6,097 research outputs found
OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering
We present a method for simultaneously learning, in an unsupervised manner,
(i) a conditional image generator, (ii) foreground extraction and segmentation,
(iii) clustering into a two-level class hierarchy, and (iv) object removal and
background completion, all done without any use of annotation. The method
combines a Generative Adversarial Network and a Variational Auto-Encoder, with
multiple encoders, generators and discriminators, and benefits from solving all
tasks at once. The input to the training scheme is a varied collection of
unlabeled images from the same domain, as well as a set of background images
without a foreground object. In addition, the image generator can mix the
background from one image, with a foreground that is conditioned either on that
of a second image or on the index of a desired cluster. The method obtains
state of the art results in comparison to the literature methods, when compared
to the current state of the art in each of the tasks.Comment: To be published in the European Conference on Computer Vision (ECCV)
202
Weakly- and Semi-Supervised Panoptic Segmentation
We present a weakly supervised model that jointly performs both semantic- and
instance-segmentation -- a particularly relevant problem given the substantial
cost of obtaining pixel-perfect annotation for these tasks. In contrast to many
popular instance segmentation approaches based on object detectors, our method
does not predict any overlapping instances. Moreover, we are able to segment
both "thing" and "stuff" classes, and thus explain all the pixels in the image.
"Thing" classes are weakly-supervised with bounding boxes, and "stuff" with
image-level tags. We obtain state-of-the-art results on Pascal VOC, for both
full and weak supervision (which achieves about 95% of fully-supervised
performance). Furthermore, we present the first weakly-supervised results on
Cityscapes for both semantic- and instance-segmentation. Finally, we use our
weakly supervised framework to analyse the relationship between annotation
quality and predictive performance, which is of interest to dataset creators.Comment: ECCV 2018. The first two authors contributed equall
Whole exome sequencing reveals novel COL4A3 and COL4A4 mutations and resolves diagnosis in Chinese families with kidney disease.
Collagen IV-related nephropathies, including thin basement membrane nephropathy and Alport Syndrome (AS), are caused by defects in the genes COL4A3, COL4A4 and COL4A5. Diagnosis of these conditions can be hindered by variable penetrance and the presence of non-specific clinical or pathological features
Tracking Target Signal Strengths on a Grid using Sparsity
Multi-target tracking is mainly challenged by the nonlinearity present in the
measurement equation, and the difficulty in fast and accurate data association.
To overcome these challenges, the present paper introduces a grid-based model
in which the state captures target signal strengths on a known spatial grid
(TSSG). This model leads to \emph{linear} state and measurement equations,
which bypass data association and can afford state estimation via
sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of
the novel model, two types of sparsity-cognizant TSSG-KF trackers are
developed: one effects sparsity through -norm regularization, and the
other invokes sparsity as an extra measurement. Iterative extended KF and
Gauss-Newton algorithms are developed for reduced-complexity tracking, along
with accurate error covariance updates for assessing performance of the
resultant sparsity-aware state estimators. Based on TSSG state estimates, more
informative target position and track estimates can be obtained in a follow-up
step, ensuring that track association and position estimation errors do not
propagate back into TSSG state estimates. The novel TSSG trackers do not
require knowing the number of targets or their signal strengths, and exhibit
considerably lower complexity than the benchmark hidden Markov model filter,
especially for a large number of targets. Numerical simulations demonstrate
that sparsity-cognizant trackers enjoy improved root mean-square error
performance at reduced complexity when compared to their sparsity-agnostic
counterparts.Comment: Submitted to IEEE Trans. on Signal Processin
Generalized Mittag-Leffler Distributions and Processes for Applications in Astrophysics and Time Series Modeling
Geometric generalized Mittag-Leffler distributions having the Laplace
transform is
introduced and its properties are discussed. Autoregressive processes with
Mittag-Leffler and geometric generalized Mittag-Leffler marginal distributions
are developed. Haubold and Mathai (2000) derived a closed form representation
of the fractional kinetic equation and thermonuclear function in terms of
Mittag-Leffler function. Saxena et al (2002, 2004a,b) extended the result and
derived the solutions of a number of fractional kinetic equations in terms of
generalized Mittag-Leffler functions. These results are useful in explaining
various fundamental laws of physics. Here we develop first-order autoregressive
time series models and the properties are explored. The results have
applications in various areas like astrophysics, space sciences, meteorology,
financial modeling and reliability modeling.Comment: 12 pages, LaTe
Integrated multiple mediation analysis: A robustness–specificity trade-off in causal structure
Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two-way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and indirect effects of each strategy are explicitly and correctly interpreted as path-specific effects under different causal mediation structures. In the integrated framework, we further verify the utility of the interventional analogues of direct and indirect effects, especially when natural direct and indirect effects cannot be identified or when cross-world exchangeability is invalid. Consequently, this study yields a robustness–specificity trade-off in the choice of strategies. Inverse probability weighting is considered for estimation. The four strategies are further applied to a simulation study for performance evaluation and for analyzing the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer data set from Taiwan to investigate the causal effect of hepatitis C virus infection on mortality
Learning Visual Context by Comparison
Finding diseases from an X-ray image is an important yet highly challenging
task. Current methods for solving this task exploit various characteristics of
the chest X-ray image, but one of the most important characteristics is still
missing: the necessity of comparison between related regions in an image. In
this paper, we present Attend-and-Compare Module (ACM) for capturing the
difference between an object of interest and its corresponding context. We show
that explicit difference modeling can be very helpful in tasks that require
direct comparison between locations from afar. This module can be plugged into
existing deep learning models. For evaluation, we apply our module to three
chest X-ray recognition tasks and COCO object detection & segmentation tasks
and observe consistent improvements across tasks. The code is available at
https://github.com/mk-minchul/attend-and-compare.Comment: ECCV 2020 spotlight pape
The phylogenetically-related pattern recognition receptors EFR and XA21 recruit similar immune signaling components in monocots and dicots
During plant immunity, surface-localized pattern recognition receptors (PRRs) recognize pathogen-associated molecular patterns (PAMPs). The transfer of PRRs between plant species is a promising strategy for engineering broad-spectrum disease resistance. Thus, there is a great interest in understanding the mechanisms of PRR-mediated resistance across different plant species. Two well-characterized plant PRRs are the leucine-rich repeat receptor kinases (LRR-RKs) EFR and XA21 from Arabidopsis thaliana (Arabidopsis) and rice, respectively. Interestingly, despite being evolutionary distant, EFR and XA21 are phylogenetically closely related and are both members of the sub-family XII of LRR-RKs that contains numerous potential PRRs. Here, we compared the ability of these related PRRs to engage immune signaling across the monocots-dicots taxonomic divide. Using chimera between Arabidopsis EFR and rice XA21, we show that the kinase domain of the rice XA21 is functional in triggering elf18-induced signaling and quantitative immunity to the bacteria Pseudomonas syringae pv. tomato (Pto) DC3000 and Agrobacterium tumefaciens in Arabidopsis. Furthermore, the EFR:XA21 chimera associates dynamically in a ligand-dependent manner with known components of the EFR complex. Conversely, EFR associates with Arabidopsis orthologues of rice XA21-interacting proteins, which appear to be involved in EFR-mediated signaling and immunity in Arabidopsis. Our work indicates the overall functional conservation of immune components acting downstream of distinct LRR-RK-type PRRs between monocots and dicots
Optofluidic waveguide as a transformation optics device for lightwave bending and manipulation
Author name used in this publication: Zhang X. M.2011-2012 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
miR-K12-7-5p Encoded by Kaposi's Sarcoma-Associated Herpesvirus Stabilizes the Latent State by Targeting Viral ORF50/RTA
Seventeen miRNAs encoded by Kaposi's sarcoma-associated herpesvirus (KSHV) have been identified and their functions have begun to be characterized. Among these miRNAs, we report here that miR-K12-7 directly targets the replication and transcription activator (RTA) encoded by open reading frame 50. We found that miR-K12-7 targeted the RTA 3′ untranslated region (RTA3′UTR) in a seed sequence-dependent manner. miR-K12-7-5p derived from miR-K12-7 mediates the inhibition of RTA expression, and the mutation of the seed match site totally abrogated the inhibitory effect of miR-K12-7 on RTA3′UTR. The inhibition of RTA expression by miR-K12-7 was further confirmed in the latently KSHV-infected 293/Bac36 cell line through transient transfection of miR-K12-7 expression plasmid or specific inhibitor of miR-K12-7-5p, respectively. The transient transfection of miR-K12-7 into 293/Bac36 cells reduced RTA expression and the expression of the downstream early genes regulated by RTA, and also the production of progeny virus was significantly reduced after treatment with chemical inducers. Our study revealed that another miRNA, miR-K12-7-5p, targets the viral immediate early gene RTA and that this miRNA contributes to the maintenance of viral latency
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