The Experts below are selected from a list of 36384 Experts worldwide ranked by ideXlab platform
Philip Whiting - One of the best experts on this subject based on the ideXlab platform.
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millimeter wave beam alignment large deviations analysis and design insights
IEEE Journal on Selected Areas in Communications, 2017Co-Authors: Chunshan Liu, Stephen V Hanly, Iain B Collings, Philip WhitingAbstract:In millimeter wave cellular communication, fast and reliable beam alignment via beam training is crucial to harvest sufficient beamforming gain for the subsequent data transmission. In this paper, we establish fundamental limits in beam-alignment performance under both the Exhaustive Search and the hierarchical Search that adopts multi-resolution beamforming codebooks, accounting for time-domain training overhead. Specifically, we derive lower and upper bounds on the probability of misalignment for an arbitrary level in the hierarchical Search, based on a single-path channel model. Using the method of large deviations, we characterize the decay rate functions of both bounds and show that the bounds coincide as the training sequence length goes large. We go on to characterize the asymptotic misalignment probability of both the hierarchical and Exhaustive Search, and show that the latter asymptotically outperforms the former, subject to the same training overhead and codebook resolution. We show via numerical results that this relative performance behavior holds in the non-asymptotic regime. Moreover, the Exhaustive Search is shown to achieve significantly higher worst case spectrum efficiency than the hierarchical Search, when the pre-beamforming signal-to-noise ratio (SNR) is relatively low. This paper hence implies that the Exhaustive Search is more effective for users situated further from base stations, as they tend to have low SNR.
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millimeter wave beam alignment large deviations analysis and design insights
arXiv: Information Theory, 2016Co-Authors: Chunshan Liu, Stephen V Hanly, Iain B Collings, Philip WhitingAbstract:In millimeter wave cellular communication, fast and reliable beam alignment via beam training is crucial to harvest sufficient beamforming gain for the subsequent data transmission. In this paper, we establish fundamental limits in beam-alignment performance under both the Exhaustive Search and the hierarchical Search that adopts multi-resolution beamforming codebooks, accounting for time-domain training overhead. Specifically, we derive lower and upper bounds on the probability of misalignment for an arbitrary level in the hierarchical Search, based on a single-path channel model. Using the method of large deviations, we characterize the decay rate functions of both bounds and show that the bounds coincide as the training sequence length goes large. With these results, we characterize the asymptotic misalignment probability of both the hierarchical and Exhaustive Search, and show that the latter asymptotically outperforms the former, subject to the same training overhead and codebook resolution. We show via numerical results that this relative performance behavior holds in the non-asymptotic regime. Moreover, the Exhaustive Search is shown to achieve significantly higher worst-case spectrum efficiency than the hierarchical Search, when the pre-beamforming signal-to-noise ratio (SNR) is relatively low. This study hence implies that the Exhaustive Search is more effective for users situated not so close to base stations, as they tend to have low SNR.
Chunshan Liu - One of the best experts on this subject based on the ideXlab platform.
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millimeter wave beam alignment large deviations analysis and design insights
IEEE Journal on Selected Areas in Communications, 2017Co-Authors: Chunshan Liu, Stephen V Hanly, Iain B Collings, Philip WhitingAbstract:In millimeter wave cellular communication, fast and reliable beam alignment via beam training is crucial to harvest sufficient beamforming gain for the subsequent data transmission. In this paper, we establish fundamental limits in beam-alignment performance under both the Exhaustive Search and the hierarchical Search that adopts multi-resolution beamforming codebooks, accounting for time-domain training overhead. Specifically, we derive lower and upper bounds on the probability of misalignment for an arbitrary level in the hierarchical Search, based on a single-path channel model. Using the method of large deviations, we characterize the decay rate functions of both bounds and show that the bounds coincide as the training sequence length goes large. We go on to characterize the asymptotic misalignment probability of both the hierarchical and Exhaustive Search, and show that the latter asymptotically outperforms the former, subject to the same training overhead and codebook resolution. We show via numerical results that this relative performance behavior holds in the non-asymptotic regime. Moreover, the Exhaustive Search is shown to achieve significantly higher worst case spectrum efficiency than the hierarchical Search, when the pre-beamforming signal-to-noise ratio (SNR) is relatively low. This paper hence implies that the Exhaustive Search is more effective for users situated further from base stations, as they tend to have low SNR.
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millimeter wave beam alignment large deviations analysis and design insights
arXiv: Information Theory, 2016Co-Authors: Chunshan Liu, Stephen V Hanly, Iain B Collings, Philip WhitingAbstract:In millimeter wave cellular communication, fast and reliable beam alignment via beam training is crucial to harvest sufficient beamforming gain for the subsequent data transmission. In this paper, we establish fundamental limits in beam-alignment performance under both the Exhaustive Search and the hierarchical Search that adopts multi-resolution beamforming codebooks, accounting for time-domain training overhead. Specifically, we derive lower and upper bounds on the probability of misalignment for an arbitrary level in the hierarchical Search, based on a single-path channel model. Using the method of large deviations, we characterize the decay rate functions of both bounds and show that the bounds coincide as the training sequence length goes large. With these results, we characterize the asymptotic misalignment probability of both the hierarchical and Exhaustive Search, and show that the latter asymptotically outperforms the former, subject to the same training overhead and codebook resolution. We show via numerical results that this relative performance behavior holds in the non-asymptotic regime. Moreover, the Exhaustive Search is shown to achieve significantly higher worst-case spectrum efficiency than the hierarchical Search, when the pre-beamforming signal-to-noise ratio (SNR) is relatively low. This study hence implies that the Exhaustive Search is more effective for users situated not so close to base stations, as they tend to have low SNR.
Yasuhiko Igarashi - One of the best experts on this subject based on the ideXlab platform.
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material Search for li ion battery electrolytes through an Exhaustive Search with a gaussian process
Chemical Physics Letters, 2019Co-Authors: Tomofumi Nakayama, Yasuhiko Igarashi, Masato Okada, Keitaro SodeyamaAbstract:Abstract When creating an estimation model, determining which variables are efficient is of considerable importance. To strictly select efficient variables, it is necessary to define appropriate criteria for the task and to perform an Exhaustive Search, which is the Search method that evaluates and compares all variable combinations. In this study, we apply an Exhaustive Search with a Gaussian process (ES-GP) to estimate coordination energy, which is related to performance of a Li-ion battery, and show that the estimation accuracy of ES-GP is significantly better than that of other methods in previous studies, such as MLR, LASSO and ES-LiR.
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liquid electrolyte informatics using an Exhaustive Search with linear regression
Physical Chemistry Chemical Physics, 2018Co-Authors: Yasuhiko Igarashi, Tomofumi Nakayama, Keitaro Sodeyama, Yoshitaka Tateyama, Masato OkadaAbstract:Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast to solid materials, disordered liquid solution properties have been less studied by data-driven information techniques. Here, we examined the estimation accuracy and efficiency of three information techniques, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and Exhaustive Search with linear regression (ES-LiR), by using coordination energy and melting point as test liquid properties. We then confirmed that ES-LiR gives the most accurate estimation among the techniques. We also found that ES-LiR can provide the relationship between the “prediction accuracy” and “calculation cost” of the properties via a weight diagram of descriptors. This technique makes it possible to choose the balance of the “accuracy” and “cost” when the Search of a huge amount of new materials was carried out.
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es dos Exhaustive Search and density of states estimation as a general framework for sparse variable selection
International Meeting on High-Dimensional Data-Driven Science HD3 2017, 2018Co-Authors: Yasuhiko Igarashi, Hikaru Takenaka, Yoshinori Nakanishiohno, Hiroko Ichikawa, Daiki Kawabata, Satoshi Eifuku, Ryoi TamuraAbstract:In this paper, we propose an Exhaustive Search with density-of-states estimation (ES-DoS) method for sparse variable selection in a wide range of learning tasks with various learning machines. We applied this ES-DoS method to synthetic and real data as an example of the regression and classification problems and discuss the results in this paper. The most important aspect of our ES-DoS method is to extract not only the optimal solution but also density of states (DoS) in terms of machine learning and data-driven science. Mapping the solutions of various approximate methods or scientists' hypotheses onto the DoS, we can comprehensively discuss and evaluate these methods and hypotheses. Our ES-DoS method opens the way for sparse variable selection in various fields, which promotes the high-dimensional data-driven science.
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Exhaustive Search for sparse variable selection in linear regression
Journal of the Physical Society of Japan, 2018Co-Authors: Yasuhiko Igarashi, Hikaru Takenaka, Yoshinori Nakanishiohno, Makoto Uemura, Shiro Ikeda, Masato OkadaAbstract:We propose a K-sparse Exhaustive Search (ES-K) method and a K-sparse approximate Exhaustive Search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested Exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of Exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, w...
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Exhaustive Search for sparse variable selection in linear regression
arXiv: Machine Learning, 2017Co-Authors: Yasuhiko Igarashi, Hikaru Takenaka, Yoshinori Nakanishiohno, Makoto Uemura, Shiro Ikeda, Masato OkadaAbstract:We propose a K-sparse Exhaustive Search (ES-K) method and a K-sparse approximate Exhaustive Search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested Exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of Exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.
Masato Okada - One of the best experts on this subject based on the ideXlab platform.
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material Search for li ion battery electrolytes through an Exhaustive Search with a gaussian process
Chemical Physics Letters, 2019Co-Authors: Tomofumi Nakayama, Yasuhiko Igarashi, Masato Okada, Keitaro SodeyamaAbstract:Abstract When creating an estimation model, determining which variables are efficient is of considerable importance. To strictly select efficient variables, it is necessary to define appropriate criteria for the task and to perform an Exhaustive Search, which is the Search method that evaluates and compares all variable combinations. In this study, we apply an Exhaustive Search with a Gaussian process (ES-GP) to estimate coordination energy, which is related to performance of a Li-ion battery, and show that the estimation accuracy of ES-GP is significantly better than that of other methods in previous studies, such as MLR, LASSO and ES-LiR.
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liquid electrolyte informatics using an Exhaustive Search with linear regression
Physical Chemistry Chemical Physics, 2018Co-Authors: Yasuhiko Igarashi, Tomofumi Nakayama, Keitaro Sodeyama, Yoshitaka Tateyama, Masato OkadaAbstract:Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast to solid materials, disordered liquid solution properties have been less studied by data-driven information techniques. Here, we examined the estimation accuracy and efficiency of three information techniques, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and Exhaustive Search with linear regression (ES-LiR), by using coordination energy and melting point as test liquid properties. We then confirmed that ES-LiR gives the most accurate estimation among the techniques. We also found that ES-LiR can provide the relationship between the “prediction accuracy” and “calculation cost” of the properties via a weight diagram of descriptors. This technique makes it possible to choose the balance of the “accuracy” and “cost” when the Search of a huge amount of new materials was carried out.
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Exhaustive Search for sparse variable selection in linear regression
Journal of the Physical Society of Japan, 2018Co-Authors: Yasuhiko Igarashi, Hikaru Takenaka, Yoshinori Nakanishiohno, Makoto Uemura, Shiro Ikeda, Masato OkadaAbstract:We propose a K-sparse Exhaustive Search (ES-K) method and a K-sparse approximate Exhaustive Search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested Exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of Exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, w...
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Exhaustive Search for sparse variable selection in linear regression
arXiv: Machine Learning, 2017Co-Authors: Yasuhiko Igarashi, Hikaru Takenaka, Yoshinori Nakanishiohno, Makoto Uemura, Shiro Ikeda, Masato OkadaAbstract:We propose a K-sparse Exhaustive Search (ES-K) method and a K-sparse approximate Exhaustive Search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested Exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of Exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.
Michele Parrinello - One of the best experts on this subject based on the ideXlab platform.
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Exhaustive Search of ligand binding pathways via volume based metadynamics
Journal of Physical Chemistry Letters, 2019Co-Authors: Riccardo Capelli, Paolo Carloni, Michele ParrinelloAbstract:Determining the complete set of ligands’ binding–unbinding pathways is important for drug discovery and for rational interpretation of mutation data. Here we have developed a metadynamics-based technique that addresses this issue and allows estimating affinities in the presence of multiple escape pathways. Our approach is shown on a lysozyme T4 variant in complex with a benzene molecule. The calculated binding free energy is in agreement with experimental data. Remarkably, not only were we able to find all the previously identified ligand binding pathways, but also we identified three pathways previously not identified as such. These results were obtained at a small computational cost, making this approach valuable for practical applications, such as screening of small compound libraries.
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Exhaustive Search of ligand binding pathways via volume based metadynamics
arXiv: Chemical Physics, 2019Co-Authors: Riccardo Capelli, Paolo Carloni, Michele ParrinelloAbstract:Determining the complete set of ligands' binding/unbinding pathways is important for drug discovery and to rationally interpret mutation data. Here we have developed a metadynamics-based technique that addressed this issue and allows estimating affinities in the presence of multiple escape pathways. Our approach is shown on a Lysozyme T4 variant in complex with the benzene molecule. The calculated binding free energy is in agreement with experimental data. Remarkably, not only we were able to find all the previously identified ligand binding pathways, but also we uncovered 3 new ones. This results were obtained at a small computational cost, making this approach valuable for practical applications, such as screening of small compounds libraries.
