Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key points.These logs are valuable for analyzing performance issues and understanding th...Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key points.These logs are valuable for analyzing performance issues and understanding the status of the system.Anomaly detection plays an important role in service management and system maintenance,and guarantees the reliability and security of online systems.Logs are universal semi-structured data,which causes difficulties for traditional manual detection and pattern-matching algorithms.While some deep learning algorithms utilize neural networks to detect anomalies,these approaches have an over-reliance on manually designed features,resulting in the effectiveness of anomaly detection depending on the quality of the features.At the same time,the aforementioned methods ignore the underlying contextual information present in adjacent log entries.We propose a novel model called Logformer with two cascaded transformer-based heads to capture latent contextual information from adjacent log entries,and leverage pre-trained embeddings based on logs to improve the representation of the embedding space.The proposed model achieves comparable results on HDFS and BGL datasets in terms of metric accuracy,recall and F1-score.Moreover,the consistent rise in F1-score proves that the representation of the embedding spacewith pre-trained embeddings is closer to the semantic information of the log.展开更多
The development of optoelectronic technologies demands photodetectors with miniaturization,broadband operation,high sensitivity,and low power consumption.Although 2D van der Waals(vd W)heterostructures are promising c...The development of optoelectronic technologies demands photodetectors with miniaturization,broadband operation,high sensitivity,and low power consumption.Although 2D van der Waals(vd W)heterostructures are promising candidates due to their built-in electric fields,ultrafast photocarrier separation,and tunable bandgaps,defect states limit their performance.Therefore,the modulation of the optoelectronic properties in such heterostructures is imperative.Surface charge transfer doping(SCTD)has emerged as a promising strategy for non-destructive modulation of electronic and optoelectronic characteristics in two-dimensional materials.In this work,we demonstrate the construction of high-performance p-i-n vertical heterojunction photodetectors through SCTD of MoTe_(2)/ReS_(2)heterostructure using p-type F_(4)-TCNQ.Systematic characterization reveals that the interfacial doping process effectively amplifies the built-in electric field,enhancing photogenerated carrier separation efficiency.Compared to the pristine heterojunction device,the doped photodetector exhibits remarkable visible to nearinfrared(635-1064 nm)performance.Particularly under 1064 nm illumination at zero bias,the device achieves a responsivity of 2.86 A/W and specific detectivity of 1.41×10^(12)Jones.Notably,the external quantum efficiency reaches an exceptional value of 334%compared to the initial 11.5%,while maintaining ultrafast response characteristics with rise/fall times of 11.6/15.6μs.This work provides new insights into interface engineering through molecular doping for developing high-performance vd W optoelectronic devices.展开更多
基金supported by the National Natural Science Foundation of China (Nos.62072074,62076054,62027827,61902054,62002047)the Frontier Science and Technology Innovation Projects of National Key R&D Program (No.2019QY1405)+1 种基金the Sichuan Science and Technology Innovation Platform and Talent Plan (No.2020TDT00020)the Sichuan Science and Technology Support Plan (No.2020YFSY0010).
文摘Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key points.These logs are valuable for analyzing performance issues and understanding the status of the system.Anomaly detection plays an important role in service management and system maintenance,and guarantees the reliability and security of online systems.Logs are universal semi-structured data,which causes difficulties for traditional manual detection and pattern-matching algorithms.While some deep learning algorithms utilize neural networks to detect anomalies,these approaches have an over-reliance on manually designed features,resulting in the effectiveness of anomaly detection depending on the quality of the features.At the same time,the aforementioned methods ignore the underlying contextual information present in adjacent log entries.We propose a novel model called Logformer with two cascaded transformer-based heads to capture latent contextual information from adjacent log entries,and leverage pre-trained embeddings based on logs to improve the representation of the embedding space.The proposed model achieves comparable results on HDFS and BGL datasets in terms of metric accuracy,recall and F1-score.Moreover,the consistent rise in F1-score proves that the representation of the embedding spacewith pre-trained embeddings is closer to the semantic information of the log.
基金financial support from 2024 Domestic Visiting Scholar Program for Teachers'Professional Development in Universities(Grant No.FX2024022)National Natural Science Foundation of China(Grant No.61904043)。
文摘The development of optoelectronic technologies demands photodetectors with miniaturization,broadband operation,high sensitivity,and low power consumption.Although 2D van der Waals(vd W)heterostructures are promising candidates due to their built-in electric fields,ultrafast photocarrier separation,and tunable bandgaps,defect states limit their performance.Therefore,the modulation of the optoelectronic properties in such heterostructures is imperative.Surface charge transfer doping(SCTD)has emerged as a promising strategy for non-destructive modulation of electronic and optoelectronic characteristics in two-dimensional materials.In this work,we demonstrate the construction of high-performance p-i-n vertical heterojunction photodetectors through SCTD of MoTe_(2)/ReS_(2)heterostructure using p-type F_(4)-TCNQ.Systematic characterization reveals that the interfacial doping process effectively amplifies the built-in electric field,enhancing photogenerated carrier separation efficiency.Compared to the pristine heterojunction device,the doped photodetector exhibits remarkable visible to nearinfrared(635-1064 nm)performance.Particularly under 1064 nm illumination at zero bias,the device achieves a responsivity of 2.86 A/W and specific detectivity of 1.41×10^(12)Jones.Notably,the external quantum efficiency reaches an exceptional value of 334%compared to the initial 11.5%,while maintaining ultrafast response characteristics with rise/fall times of 11.6/15.6μs.This work provides new insights into interface engineering through molecular doping for developing high-performance vd W optoelectronic devices.
