329 research outputs found
Research on Method of Health Assessment about the Destruction Equipment for High-risk Hazardous Chemical Waste
AbstractThe destroying tasks of high-risk hazardous chemical waste have a strict request to the health status of destruction equipment.The paper proposes the health status classification method based on time between failures for the destruction of equipment, set up health status assessment model based on Time-varying Bayesian Networks and the time slice, which can take advantage of history fault information and health status monitoring indicator information to health status assessment for the destruction equipment, and which provides a reliable and safe evaluation method
The upstream sequence of the phycocyanin \u3b2 subunit gene from Arthrospira platensis regulates expression of gfp gene in response to light intensity
In cyanobacteria, few details are known of the mechanisms through which
the expression of the light-harvesting pigment c-phycocyanin is
regulated. In the present study, a 419 bp upstream sequence of the
phycocyanin \u3b2 subunit (cpcB) gene from Arthrospira platensis
FACHB341 was fused with green fluorescent protein (gfp) gene, and a
heterologous reporting system was built up to investigate the influence
of light intensity on the expression of gfp gene, and the regulation
function of different region of the upstream sequence of cpcB gene.
Results showed that the upstream sequence of cpcB gene could drive the
expression of gfp gene in Synechococcus sp. strain PCC7942, and the
expression was influenced by light intensity, the lower the light
intensity, the higher the gfp level. Deletion analysis revealed that a
light-responsive element was located in the region -276 to-218, a
promoter sequence was in the region -85 to -1, and two positive cis
elements were in the -419 to -276 and the -218 to -130 regions,
respectively
Towards Consistent Video Editing with Text-to-Image Diffusion Models
Existing works have advanced Text-to-Image (TTI) diffusion models for video
editing in a one-shot learning manner. Despite their low requirements of data
and computation, these methods might produce results of unsatisfied consistency
with text prompt as well as temporal sequence, limiting their applications in
the real world. In this paper, we propose to address the above issues with a
novel EI model towards \textbf{E}nhancing v\textbf{I}deo \textbf{E}diting
cons\textbf{I}stency of TTI-based frameworks. Specifically, we analyze and find
that the inconsistent problem is caused by newly added modules into TTI models
for learning temporal information. These modules lead to covariate shift in the
feature space, which harms the editing capability. Thus, we design EI to
tackle the above drawbacks with two classical modules: Shift-restricted
Temporal Attention Module (STAM) and Fine-coarse Frame Attention Module (FFAM).
First, through theoretical analysis, we demonstrate that covariate shift is
highly related to Layer Normalization, thus STAM employs a \textit{Instance
Centering} layer replacing it to preserve the distribution of temporal
features. In addition, {STAM} employs an attention layer with normalized
mapping to transform temporal features while constraining the variance shift.
As the second part, we incorporate {STAM} with a novel {FFAM}, which
efficiently leverages fine-coarse spatial information of overall frames to
further enhance temporal consistency. Extensive experiments demonstrate the
superiority of the proposed EI model for text-driven video editing
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