3,398 research outputs found
Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models
In this study, we present an efficient and effective approach for achieving
temporally consistent synthetic-to-real video translation in videos of varying
lengths. Our method leverages off-the-shelf conditional image diffusion models,
allowing us to perform multiple synthetic-to-real image generations in
parallel. By utilizing the available optical flow information from the
synthetic videos, our approach seamlessly enforces temporal consistency among
corresponding pixels across frames. This is achieved through joint noise
optimization, effectively minimizing spatial and temporal discrepancies. To the
best of our knowledge, our proposed method is the first to accomplish diverse
and temporally consistent synthetic-to-real video translation using conditional
image diffusion models. Furthermore, our approach does not require any training
or fine-tuning of the diffusion models. Extensive experiments conducted on
various benchmarks for synthetic-to-real video translation demonstrate the
effectiveness of our approach, both quantitatively and qualitatively. Finally,
we show that our method outperforms other baseline methods in terms of both
temporal consistency and visual quality
siPRED: Predicting siRNA Efficacy Using Various Characteristic Methods
Small interfering RNA (siRNA) has been used widely to induce gene silencing in cells. To predict the efficacy of an siRNA with respect to inhibition of its target mRNA, we developed a two layer system, siPRED, which is based on various characteristic methods in the first layer and fusion mechanisms in the second layer. Characteristic methods were constructed by support vector regression from three categories of characteristics, namely sequence, features, and rules. Fusion mechanisms considered combinations of characteristic methods in different categories and were implemented by support vector regression and neural networks to yield integrated methods. In siPRED, the prediction of siRNA efficacy through integrated methods was better than through any method that utilized only a single method. Moreover, the weighting of each characteristic method in the context of integrated methods was established by genetic algorithms so that the effect of each characteristic method could be revealed. Using a validation dataset, siPRED performed better than other predictive systems that used the scoring method, neural networks, or linear regression. Finally, siPRED can be improved to achieve a correlation coefficient of 0.777 when the threshold of the whole stacking energy is ≥−34.6 kcal/mol. siPRED is freely available on the web at http://predictor.nchu.edu.tw/siPRED
MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance
This study introduces an efficient and effective method, MeDM, that utilizes
pre-trained image Diffusion Models for video-to-video translation with
consistent temporal flow. The proposed framework can render videos from scene
position information, such as a normal G-buffer, or perform text-guided editing
on videos captured in real-world scenarios. We employ explicit optical flows to
construct a practical coding that enforces physical constraints on generated
frames and mediates independent frame-wise scores. By leveraging this coding,
maintaining temporal consistency in the generated videos can be framed as an
optimization problem with a closed-form solution. To ensure compatibility with
Stable Diffusion, we also suggest a workaround for modifying observed-space
scores in latent-space Diffusion Models. Notably, MeDM does not require
fine-tuning or test-time optimization of the Diffusion Models. Through
extensive qualitative, quantitative, and subjective experiments on various
benchmarks, the study demonstrates the effectiveness and superiority of the
proposed approach
Magnetic phase diagrams of the Kagome staircase compound Co3V2O8
At zero magnetic field, a series of five phase transitions occur in Co3V2O8.
The Neel temperature, TN=11.4 K, is followed by four additional phase changes
at T1=8.9 K, T2=7.0 K, T3=6.9 K, and T4=6.2 K. The different phases are
distinguished by the commensurability of the b-component of its spin density
wave vector. We investigate the stability of these various phases under
magnetic fields through dielectric constant and magnetic susceptibility
anomalies. The field-temperature phase diagram of Co3V2O8 is completely
resolved. The complexity of the phase diagram results from the competition of
different magnetic states with almost equal ground state energies due to
competing exchange interactions and frustration.Comment: Proceedings of the 2007 Conference on Strongly Correlated Electron
Systems, 2 pages, 2 figure
MADS-Box Gene Classification in Angiosperms by Clustering and Machine Learning Approaches
The MADS-box gene family is an important transcription factor family involved in floral organogenesis. The previously proposed ABCDE model suggests that different floral organ identities are controlled by various combinations of classes of MADS-box genes. The five-class ABCDE model cannot cover all the species of angiosperms, especially the orchid. Thus, we developed a two-stage approach for MADS-box gene classification to advance the study of floral organogenesis of angiosperms. First, eight classes of reference datasets (A, AGL6, B12, B34, BPI, C, D, and E) were curated and clustered by phylogenetic analysis and unsupervised learning, and they were confirmed by the literature. Second, feature selection and multiple prediction models were curated according to sequence similarity and the characteristics of the MADS-box gene domain using support vector machines. Compared with the BindN and COILS features, the local BLAST model yielded the best accuracy. For performance evaluation, the accuracy of Phalaenopsis aphrodite MADS-box gene classification was 93.3%, which is higher than 86.7% of our previous classification prediction tool, iMADS. Phylogenetic tree construction – the most common method for gene classification yields classification errors and is time-consuming for analysis of massive, multi-species, or incomplete sequences. In this regard, our new system can also confirm the classification errors of all the random selection that were incorrectly classified by phylogenetic tree analysis. Our model constitutes a reliable and efficient MADS-box gene classification system for angiosperms
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