Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Van Oort, Colin"'
Autor:
Ratliff-Crain, Ethan, Van Oort, Colin M., Bagrow, James, Koehler, Matthew T. K., Tivnan, Brian F.
In 2001, Rama Cont introduced a now-widely used set of 'stylized facts' to synthesize empirical studies of financial price changes (returns), resulting in 11 statistical properties common to a large set of assets and markets. These properties are vie
Externí odkaz:
http://arxiv.org/abs/2311.07738
Autor:
Alshaabi, Thayer, Van Oort, Colin M., Fudolig, Mikaela Irene, Arnold, Michael V., Danforth, Christopher M., Dodds, Peter Sheridan
Publikováno v:
Front. Artif. Intell. 4:783778 (2022)
Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dom
Externí odkaz:
http://arxiv.org/abs/2109.09010
Autor:
Wilson, Daniel, Alshaabi, Thayer, Van Oort, Colin, Zhang, Xiaohan, Nelson, Jonathan, Wshah, Safwan
Geo-localizing static objects from street images is challenging but also very important for road asset mapping and autonomous driving. In this paper we present a two-stage framework that detects and geolocalizes traffic signs from low frame rate stre
Externí odkaz:
http://arxiv.org/abs/2107.06257
We adopt the deep learning method CASI-3D (Convolutional Approach to Structure Identification-3D) to identify protostellar outflows in molecular line spectra. We conduct magneto-hydrodynamics simulations that model forming stars that launch protostel
Externí odkaz:
http://arxiv.org/abs/2010.12525
We adopt the deep learning method CASI (Convolutional Approach to Shell Identification) and extend it to 3D (CASI-3D) to identify signatures of stellar feedback in molecular line spectra, such as 13CO. We adopt magneto-hydrodynamics simulations that
Externí odkaz:
http://arxiv.org/abs/2001.04506
We utilize techniques from deep learning to identify signatures of stellar feedback in simulated molecular clouds. Specifically, we implement a deep neural network with an architecture similar to U-Net and apply it to the problem of identifying wind-
Externí odkaz:
http://arxiv.org/abs/1905.09310
Autor:
Ring IV, John H., Van Oort, Colin M., Dewhurst, David R., Gray, Tyler J., Danforth, Christopher M., Tivnan, Brian F.
Using the most comprehensive, commercially-available dataset of trading activity in U.S. equity markets, we catalog and analyze quote dislocations between the SIP National Best Bid and Offer (NBBO) and a synthetic BBO constructed from direct feeds. W
Externí odkaz:
http://arxiv.org/abs/1902.04691
Autor:
Tivnan, Brian F., Dewhurst, David Rushing, Van Oort, Colin M., Ring IV, John H., Gray, Tyler J., Tivnan, Brendan F., Koehler, Matthew T. K., McMahon, Matthew T., Slater, David, Veneman, Jason, Danforth, Christopher M.
Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference. We find that
Externí odkaz:
http://arxiv.org/abs/1902.04690
Publikováno v:
In Proceedings of the Genetic and Evolutionary Computation Conference (2019) 90-98
Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-inte
Externí odkaz:
http://arxiv.org/abs/1812.05657
Autor:
Dodds, Peter Sheridan, Dewhurst, David Rushing, Hazlehurst, Fletcher F., Van Oort, Colin M., Mitchell, Lewis, Reagan, Andrew J., Williams, Jake Ryland, Danforth, Christopher M.
Publikováno v:
Phys. Rev. E 95, 052301 (2017)
Herbert Simon's classic rich-get-richer model is one of the simplest empirically supported mechanisms capable of generating heavy-tail size distributions for complex systems. Simon argued analytically that a population of flavored elements growing by
Externí odkaz:
http://arxiv.org/abs/1608.06313