On The Robustness of a Neural Network

With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the \textit{black box} aspect of neural networks as it becomes crucial to understand their limits and capabilities. With the rise of neuromorphic hardware, it is even more critical to understand how a neural network, as a distributed system, tolerates the failures of its computing nodes, neurons, and its communication channels, synapses. Experimentally assessing the robustness of neural networks involves the quixotic venture of testing all the possible failures, on all the possible inputs, which ultimately hits a combinatorial explosion for the first, and the impossibility to gather all the possible inputs for the second. In this paper, we prove an upper bound on the expected error of the output when a subset of neurons crashes. This bound involves dependencies on the network parameters that can be seen as being too pessimistic in the average case. It involves a polynomial dependency on the Lipschitz coefficient of the neurons activation function, and an exponential dependency on the depth of the layer where a failure occurs. We back up our theoretical results with experiments illustrating the extent to which our prediction matches the dependencies between the network parameters and robustness. Our results show that the robustness of neural networks to the average crash can be estimated without the need to neither test the network on all failure configurations, nor access the training set used to train the network, both of which are practically impossible requirements.Comment: 36th IEEE International Symposium on Reliable Distributed Systems 26 - 29 September 2017. Hong Kong, Chin

AMAPstudio: a 3D Interactive Software Suite for Plants Architecture Modelling

Plants architecture modelling results in building complex models. Turning them into simulators requires strong interaction between scientists and software developers. The AMAPstudio project adapts a methodology that has been successfully conducted in the forestry modelling field for 12 years. It focuses on a long-term supported software environment and a strong customized technical backing to help modellers integrate their simulators in highly 3D interactive softwar

AMAPstudio : a software suite for plants architecture modelling

AMAPstudio is a user-friendly software suite designed for botanists and agronomists to edit, visualize, explore and simulate multi-scale plant architecture. It contains interactive tools to handle the topology (e.g.organs addition or deletion), the geometry (e.g. 3D selection, edition, rotation) and the dynamics (i.e. time line, scenarios) of plants at the individual or scene scale. AMAPstudio is based on the Multi-scale Tree Graph (MTG) data structure, which is commonly used to represent plant topology. Users can explore this data structure to test or to improve hypotheses on plant development. Specific data can be extracted with combinations of criteria and can be visualized in tables and graphs. Simple analysis functions can be launched or data can be exported to external tools, e.g. R, or any other statistical computing environment, for more specific analyses. AMAPstudio is also a framework in which modellers can integrate their own plant simulation models. Different scenarios can be computed for a growth model by interactively modifying model parameters or plant structure (e.g. by pruning) at particular time steps. AMAPstudio is an open software built according to the flexible Capsis methodology. It is a free open-source software (LGPL) available on all Java compatible operating systems and it can be downloaded on http://amapstudio.cirad.fr

Evidence of a causal and modifiable relationship between kidney function and circulating trimethylamine N-oxide

The host-microbiota co-metabolite trimethylamine N-oxide (TMAO) is linked to increased cardiovascular risk but how its circulating levels are regulated remains unclear. We applied "explainable" machine learning, univariate, multivariate and mediation analyses of fasting plasma TMAO concentration and a multitude of phenotypes in 1,741 adult Europeans of the MetaCardis study. Here we show that next to age, kidney function is the primary variable predicting circulating TMAO, with microbiota composition and diet playing minor, albeit significant, roles. Mediation analysis suggests a causal relationship between TMAO and kidney function that we corroborate in preclinical models where TMAO exposure increases kidney scarring. Consistent with our findings, patients receiving glucose-lowering drugs with reno-protective properties have significantly lower circulating TMAO when compared to propensity-score matched control individuals. Our analyses uncover a bidirectional relationship between kidney function and TMAO that can potentially be modified by reno-protective anti-diabetic drugs and suggest a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk

Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology

Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism

AMAPstudio: an editing and simulation software suite for plants architecture modelling

AMAPstudio is a software suite dedicated to plants architecture modelling, designed for botanists and agronomists, providing features to edit, visualise, explore and simulate multi-scale plant descriptions.AMAPstudio is based on the multi-scale tree graph (MTG) data structure, which is commonly used to represent plant topology. The user can explore and edit the topology and the geometry of one or several plants. Specific data can be extracted with combinations of criteria and can be visualised in tables and graphs. Simple analysis functions can be run and data can be exported to external tools, e.g. R or any other statistical computing environment, for more specific analyses.AMAPstudio is also a framework in which modellers can integrate their own plant simulation models to build plant growth or scene dynamics scenarios and explore the results.Models can be of different kinds, they can address more or less functioning and interaction with other plants or with the environment, possibly enabling to run ecological studies.AMAPstudio is an open software built according to the Capsis methodology. It is scenario oriented and brings particularly interactive editors easing the daily work and knowledge transfer. It is a free open-source software (LGPL) available on all Java compatible operating systems and it can be downloaded on http://amapstudio.cirad.fr

AMAPstudio-Scan -An Interactive Software to Build and Edit a Plant Architecture from a TLS Cloud

AMAPstudio-Scan -An Interactive Software to Build and Edit a Plant Architecture from a TLS Cloud. FSPMA2016, International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications, IEE

Fast and Robust Distributed Learning in High Dimension

Could a gradient aggregation rule (GAR) for distributed machine learning be both robust and fast? This paper answers by the affirmative through Multi-Bulyan. Given n workers, f of which are arbitrary malicious (Byzantine) and m = n − f are not, we prove that Multi-Bulyan can ensure a strong form of Byzantine resilience, as well as an m / n slowdown, compared to averaging, the fastest (but non Byzantine resilient) rule for distributed machine learning. When m ≈ n (almost all workers are correct), Multi-Bulyan reaches the speed of averaging. We also prove that Multi-Bulyan's cost in local computation is O(d) (like averaging), an important feature for ML where d commonly reaches 10⁹, while robust alternatives have at least quadratic cost in d. Our theoretical findings are complemented with an experimental evaluation which, in addition to supporting the linear O(d) complexity argument, conveys the fact that Multi-Bulyan's parallelisability further adds to its efficiency

Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach

1.Calibration of local, regional or global allometric equations to estimate biomass at the tree level constitutes a significant burden on projects aiming at reducing Carbon emissions from forest degradation and deforestation. The objective of this contribution is to assess the precision and accuracy of Terrestrial Laser Scanning (TLS) for estimating volumes and aboveground biomass (AGB) of the woody parts of tropical trees, and for the calibration of allometric models. 2.We used a destructive dataset of 61 trees, with diameters and AGB of up to 186.6 cm and 60 Mg respectively, which were scanned, felled and weighed in the semi-deciduous forests of eastern Cameroon. We present an operational approach based on available software allowing to retrieve TLS volume with low bias and high accuracy for large tropical trees. Edition of the obtained models proved necessary, mainly to account for the complexity of buttressed parts of tree trunks, which were separately modelled through a meshing approach, and to bring a few corrections in the topology and geometry of branches, thanks to the AMAPStudio-Scan software. 3.Over the entire dataset, TLS derived volumes proved highly reliable for branches larger than 5 cm in diameter. The volumes of the remaining woody parts estimated for stumps, stems and crowns as well as for the whole tree proved very accurate (RMSE below 2.81% and R² above of 0.98) and unbiased. Once converted to AGB using mean local specific wood density values, TLS estimates allowed calibrating a biomass allometric model with coefficients statistically undistinguishable from those of a model based on destructive data. Un-edited Quantitative Structure Model (QSM) however lead to systematic overestimations of woody volumes and subsequently to significantly different allometric parameters. 4.We can therefore conclude that the non-destructive TLS approach can now be used as an operational alternative to traditional destructive sampling to build the allometric equations, although attention must be paid to the quality of QSM model adjustments to avoid systematic bia