking services enables
geo-social group queries for group-based activity planning and marketing. This
paper proposes a new family of geo-social group queries with minimum
acquaintance constraint (GSGQs), which are more appealing than existing
geo-social group queries in terms of producing a cohesive group that guarantees
the worst-case acquaintance level. GSGQs, also specified with various spatial
constraints, are more complex than conventional spatial queries; particularly,
those with a strict NN spatial constraint are proved to be NP-hard. For
efficient processing of general GSGQ queries on large location-based social
networks, we devise two social-aware index structures, namely SaR-tree and
SaR*-tree. The latter features a novel clustering technique that considers both
spatial and social factors. Based on SaR-tree and SaR*-tree, efficient
algorithms are developed to process various GSGQs. Extensive experiments on
real-world Gowalla and Dianping datasets show that our proposed methods
substantially outperform the baseline algorithms based on R-tree.Comment: This is the preprint version that is accepted by the Very Large Data
Bases Journa
Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning
Inspired by expert evaluation policy for urban perception, we proposed a
novel inverse reinforcement learning (IRL) based framework for predicting urban
safety and recovering the corresponding reward function. We also presented a
scalable state representation method to model the prediction problem as a
Markov decision process (MDP) and use reinforcement learning (RL) to solve the
problem. Additionally, we built a dataset called SmallCity based on the
crowdsourcing method to conduct the research. As far as we know, this is the
first time the IRL approach has been introduced to the urban safety perception
and planning field to help experts quantitatively analyze perceptual features.
Our results showed that IRL has promising prospects in this field. We will
later open-source the crowdsourcing data collection site and the model proposed
in this paper.Comment: ACML2022 Camera-ready Versio
Di-μ-methanolato-κ4 O:O-bis[trichlorido(dimethylformamide-κO)tin(IV)]
The title compound, [Sn2(CH3O)2Cl6(C3H7NO)2], contains two hexacoordinated SnIV atoms symmetrically bridged by two deprotonated methanol ligands, with an inversion center in the middle of the planar Sn2O2 ring. The other sites of the distorted octahedral coordination geometry of the SnIV atom are occupied by three Cl atoms and one O atom from a dimethylformamide molecule. The complex molecules are connected by weak C—H⋯Cl hydrogen bonds into a two-dimensional supramolecular network parallel to (10)
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