Abstract. Electroencephalography (EEG) source imaging aims to reconstruct brain activity maps from the neuroelectric potential difference measured on the skull. To obtain the brain activity map, we need to solve an ill-posed and ill-conditioned inverse problem that requires regularization techniques to make the solution viable. When dealing with real-time applications, dimensionality reduction techniques can be used to reduce the computational load required to evaluate the numerical solution of the EEG inverse problem.

The ongoing reproducibility crisis in psychology and cognitive neuroscience has sparked increasing calls to re-evaluate and reshape scientific culture and practices. Heeding those calls, we have recently launched the EEGManyPipelines project as a means to assess the robustness of EEG research in naturalistic conditions and experiment with an alternative model of conducting scientific research.

In this paper, we aim at developing new methods to join machine learning techniques and macroscopic differential models for vehicular traffic estimation and forecast. It is well known that data-driven and model- driven approaches have (sometimes complementary) advantages and drawbacks. We consider here a dataset with flux and velocity data of vehicles moving on a highway, collected by fixed sensors and classified by lane and by class of vehicle.

SRv6 can provide hybrid cooperation between a centralized network controller and network nodes. IPv6 routers maintain multi-hop ECMP-aware segments, whereas the controller establishes a source-routed path through the network. Since the state of the flow is defined at the ingress to the network and then is contained in a specific packet header, called Segment Routing Header (SRH), the importance of such a header itself is vital. Motivated by the need to study and investigate this technology, this paper discusses some security-related issues of Segment Routing.

Networks are pervasive in computer science and in real world applications. It is often useful to leverage distinctive node features to regroup such data in clusters, by making use of a single representative node per cluster. Such contracted graphs can help identify features of the original networks that were not visible before. As an example, we can identify contiguous nodes having the same discrete property in a social network. Contracting a graph allows a more scalable analysis of the interactions and structure of the network nodes.

Advanced persistent threats (APTs) have rocketed over the last years. Unfortunately, their technical characterization is incomplete--it is still unclear if they are advanced usages of regular malware or a different form of malware. This is key to develop an effective cyberdefense. To address this issue, in this paper we analyze the techniques and tactics at stake for both regular and APT-linked malware. To enable reproducibility, our approach leverages only publicly available datasets and analysis tools. Our study involves 11,651 regular malware and 4686 APT-linked ones.

This manuscript relates to the exploiting of the abstract calculus pattern (ACP) for the (numerical) solution of ordinary differential equation (ODEs) systems, which are ubiquitous mathematical formulations of many physical (dynamical) phenomena. We present FOODIE, a software suite aimed to numerically solve ODE problems by means of a clear, concise, and efficient abstract interface.

Background: The oral food challenge test (OFC) represents the gold standard for the diagnosis of food allergy (FA). It is also necessary for the safe reintroduction of the allergen into the diet of the patient. However, it is burdened with serious side effects.

Graphical models are well-known mathematical objects for describing conditional dependency relationships between random variables of a complex system. Gaussian graphical models refer to the case of multivariate Gaussian variable for which the graphical model is encoded through the support of corresponding inverse covariance (precision) matrix. We consider a problem of estimating multiple Gaussian graphical models from high- dimensional data sets under the assumption that they share the same conditional independence structure. However, the individual correlation matrices can differ.

This paper considers a Multiple Objective variant of the Critical Disruption Path problem to extend its suitability in a range of security operations relying on path-based network interdiction, including flight pattern optimisation for surveillance.