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

In-vivo Optical Tomography of Small Scattering Specimens: time-lapse 3D imaging of the head eversion process in Drosophila melanogaster

5 fig.Even though in vivo imaging approaches have witnessed several new and important developments, specimens that exhibit high light scattering properties such as Drosophila melanogaster pupae are still not easily accessible with current optical imaging techniques, obtaining images only from subsurface features. This means that in order to obtain 3D volumetric information these specimens need to be studied either after fixation and a chemical clearing process, through an imaging window - thus perturbing physiological development -, or during early stages of development when the scattering contribution is negligible. In this paper we showcase how Optical Projection Tomography may be used to obtain volumetric images of the head eversion process in vivo in Drosophila melanogaster pupae, both in control and headless mutant specimens. Additionally, we demonstrate the use of Helical Optical Projection Tomography (hOPT) as a tool for high throughput 4D-imaging of several specimens simultaneously.This work was supported in part by Project ‘‘THALES – BSRC ‘Alexander Fleming’ – Development and employment of Minos-based genetic and functional genomic technologies in model organisms (MINOS)’’ – MIS: 376898, the Fellowship for Young International Scientist of the Chinese Academy of Sciences Grant No. 2010Y2GA03 and the NSFC-NIH Biomedical collaborative research program 81261120414. A. Arranz acknowledges support from Marie Curie Intra-European Fellowship Program FP7-PEOPLE-2010-IEF. J. Ripoll acknowledges support from EC FP7 CIG grant HIGH-THROUGHPUT TOMO, and Spanish MINECO grant MESO-IMAGING FIS2013-41802-R. The authors would like to thank Dr. S. Oehler for the help with the GFP-expressing flies, and G. Livadaras and G. Zacharakis for help with the Drosophila stocks

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

Slr1670 from Synechocystis sp. PCC 6803 Is Required for the Re-assimilation of the Osmolyte Glucosylglycerol

When subjected to mild salt stress, the cyanobacterium Synechocystis sp. PCC 6803 produces small amounts of glycerol through an as of yet unidentified pathway. Here, we show that this glycerol is a degradation product of the main osmolyte of this organism, glucosylglycerol (GG). Inactivation of ggpS, encoding the first step of GG-synthesis, abolished de novo synthesis of glycerol, while the ability to hydrolyze exogenously supplied glucoslylglycerol was unimpaired. Inactivation of glpK, encoding glycerol kinase, had no effect on glycerol synthesis. Inactivation of slr1670, encoding a GHL5-type putative glycoside hydrolase, abolished de novo synthesis of glycerol, as well as hydrolysis of GG, and led to increased intracellular concentrations of this osmolyte. Slr1670 therefore presumably displays GG hydrolase activity. A gene homologous to the one encoded by slr1670 occurs in a wide range of cyanobacteria, proteobacteria, and archaea. In cyanobacteria, it co-occurs with genes involved in GG-synthesis

Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation

We propose a new efficient architecture for semantic segmentation, based on a "Waterfall" Atrous Spatial Pooling architecture, that achieves a considerable accuracy increase while decreasing the number of network parameters and memory footprint. The proposed Waterfall architecture leverages the efficiency of progressive filtering in the cascade architecture while maintaining multiscale fields-of-view comparable to spatial pyramid configurations. Additionally, our method does not rely on a postprocessing stage with Conditional Random Fields, which further reduces complexity and required training time. We demonstrate that the Waterfall approach with a ResNet backbone is a robust and efficient architecture for semantic segmentation obtaining state-of-the-art results with significant reduction in the number of parameters for the Pascal VOC dataset and the Cityscapes dataset.Comment: 17 pages, 11 figure

MoPEFT: A Mixture-of-PEFTs for the Segment Anything Model

The emergence of foundation models, such as the Segment Anything Model (SAM), has sparked interest in Parameter-Efficient Fine-Tuning (PEFT) methods that tailor these large models to application domains outside their training data. However, different PEFT techniques modify the representation of a model differently, making it a non-trivial task to select the most appropriate method for the domain of interest. We propose a new framework, Mixture-of-PEFTs methods (MoPEFT), that is inspired by traditional Mixture-of-Experts (MoE) methodologies and is utilized for fine-tuning SAM. Our MoPEFT framework incorporates three different PEFT techniques as submodules and dynamically learns to activate the ones that are best suited for a given data-task setup. We test our method on the Segment Anything Model and show that MoPEFT consistently outperforms other fine-tuning methods on the MESS benchmark.Comment: Workshop on Foundation Models, CVPR 202