Projection sous les contrôles de distance par paires

International audienceVisualization of high-dimensional and possibly complex (non continuous for instance) data onto a low-dimensional space may be difficult. Several projection methods have been already proposed for displaying such high-dimensional structures on a lower-dimensional space, but the information lost is not always easy to use. Here, a new projection paradigm is presented to describe a non-linear projection method that takes into account the projection quality of each projected point in the reduced space, this quality being directly available in the same scale as this reduced space. More specifically, this novel method allows a straightforward visualization data in R 2 with a simple reading of the approximation quality, and provides then a novel variant of dimensionality reduction

Projection under pairwise distance control

International audienceVisualization of high-dimensional and possibly complex (non continuous for instance) data onto a low-dimensional space may be difficult. Severalprojection methods have been already proposed for displaying such high-dimensional structures on a lower-dimensional space, but informationlost on initial data is not always easy to use. A new projection paradigm is presented to describe a non-linear projection method that takes intoaccount the projection quality of each projected point in the reduced space, this quality being directly available in the same scale as this reducedspace. More specifically, this novel method allows a straightforward visualization data in R^2 with a simple reading of the approximation quality,and provides then a novel variant of dimensionality reduction

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.Comment: The 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018

The CACTA transposon Bot1 played a major role in Brassica genome divergence and gene proliferation

We isolated and characterized a Brassica C genome-specific CACTA element, which was designated Bot1 (Brassica oleracea transposon 1). After analysing phylogenetic relationships, copy numbers and sequence similarity of Bot1 and Bot1 analogues in B. oleracea (C genome) versus Brassica rapa (A genome), we concluded that Bot1 has encountered several rounds of amplification in the oleracea genome only, and has played a major role in the recent rapa and oleracea genome divergence. We performed in silico analyses of the genomic organization and internal structure of Bot1, and established which segment of Bot1 is C-genome specific. Our work reports a fully characterized Brassica repetitive sequence that can distinguish the Brassica A and C chromosomes in the allotetraploid Brassica napus, by fluorescent in situ hybridization. We demonstrated that Bot1 carries a host S locus-associated SLL3 gene copy. We speculate that Bot1 was involved in the proliferation of SLL3 around the Brassica genome. The present study reinforces the assumption that transposons are a major driver of genome and gene evolution in higher plants

Mobile Air Quality Studies (MAQS) - an international project

Due to an increasing awareness of the potential hazardousness of air pollutants, new laws, rules and guidelines have recently been implemented globally. In this respect, numerous studies have addressed traffic-related exposure to particulate matter using stationary technology so far. By contrast, only few studies used the advanced technology of mobile exposure analysis. The Mobile Air Quality Study (MAQS) addresses the issue of air pollutant exposure by combining advanced high-granularity spatial-temporal analysis with vehicle-mounted, person-mounted and roadside sensors. The MAQS-platform will be used by international collaborators in order 1) to assess air pollutant exposure in relation to road structure, 2) to assess air pollutant exposure in relation to traffic density, 3) to assess air pollutant exposure in relation to weather conditions, 4) to compare exposure within vehicles between front and back seat (children) positions, and 5) to evaluate "traffic zone"- exposure in relation to non-"traffic zone"-exposure. Primarily, the MAQS-platform will focus on particulate matter. With the establishment of advanced mobile analysis tools, it is planed to extend the analysis to other pollutants including including NO2, SO2, nanoparticles, and ozone

DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks

Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks. To generate adversarial inputs, there has emerged a wide variety of techniques, such as black- and whitebox attacks for neural networks. In this paper, we present DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image classifiers. Despite its simplicity, DeepSearch is shown to be more effective in finding adversarial inputs than state-of-the-art blackbox approaches. DeepSearch is additionally able to generate the most subtle adversarial inputs in comparison to these approaches

℮-conome: an automated tissue counting platform of cone photoreceptors for rodent models of retinitis pigmentosa

<p>Abstract</p> <p>Background</p> <p>Retinitis pigmentosa is characterized by the sequential loss of rod and cone photoreceptors. The preservation of cones would prevent blindness due to their essential role in human vision. Rod-derived Cone Viability Factor is a thioredoxin-like protein that is secreted by rods and is involved in cone survival. To validate the activity of Rod-derived Cone Viability Factors (RdCVFs) as therapeutic agents for treating retinitis Pigmentosa, we have developed e-conome, an automated cell counting platform for retinal flat mounts of rodent models of cone degeneration. This automated quantification method allows for faster data analysis thereby accelerating translational research.</p> <p>Methods</p> <p>An inverted fluorescent microscope, motorized and coupled to a CCD camera records images of cones labeled with fluorescent peanut agglutinin lectin on flat-mounted retinas. In an average of 300 fields per retina, nine Z-planes at magnification X40 are acquired after two-stage autofocus individually for each field. The projection of the stack of 9 images is subject to a threshold, filtered to exclude aberrant images based on preset variables. The cones are identified by treating the resulting image using 13 variables empirically determined. The cone density is calculated over the 300 fields.</p> <p>Results</p> <p>The method was validated by comparison to the conventional stereological counting. The decrease in cone density in <it>rd1 </it>mouse was found to be equivalent to the decrease determined by stereological counting. We also studied the spatiotemporal pattern of the degeneration of cones in the <it>rd1 </it>mouse and show that while the reduction in cone density starts in the central part of the retina, cone degeneration progresses at the same speed over the whole retinal surface. We finally show that for mice with an inactivation of the Nucleoredoxin-like genes <it>Nxnl1 </it>or <it>Nxnl2 </it>encoding RdCVFs, the loss of cones is more pronounced in the ventral retina.</p> <p>Conclusion</p> <p>The automated platform ℮-conome used here for retinal disease is a tool that can broadly accelerate translational research for neurodegenerative diseases.</p