341 research outputs found
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ClpXP-regulated Proteins Suppress Requirement for RecA in Dam Mutants of Escherichia coli K-12
Double strand breaks (DSB) are a common source of DNA damage in both prokaryotes and eukaryotes. If they are not repaired or are repaired incorrectly, they can lead to cell death (bacteria) or cancer (humans). In Escherichia coli, repair of DSB are typically accomplished via homologous recombination and mediated by RecA. This repair pathway, among others, is associated with activation of the SOS response. DNA adenine methyltransferase (dam) mutants have an increased number of DSB and, therefore, are notorious for being RecA-dependent for viability. Here, we show that the synthetic lethality of Δdam/ΔrecA is suppressed when clpP is removed, suggesting that there is a protein, normally degraded by ClpXP, which is preventing DSB from occurring
Curriculum Guided Domain Adaptation in the Dark
Addressing the rising concerns of privacy and security, domain adaptation in
the dark aims to adapt a black-box source trained model to an unlabeled target
domain without access to any source data or source model parameters. The need
for domain adaptation of black-box predictors becomes even more pronounced to
protect intellectual property as deep learning based solutions are becoming
increasingly commercialized. Current methods distill noisy predictions on the
target data obtained from the source model to the target model, and/or separate
clean/noisy target samples before adapting using traditional noisy label
learning algorithms. However, these methods do not utilize the easy-to-hard
learning nature of the clean/noisy data splits. Also, none of the existing
methods are end-to-end, and require a separate fine-tuning stage and an initial
warmup stage. In this work, we present Curriculum Adaptation for Black-Box
(CABB) which provides a curriculum guided adaptation approach to gradually
train the target model, first on target data with high confidence (clean)
labels, and later on target data with noisy labels. CABB utilizes
Jensen-Shannon divergence as a better criterion for clean-noisy sample
separation, compared to the traditional criterion of cross entropy loss. Our
method utilizes co-training of a dual-branch network to suppress error
accumulation resulting from confirmation bias. The proposed approach is
end-to-end trainable and does not require any extra finetuning stage, unlike
existing methods. Empirical results on standard domain adaptation datasets show
that CABB outperforms existing state-of-the-art black-box DA models and is
comparable to white-box domain adaptation models
Blur Identification Based on Higher Order Spectral Nulls
The identification of the point spread function (PSF) from the degraded image data constitutes an important first step in image restoration that is known as blur identification. Though a number of blur identification algorithms have been developed in recent years, two of the earlier methods based on the power spectrum and power cepstrum remain popular, because they are easy to implement and have proved to be effective in practical situations. Both methods are limited to PSF\u27s which exhibit spectral nulls, such as due to defocused lens and linear motion blur. Another limitation of these methods is the degradation of their performance in the presence of observation noise. The central slice of the power bispectrum has been employed as an alternative to the power spectrum which can suppress the effects of additive Gaussian noise. In this paper, we utilize the bicepstrum for the identification of linear motion and defocus blurs. We present simulation results where the performance of the blur identification methods based on the spectrum, the cepstrum, the bispectrum and the bicepstrum is compared for different blur sizes and signal-to-noise ratio levels
Real-time video annotation using MPEG-7 motion activity descriptors
The MPEG-7 standard provides a framework of standardized tools that can be used to describe and efficiently manage multimedia content. Visual descriptors include color, texture, shape and motion. In this paper, we address the hardware implementation of MPEG-7 motion descriptors using Handel-C. In particular, descriptors for motion intensity and spatial distribution of motion activity are generated and implemented
Continual Domain Adaptation on Aerial Images under Gradually Degrading Weather
Domain adaptation (DA) strives to mitigate the domain gap between the source
domain where a model is trained, and the target domain where the model is
deployed. When a deep learning model is deployed on an aerial platform, it may
face gradually degrading weather conditions during operation, leading to
widening domain gaps between the training data and the encountered evaluation
data. We synthesize two such gradually worsening weather conditions on real
images from two existing aerial imagery datasets, generating a total of four
benchmark datasets. Under the continual, or test-time adaptation setting, we
evaluate three DA models on our datasets: a baseline standard DA model and two
continual DA models. In such setting, the models can access only one small
portion, or one batch of the target data at a time, and adaptation takes place
continually, and over only one epoch of the data. The combination of the
constraints of continual adaptation, and gradually deteriorating weather
conditions provide the practical DA scenario for aerial deployment. Among the
evaluated models, we consider both convolutional and transformer architectures
for comparison. We discover stability issues during adaptation for existing
buffer-fed continual DA methods, and offer gradient normalization as a simple
solution to curb training instability
Semantically Invariant Text-to-Image Generation
Image captioning has demonstrated models that are capable of generating
plausible text given input images or videos. Further, recent work in image
generation has shown significant improvements in image quality when text is
used as a prior. Our work ties these concepts together by creating an
architecture that can enable bidirectional generation of images and text. We
call this network Multi-Modal Vector Representation (MMVR). Along with MMVR, we
propose two improvements to the text conditioned image generation. Firstly, a
n-gram metric based cost function is introduced that generalizes the caption
with respect to the image. Secondly, multiple semantically similar sentences
are shown to help in generating better images. Qualitative and quantitative
evaluations demonstrate that MMVR improves upon existing text conditioned image
generation results by over 20%, while integrating visual and text modalities.Comment: 5 papers, 5 figures, Published in 2018 25th IEEE International
Conference on Image Processing (ICIP
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