Unitarity of the Leptonic Mixing Matrix

We determine the elements of the leptonic mixing matrix, without assuming unitarity, combining data from neutrino oscillation experiments and weak decays. To that end, we first develop a formalism for studying neutrino oscillations in vacuum and matter when the leptonic mixing matrix is not unitary. To be conservative, only three light neutrino species are considered, whose propagation is generically affected by non-unitary effects. Precision improvements within future facilities are discussed as well.Comment: Standard Model radiative corrections to the invisible Z width included. Some numerical results modified at the percent level. Updated with latest bounds on the rare tau decay. Physical conculsions unchange

Herding on the Steppe: A Study Aboard Experience in Mongolia

My experiences studying abroad and the cultural differences that I discovered and lived though in Mongolia. Learning by doing in order to gain experience, I herded with a Mongolian host family of nomadic herders. Herding and living with them in the preparation for winter as I worked to learn each day. This paper highlights studying abroad as an opportunity to live in a radically different way in order to see new versions of the world

Keyed Non-Parametric Hypothesis Tests

The recent popularity of machine learning calls for a deeper understanding of AI security. Amongst the numerous AI threats published so far, poisoning attacks currently attract considerable attention. In a poisoning attack the opponent partially tampers the dataset used for learning to mislead the classifier during the testing phase. This paper proposes a new protection strategy against poisoning attacks. The technique relies on a new primitive called keyed non-parametric hypothesis tests allowing to evaluate under adversarial conditions the training input's conformance with a previously learned distribution $\mathfrak{D}$. To do so we use a secret key $\kappa$ unknown to the opponent. Keyed non-parametric hypothesis tests differs from classical tests in that the secrecy of $\kappa$ prevents the opponent from misleading the keyed test into concluding that a (significantly) tampered dataset belongs to $\mathfrak{D}$.Comment: Paper published in NSS 201

Neutrino masses from higher than d=5 effective operators

We discuss the generation of small neutrino masses from effective operators higher than dimension five, which open new possibilities for low scale see-saw mechanisms. In order to forbid the radiative generation of neutrino mass by lower dimensional operators, extra fields are required, which are charged under a new symmetry. We discuss this mechanism in the framework of a two Higgs doublet model. We demonstrate that the tree level generation of neutrino mass from higher dimensional operators often leads to inverse see-saw scenarios in which small lepton number violating terms are naturally suppressed by the new physics scale. Furthermore, we systematically discuss tree level generalizations of the standard see-saw scenarios from higher dimensional operators. Finally, we point out that higher dimensional operators can also be generated at the loop level. In this case, we obtain the TeV scale as new physics scale even with order one couplings.Comment: 22 pages, 3 figures, 2 tables. Some references adde

Equivalent effective Lagrangians for Scherk-Schwarz compactifications

We discuss the general form of the mass terms that can appear in the effective field theories of coordinate-dependent compactifications a la Scherk-Schwarz. As an illustrative example, we consider an interacting five-dimensional theory compactified on the orbifold S^1/Z_2, with a fermion subject to twisted periodicity conditions. We show how the same physics can be described by equivalent effective Lagrangians for periodic fields, related by field redefinitions and differing only in the form of the five-dimensional mass terms. In a suitable limit, these mass terms can be localized at the orbifold fixed points. We also show how to reconstruct the twist parameter from any given mass terms of the allowed form. Finally, after mentioning some possible generalizations of our results, we re-discuss the example of brane-induced supersymmetry breaking in five-dimensional Poincare' supergravity, and comment on its relation with gaugino condensation in M-theory.Comment: 17 pages, 3 figures. Published versio

Scherk-Schwarz SUSY breaking from the viewpoint of 5D conformal supergravity

We reinterpret the Scherk-Schwarz (SS) boundary condition for SU(2)_R in a compactified five-dimensional (5D) Poincare supergravity in terms of the twisted SU(2)_U gauge fixing in 5D conformal supergravity. In such translation, only the compensator hypermultiplet is relevant to the SS twist, and various properties of the SS mechanism can be easily understood. Especially, we show the correspondence between the SS twist and constant superpotentials within our framework.Comment: 16 pages, no figur

Societal issues in machine learning: When learning from data is not enough

It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. Such characterization is on the interest of big IT companies, but it correctly reflects the current industrialization of AI. This phenomenon means that AI systems and products are reaching the society at large and, therefore, that societal issues related to the use of AI and Machine Learning (ML) cannot be ignored any longer. Designing ML models from this human-centered perspective means incorporating human-relevant requirements such as safety, fairness, privacy, and interpretability, but also considering broad societal issues such as ethics and legislation. These are essential aspects to foster the acceptance of ML-based technologies, as well as to ensure compliance with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. The ESANN special session for which this tutorial acts as an introduction aims to showcase the state of the art on these increasingly relevant topics among ML theoreticians and practitioners. For this purpose, we welcomed both solid contributions and preliminary relevant results showing the potential, the limitations and the challenges of new ideas, as well as refinements, or hybridizations among the different fields of research, ML and related approaches in facing real-world problems involving societal issues

Empirical assessment of generating adversarial configurations for software product lines

Software product line (SPL) engineering allows the derivation of products tailored to stakeholders’ needs through the setting of a large number of configuration options. Unfortunately, options and their interactions create a huge configuration space which is either intractable or too costly to explore exhaustively. Instead of covering all products, machine learning (ML) approximates the set of acceptable products (e.g., successful builds, passing tests) out of a training set (a sample of configurations). However, ML techniques can make prediction errors yielding non-acceptable products wasting time, energy and other resources. We apply adversarial machine learning techniques to the world of SPLs and craft new configurations faking to be acceptable configurations but that are not and vice-versa. It allows to diagnose prediction errors and take appropriate actions. We develop two adversarial configuration generators on top of state-of-the-art attack algorithms and capable of synthesizing configurations that are both adversarial and conform to logical constraints. We empirically assess our generators within two case studies: an industrial video synthesizer (MOTIV) and an industry-strength, open-source Web-app configurator (JHipster). For the two cases, our attacks yield (up to) a 100% misclassification rate without sacrificing the logical validity of adversarial configurations. This work lays the foundations of a quality assurance framework for ML-based SPLs

DeltaPhish: Detecting Phishing Webpages in Compromised Websites

The large-scale deployment of modern phishing attacks relies on the automatic exploitation of vulnerable websites in the wild, to maximize profit while hindering attack traceability, detection and blacklisting. To the best of our knowledge, this is the first work that specifically leverages this adversarial behavior for detection purposes. We show that phishing webpages can be accurately detected by highlighting HTML code and visual differences with respect to other (legitimate) pages hosted within a compromised website. Our system, named DeltaPhish, can be installed as part of a web application firewall, to detect the presence of anomalous content on a website after compromise, and eventually prevent access to it. DeltaPhish is also robust against adversarial attempts in which the HTML code of the phishing page is carefully manipulated to evade detection. We empirically evaluate it on more than 5,500 webpages collected in the wild from compromised websites, showing that it is capable of detecting more than 99% of phishing webpages, while only misclassifying less than 1% of legitimate pages. We further show that the detection rate remains higher than 70% even under very sophisticated attacks carefully designed to evade our system.Comment: Preprint version of the work accepted at ESORICS 201

Societal issues in machine learning: when learning from data is not enough

It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. Such characterization is on the interest of big IT companies, but it correctly reflects the current industrialization of AI. This phenomenon means that AI systems and products are reaching the society at large and, therefore, that societal issues related to the use of AI and Machine Learning (ML) cannot be ignored any longer. Designing ML models from this human-centered perspective means incorporating human-relevant requirements such as safety, fairness, privacy, and interpretability, but also considering broad societal issues such as ethics and legislation. These are essential aspects to foster the acceptance of ML-based technologies, as well as to ensure compliance with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. The ESANN special session for which this tutorial acts as an introduction aims to showcase the state of the art on these increasingly relevant topics among ML theoreticians and practitioners. For this purpose, we welcomed both solid contributions and preliminary relevant results showing the potential, the limitations and the challenges of new ideas, as well as refinements, or hybridizations among the different fields of research, ML and related approaches in facing real-world problems involving societal issues.Peer ReviewedPostprint (published version