3,027 research outputs found
Egocentric Scene Understanding via Multimodal Spatial Rectifier
In this paper, we study a problem of egocentric scene understanding, i.e.,
predicting depths and surface normals from an egocentric image. Egocentric
scene understanding poses unprecedented challenges: (1) due to large head
movements, the images are taken from non-canonical viewpoints (i.e., tilted
images) where existing models of geometry prediction do not apply; (2) dynamic
foreground objects including hands constitute a large proportion of visual
scenes. These challenges limit the performance of the existing models learned
from large indoor datasets, such as ScanNet and NYUv2, which comprise
predominantly upright images of static scenes. We present a multimodal spatial
rectifier that stabilizes the egocentric images to a set of reference
directions, which allows learning a coherent visual representation. Unlike
unimodal spatial rectifier that often produces excessive perspective warp for
egocentric images, the multimodal spatial rectifier learns from multiple
directions that can minimize the impact of the perspective warp. To learn
visual representations of the dynamic foreground objects, we present a new
dataset called EDINA (Egocentric Depth on everyday INdoor Activities) that
comprises more than 500K synchronized RGBD frames and gravity directions.
Equipped with the multimodal spatial rectifier and the EDINA dataset, our
proposed method on single-view depth and surface normal estimation
significantly outperforms the baselines not only on our EDINA dataset, but also
on other popular egocentric datasets, such as First Person Hand Action (FPHA)
and EPIC-KITCHENS.Comment: Appearing in the Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR), 202
ANALYSIS OF IMMUNE GENE EXPRESSION IN ROCK BREAM SPLEEN AFTER ROCK BREAM IRIDOVIRUS INFECTION BY TRANSCRIPTOME SEQUENCING
ANALYSIS OF IMMUNE GENE EXPRESSION IN ROCK BREAM SPLEEN AFTER ROCK BREAM IRIDOVIRUS INFECTION BY TRANSCRIPTOME SEQUENCING
Surface Normal Estimation of Tilted Images via Spatial Rectifier
In this paper, we present a spatial rectifier to estimate surface normals of
tilted images. Tilted images are of particular interest as more visual data are
captured by arbitrarily oriented sensors such as body-/robot-mounted cameras.
Existing approaches exhibit bounded performance on predicting surface normals
because they were trained using gravity-aligned images. Our two main hypotheses
are: (1) visual scene layout is indicative of the gravity direction; and (2)
not all surfaces are equally represented by a learned estimator due to the
structured distribution of the training data, thus, there exists a
transformation for each tilted image that is more responsive to the learned
estimator than others. We design a spatial rectifier that is learned to
transform the surface normal distribution of a tilted image to the rectified
one that matches the gravity-aligned training data distribution. Along with the
spatial rectifier, we propose a novel truncated angular loss that offers a
stronger gradient at smaller angular errors and robustness to outliers. The
resulting estimator outperforms the state-of-the-art methods including data
augmentation baselines not only on ScanNet and NYUv2 but also on a new dataset
called Tilt-RGBD that includes considerable roll and pitch camera motion.Comment: 16 page
Effects of noise on a model of oscillatory chemical reaction
A simple oscillating reaction model subject to additive Gaussian white noise is investigated as the model is located in the dynamic region of oscillations. The model is composed of three ordinary differential equations representing the time evolutions of X, Y, and Z, respectively. Initially, a uniform random noise is separately added to the three equations to study the effect of noise on the oscillatory cycle of X, Y, and Z. For a given value of noise intensity, the amplitude of oscillation increases monotonically with time. Furthermore, the noise is added to any one of the three equations to study the impact of noise on one species on the bifurcation behavior of the other
STAT1 and Nmi are downstream targets of Ets-1 transcription factor in MCF-7 human breast cancer cell
AbstractEts-1 is a cellular homologue of the product of the viral ets oncogene of the E26 virus, and it functions as a tissue-specific transcription factor. It plays an important role in cell proliferation, differentiation, lymphoid cell development, transformation, angiogenesis, and apoptosis. Ets-1 controls the expression of critical genes involved in these processes by binding to ets binding sites present in the transcriptional regulatory regions. Here, we transiently overexpressed Ets-1 in MCF-7 and comprehensively searched for potential downstream targets of Ets-1 by cDNA microarray analysis. The expressions of several interferon-related genes including STAT1 and Nmi were augmented by the overexpression of Ets-1. RT-PCR and Western blotting confirmed the increase in the levels of STAT1 and Nmi mRNA and protein. In contrast, Ets-1 siRNA decreased the expression of STAT1 and Nmi proteins. As in our transient transfection experiments, stable overexpression of Ets-1, also increased the protein expression of STAT1 and Nmi in MCF-7 cells. Taken together, our results indicate that STAT1 and Nmi are downstream targets of Ets-1 in MCF-7 human breast cancer cells
GIST-AiTeR Speaker Diarization System for VoxCeleb Speaker Recognition Challenge (VoxSRC) 2023
This report describes the submission system by the GIST-AiTeR team for the
VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23) Track 4. Our submission
system focuses on implementing diverse speaker diarization (SD) techniques,
including ResNet293 and MFA-Conformer with different combinations of segment
and hop length. Then, those models are combined into an ensemble model. The
ResNet293 and MFA-Conformer models exhibited the diarization error rates (DERs)
of 3.65% and 3.83% on VAL46, respectively. The submitted ensemble model
provided a DER of 3.50% on VAL46, and consequently, it achieved a DER of 4.88%
on the VoxSRC-23 test set.Comment: 2023 VoxSRC Track
Regional outbreak of staphylococcal scalded skin syndrome in healthy children
Purpose : Staphylococcal scalded skin syndrome (SSSS) is a relatively uncommon superficial blistering skin disease that is due to Staphylococcus aureus. We had experienced a regional outbreak of SSSS over 3 years in healthy children. Methods : We retrospectively reviewed the medical records of those patients diagnosed as SSSS. Most of neonatal cases were nosocomial infections and excluded from the analysis. The clinical features, laboratory findings, the isolation and antibiotic resistance of S. aureus, the antibiotic management and other supportive treatments were analyzed. Results : Fifty-five patients with SSSS were admitted to our hospital from October 2001 to September 2004. The median age of patients was 3.0 years. Of the 55 patients, 9 were the generalized type, 13 were the intermediate type and 33 were the scarletiniform rash. All the patients were living in neighborhood of the Jinju area. S. aureus were isolated from 9 of the patients and all of the isolated S. aureus were methicillin resistant. All the patients except two were treated with intravenous flocloxacillin or nafcillin and/or cefotaxime. All the patients recovered during the follow-up period of 2 to 3 weeks. Conclusion : We experienced a regional outbreak of SSSS in previous healthy children. Further study for finding the carriers of S. aureus caused SSSS and preventing the spread of this disease is needed. Additionally, guidelines for treating SSSS due to methicillin resistant S. aureus should be established
- …