Zobrazeno 1 - 10
of 371
pro vyhledávání: '"SAMET, HANAN"'
Visualizations such as bar charts, scatter plots, and objects on geographical maps often convey critical information, including exact and relative numeric values, using shapes. The choice of shape and method of encoding information is often arbitrari
Externí odkaz:
http://arxiv.org/abs/2211.05965
Autor:
Ondov, Brian, Patel, Harsh B., Kuo, Ai-Te, Samet, Hanan, Kastner, John, Han, Yunheng, Wei, Hong, Elmqvist, Niklas
While many dashboards for visualizing COVID-19 data exist, most separate geospatial and temporal data into discrete visualizations or tables. Further, the common use of choropleth maps or space-filling map overlays supports only a single geospatial v
Externí odkaz:
http://arxiv.org/abs/2211.05823
User trajectory data is becoming increasingly accessible due to the prevalence of GPS-equipped devices such as smartphones. Many existing studies focus on querying trajectories that are similar to each other in their entirety. We observe that traject
Externí odkaz:
http://arxiv.org/abs/2106.03355
Autor:
Han, Yunheng, Samet, Hanan
Trajectories represent the mobility of moving objects and thus is of great value in data mining applications. However, trajectory data is enormous in volume, so it is expensive to store and process the raw data directly. Trajectories are also redunda
Externí odkaz:
http://arxiv.org/abs/2010.08622
An efficient algorithm for computing the choropleth map classification scheme known as equal area breaks or geographical quantiles is introduced. An equal area breaks classification aims to obtain a coloring for the map such that the area associated
Externí odkaz:
http://arxiv.org/abs/2005.01653
With the rapid continuing spread of COVID-19, it is clearly important to be able to track the progress of the virus over time in order to be better prepared to anticipate its emergence and spread in new regions as well as declines in its presence in
Externí odkaz:
http://arxiv.org/abs/2003.00107
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Ondov, Brian, Samet, Hanan
Publikováno v:
In Pattern Recognition Letters April 2023 168:1-7
Currently, deep neural networks are deployed on low-power portable devices by first training a full-precision model using powerful hardware, and then deriving a corresponding low-precision model for efficient inference on such systems. However, train
Externí odkaz:
http://arxiv.org/abs/1706.02379