Place embeddings generated from human mobility trajectories have become a popular method to understand the functionality of places. Place embeddings with high spatial resolution are desirable for many applications, however, downscaling the spatial resolution deteriorates the quality of embeddings due to data sparsity, especially in less populated areas. We address this issue by proposing a method that generates fine grained place embeddings, which leverages spatial hierarchical information according to the local density of observed data points. The effectiveness of our fine grained place embeddings are compared to baseline methods via next place prediction tasks using real world trajectory data from 3 cities in Japan. In addition, we demonstrate the value of our fine grained place embeddings for land use classification applications. We believe that our technique of incorporating spatial hierarchical information can complement and reinforce various place embedding generating methods.
Predicting the number of infections in the anti-epidemic process is extremely beneficial to the government in developing anti-epidemic strategies, especially in fine-grained geographic units. Previous works focus on low spatial resolution prediction, e.g., county-level, and preprocess data to the same geographic level, which loses some useful information. In this paper, we propose a fine-grained population mobility data-based model (FGC-COVID) utilizing data of two geographic levels for community-level COVID-19 prediction. We use the population mobility data between Census Block Groups (CBGs),…
Laboratoire d’Informatique de Paris Nord (LIPN)（巴黎北信息学实验室）；Riken AIP（理化学研究所人工智能项目中心）；ILPS, Amsterdam University（阿姆斯特丹大学信息学研究所）
Wikipedia is a huge opportunity for machine learning, being the largest semi-structured base of knowledge available. Because of this, many works examine its contents, and focus on structuring it in order to make it usable in learning tasks, for example by classifying it into an ontology. Beyond its textual contents, Wikipedia also displays a typical graph structure, where pages are linked together through citations. In this paper, we address the task of integrating graph (i.e. structure) information to classify Wikipedia into a fine-grained named entity ontology (NE), the Extended Named Entity…
Current approaches for points-of-interest (POI) recommendation learn the preferences of a user via the standard spatial features such as the POI coordinates, the social network, etc. These models ignore a crucial aspect of spatial mobility -- every user carries their smartphones wherever they go. In addition, with growing privacy concerns, users refrain from sharing their exact geographical coordinates and their social media activity. In this paper, we present REVAMP, a sequential POI recommendation approach that utilizes the user activity on smartphone applications (or apps) to identify their…
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；University of Helsinki（赫尔辛基大学）；The Hong Kong University of Science and Technology（香港科技大学）
Understanding urban dynamics, i.e., how the types and intensity of urban residents' activities in the city change along with time, is of urgent demand for building an efficient and livable city. Nonetheless, this is challenging due to the expanding urban population and the complicated spatial distribution of residents. In this paper, to reveal urban dynamics, we propose a novel system UrbanRhythm to reveal the urban dynamics hidden in human mobility data. UrbanRhythm addresses three questions: 1) What mobility feature should be used to present residents' high-dimensional activities in the city…
A considerable amount of mobility data has been accumulated due to the proliferation of location-based service. Nevertheless, compared with mobility data from transportation systems like the GPS module in taxis, this kind of data is commonly sparse in terms of individual trajectories in the sense that users do not access mobile services and contribute their data all the time. Consequently, the sparsity inevitably weakens the practical value of the data even it has a high user penetration rate. To solve this problem, we propose a novel attentional neural network-based model, named AttnMove, to …
Mobility-on-demand (MoD) systems have recently emerged as a promising paradigm of one-way vehicle sharing for sustainable personal urban mobility in densely populated cities. In this paper, we enhance the capability of a MoD system by deploying robotic shared vehicles that can autonomously cruise the streets to be hailed by users. A key challenge to managing the MoD system effectively is that of real-time, fine-grained mobility demand sensing and prediction. This paper presents a novel decentralized data fusion and active sensing algorithm for real-time, fine-grained mobility demand sensing an…
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach
School of Software, Dalian University of Technology（大连理工大学软件学院）；School of Engineering, IT and Physical Sciences, Federation University Australia（澳大利亚联邦大学工程、信息技术与物理科学学院）
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories. To disentangle the factors without supervision, our key idea is to use information theory to associate each factor to a latent code, and to condition the relationships between the codes in a specific way to induce the desired hierarchy. Through extensive experiments, we show that FineGAN achieves the desired disentanglement to generate realistic and diverse images belonging to fine-grained classes of …
Plan-to-Synthesis: Cross-City Human Mobility Generation via Semantic Latent Flow Matching
Singapore-MIT Alliance for Research and Technology Centre, MIT（新加坡-麻省理工学院研究与技术联盟中心（麻省理工学院））；Nanyang Technological University（南洋理工大学）；Hong Kong University of Science & Technology (Guangzhou)（香港科技大学（广州））；Korea Advanced Institute of Science & Technology（韩国高等科学技术研究院）；University of Florida（佛罗里达大学）；Massachusetts Institute of Technology（麻省理工学院）
University of Toronto（多伦多大学）；Twenty Billion Neurons（两百亿神经元公司）
We introduce a hierarchical architecture for video understanding that exploits the structure of real world actions by capturing targets at different levels of granularity. We design the model such that it first learns simpler coarse-grained tasks, and then moves on to learn more fine-grained targets. The model is trained with a joint loss on different granularity levels. We demonstrate empirical results on the recent release of Something-Something dataset, which provides a hierarchy of targets, namely coarse-grained action groups, fine-grained action categories, and captions. Experiments sugge…
Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server ca…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）；IBM-Illinois Center for Cognitive Computing Systems Research（IBM-伊利诺伊认知计算系统研究中心）
We propose to measure fine-grained domain relevance - the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. Such measurement is crucial for many downstream tasks in natural language processing. To handle long-tail terms, we build a core-anchored semantic graph, which uses core terms with rich description information to bridge the vast remaining fringe terms semantically. To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms j…
Purdue University（普渡大学）；University of Tübingen（蒂宾根大学）；Indiana University - Purdue University（印第安纳大学-普渡大学）
Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes. As manually annotated visual attributes are extremely costly and often impractical to obtain for a large number of classes, in this study we use DNA as side information for the first time for fine-grained zero-shot classification of species. Mitochondrial DNA plays an important role as a genetic marker in evolutionary biology and has been used to achieve near-perfect accuracy in the species classification of living organisms. We implement a simple hier…
