A nerve cell receives multiple inputs from upstream neurons by way of its synapses. Neuron processing functions are thus influenced by changes in the biophysical properties of the synapse, such as long-term potentiation (LTP) or depression (LTD). This observation has opened new perspectives on the biophysical basis of learning and memory, but its quantitative impact on the information transmission of a neuron remains partially elucidated. One major obstacle is the high dimensionality of the neuronal input-output space, which makes it unfeasible to perform a thorough computational analysis of a neuron with multiple synaptic inputs. In this work, information theory was employed to characterize the information transmission of a cerebellar granule cell over a region of its excitatory input space following synaptic changes. Granule cells have a small dendritic tree (on average, they receive only four mossy fiber afferents), which greatly bounds the input combinatorial space, reducing the complexity of information-theoretic calculations. Numerical simulations and LTP experiments quantified how changes in neurotransmitter release probability (p) modulated information transmission of a cerebellar granule cell. Numerical simulations showed that p shaped the neurotransmission landscape in unexpected ways. As p increased, the optimality of the information transmission of most stimuli did not increase strictly monotonically; instead it reached a plateau at intermediate p levels. Furthermore, our results showed that the spatiotemporal characteristics of the inputs determine the effect of p on neurotransmission, thus permitting the selection of distinctive preferred stimuli for different p values. These selective mechanisms may have important consequences on the encoding of cerebellar mossy fiber inputs and the plasticity and computation at the next circuit stage, including the parallel fiber-Purkinje cell synapses.

The lack of reliable sources of detailed information on the vulnerabilities of open-source software (OSS) components is a major obstacle to maintaining a secure software supply chain and an effective vulnerability management process. Standard sources of advisories and vulnerability data, such as the National Vulnerability Database (NVD), are known to suffer from poor coverage and inconsistent quality. To reduce our dependency on these sources, we propose an approach that uses machine-learning to analyze source code repositories and to automatically identify commits that are security-relevant (i.e., that are likely to fix a vulnerability). We treat the source code changes introduced by commits as documents written in natural language, classifying them using standard document classification methods. Combining independent classifiers that use information from different facets of commits, our method can yield high precision (80%) while ensuring acceptable recall (43%). In particular, the use of information extracted from the source code changes yields a substantial improvement over the best known approach in state of the art, while requiring a significantly smaller amount of training data and employing a simpler architecture.

Advancing our understanding of software vulnerabilities, automating their identification, the analysis of their impact, and ultimately their mitigation is necessary to enable the development of software that is more secure. While operating a vulnerability assessment tool that we developed and that is currently used by hundreds of development units at SAP, we manually collected and curated a dataset of vulnerabilities of open-source software and the commits fixing them. The data was obtained both from the National Vulnerability Database (NVD) and from project-specific Web resources that we monitor on a continuous basis. From that data, we extracted a dataset that maps 624 publicly disclosed vulnerabilities affecting 205 distinct open-source Java projects, used in SAP products or internal tools, onto the 1282 commits that fix them. Out of 624 vulnerabilities, 29 do not have a CVE identifier at all and 46, which do have a CVE identifier assigned by a numbering authority, are not available in the NVD yet. The dataset is released under an open-source license, together with supporting scripts that allow researchers to automatically retrieve the actual content of the commits from the corresponding repositories and to augment the attributes available for each instance. Also, these scripts allow to complement the dataset with additional instances that are not security fixes (which is useful, for example, in machine learning applications). Our dataset has been successfully used to train classifiers that could automatically identify security-relevant commits in code repositories. The release of this dataset and the supporting code as open-source will allow future research to be based on data of industrial relevance; also, it represents a concrete step towards making the maintenance of this dataset a shared effort involving open-source communities, academia, and the industry.

The Big Data revolution has promised to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, critical issues still need to be solved in the road that leads to commodization of Big Data Analytics, such as the management of Big Data complexity and the protection of data security and privacy. In this paper, we focus on the first issue and propose a methodology based on Model Driven Engineering (MDE) that aims to substantially lower the amount of competences needed in the management of a Big Data pipeline and to support automation of Big Data analytics. The proposal is experimentally evaluated in a real-world scenario: the implementation of novel functionality for Threat Detection Systems.

We investigate the problem of speciation and coexistence in simple ecosystems when the competition among individuals is included in the Eigen model for quasispecies. By suggesting an analogy between the competition among strains and the diffusion of a chemical inhibitor in a reaction-diffusion system, the speciation phenomenon is considered the analogous of chemical pattern formation in genetic space. In the limit of vanishing mutation rate we obtain analytically the conditions for speciation. Using different forms of the competition interaction we show that the speciation is absent for the genetic equivalent of a normal diffusing inhibitor, and is present for shorter-range interactions.[S0031-9007(97)04338-X] PACS numbers: 87.10. + e, 82.20.Mj In this work we address the problem of speciation (species formation) in simple ecosystems, mirroring some aspects of bacterial and viral evolution. Our model can be considered as an extension of the Eigen model [1,2]. With respect to the latter, we introduce the competition among individuals.Eigen's phenomenological theory of self-reproducing macromolecules (or haploid organisms) illustrates the concept of stable quasispecies, i.e., a peaked distribution of genomes around a master sequence, its width being determined by mutations. In its simpler formulation, the various genomes have different reproductive rates, the logarithm of which constitutes the fitness landscape [3][4][5]. The master sequence is located in correspondence of the maximum of the fitness. In general, a one to one correspondence between a given phenotype and a genotype is assumed (no polymorphism nor age structure). The genomes are coupled by mutations and by a global constraint on the total number of individuals (constant organization). One usually considers only point mutations (the most common ones), which correspond to a diffusion process in genetic space. In this way one can define the concept of distance in genetic space as the number of mutations needed to connect two genomes. The Eigen model has also been studied in the context of statistical mechanics [6][7][8][9].Epstein [10] studied the problem without considering mutations; he showed that the coexistence is possible if the species are self-limiting (i.e., there exists a form of self-competition, modeled, for instance, by a logistic term) and coexisting species does not compete directly. On the contrary, when two species are in competition (because they share some resource-an enzyme in Epstein's case), only the fittest one survives. However, he did not introduce the genetic distance among species nor presented any evolutionary mechanism for the speciation phenomenon.We think that the direct competition for local resources among strains, coupled with a weak mutation rate, is the simplest mechanism for modeling both speciation and stable coexistence in simple smooth landscapes. The mu-tations are needed to populate newly formed niches, while the competition actively separates the strains into quasispecies. One can consider the following analo...

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The Big Data revolution promises to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, major hurdles still need to be overcome on the road that leads to commoditization and wide adoption of Big Data Analytics (BDA). Big Data complexity is the first factor hampering the full potential of BDA. The opacity and variety of Big Data technologies and computations, in fact, make BDA a failure prone and resource-intensive process, which requires a trial-and-error approach. This problem is even exacerbated by the fact that current solutions to Big Data application development take a bottom-up approach, where the last technology release drives application development. Selection of the best Big Data platform, as well as of the best pipeline to execute analytics, represents then a deal breaker. In this paper, we propose a return to roots by defining a Model-Driven Engineering (MDE) methodology that supports automation of BDA based on model specification. Our approach lets customers declare requirements to be achieved by an abstract Big Data platform and smart engines deploy the Big Data pipeline carrying out the analytics on a specific instance of such platform. Driven by customers' requirements, our methodology is based on an OWLS ontology of Big Data services and on a compiler transforming OWLS service compositions in workflows that can be directly executed on the selected platform. The proposal is experimentally evaluated in a real-world scenario focusing on the threat detection system of SAP.

In this paper we extend the Celada-Seiden (CS) model of the humoral immune response to include infectious virus and cytotoxic T lymphocytes (cellular response). The response of the system to virus involves a competition between the ability of the virus to kill the host cells and the host's ability to eliminate the virus. We find two basins of attraction in the dynamics of this system, one is identified with disease and the other with the immune state. There is also an oscillating state that exists on the border of these two stable states.Fluctuations in the population of virus or antibody can end the oscillation and 1 drive the system into one of the stable states. The introduction of mechanisms of cross-regulation between the two responses can bias the system towards one of them. We also study a mean field model, based on coupled maps, to investigate virus-like infections. This simple model reproduces the attractors for average populations observed in the cellular automaton. All the dynamical behavior connected to spatial extension is lost, as is the oscillating feature.Thus the mean field approximation introduced with coupled maps destroys oscillations.