One-Loop Integrals for Purely Virtual Particles

Quantum field theories with purely virtual particles, or fakeons, require suitable modifications in one-loop integrals. We provide the expressions for the modified scalar integrals in the case of the bubble, triangle and box diagrams. The new functions are defined by means of their difference with the `t Hooft-Veltman scalar integrals. The modifications do not affect the derivation of the Passarino-Veltman reduction and one-loop integrals with nontrivial numerators can be decomposed in the same fashion. Therefore, the new functions can be directly used to study the phenomenology of any models with standard particles and fakeons. We compare our results with standard amplitudes and show that the largest differences are often localized in relatively small energy ranges and are characterized by additional nonanalyticities. Finally, we give explicit examples in the context of a toy model, where cross sections and decay widths of standard particles are modified by the presence of fakeons.Comment: 34 pages, 9 figures. Sec. 2 rearranged, new example with plot in sec. 3, other minor corrections. Published version, pr

Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid

Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains. It has however been shown that they can be fooled by adversarial examples, i.e., images altered by a barely-perceivable adversarial noise, carefully crafted to mislead classification. In this work, we aim to evaluate the extent to which robot-vision systems embodying deep-learning algorithms are vulnerable to adversarial examples, and propose a computationally efficient countermeasure to mitigate this threat, based on rejecting classification of anomalous inputs. We then provide a clearer understanding of the safety properties of deep networks through an intuitive empirical analysis, showing that the mapping learned by such networks essentially violates the smoothness assumption of learning algorithms. We finally discuss the main limitations of this work, including the creation of real-world adversarial examples, and sketch promising research directions.Comment: Accepted for publication at the ICCV 2017 Workshop on Vision in Practice on Autonomous Robots (ViPAR

Gauge-invariant coefficients in perturbative quantum gravity

Heat kernel methods are useful for studying properties of quantum gravity. We recompute the first three heat kernel coefficients in perturbative quantum gravity with cosmological constant to ascertain which ones are correctly reported in the literature. They correspond to the counterterms needed to renormalize the one-loop effective action in four dimensions. They may be evaluated at arbitrary dimensions D, in which case they identify only a subset of the divergences appearing in the effective action for D ≥ 6. Generically, these coefficients depend on the gauge-fixing choice adopted in quantizing the Einstein–Hilbert action. However, they become gauge-invariant once evaluated on-shell, i.e. using Einstein’s equations with cosmological constant. Thus, we identify these gauge invariant coefficients and use them as a benchmark for testing alternative approaches to perturbative quantum gravity. One of these approaches describes the graviton in first-quantization through the N = 4 spinning particle, characterized by four supersymmetries on the worldline and a set of worldline gauge invariances. This description has been used for computing the gauge-invariant coefficients as well. We verify their correctness at D = 4, but find a mismatch at arbitrary D when comparing with the benchmark fixed previously. We interpret this result as signaling that the path integral quantization of the N = 4 spinning particle should be amended. We perform this task by fixing the correct counterterm that must be used in the worldline path integral quantization of the N = 4 spinning particle to make it consistent in arbitrary dimensions

A literature review on the links between environmental regulation and competitiveness

The effects of environmental regulation on competitiveness is always a topic under debate for policymakers and practitioners. The article describes the different ways of defining and measuring the effects of environmental regulation on competition and market forces and synthesizes the most updated findings on the relationship between these dimensions. It also proposes an in depth analysis of the most recent empirical studies, with a particular focus on the buildings and construction (B&C) sector, which often is a substantial contributor to the most important countries’ economic indicators. We find that two variables have proved to be both (i) key in defining to what extent and under what conditions environmental regulation exerts adverse or positive effects on competitiveness and (ii) difficult to nail down: forms of regulation and responses by business.

Gauge invariant coefficients in perturbative quantum gravity

Perturbative quantum gravity can be studied in many ways. A traditional approach is to apply covariant quantization schemes to the Einstein-Hilbert action and use heat kernel methods, as pioneered by DeWitt. An alternative approach is to consider the graviton as arising from the first quantization of particle actions, following the same methods used in string theory. An interesting model to describe the graviton is based on the so-called N = 4 spinning particle, which has been used recently to study perturbative properties of quantum gravity, allowing in particular for the calculation of certain gauge-invariant coefficients. The latter are related to the counterterms that renormalize the one-loop effective action of pure quantum gravity with a cosmological constant. Such coefficients have already been tested in D = 4 dimensions. Here we study the general case of arbitrary D. We derive the gauge-invariant coefficients —the simplest one being the number of physical degrees of freedom of the graviton—using the traditional heat kernel method. We compare them with the ones obtained by using the N = 4 spinning particle and discover that the latter fails to reproduce some of those coefficients at arbitrary dimension, suggesting the need of improving that first quantized model. This constitutes a first original result of this thesis. In the second part, we try to find an alternative worldline path integral treatment of the heat kernel, extending a previous worldline construction that was tailored to 4 dimensions only. We succeed in finding suitable worldline actions for the gauge-fixed graviton fluctuations and related ghosts. The action for the graviton fluctuations that we construct reproduces the expected Hamiltonian but does not seem to admit a perturbative path integral treatment

Recognition of landslides in lunar impact craters

Landslides have been observed on several planets and minor bodies of the solar System, including the Moon. Notwithstanding different types of slope failures have been studied on the Moon, a detailed lunar landslide inventory is still pending. Undoubtedly, such will be in a benefit for future geological and morphological studies, as well in hazard, risk and suscept- ibility assessments. A preliminary survey of lunar landslides in impact craters has been done using visual inspection on images and digital elevation model (DEM) (Brunetti et al. 2015) but this method suffers from subjective interpretation. A new methodology based on polynomial interpolation of crater cross-sections extracted from global lunar DEMs is presented in this paper. Because of their properties, Chebyshev polynomials were already exploited for para- metric classification of different crater morphologies (Mahanti et al., 2014). Here, their use has been extended to the discrimination of slumps in simple impact craters. Two criteria for recognition have provided the best results: one based on fixing an empirical absolute thresholding and a second based on statistical adaptive thresholding. The application of both criteria to a data set made up of 204 lunar craters’ cross-sections has demonstrated that the former criterion provides the best recognition

Preliminary archeological site survey by UAV-borne lidar. A case study

Preliminary analysis of an archaeological site requires the acquisition of information by several diverse diagnostic techniques. Remote sensing plays an important role especially in spatially ex-tended and not easily accessible sites for the purposes of preventive and rescue archaeology, landscape archaeology, and intervention planning. In this paper, we present a case study of a de-tailed topographic survey based on a light detection and ranging (LiDAR) sensor carried by an unmanned aerial vehicle (UAV; also known as drone). The high-resolution digital terrain model, obtained from the cloud of points automatically labeled as ground, was searched exhaustively by an expert operator looking for entrances to prehistoric hypogea. The study documents the useful-ness of such a technique to reveal anthropogenic structures hidden by vegetation and perform fast topographic documentation of the ground surface

Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks

Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transferability has been shown in previous work, but the underlying reasons why an attack transfers or not are not yet well understood. In this paper, we present a comprehensive analysis aimed to investigate the transferability of both test-time evasion and training-time poisoning attacks. We provide a unifying optimization framework for evasion and poisoning attacks, and a formal definition of transferability of such attacks. We highlight two main factors contributing to attack transferability: the intrinsic adversarial vulnerability of the target model, and the complexity of the surrogate model used to optimize the attack. Based on these insights, we define three metrics that impact an attack's transferability. Interestingly, our results derived from theoretical analysis hold for both evasion and poisoning attacks, and are confirmed experimentally using a wide range of linear and non-linear classifiers and datasets

Insider Threats in Emerging Mobility-as-a-Service Scenarios

Mobility as a Service (MaaS) applies the everything-as- \ a-service paradigm of Cloud Computing to transportation: a MaaS \ provider offers to its users the dynamic composition of solutions of \ different travel agencies into a single, consistent interface. \ Traditionally, transits and data on mobility belong to a scattered \ plethora of operators. Thus, we argue that the economic model of \ MaaS is that of federations of providers, each trading its resources to \ coordinate multi-modal solutions for mobility. Such flexibility comes \ with many security and privacy concerns, of which insider threat is \ one of the most prominent. In this paper, we follow a tiered structure \ — from individual operators to markets of federated MaaS providers \ — to classify the potential threats of each tier and propose the \ appropriate countermeasures, in an effort to mitigate the problems

ESTIMATE OF TRUNK INCLINATION DURING FAST MOVEMENTS BY INERTIAL SENSING

The purpose of this study was to identify a reliable algorithm to estimate the inclination of a trunk-mounted inertial measurement unit (IMU) during fast movements and to test its subject- and task-specificity. Ten amateur football players performed three times the approach phase of the drive block technique and a fast sit-to-stand-to-sit task. IMU data were processed using an ad hoc adaptive Kaman filter, and pitch angular displacements were obtained and compared to stereophotogrammetric reference estimates. Tuning of the algorithm parameters was performed and relevant accuracy was tested in terms of root mean squared difference (RMSD) and correlation coefficient. Strong correlation (>0.978) were observed for both motor tasks, together with RMSD smaller than 4.4±1.7 deg. The tuned algorithm proved to be neither subject- nor task-specific (p>0.05)