We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is know...We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.展开更多
Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and sha...Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously.Specifically,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.展开更多
For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform(SWT). First, the mixed signal of chaotic signal, harmonic signal...For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform(SWT). First, the mixed signal of chaotic signal, harmonic signal, and noise is decomposed into a series of intrinsic mode-type functions by synchrosqueezed wavelet transform(SWT) then the instantaneous frequency of intrinsic mode-type functions is analyzed by using of Hilbert transform, and the harmonic extraction is realized. In experiments of harmonic signal extraction, the Duffing and Lorenz chaotic signals are selected as interference signal, and the mixed signal of chaotic signal and harmonic signal is added by Gauss white noises of different intensities.The experimental results show that when the white noise intensity is in a certain range, the extracting harmonic signals measured by the proposed SWT method have higher precision, the harmonic signal extraction effect is obviously superior to the classical empirical mode decomposition method.展开更多
In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance...In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.展开更多
We explore the entropy uncertainty for qutrit system under non-Markov noisy environment and discuss the effects of the quantum memory system and the spontaneously generated interference(SGI)on the entropy uncertainty ...We explore the entropy uncertainty for qutrit system under non-Markov noisy environment and discuss the effects of the quantum memory system and the spontaneously generated interference(SGI)on the entropy uncertainty in detail.The results show that,the entropy uncertainty can be reduced by using the methods of quantum memory system and adjusting of SGI.Particularly,the entropy uncertainty can be decreased obviously when both the quantum memory system and the SGI are simultaneously applied.展开更多
In the past years, great progresses have been made on quantum computation and quantum simulation. Increasing the number of qubits in the quantum processors is expected to be one of the main motivations in the next yea...In the past years, great progresses have been made on quantum computation and quantum simulation. Increasing the number of qubits in the quantum processors is expected to be one of the main motivations in the next years, while noises in manipulation of quantum states may still be inevitable even the precision will improve. For research in this direction, it is necessary to review the available results about noisy multiqubit quantum computation and quantum simulation. The review focuses on multiqubit state generations, quantum computational advantage, and simulating physics of quantum many-body systems. Perspectives of near term noisy intermediate-quantum processors will be discussed.展开更多
Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-f...Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation.By two-scale image decomposition,the input image is decomposed into base layer and detail layer.For the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge information.Besides,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization problems.For the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain.The sum of the above base and detail layers is as the final fused image.Experimental results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.展开更多
The effects of distributing entanglement through the amplitude damping channel or the phase damping channel on the teleportation of a single-qubit state via the Greenberger-Horne-Zeilinger state and the W state are di...The effects of distributing entanglement through the amplitude damping channel or the phase damping channel on the teleportation of a single-qubit state via the Greenberger-Horne-Zeilinger state and the W state are discussed.It is found that the average fidelity of teleportation depends on the type and rate of the damping in the channel.For the one-qubit affected case,the Greenberger-Horne-Zeilinger state is as robust as the W state,i.e.,the same quantum information is preserved through teleportation.For the two-qubit affected case,the W state is more robust when the entanglement is distributed via the amplitude damping channel;if the entanglement is distributed via the phase damping channel,the W state is more robust when the noisy parameter is small while the Greenberger-Horne-Zeilinger state becomes more robust when it is large.For the three-qubit affected case,the Greenberger-Horne-Zeilinger state is more robust than the W state.展开更多
Network localization is a fundamental problem in wireless sensor networks, mainly in location dependent applications. A common family of solutions to this problem is the range-based network localization. The resulting...Network localization is a fundamental problem in wireless sensor networks, mainly in location dependent applications. A common family of solutions to this problem is the range-based network localization. The resulting localization algorithms are noise sensitive and thus lacking in terms of robustness. Our contribution provides an algorithm which is robust to measurement errors. We propose an analytical tool to analyze the effect of range errors in the final location and use a distributed method to solve the noisy range localization problem.展开更多
In real life, when a noise problem occurs, it is important to identify the cause and measure the noise of the source, since it may affect human beings or other constructions due to vibration generated from noise, so i...In real life, when a noise problem occurs, it is important to identify the cause and measure the noise of the source, since it may affect human beings or other constructions due to vibration generated from noise, so it is necessary to determine the noise related to a specific source like a machine in the presence of other sources which is a very important approach in noise control engineering. In this article a full experiment was executed to measure the sound pressure levels of various sources (stationary and non-stationary), in both an anechoic chamber and a non-ideal noisy environment. The sound pressure level was extracted for different sources and compared for both ideal and non-ideal environment. The results showed that acoustical free field of the space is the best field to do measurements to avoid reflection, on the other hand the difference between the source and the background should be more than 3 dB to get better results.展开更多
We theoretically investigate the collective response of an ensemble of leaky integrate-and-fire neuron units to a noisy periodic signal by including local spatially correlated noise. By using the linear response theor...We theoretically investigate the collective response of an ensemble of leaky integrate-and-fire neuron units to a noisy periodic signal by including local spatially correlated noise. By using the linear response theory, we obtained the analytic expression of signal-to-noise ratio (SNR). Numerical simulation results show that the rms amplitude of internal noise can be increased up to?an optimal value where the output SNR reaches a maximum value. Due to the existence of the local spatially correlated noise in the units of the ensemble, the SNR gain of the collective ensemble response can exceed unity and can be optimized when the nearest-neighborhood correlation is negative. This nonlinear collective phenomenon of SNR gain amplification in an ensemble of leaky integrate-and-fire neuron units can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and?amplitude of the weak periodic signal. The present study illustrates the potential to utilize the local spatially correlation noise and the number of ensemble units for optimizing the collective response of the neuron to inputs, as well as a guidance in the design of information processing devices to weak signal detection.展开更多
Cross-lingual summarization(CLS)is the task of generating a summary in a target language from a document in a source language.Recently,end-to-end CLS models have achieved impressive results using large-scale,high-qual...Cross-lingual summarization(CLS)is the task of generating a summary in a target language from a document in a source language.Recently,end-to-end CLS models have achieved impressive results using large-scale,high-quality datasets typically constructed by translating monolingual summary corpora into CLS corpora.However,due to the limited performance of low-resource language translation models,translation noise can seriously degrade the performance of these models.In this paper,we propose a fine-grained reinforcement learning approach to address low-resource CLS based on noisy data.We introduce the source language summary as a gold signal to alleviate the impact of the translated noisy target summary.Specifically,we design a reinforcement reward by calculating the word correlation and word missing degree between the source language summary and the generated target language summary,and combine it with cross-entropy loss to optimize the CLS model.To validate the performance of our proposed model,we construct Chinese-Vietnamese and Vietnamese-Chinese CLS datasets.Experimental results show that our proposed model outperforms the baselines in terms of both the ROUGE score and BERTScore.展开更多
The gravimeter based on atom interferometry has potential wide applications on building gravity networks and geophysics as well as gravity assisted navigation. Here, we demonstrate experimentally a portable atomic gra...The gravimeter based on atom interferometry has potential wide applications on building gravity networks and geophysics as well as gravity assisted navigation. Here, we demonstrate experimentally a portable atomic gravimeter operating in the noisy urban environment. Despite the influence of noisy external vibrations, our portable atomic gravimeter reaches a sensitivity as good as 65 μGal/√Hz and a resolution of 1.1 μGal after 4000 s integration, being comparable to state-of-the-art atomic gravimeters. Our achievement paves the way for bringing the portable atomic gravimeter to field applications.展开更多
Level-set-based image segmentation has been widely used in unsupervised segmentation tasks.Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local stati...Level-set-based image segmentation has been widely used in unsupervised segmentation tasks.Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models.Most existing methods are based on the assumption that the distribution of image noise is known or observable.However,real-time images do not meet this assumption.To bridge this gap,we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism.First,a denoising filter is acquired under the unsupervised learning paradigm.Second,the denoising filter is integrated into the level-set framework to separate noise from the noisy image input.Finally,the level-set energy function is minimized to acquire segmentation contours.Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.展开更多
This paper describes our approach for the Chinese clinical named entity recognition(CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing(CCKS) competition. In this task, we need ...This paper describes our approach for the Chinese clinical named entity recognition(CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing(CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record(EMR). We constructed a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule post-processing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we used post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.展开更多
Point-based geometry representations have become widely used in numerous contexts,ranging from particle-based simulations,over stereo image matching,to depth sensing via light detection and ranging.Our application foc...Point-based geometry representations have become widely used in numerous contexts,ranging from particle-based simulations,over stereo image matching,to depth sensing via light detection and ranging.Our application focus is on the reconstruction of curved line structures in noisy 3D point cloud data.Respective algorithms operating on such point clouds often rely on the notion of a local neighborhood.Regarding the latter,our approach employs multi-scale neighborhoods,for which weighted covariance measures of local points are determined.Curved line structures are reconstructed via vector field tracing,using a bidirectional piecewise streamline integration.We also introduce an automatic selection of optimal starting points via multi-scale geometric measures.The pipeline development and choice of parameters was driven by an extensive,automated initial analysis process on over a million prototype test cases.The behavior of our approach is controlled by several parameters—the majority being set automatically,leaving only three to be controlled by a user.In an extensive,automated final evaluation,we cover over one hundred thousand parameter sets,including 3D test geometries with varying curvature,sharp corners,intersections,data holes,and systematically applied varying types of noise.Further,we analyzed different choices for the point of reference in the co-variance computation;using a weighted mean performed best in most cases.In addition,we compared our method to current,publicly available line reconstruction frameworks.Up to thirty times faster execution times were achieved in some cases,at comparable error measures.Finally,we also demonstrate an exemplary application on four real-world 3D light detection and ranging datasets,extracting power line cables.展开更多
In this article,we consider the primal-dual path-following method and the trust-region updating strategy for the standard linear programming problem.For the rank-deficient problem with the small noisy data,we also giv...In this article,we consider the primal-dual path-following method and the trust-region updating strategy for the standard linear programming problem.For the rank-deficient problem with the small noisy data,we also give the preprocessing method based on the QR decomposition with column pivoting.Then,we prove the global convergence of the new method when the initial point is strictly primal-dual feasible.Finally,for some rankdeficient problems with or without the small noisy data from the NETLIB collection,we compare it with other two popular interior-point methods,i.e.the subroutine pathfollow.m and the built-in subroutine linprog.m of the MATLAB environment.Numerical results show that the new method is more robust than the other two methods for the rank-deficient problem with the small noise data.展开更多
基金Project supported by Indian Institute of Engineering Science and Technology, Shibpur, India
文摘We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.
基金supported by STI 2030-Major Projects 2021ZD0200400National Natural Science Foundation of China(62276233 and 62072405)Key Research Project of Zhejiang Province(2023C01048).
文摘Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously.Specifically,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.
基金supported by the National Natural Science Foundation of China(Grant No.61171075)the Natural Science Foundation of Hubei Province,China(Grant No.2015CFB424)+1 种基金the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics,China(Grant No.SOED1405)the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science,China(Grant No.Z201303)
文摘For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform(SWT). First, the mixed signal of chaotic signal, harmonic signal, and noise is decomposed into a series of intrinsic mode-type functions by synchrosqueezed wavelet transform(SWT) then the instantaneous frequency of intrinsic mode-type functions is analyzed by using of Hilbert transform, and the harmonic extraction is realized. In experiments of harmonic signal extraction, the Duffing and Lorenz chaotic signals are selected as interference signal, and the mixed signal of chaotic signal and harmonic signal is added by Gauss white noises of different intensities.The experimental results show that when the white noise intensity is in a certain range, the extracting harmonic signals measured by the proposed SWT method have higher precision, the harmonic signal extraction effect is obviously superior to the classical empirical mode decomposition method.
文摘In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.
基金Project supported by the National Natural Science Foundation of China(Grant No.11374096)。
文摘We explore the entropy uncertainty for qutrit system under non-Markov noisy environment and discuss the effects of the quantum memory system and the spontaneously generated interference(SGI)on the entropy uncertainty in detail.The results show that,the entropy uncertainty can be reduced by using the methods of quantum memory system and adjusting of SGI.Particularly,the entropy uncertainty can be decreased obviously when both the quantum memory system and the SGI are simultaneously applied.
基金supported in part by the National Natural Science Foundation of China (Grant Nos. 11934018, T2121001, 11904393, and 92065114)the CAS Strategic Priority Research Program (Grant No. XDB28000000)+1 种基金Beijing Natural Science Foundation (Grant No. Z200009)Scientific Instrument Developing Project of Chinese Academy of Sciences (Grant No. YJKYYQ20200041)。
文摘In the past years, great progresses have been made on quantum computation and quantum simulation. Increasing the number of qubits in the quantum processors is expected to be one of the main motivations in the next years, while noises in manipulation of quantum states may still be inevitable even the precision will improve. For research in this direction, it is necessary to review the available results about noisy multiqubit quantum computation and quantum simulation. The review focuses on multiqubit state generations, quantum computational advantage, and simulating physics of quantum many-body systems. Perspectives of near term noisy intermediate-quantum processors will be discussed.
基金supported in part by National Key Research and Development Program of China(2018YFB0804202,2018YFB0804203)Regional Joint Fund of NSFC(U19A2057)+1 种基金National Natural Science Foundation of China(61876070)Jilin Province Science and Technology Development Plan Project(20190303134SF).
文摘Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation.By two-scale image decomposition,the input image is decomposed into base layer and detail layer.For the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge information.Besides,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization problems.For the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain.The sum of the above base and detail layers is as the final fused image.Experimental results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.
基金Project supported by the National Natural Science Foundation of China (Grant No.10374025)the Natural Science Foundation of Hunan Province of China (Grant No.07JJ3013)the Scientific Research Fund of the Educational Department of Hunan Province of China (Grant No.08C580)
文摘The effects of distributing entanglement through the amplitude damping channel or the phase damping channel on the teleportation of a single-qubit state via the Greenberger-Horne-Zeilinger state and the W state are discussed.It is found that the average fidelity of teleportation depends on the type and rate of the damping in the channel.For the one-qubit affected case,the Greenberger-Horne-Zeilinger state is as robust as the W state,i.e.,the same quantum information is preserved through teleportation.For the two-qubit affected case,the W state is more robust when the entanglement is distributed via the amplitude damping channel;if the entanglement is distributed via the phase damping channel,the W state is more robust when the noisy parameter is small while the Greenberger-Horne-Zeilinger state becomes more robust when it is large.For the three-qubit affected case,the Greenberger-Horne-Zeilinger state is more robust than the W state.
文摘Network localization is a fundamental problem in wireless sensor networks, mainly in location dependent applications. A common family of solutions to this problem is the range-based network localization. The resulting localization algorithms are noise sensitive and thus lacking in terms of robustness. Our contribution provides an algorithm which is robust to measurement errors. We propose an analytical tool to analyze the effect of range errors in the final location and use a distributed method to solve the noisy range localization problem.
文摘In real life, when a noise problem occurs, it is important to identify the cause and measure the noise of the source, since it may affect human beings or other constructions due to vibration generated from noise, so it is necessary to determine the noise related to a specific source like a machine in the presence of other sources which is a very important approach in noise control engineering. In this article a full experiment was executed to measure the sound pressure levels of various sources (stationary and non-stationary), in both an anechoic chamber and a non-ideal noisy environment. The sound pressure level was extracted for different sources and compared for both ideal and non-ideal environment. The results showed that acoustical free field of the space is the best field to do measurements to avoid reflection, on the other hand the difference between the source and the background should be more than 3 dB to get better results.
文摘We theoretically investigate the collective response of an ensemble of leaky integrate-and-fire neuron units to a noisy periodic signal by including local spatially correlated noise. By using the linear response theory, we obtained the analytic expression of signal-to-noise ratio (SNR). Numerical simulation results show that the rms amplitude of internal noise can be increased up to?an optimal value where the output SNR reaches a maximum value. Due to the existence of the local spatially correlated noise in the units of the ensemble, the SNR gain of the collective ensemble response can exceed unity and can be optimized when the nearest-neighborhood correlation is negative. This nonlinear collective phenomenon of SNR gain amplification in an ensemble of leaky integrate-and-fire neuron units can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and?amplitude of the weak periodic signal. The present study illustrates the potential to utilize the local spatially correlation noise and the number of ensemble units for optimizing the collective response of the neuron to inputs, as well as a guidance in the design of information processing devices to weak signal detection.
基金Project supported by the National Natural Science Foundation of China(Nos.U21B2027,62266027,61972186,62241604)the Yunnan Provincial Major Science and Technology Special Plan Projects,China(Nos.202302AD080003,202103AA080015,and 202202AD080003)+1 种基金the General Projects of Basic Research in Yunnan Province,China(Nos.202301AT070471 and 202301AT070393)the Kunming University of Science and Technology“Double First-Class”Joint Project,China(No.202201BE070001-021)。
文摘Cross-lingual summarization(CLS)is the task of generating a summary in a target language from a document in a source language.Recently,end-to-end CLS models have achieved impressive results using large-scale,high-quality datasets typically constructed by translating monolingual summary corpora into CLS corpora.However,due to the limited performance of low-resource language translation models,translation noise can seriously degrade the performance of these models.In this paper,we propose a fine-grained reinforcement learning approach to address low-resource CLS based on noisy data.We introduce the source language summary as a gold signal to alleviate the impact of the translated noisy target summary.Specifically,we design a reinforcement reward by calculating the word correlation and word missing degree between the source language summary and the generated target language summary,and combine it with cross-entropy loss to optimize the CLS model.To validate the performance of our proposed model,we construct Chinese-Vietnamese and Vietnamese-Chinese CLS datasets.Experimental results show that our proposed model outperforms the baselines in terms of both the ROUGE score and BERTScore.
基金supported by the National Key R&D Program of China (No. 2016YFA0301601)National Natural Science Foundation of China (No. 11674301)+3 种基金Strategic Priority Research Program on Space Science of the Chinese Academy of Sciences (No. XDA15020000)Anhui Initiative in Quantum Information Technologies (No. AHY120000)Shanghai Municipal Science and Technology Major Project (No. 2019SHZDZX01)funded by the Youth Program of National Natural Science Foundation of China (No. 11804019)。
文摘The gravimeter based on atom interferometry has potential wide applications on building gravity networks and geophysics as well as gravity assisted navigation. Here, we demonstrate experimentally a portable atomic gravimeter operating in the noisy urban environment. Despite the influence of noisy external vibrations, our portable atomic gravimeter reaches a sensitivity as good as 65 μGal/√Hz and a resolution of 1.1 μGal after 4000 s integration, being comparable to state-of-the-art atomic gravimeters. Our achievement paves the way for bringing the portable atomic gravimeter to field applications.
基金supported by the National Natural Science Foundation of China(No.61976150)the Natural Science Foundation of Shanxi Province(Nos.201901D111091 and 201801D21135)。
文摘Level-set-based image segmentation has been widely used in unsupervised segmentation tasks.Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models.Most existing methods are based on the assumption that the distribution of image noise is known or observable.However,real-time images do not meet this assumption.To bridge this gap,we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism.First,a denoising filter is acquired under the unsupervised learning paradigm.Second,the denoising filter is integrated into the level-set framework to separate noise from the noisy image input.Finally,the level-set energy function is minimized to acquire segmentation contours.Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.
基金This work is supported by the National Key R&D Program of China(2020AAA0106400)the National Natural Science Foundation of China(No.61831022,No.61806201)+1 种基金the Key Research Program of the Chinese Academy of Sciences(Grant No.ZDBS-SSW-JSC006)This work is also supported by Beijing Academy of Artificial Intelligence(BAAI).
文摘This paper describes our approach for the Chinese clinical named entity recognition(CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing(CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record(EMR). We constructed a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule post-processing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we used post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.
基金This research was funded through the Vice Rectorate of Research of the University of Innsbruck within the scope of the doctoral program Computational Interdisciplinary Modelling(DK CIM).
文摘Point-based geometry representations have become widely used in numerous contexts,ranging from particle-based simulations,over stereo image matching,to depth sensing via light detection and ranging.Our application focus is on the reconstruction of curved line structures in noisy 3D point cloud data.Respective algorithms operating on such point clouds often rely on the notion of a local neighborhood.Regarding the latter,our approach employs multi-scale neighborhoods,for which weighted covariance measures of local points are determined.Curved line structures are reconstructed via vector field tracing,using a bidirectional piecewise streamline integration.We also introduce an automatic selection of optimal starting points via multi-scale geometric measures.The pipeline development and choice of parameters was driven by an extensive,automated initial analysis process on over a million prototype test cases.The behavior of our approach is controlled by several parameters—the majority being set automatically,leaving only three to be controlled by a user.In an extensive,automated final evaluation,we cover over one hundred thousand parameter sets,including 3D test geometries with varying curvature,sharp corners,intersections,data holes,and systematically applied varying types of noise.Further,we analyzed different choices for the point of reference in the co-variance computation;using a weighted mean performed best in most cases.In addition,we compared our method to current,publicly available line reconstruction frameworks.Up to thirty times faster execution times were achieved in some cases,at comparable error measures.Finally,we also demonstrate an exemplary application on four real-world 3D light detection and ranging datasets,extracting power line cables.
基金This work was supported in part by Grant 61876199 from National Natural Science Foundation of China,Grant YBWL2011085 from Huawei Technologies Co.,Ltd.,and Grant YJCB2011003HIInnovation Research Program of Huawei Technologies Co.,Ltd..The first author is grateful to professor Li-Zhi Liao for introducing him the interiorpoint methods when he visited Hong Kong Baptist University in July,2012.
文摘In this article,we consider the primal-dual path-following method and the trust-region updating strategy for the standard linear programming problem.For the rank-deficient problem with the small noisy data,we also give the preprocessing method based on the QR decomposition with column pivoting.Then,we prove the global convergence of the new method when the initial point is strictly primal-dual feasible.Finally,for some rankdeficient problems with or without the small noisy data from the NETLIB collection,we compare it with other two popular interior-point methods,i.e.the subroutine pathfollow.m and the built-in subroutine linprog.m of the MATLAB environment.Numerical results show that the new method is more robust than the other two methods for the rank-deficient problem with the small noise data.
