The optical rogue wave(RW),known as a short-lived extraordinarily high amplitude dynamics phenomenon with small appearing probabilities,plays an important role in revealing and understanding the fundamental physics of...The optical rogue wave(RW),known as a short-lived extraordinarily high amplitude dynamics phenomenon with small appearing probabilities,plays an important role in revealing and understanding the fundamental physics of nonlinear wave propagations in optical systems.The random fiber laser(RFL),featured with cavity-free and“modeless”structure,has opened up new avenues for fundamental physics research and potential practical applications combining nonlinear optics and laser physics.Here,the extreme event of optical RW induced by noise-driven modulation instability that interacts with the cascaded stimulated Brillouin scattering,the quasi-phase-matched four-wave mixing as well as the random mode resonance process is observed in a Brillouin random fiber laser comb(BRFLC).Temporal and statistical characteristics of the RWs concerning their emergence and evolution are experimentally explored and analyzed.Specifically,temporally localized structures with high intensities including chair-like pulses with a sharp leading edge followed by a trailing plateau appear frequently in the BRFLC output,which can evolve to chair-like RW pulses with adjustable pulse duration and amplitude under controlled conditions.This investigation provides a deep insight into the extreme event of RWs and paves the way for RW manipulation for its generation and elimination in RFLs through adapted laser configuration.展开更多
The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are or...The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood（SL） is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit（GPU）-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes,delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals（like EEG and f MRI） using similar recording techniques.展开更多
Temporal action localization (TAL) is a task of detecting the start and end timestamps of action instances and classifying them in an untrimmed video. As the number of action categories per video increases, existing w...Temporal action localization (TAL) is a task of detecting the start and end timestamps of action instances and classifying them in an untrimmed video. As the number of action categories per video increases, existing weakly-supervised TAL (W-TAL) methods with only video-level labels cannot provide sufficient supervision. Single-frame supervision has attracted the interest of researchers. Existing paradigms model single-frame annotations from the perspective of video snippet sequences, neglect action discrimination of annotated frames, and do not pay sufficient attention to their correlations in the same category. Considering a category, the annotated frames exhibit distinctive appearance characteristics or clear action patterns.Thus, a novel method to enhance action discrimination via category-specific frame clustering for W-TAL is proposed. Specifically,the K-means clustering algorithm is employed to aggregate the annotated discriminative frames of the same category, which are regarded as exemplars to exhibit the characteristics of the action category. Then, the class activation scores are obtained by calculating the similarities between a frame and exemplars of various categories. Category-specific representation modeling can provide complimentary guidance to snippet sequence modeling in the mainline. As a result, a convex combination fusion mechanism is presented for annotated frames and snippet sequences to enhance the consistency properties of action discrimination,which can generate a robust class activation sequence for precise action classification and localization. Due to the supplementary guidance of action discriminative enhancement for video snippet sequences, our method outperforms existing single-frame annotation based methods. Experiments conducted on three datasets (THUMOS14, GTEA, and BEOID) show that our method achieves high localization performance compared with state-of-the-art methods.展开更多
Temporal localization is crucial for action video recognition.Since the manual annotations are expensive and time-consuming in videos,temporal localization with weak video-level labels is challenging but indispensable...Temporal localization is crucial for action video recognition.Since the manual annotations are expensive and time-consuming in videos,temporal localization with weak video-level labels is challenging but indispensable.In this paper,we propose a weakly-supervised temporal action localization approach in untrimmed videos.To settle this issue,we train the model based on the proxies of each action class.The proxies are used to measure the distances between action segments and different original action features.We use a proxy-based metric to cluster the same actions together and separate actions from backgrounds.Compared with state-of-the-art methods,our method achieved competitive results on the THUMOS14 and ActivityNet1.2 datasets.展开更多
Failures are normal rather than exceptional in the cloud computing environments. To improve system avai- lability, replicating the popular data to multiple suitable locations is an advisable choice, as users can acces...Failures are normal rather than exceptional in the cloud computing environments. To improve system avai- lability, replicating the popular data to multiple suitable locations is an advisable choice, as users can access the data from a nearby site. This is, however, not the case for replicas which must have a fixed number of copies on several locations. How to decide a reasonable number and right locations for replicas has become a challenge in the cloud computing. In this paper, a dynamic data replication strategy is put forward with a brief survey of replication strategy suitable for distributed computing environments. It includes： 1） analyzing and modeling the relationship between system availability and the number of replicas; 2） evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 3） calculating a suitable number of copies to meet a reasonable system byte effective rate requirement and placing replicas among data nodes in a balanced way; 4） designing the dynamic data replication algorithm in a cloud. Experimental results demonstrate the efficiency and effectiveness of the improved system brought by the proposed strategy in a cloud.展开更多
This paper proposes a complementary novel idea, called MiniTasking to further reduce the number of cache misses by improving the data temporal locality for multiple concurrent queries. Our idea is based on the observa...This paper proposes a complementary novel idea, called MiniTasking to further reduce the number of cache misses by improving the data temporal locality for multiple concurrent queries. Our idea is based on the observation that, in many workloads such as decision support systems （DSS）, there is usually significant amount of data sharing among different concurrent queries. MiniTasking exploits such data sharing to improve data temporal locality by scheduling query execution at three levels： query level batching, operator level grouping and mini-task level scheduling. The experimental results with various types of concurrent TPC-H query workloads show that, with the traditional N-ary Storage Model （NSM） layout, MiniTasking significantly reduces the L2 cache misses by up to 83%, and thereby achieves 24% reduction in execution time. With the Partition Attributes Across （PAX） layout, MiniTasking further reduces the cache misses by 65% and the execution time by 9%. For the TPC-H throughput test workload, MiniTasking improves the end performance up to 20%.展开更多
Based on the study of existing fair exchange protocols, this paper sets up an accurate formal model by stepwise refinement. In the process of refinement an unreliable channel is employed to simulate an attack behavior...Based on the study of existing fair exchange protocols, this paper sets up an accurate formal model by stepwise refinement. In the process of refinement an unreliable channel is employed to simulate an attack behavior. The model provides a novel formal definition of exchanged items, and presents the formal goals for fairness, accountability, etc., reflecting the inherent requirements for fair exchange protocols across-the-board. In order to check, prove, and design fair exchange protocols effectively and efficiently, the model puts forward a novel property of abuse-freeness which applies to all fair exchange protocols, gives a formal definition for trust strand of the third party, and presents general criteria of designing a secure and effective fair exchange protocol. Taking a typical fair exchange protocol as an example, this paper presents the analysis steps of fair exchange protocols appealing to our model. An unknown attack is uncovered. The analysis reveals the process of a complete attack, discovering deeper reasons for causing an attack. Finally, we modify the flawed protocol and the revised protocol ensures the desirable properties.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.62105180)the Natural Science Foundation of Shandong Province (Grant Nos.ZR2020MF110 and ZR2020MF118)+2 种基金the Taishan Scholar Foundation of Shandong Province (Grant No.tsqn202211027)the Qilu Young Scholar Program of Shandong Universitythe National Grant Program for High-level Returning Oversea Talents (2023).
文摘The optical rogue wave(RW),known as a short-lived extraordinarily high amplitude dynamics phenomenon with small appearing probabilities,plays an important role in revealing and understanding the fundamental physics of nonlinear wave propagations in optical systems.The random fiber laser(RFL),featured with cavity-free and“modeless”structure,has opened up new avenues for fundamental physics research and potential practical applications combining nonlinear optics and laser physics.Here,the extreme event of optical RW induced by noise-driven modulation instability that interacts with the cascaded stimulated Brillouin scattering,the quasi-phase-matched four-wave mixing as well as the random mode resonance process is observed in a Brillouin random fiber laser comb(BRFLC).Temporal and statistical characteristics of the RWs concerning their emergence and evolution are experimentally explored and analyzed.Specifically,temporally localized structures with high intensities including chair-like pulses with a sharp leading edge followed by a trailing plateau appear frequently in the BRFLC output,which can evolve to chair-like RW pulses with adjustable pulse duration and amplitude under controlled conditions.This investigation provides a deep insight into the extreme event of RWs and paves the way for RW manipulation for its generation and elimination in RFLs through adapted laser configuration.
基金supported by Grants from the National Natural Science Foundation of China(81230023,81571067,and 81521063)National Basic Research Development Program(973 Program)of China(2013CB531905)the‘‘111’’Project of China
文摘The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood（SL） is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit（GPU）-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes,delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals（like EEG and f MRI） using similar recording techniques.
基金supported by the National Natural Science Foundation of China(No.61672268)。
文摘Temporal action localization (TAL) is a task of detecting the start and end timestamps of action instances and classifying them in an untrimmed video. As the number of action categories per video increases, existing weakly-supervised TAL (W-TAL) methods with only video-level labels cannot provide sufficient supervision. Single-frame supervision has attracted the interest of researchers. Existing paradigms model single-frame annotations from the perspective of video snippet sequences, neglect action discrimination of annotated frames, and do not pay sufficient attention to their correlations in the same category. Considering a category, the annotated frames exhibit distinctive appearance characteristics or clear action patterns.Thus, a novel method to enhance action discrimination via category-specific frame clustering for W-TAL is proposed. Specifically,the K-means clustering algorithm is employed to aggregate the annotated discriminative frames of the same category, which are regarded as exemplars to exhibit the characteristics of the action category. Then, the class activation scores are obtained by calculating the similarities between a frame and exemplars of various categories. Category-specific representation modeling can provide complimentary guidance to snippet sequence modeling in the mainline. As a result, a convex combination fusion mechanism is presented for annotated frames and snippet sequences to enhance the consistency properties of action discrimination,which can generate a robust class activation sequence for precise action classification and localization. Due to the supplementary guidance of action discriminative enhancement for video snippet sequences, our method outperforms existing single-frame annotation based methods. Experiments conducted on three datasets (THUMOS14, GTEA, and BEOID) show that our method achieves high localization performance compared with state-of-the-art methods.
基金supported by the National Key Research and Development Program of China(2018AAA0100104 and 2018AAA0100100)the National Natural Science Foundation of China(Grant No.61702095)+1 种基金Natural Science Foundation of Jiangsu Province(BK20211164,BK20190341,and BK20210002)the Big Data Computing Center of Southeast University.
文摘Temporal localization is crucial for action video recognition.Since the manual annotations are expensive and time-consuming in videos,temporal localization with weak video-level labels is challenging but indispensable.In this paper,we propose a weakly-supervised temporal action localization approach in untrimmed videos.To settle this issue,we train the model based on the proxies of each action class.The proxies are used to measure the distances between action segments and different original action features.We use a proxy-based metric to cluster the same actions together and separate actions from backgrounds.Compared with state-of-the-art methods,our method achieved competitive results on the THUMOS14 and ActivityNet1.2 datasets.
基金Supported by the National Natural Science Foundation of China under Grant Nos. 61070162, 71071028 and 70931001the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant Nos. 20110042110024 and 20100042110025the Fundamental Research Funds for the Central Universities of China under Grant Nos. N100604012, N090504003 and N090504006
文摘Failures are normal rather than exceptional in the cloud computing environments. To improve system avai- lability, replicating the popular data to multiple suitable locations is an advisable choice, as users can access the data from a nearby site. This is, however, not the case for replicas which must have a fixed number of copies on several locations. How to decide a reasonable number and right locations for replicas has become a challenge in the cloud computing. In this paper, a dynamic data replication strategy is put forward with a brief survey of replication strategy suitable for distributed computing environments. It includes： 1） analyzing and modeling the relationship between system availability and the number of replicas; 2） evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 3） calculating a suitable number of copies to meet a reasonable system byte effective rate requirement and placing replicas among data nodes in a balanced way; 4） designing the dynamic data replication algorithm in a cloud. Experimental results demonstrate the efficiency and effectiveness of the improved system brought by the proposed strategy in a cloud.
文摘This paper proposes a complementary novel idea, called MiniTasking to further reduce the number of cache misses by improving the data temporal locality for multiple concurrent queries. Our idea is based on the observation that, in many workloads such as decision support systems （DSS）, there is usually significant amount of data sharing among different concurrent queries. MiniTasking exploits such data sharing to improve data temporal locality by scheduling query execution at three levels： query level batching, operator level grouping and mini-task level scheduling. The experimental results with various types of concurrent TPC-H query workloads show that, with the traditional N-ary Storage Model （NSM） layout, MiniTasking significantly reduces the L2 cache misses by up to 83%, and thereby achieves 24% reduction in execution time. With the Partition Attributes Across （PAX） layout, MiniTasking further reduces the cache misses by 65% and the execution time by 9%. For the TPC-H throughput test workload, MiniTasking improves the end performance up to 20%.
基金the Natural Science Foundation ofBeijing(Grant No.4052016)the National Natural Science Foundation of China(Grant No.60083007) the National Grand Fundamental Research 973 Program ofChina(Grant No.G1999035802).
文摘Based on the study of existing fair exchange protocols, this paper sets up an accurate formal model by stepwise refinement. In the process of refinement an unreliable channel is employed to simulate an attack behavior. The model provides a novel formal definition of exchanged items, and presents the formal goals for fairness, accountability, etc., reflecting the inherent requirements for fair exchange protocols across-the-board. In order to check, prove, and design fair exchange protocols effectively and efficiently, the model puts forward a novel property of abuse-freeness which applies to all fair exchange protocols, gives a formal definition for trust strand of the third party, and presents general criteria of designing a secure and effective fair exchange protocol. Taking a typical fair exchange protocol as an example, this paper presents the analysis steps of fair exchange protocols appealing to our model. An unknown attack is uncovered. The analysis reveals the process of a complete attack, discovering deeper reasons for causing an attack. Finally, we modify the flawed protocol and the revised protocol ensures the desirable properties.
