A basic assumption of most recently proposed waveform design algorithms is that the target impulse response is a known deterministic function or a stochastic process with a known power spectral density （PSD）. Howeve...A basic assumption of most recently proposed waveform design algorithms is that the target impulse response is a known deterministic function or a stochastic process with a known power spectral density （PSD）. However, it is well-known that a target impulse response is neither easily nor accurately obtained; besides it changes sharply with attitude angles. Both of the aforementioned cases complicate the waveform design process. In this paper, an adaptive robust waveform selection method for unknown target detection in clutter is proposed. The target impulse response is considered to be unknown but belongs to a known uncertainty set. An adaptive waveform library is devised by using a signal-to-clutter-plus-noise ratio （SCNR）- based optimal waveform design method. By applying the minimax robust waveform selection method, the optimal robust waveform is selected to ensure the lowest performance bound of the unknown target detection in clutter. Results show that the adaptive waveform library outperforms the predefined linear frequency modulation （LFM） waveform library on the SCNR bound.展开更多
We introduce a new approach for optimal portfolio choice under model ambiguity by incorporating predictable forward preferences in the framework of Angoshtari et al.[2].The investor reassesses and revises the model am...We introduce a new approach for optimal portfolio choice under model ambiguity by incorporating predictable forward preferences in the framework of Angoshtari et al.[2].The investor reassesses and revises the model ambiguity set incrementally in time while,also,updating his risk preferences forward in time.This dynamic alignment of preferences and ambiguity updating results in time-consistent policies and provides a richer,more accurate learning setting.For each investment period,the investor solves a worst-case portfolio optimization over possible market models,which are represented via a Wasserstein neighborhood centered at a binomial distribution.Duality methods from Gao and Kleywegt[10];Blanchet and Murthy[8]are used to solve the optimization problem over a suitable set of measures,yielding an explicit optimal portfolio in the linear case.We analyze the case of linear and quadratic utilities,and provide numerical results.展开更多
Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cr...Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cropping attacks,rotation attacks,etc.We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further.First,to extract more sparse and stable interest points,we use the Superpoint algorithm for generation and design two steps to perform the screening procedure.We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability.The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework.Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks.We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks.Through extensive experimental results,we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image.Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61171133the Natural Science Fund for Distinguished Young Scholars of Hunan Province under Grant No.11JJ1010the Research Fund for the Doctoral Program of Higher Education of China under Grant No.20124307110013
文摘A basic assumption of most recently proposed waveform design algorithms is that the target impulse response is a known deterministic function or a stochastic process with a known power spectral density （PSD）. However, it is well-known that a target impulse response is neither easily nor accurately obtained; besides it changes sharply with attitude angles. Both of the aforementioned cases complicate the waveform design process. In this paper, an adaptive robust waveform selection method for unknown target detection in clutter is proposed. The target impulse response is considered to be unknown but belongs to a known uncertainty set. An adaptive waveform library is devised by using a signal-to-clutter-plus-noise ratio （SCNR）- based optimal waveform design method. By applying the minimax robust waveform selection method, the optimal robust waveform is selected to ensure the lowest performance bound of the unknown target detection in clutter. Results show that the adaptive waveform library outperforms the predefined linear frequency modulation （LFM） waveform library on the SCNR bound.
文摘We introduce a new approach for optimal portfolio choice under model ambiguity by incorporating predictable forward preferences in the framework of Angoshtari et al.[2].The investor reassesses and revises the model ambiguity set incrementally in time while,also,updating his risk preferences forward in time.This dynamic alignment of preferences and ambiguity updating results in time-consistent policies and provides a richer,more accurate learning setting.For each investment period,the investor solves a worst-case portfolio optimization over possible market models,which are represented via a Wasserstein neighborhood centered at a binomial distribution.Duality methods from Gao and Kleywegt[10];Blanchet and Murthy[8]are used to solve the optimization problem over a suitable set of measures,yielding an explicit optimal portfolio in the linear case.We analyze the case of linear and quadratic utilities,and provide numerical results.
文摘Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cropping attacks,rotation attacks,etc.We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further.First,to extract more sparse and stable interest points,we use the Superpoint algorithm for generation and design two steps to perform the screening procedure.We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability.The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework.Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks.We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks.Through extensive experimental results,we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image.Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.
