The Experts below are selected from a list of 5430 Experts worldwide ranked by ideXlab platform
Robert Nowak - One of the best experts on this subject based on the ideXlab platform.
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socioscope spatio Temporal Signal recovery from social media
European conference on Machine Learning, 2012Co-Authors: Junming Xu, Aniruddha Bhargava, Robert NowakAbstract:Many real-world phenomena can be represented by a spatio-Temporal Signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such Signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity --- issues inadequately addressed by prior work. We formulate Signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-Temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
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ECML/PKDD (2) - Socioscope: spatio-Temporal Signal recovery from social media
Machine Learning and Knowledge Discovery in Databases, 2012Co-Authors: Junming Xu, Aniruddha Bhargava, Robert NowakAbstract:Many real-world phenomena can be represented by a spatio-Temporal Signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such Signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity --- issues inadequately addressed by prior work. We formulate Signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-Temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
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Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media
arXiv: Artificial Intelligence, 2012Co-Authors: Junming Xu, Aniruddha Bhargava, Robert NowakAbstract:Many real-world phenomena can be represented by a spatio-Temporal Signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such Signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity -- issues inadequately addressed by prior work. We formulate Signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-Temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
Junming Xu - One of the best experts on this subject based on the ideXlab platform.
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socioscope spatio Temporal Signal recovery from social media
European conference on Machine Learning, 2012Co-Authors: Junming Xu, Aniruddha Bhargava, Robert NowakAbstract:Many real-world phenomena can be represented by a spatio-Temporal Signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such Signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity --- issues inadequately addressed by prior work. We formulate Signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-Temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
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ECML/PKDD (2) - Socioscope: spatio-Temporal Signal recovery from social media
Machine Learning and Knowledge Discovery in Databases, 2012Co-Authors: Junming Xu, Aniruddha Bhargava, Robert NowakAbstract:Many real-world phenomena can be represented by a spatio-Temporal Signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such Signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity --- issues inadequately addressed by prior work. We formulate Signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-Temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
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Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media
arXiv: Artificial Intelligence, 2012Co-Authors: Junming Xu, Aniruddha Bhargava, Robert NowakAbstract:Many real-world phenomena can be represented by a spatio-Temporal Signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such Signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity -- issues inadequately addressed by prior work. We formulate Signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-Temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
Aniruddha Bhargava - One of the best experts on this subject based on the ideXlab platform.
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socioscope spatio Temporal Signal recovery from social media
European conference on Machine Learning, 2012Co-Authors: Junming Xu, Aniruddha Bhargava, Robert NowakAbstract:Many real-world phenomena can be represented by a spatio-Temporal Signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such Signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity --- issues inadequately addressed by prior work. We formulate Signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-Temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
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ECML/PKDD (2) - Socioscope: spatio-Temporal Signal recovery from social media
Machine Learning and Knowledge Discovery in Databases, 2012Co-Authors: Junming Xu, Aniruddha Bhargava, Robert NowakAbstract:Many real-world phenomena can be represented by a spatio-Temporal Signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such Signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity --- issues inadequately addressed by prior work. We formulate Signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-Temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
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Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media
arXiv: Artificial Intelligence, 2012Co-Authors: Junming Xu, Aniruddha Bhargava, Robert NowakAbstract:Many real-world phenomena can be represented by a spatio-Temporal Signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such Signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity -- issues inadequately addressed by prior work. We formulate Signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-Temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
Y. Fainman - One of the best experts on this subject based on the ideXlab platform.
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Real-time spatial-Temporal Signal processing with optical nonlinearities
IEEE Journal of Selected Topics in Quantum Electronics, 2001Co-Authors: D.m. Marom, D. Panasenko, Y.t. Mazurenko, Y. FainmanAbstract:The instantaneous response time of parametric optical nonlinearitics enable real-time processing of, and interaction between, spatial and Temporal optical waveforms. We review the various Signal-processing alternatives based on three- and four-wave-mixing arrangements among spatial and Temporal information carrying waveforms. The fast response time of the interaction permits information exchange between the time and space domains, providing the ability to correlate and convolve Signals from the two domains. We demonstrate the usefulness of real-time Signal processing with optical nonlinearities with the following experiments: converting waveforms from the time to space domain as well as from the space to time domain, spectral phase conjugation and spectral inversion of ultrafast waveforms, transmission of the spatial correlation function on an ultrafast waveform, and a suggestion for a single-shot triple autocorrelation measurement.
Y. Ichioka - One of the best experts on this subject based on the ideXlab platform.
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Two-Dimensional Image Transmission Based on the Ultrafast Optical Data Format Conversion between a Temporal Signal and a Two-Dimensional Spatial Signal.
Applied Optics, 2001Co-Authors: K. Iwamoto, T. Konishi, J. Tanida, Y. IchiokaAbstract:We have experimentally demonstrated a two-dimensional (2-D) image transmission based on the ultrafast optical data format conversion between a Temporal Signal and a spatial Signal with an ultrashort optical pulse. In the proposed system we adopt a spectral holography technique to transmit a one-dimensional (1-D) spatial Signal and use a spatial-domain time–frequency transform to realize a transform between 1-D and 2-D spatial Signals. By use of these techniques, a low-optical-loss transmission system can be constructed. To demonstrate a 2-D image transmission with this technique, we achieved experimentally transmission of the alphabet letter T as a 3 × 3 pixel 2-D spatial image.
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2D-image transmission system based on the ultrafast optical data format conversion between Temporal Signal and 2D-spatial Signal
Technical Digest. CLEO Pacific Rim 2001. 4th Pacific Rim Conference on Lasers and Electro-Optics (Cat. No.01TH8557), 2001Co-Authors: K. Iwamoto, Y. Nagasaka, T. Konishi, J. Tanida, Y. IchiokaAbstract:We study on the 2D-image transmission system for the ultrafast optical data format conversion between Temporal Signal and 2D-spatial Signal. To put emphasis on the power efficiency, we construct a system which can separate the filtering process from a transmission process.
