48 research outputs found
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
Time series anomaly detection is challenging due to the complexity and
variety of patterns that can occur. One major difficulty arises from modeling
time-dependent relationships to find contextual anomalies while maintaining
detection accuracy for point anomalies. In this paper, we propose a framework
for unsupervised time series anomaly detection that utilizes point-based and
sequence-based reconstruction models. The point-based model attempts to
quantify point anomalies, and the sequence-based model attempts to quantify
both point and contextual anomalies. Under the formulation that the observed
time point is a two-stage deviated value from a nominal time point, we
introduce a nominality score calculated from the ratio of a combined value of
the reconstruction errors. We derive an induced anomaly score by further
integrating the nominality score and anomaly score, then theoretically prove
the superiority of the induced anomaly score over the original anomaly score
under certain conditions. Extensive studies conducted on several public
datasets show that the proposed framework outperforms most state-of-the-art
baselines for time series anomaly detection.Comment: NeurIPS 2023 (https://neurips.cc/virtual/2023/poster/70582
Learning to Behave Like Clean Speech: Dual-Branch Knowledge Distillation for Noise-Robust Fake Audio Detection
Most research in fake audio detection (FAD) focuses on improving performance
on standard noise-free datasets. However, in actual situations, there is
usually noise interference, which will cause significant performance
degradation in FAD systems. To improve the noise robustness, we propose a
dual-branch knowledge distillation fake audio detection (DKDFAD) method.
Specifically, a parallel data flow of the clean teacher branch and the noisy
student branch is designed, and interactive fusion and response-based
teacher-student paradigms are proposed to guide the training of noisy data from
the data distribution and decision-making perspectives. In the noise branch,
speech enhancement is first introduced for denoising, which reduces the
interference of strong noise. The proposed interactive fusion combines
denoising features and noise features to reduce the impact of speech distortion
and seek consistency with the data distribution of clean branch. The
teacher-student paradigm maps the student's decision space to the teacher's
decision space, making noisy speech behave as clean. In addition, a joint
training method is used to optimize the two branches to achieve global
optimality. Experimental results based on multiple datasets show that the
proposed method performs well in noisy environments and maintains performance
in cross-dataset experiments
Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features
Recently, pioneer research works have proposed a large number of acoustic
features (log power spectrogram, linear frequency cepstral coefficients,
constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining
good performance, and showing that different subbands have different
contributions to audio deepfake detection. However, this lacks an explanation
of the specific information in the subband, and these features also lose
information such as phase. Inspired by the mechanism of synthetic speech, the
fundamental frequency (F0) information is used to improve the quality of
synthetic speech, while the F0 of synthetic speech is still too average, which
differs significantly from that of real speech. It is expected that F0 can be
used as important information to discriminate between bonafide and fake speech,
while this information cannot be used directly due to the irregular
distribution of F0. Insteadly, the frequency band containing most of F0 is
selected as the input feature. Meanwhile, to make full use of the phase and
full-band information, we also propose to use real and imaginary spectrogram
features as complementary input features and model the disjoint subbands
separately. Finally, the results of F0, real and imaginary spectrogram features
are fused. Experimental results on the ASVspoof 2019 LA dataset show that our
proposed system is very effective for the audio deepfake detection task,
achieving an equivalent error rate (EER) of 0.43%, which surpasses almost all
systems
Distinct double flower varieties in Camellia japonica exhibit both expansion and contraction of C-class gene expression
Double flower domestication is of great value in ornamental plants and presents an excellent system to study the mechanism of morphological alterations by human selection. The classic ABC model provides a genetic framework underlying the control of floral organ identity and organogenesis from which key regulators have been identified and evaluated in many plant species. Recent molecular studies have underscored the importance of C-class homeotic genes, whose functional attenuation contributed to the floral diversity in various species. Cultivated Camellia japonica L. possesses several types of double flowers, however the molecular mechanism underlying their floral morphological diversification remains unclear. In this study, we cloned the C-class orthologous gene CjAG in C. japonica. We analyzed the expression patterns of CjAG in wild C. japonica, and performed ectopic expression in Arabidopsis. These results revealed that CjAG shared conserved C-class function that controls stamen and carpel development. Further we analyzed the expression pattern of CjAG in two different C. japonica double-flower varieties, `Shibaxueshi’ and `Jinpanlizhi’, and showed that expression of CjAG was highly contracted in `Shibaxueshi’ but expanded in inner petals of `Jinpanlizhi’. Moreover, detailed expression analyses of B- and C-class genes have uncovered differential patterns of B-class genes in the inner organs of `Jinpanlizhi’. These results demonstrated that the contraction and expansion of CjAG expression were associated with the formation of different types of double flowers. Our studies have manifested two different trajectories of double flower domestication regarding the C-class gene expression in C. japonica.https://doi.org/10.1186/s12870-014-0288-
Matrix Protein 2 Vaccination and Protection against Influenza Viruses, Including Subtype H5N1
Vaccination of mice with influenza matrix protein 2 induced cross-reactive antibody responses