Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks

We consider the problem of the stability of saliency-based explanations of Neural Network predictions under adversarial attacks in a classification task. Saliency interpretations of deterministic Neural Networks are remarkably brittle even when the attacks fail, i.e. for attacks that do not change the classification label. We empirically show that interpretations provided by Bayesian Neural Networks are considerably more stable under adversarial perturbations of the inputs and even under direct attacks to the explanations. By leveraging recent results, we also provide a theoretical explanation of this result in terms of the geometry of the data manifold. Additionally, we discuss the stability of the interpretations of high level representations of the inputs in the internal layers of a Network. Our results demonstrate that Bayesian methods, in addition to being more robust to adversarial attacks, have the potential to provide more stable and interpretable assessments of Neural Network predictions

Programmable models of growth and mutation of cancer-cell populations

In this paper we propose a systematic approach to construct mathematical models describing populations of cancer-cells at different stages of disease development. The methodology we propose is based on stochastic Concurrent Constraint Programming, a flexible stochastic modelling language. The methodology is tested on (and partially motivated by) the study of prostate cancer. In particular, we prove how our method is suitable to systematically reconstruct different mathematical models of prostate cancer growth - together with interactions with different kinds of hormone therapy - at different levels of refinement.Comment: In Proceedings CompMod 2011, arXiv:1109.104

Characterization methodology for re-using marble slurry in industrial applications

Nowadays calcium carbonate has a great importance in different industrial fields and currently there is the opportunity of appreciate the potential value of marble waste and convert it into marketable products. Marble slurry samples, collected from different dimension stone treatment plants in Orosei marble district (Sardinia - Italy), were chemically, physically, mineralogically, and morphologically analyzed and the obtained data were evaluated for compatibility with the marketable micronized CaCO3 specifications required by some industrial sectors, estimating the prospects of recovered CaCO3 utilization. Besides the economic benefits, transforming a waste into an important economic resource involves environmental advantages, due to reduced marble waste landfills, and sustainability promotion

Polarity assessment of reflection seismic data: a Deep Learning approach

We propose a procedure for the polarity assessment in reflection seismic data based on a Neural Network approach. The algorithm is based on a fully 1D approach, which does not require any input besides the seismic data since the necessary parameters are all automatically estimated. An added benefit is that the prediction has an associated probability, which automatically quantifies the reliability of the results. We tested the proposed procedure on synthetic and real reflection seismic data sets. The algorithm is able to correctly extract the seismic horizons also in case of complex conditions, such as along the flanks of salt domes, and is able to track polarity inversions

The Mean Drift: Tailoring the Mean Field Theory of Markov Processes for Real-World Applications

The statement of the mean field approximation theorem in the mean field theory of Markov processes particularly targets the behaviour of population processes with an unbounded number of agents. However, in most real-world engineering applications one faces the problem of analysing middle-sized systems in which the number of agents is bounded. In this paper we build on previous work in this area and introduce the mean drift. We present the concept of population processes and the conditions under which the approximation theorems apply, and then show how the mean drift is derived through a systematic application of the propagation of chaos. We then use the mean drift to construct a new set of ordinary differential equations which address the analysis of population processes with an arbitrary size

Experimental Biological Protocols with Formal Semantics

Both experimental and computational biology is becoming increasingly automated. Laboratory experiments are now performed automatically on high-throughput machinery, while computational models are synthesized or inferred automatically from data. However, integration between automated tasks in the process of biological discovery is still lacking, largely due to incompatible or missing formal representations. While theories are expressed formally as computational models, existing languages for encoding and automating experimental protocols often lack formal semantics. This makes it challenging to extract novel understanding by identifying when theory and experimental evidence disagree due to errors in the models or the protocols used to validate them. To address this, we formalize the syntax of a core protocol language, which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols. We present both a deterministic and a stochastic semantics to this language, both defined in terms of hybrid processes. In particular, the stochastic semantics captures uncertainties in equipment tolerances, making it a suitable tool for both experimental and computational biologists. We illustrate how the proposed protocol language can be used for automated verification and synthesis of laboratory experiments on case studies from the fields of chemistry and molecular programming

Lumping the approximate master equation for multistate processes on complex networks

Complex networks play an important role in human society and in nature. Stochastic multistate processes provide a powerful framework to model a variety of emerging phenomena such as the dynamics of an epidemic or the spreading of information on complex networks. In recent years, mean-field type approximations gained widespread attention as a tool to analyze and understand complex network dynamics. They reduce the model\u2019s complexity by assuming that all nodes with a similar local structure behave identically. Among these methods the approximate master equation (AME) provides the most accurate description of complex networks\u2019 dynamics by considering the whole neighborhood of a node. The size of a typical network though renders the numerical solution of multistate AME infeasible. Here, we propose an efficient approach for the numerical solution of the AME that exploits similarities between the differential equations of structurally similar groups of nodes. We cluster a large number of similar equations together and solve only a single lumped equation per cluster. Our method allows the application of the AME to real-world networks, while preserving its accuracy in computing estimates of global network properties, such as the fraction of nodes in a state at a given time

Impairment of enzymatic antioxidant defenses is associated with bilirubin-induced neuronal cell death in the cerebellum of Ugt1 KO mice

Severe hyperbilirubinemia is toxic during central nervous system development. Prolonged and uncontrolled high levels of unconjugated bilirubin lead to bilirubin-induced encephalopathy and eventually death by kernicterus. Despite extensive studies, the molecular and cellular mechanisms of bilirubin toxicity are still poorly defined. To fill this gap, we investigated the molecular processes underlying neuronal injury in a mouse model of severe neonatal jaundice, which develops hyperbilirubinemia as a consequence of a null mutation in the Ugt1 gene. These mutant mice show cerebellar abnormalities and hypoplasia, neuronal cell death and die shortly after birth because of bilirubin neurotoxicity. To identify protein changes associated with bilirubin-induced cell death, we performed proteomic analysis of cerebella from Ugt1 mutant and wild-type mice. Proteomic data pointed-out to oxidoreductase activities or antioxidant processes as important intracellular mechanisms altered during bilirubin-induced neurotoxicity. In particular, they revealed that down-representation of DJ-1, superoxide dismutase, peroxiredoxins 2 and 6 was associated with hyperbilirubinemia in the cerebellum of mutant mice. Interestingly, the reduction in protein levels seems to result from post-translational mechanisms because we did not detect significant quantitative differences in the corresponding mRNAs. We also observed an increase in neuro-specific enolase 2 both in the cerebellum and in the serum of mutant mice, supporting its potential use as a biomarker of bilirubin-induced neurological damage. In conclusion, our data show that different protective mechanisms fail to contrast oxidative burst in bilirubin-affected brain regions, ultimately leading to neurodegeneration. \ua9 2015 Macmillan Publishers Limited All rights reserved