- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Rome (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Uncertainty Quantification in Imaging: When Convex Optimization Meets Bayesian Analysis
摘要： We propose to perform Bayesian uncertainty quantification via convex optimization tools (BUQO), in the context of high dimensional inverse problems. We quantify the uncertainty associated with particular structures appearing in the maximum a posteriori estimate, obtained from a log-concave Bayesian model. A hypothesis test is defined, where the null hypothesis represents the non-existence of the structure of interest in the true image. To determine if this null hypothesis is rejected, we use the data and prior knowledge. Computing such test in the context of imaging problem is often intractable due to the high dimensionality involved. In this work, we propose to leverage probability concentration phenomena and the underlying convex geometry to formulate the Bayesian hypothesis test as a convex minimization problem. This problem is subsequently solved using a proximal primal-dual algorithm. The proposed method is applied to astronomical radio-interferometric imaging.
关键词： astronomical imaging,proximal primal-dual algorithm,inverse problem,hypothesis testing,Bayesian uncertainty quantification
更新于2025-09-23 15:23:52
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A hypothesis testing approach for communication over entanglement assisted compound quantum channel
摘要： We study the problem of communication over a compound quantum channel in the presence of entanglement. Classically such a channel is modeled as a collection of conditional probability distributions wherein neither the sender nor the receiver is aware of the channel being used for transmission, except for the fact that it belongs to this collection. We provide near optimal achievability and converse bounds for this problem in the one-shot quantum setting in terms of the quantum hypothesis testing divergence. We also consider the case of informed sender, showing a one-shot achievability result that converges appropriately in the asymptotic and i.i.d. setting. Our achievability proof is similar in spirit to its classical counterpart. To arrive at our result, we use the technique of position-based decoding along with a new approach for constructing a union of two projectors, which might be of independent interest. We give another application of the union of projectors to the problem of testing composite quantum hypotheses.
关键词： quantum hypothesis testing,one-shot information theory,position-based decoding,compound quantum channel,entanglement-assisted communication
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - On the Modelling of Urban Infrastructure Deformation Profiles Using the Applied Element Method and Multiple Hypothesis Testing
摘要： Although persistent scatterer interferometry can be used for infrastructure monitoring, interpretation of the results can be difficult. We propose to use the applied element method for modelling building deformation and multiple hypothesis testing for finding deformation model in data obtained from persistent scatterer interferometry. Simulation results are presented.
关键词： deformation,multiple hypothesis testing,building,persistent scatterer interferometry
更新于2025-09-23 15:21:21
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Highly accelerated stress method for measuring water vapor transmission rate of PV backsheet
摘要： The increasing availability of network data is leading to a growing interest in processing of signals on graphs. One notable tool for extending conventional signal-processing operations to networks is the graph Fourier transform that can be obtained as the eigendecomposition of the graph Laplacian. In this letter, we used the graph Fourier transform to define a new method for generating surrogate graph signals. The approach is based on sign-randomization of the graph Fourier coefficients and, therefore, the correlation structure of the surrogate graph signals (i.e., smoothness on the graph topology) is imposed by the measured data. The proposed method of surrogate data generation can be widely applied for nonparametric statistical hypothesis testing. Here, we showed a proof-of-concept with a high-density electroencephalography dataset.
关键词： nonparametric hypothesis testing,Electroencephalography (EEG),graph Laplacian,surrogate data,phase randomization,graph signals
更新于2025-09-23 15:21:01
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Increase the Quantum Dots Sensitized ${\rm{TiO_{2}}}$ Solar Cell Efficiency Adding n%Yb3+a??1%Er3+ Doped ${\rm{NaYF_{4}}}$: Submicrometer-Sized Rods
摘要： The increasing availability of network data is leading to a growing interest in processing of signals on graphs. One notable tool for extending conventional signal-processing operations to networks is the graph Fourier transform that can be obtained as the eigendecomposition of the graph Laplacian. In this letter, we used the graph Fourier transform to define a new method for generating surrogate graph signals. The approach is based on sign-randomization of the graph Fourier coefficients and, therefore, the correlation structure of the surrogate graph signals (i.e., smoothness on the graph topology) is imposed by the measured data. The proposed method of surrogate data generation can be widely applied for nonparametric statistical hypothesis testing. Here, we showed a proof-of-concept with a high-density electroencephalography dataset.
关键词： nonparametric hypothesis testing,surrogate data,Electroencephalography (EEG),graph Laplacian,graph signals,phase randomization
更新于2025-09-23 15:19:57
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Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization
摘要： We propose a Bayesian uncertainty quanti?cation method for large-scale imaging inverse problems. Our method applies to all Bayesian models that are log-concave, where maximum a posteriori (MAP) estimation is a convex optimization problem. The method is a framework to analyze the con?dence in speci?c structures observed in MAP estimates (e.g., lesions in medical imaging, celestial sources in astronomical imaging), to enable using them as evidence to inform decisions and conclusions. Precisely, following Bayesian decision theory, we seek to assert the structures under scrutiny by performing a Bayesian hypothesis test that proceeds as follows: ?rst, it postulates that the structures are not present in the true image, and then seeks to use the data and prior knowledge to reject this null hypothesis with high probability. Computing such tests for imaging problems is generally very di?cult because of the high dimensionality involved. A main feature of this work is to leverage probability concentration phenomena and the underlying convex geometry to formulate the Bayesian hypothesis test as a convex problem, which we then e?ciently solve by using scalable optimization algorithms. This allows scaling to high-resolution and high-sensitivity imaging problems that are computationally una?ordable for other Bayesian computation approaches. We illustrate our methodology, dubbed BUQO (Bayesian Uncertainty Quanti?cation by Optimization), on a range of challenging Fourier imaging problems arising in astronomy and medicine. MATLAB code for the proposed uncertainty quanti?cation method is available on GitHub.
关键词： Bayesian inference,inverse problems,image processing,hypothesis testing,uncertainty quanti?cation,convex optimization
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Automatic Insar Phase Modeling and Quality Assessment Using Machine Learning and Hypothesis Testing
摘要： PS-InSAR time series yield large volumes of data points, observed during many epochs. While traditional processing algorithms use a single parameterization for the behavior of all points, in reality this behavior will differ significantly between points and over time. It is a challenge to find the optimal parameterization for this behavior, and to assess the quality of the measurements per point and per epoch. Here we propose a post-processing method to improve the model estimation of PS-InSAR phase time series. The method combines machine learning (ML) algorithms and hypothesis testing (HT) into the ML/HT method efficiently leading to significant improvements in data interpretation, parameterization, as well as the quality of the estimated parameters. Moreover we show that we can find structure in the data regardless of spatial location and temporal complexity. In contrast to conventional assumptions that nearby points behave in the same way, with unchanged characteristics over time, a method is developed that takes individual behavior into account. Demonstrating that we can move from spatial and temporal analysis tools to semantic-based analysis.
关键词： stochastics,machine learning,InSAR,hypothesis testing
更新于2025-09-10 09:29:36
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Symmetric and asymmetric discrimination of bosonic loss: Toy applications to biological samples and photodegradable materials
摘要： We consider quantum discrimination of bosonic loss based on both symmetric and asymmetric hypothesis testing. In both approaches, an entangled resource is able to outperform any classical strategy based on coherent-state transmitters in the regime of low photon numbers. In the symmetric case, we then consider the low-energy detection of bacterial growth in culture media. Assuming an exponential growth law for the bacterial concentration and the Beer-Lambert law for the optical transmissivity of the sample, we find that the use of entanglement allows one to achieve a much faster detection of growth with respect to the use of coherent states. This performance is also studied by assuming an exponential photo degradable model, where the concentration is reduced by increasing the number of photons irradiated over the sample. This investigation is then extended to the readout of classical information from suitably designed photodegradable optical memories.
关键词： coherent-state transmitters,optical memories,bacterial growth,entangled resource,quantum discrimination,bosonic loss,Beer-Lambert law,photodegradable model,optical transmissivity,symmetric and asymmetric hypothesis testing
更新于2025-09-10 09:29:36
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Advances in photonic quantum sensing
摘要： Quantum sensing has become a broad field. It is generally related with the idea of using quantum resources to boost the performance of a number of practical tasks, including the radar-like detection of faint objects, the readout of information from optical memories, and the optical resolution of extremely close point-like sources. Here, we first focus on the basic tools behind quantum sensing, discussing the most recent and general formulations for the problems of quantum parameter estimation and hypothesis testing. With this basic background in hand, we then review emerging applications of quantum sensing in the photonic regime both from a theoretical and experimental point of view. Besides the state of the art, we also discuss open problems and potential next steps.
关键词： quantum hypothesis testing,quantum parameter estimation,photonic quantum sensing,optical resolution,quantum reading,quantum sensing,quantum illumination
更新于2025-09-09 09:28:46