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Marco Romanelli

Publications and source records attributed to Marco Romanelli.

34 records · Page 2Linked to original sources
arXiv2022

Advances in ultrafast plasmonics

In the past twenty years, we have reached a broad understanding of many light-driven phenomena in nanoscale systems. The temporal dynamics of the excited states are instead quite challenging to explore, and, at the same time, crucial to study for understanding the origin of fundamental physical and chemical processes. In this review we examine the current state and prospects of ultrafast phenomena driven by plasmons both from a fundamental and applied point of view. This research area is referred to as ultrafast plasmonics and represents an outstanding playground to tailor and control fast optical and electronic processes at the nanoscale, such as ultrafast optical switching, single photon emission and strong coupling interactions to tailor photochemical reactions. Here, we provide an overview of the field, and describe the methodologies to monitor and control nanoscale phenomena with plasmons at ultrafast timescales in terms of both modeling and experimental characterization. Various directions are showcased, among others recent advances in ultrafast plasmon-driven chemistry and multi-functional plasmonics, in which charge, spin, and lattice degrees of freedom are exploited to provide active control of the optical and electronic properties of nanoscale materials. As the focus shifts to the development of practical devices, such as all-optical transistors, we also emphasize new materials and applications in ultrafast plasmonics and highlight recent development in the relativistic realm. The latter is a promising research field with potential applications in fusion research or particle and light sources providing properties such as attosecond duration.

physics.optics↗
arXiv2022

MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors

Detection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications. However, the detection methods are generally validated by assuming a single implicitly known attack strategy, which does not necessarily account for real-life threats. Indeed, this can lead to an overoptimistic assessment of the detectors' performance and may induce some bias in the comparison between competing detection schemes. We propose a novel multi-armed framework, called MEAD, for evaluating detectors based on several attack strategies to overcome this limitation. Among them, we make use of three new objectives to generate attacks. The proposed performance metric is based on the worst-case scenario: detection is successful if and only if all different attacks are correctly recognized. Empirically, we show the effectiveness of our approach. Moreover, the poor performance obtained for state-of-the-art detectors opens a new exciting line of research.

cs.CV↗
arXiv2022

Ultrafast collapse of molecular polaritons in photoswitch-nanoantennas at room temperature

Molecular polaritons are hybrid light-matter states that emerge when a molecular transition strongly interacts with photons in a resonator. At optical frequencies, this interaction unlocks a way to explore and control new chemical phenomena at the nanoscale. Achieving such a control at ultrafast timescales, however, is an outstanding challenge, as it requires a deep understanding of the dynamics of the collectively coupled molecular excitation and the nanoconfined electromagnetic fields. Here, we investigate the dynamics of collective polariton states, realized by coupling molecular photoswitches to optically anisotropic plasmonic nanoantennas. Pump-probe experiments reveal an ultrafast collapse of polaritons to a single-molecule transition triggered by femtosecond-pulse excitation at room-temperature. Through a synergistic combination of experiments and quantum mechanical modelling, we show that the response of the system is governed by intramolecular dynamics, occurring one order of magnitude faster with respect to the unperturbed excited molecule relaxation to the ground state.

cond-mat.mes-hall↗
arXiv2021

Estimating g-Leakage via Machine Learning

This paper considers the problem of estimating the information leakage of a system in the black-box scenario. It is assumed that the system's internals are unknown to the learner, or anyway too complicated to analyze, and the only available information are pairs of input-output data samples, possibly obtained by submitting queries to the system or provided by a third party. Previous research has mainly focused on counting the frequencies to estimate the input-output conditional probabilities (referred to as frequentist approach), however this method is not accurate when the domain of possible outputs is large. To overcome this difficulty, the estimation of the Bayes error of the ideal classifier was recently investigated using Machine Learning (ML) models and it has been shown to be more accurate thanks to the ability of those models to learn the input-output correspondence. However, the Bayes vulnerability is only suitable to describe one-try attacks. A more general and flexible measure of leakage is the g-vulnerability, which encompasses several different types of adversaries, with different goals and capabilities. In this paper, we propose a novel approach to perform black-box estimation of the g-vulnerability using ML. A feature of our approach is that it does not require to estimate the conditional probabilities, and that it is suitable for a large class of ML algorithms. First, we formally show the learnability for all data distributions. Then, we evaluate the performance via various experiments using k-Nearest Neighbors and Neural Networks. Our results outperform the frequentist approach when the observables domain is large.

cs.CR↗
arXiv2021

DOCTOR: A Simple Method for Detecting Misclassification Errors

Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations where DNN are implemented as "black boxes". A promising approach to secure their use is to accept decisions that are likely to be correct while discarding the others. In this work, we propose DOCTOR, a simple method that aims to identify whether the prediction of a DNN classifier should (or should not) be trusted so that, consequently, it would be possible to accept it or to reject it. Two scenarios are investigated: Totally Black Box (TBB) where only the soft-predictions are available and Partially Black Box (PBB) where gradient-propagation to perform input pre-processing is allowed. Empirically, we show that DOCTOR outperforms all state-of-the-art methods on various well-known images and sentiment analysis datasets. In particular, we observe a reduction of up to $4\%$ of the false rejection rate (FRR) in the PBB scenario. DOCTOR can be applied to any pre-trained model, it does not require prior information about the underlying dataset and is as simple as the simplest available methods in the literature.

cs.CV↗
arXiv2020

Optimal Obfuscation Mechanisms via Machine Learning

We consider the problem of obfuscating sensitive information while preserving utility, and we propose a machine learning approach inspired by the generative adversarial networks paradigm. The idea is to set up two nets: the generator, that tries to produce an optimal obfuscation mechanism to protect the data, and the classifier, that tries to de-obfuscate the data. By letting the two nets compete against each other, the mechanism improves its degree of protection, until an equilibrium is reached. We apply our method to the case of location privacy, and we perform experiments on synthetic data and on real data from the Gowalla dataset. We evaluate the privacy of the mechanism not only by its capacity to defeat the classifier, but also in terms of the Bayes error, which represents the strongest possible adversary. We compare the privacy-utility tradeoff of our method to that of the planar Laplace mechanism used in geo-indistinguishability, showing favorable results. Like the Laplace mechanism, our system can be deployed at the user end for protecting his location.

cs.LG↗
arXiv2020

Feature selection in machine learning: Rényi min-entropy vs Shannon entropy

Feature selection, in the context of machine learning, is the process of separating the highly predictive feature from those that might be irrelevant or redundant. Information theory has been recognized as a useful concept for this task, as the prediction power stems from the correlation, i.e., the mutual information, between features and labels. Many algorithms for feature selection in the literature have adopted the Shannon-entropy-based mutual information. In this paper, we explore the possibility of using Rényi min-entropy instead. In particular, we propose an algorithm based on a notion of conditional Rényi min-entropy that has been recently adopted in the field of security and privacy, and which is strictly related to the Bayes error. We prove that in general the two approaches are incomparable, in the sense that we show that we can construct datasets on which the Rényi-based algorithm performs better than the corresponding Shannon-based one, and datasets on which the situation is reversed. In practice, however, when considering datasets of real data, it seems that the Rényi-based algorithm tends to outperform the other one. We have effectuate several experiments on the BASEHOCK, SEMEION, and GISETTE datasets, and in all of them we have indeed observed that the Rényi-based algorithm gives better results.

cs.LG↗
arXiv2019

Synchronization of two DFB lasers using frequency-shifted feedback for microwave photonics

The phase synchronization of two semiconductor DFB lasers submitted to both frequency-shifted cross-injection and self-feedback is investigated, both numerically and experimentally. A dedicated setup based on monolithic DFB lasers permits to effectively lock the frequency difference of the two lasers on an external reference over a broad range of parameters. In agreement with a delayed rate equation model, it is shown that the sensitivity of the locking to the optical feedback phases is mitigated by using strong cross-injection, thus paving the way to applications for microwave photonics. Other synchronization dynamics can also be observed, such as bounded-phase oscillations.

physics.optics↗
arXiv2017

Linewidth enhancement factor measurement based on FM-modulated optical injection: application to rare-earth doped active medium

A new method for measuring the linewidth enhancement factor of a laser is proposed. It is based on frequency-modulated optical injection, combined with dual-frequency laser operation. The linewidth enhancement factor α is deduced from the experimental data using a theoretical analysis based on a standard rate equation model. As the intracavity power is kept constant, the method allows to free the process from the thermal effects that are usually present in AM/FM techniques. Measurement of α=0.28$\pm$0.04 in a diode-pumped Nd:YAG laser demonstrates that the method is well-suited for characterizing small values of α.

physics.optics↗
arXiv2016

Excitable-like chaotic pulses in the bounded-phase regime of an opto-radiofrequency oscillator

We report theoretical and experimental evidence of chaotic pulses with excitable-like properties in an opto-radiofrequency oscillator based on a self-injected dual-frequency laser. The chaotic attractor involved in the dynamics produces pulses that, albeit chaotic, are quite regular: They all have similar amplitudes, and are almost periodic in time. Thanks to these features, the system displays properties that are similar to those of excitable systems. In particular, the pulses exhibit a threshold-like response, of well-defined amplitude, to perturbations, and it appears possible to define a refractory time. At variance with excitability in injected lasers, here the excitable-like pulses are not accompanied by phase slips.

physics.optics↗
arXiv2016

Frequency-locked chaotic opto-RF oscillator

A driven opto-RF oscillator, consisting of a dual-frequency laser (DFL) submitted to frequency-shifted feedback, is studied experimentally and numerically in a chaotic regime. Precise control of the reinjection strength and detuning permits to isolate a parameter region of bounded-phase chaos, where the opto-RF oscillator is frequency-locked to the master oscillator, in spite of chaotic phase and intensity oscillations. Robust experimental evidence of this synchronization regime is found and phase noise spectra allows to compare phase-locking and bounded-phase chaos regimes. In particular, it is found that the long-term phase stability of the master oscillator is well transferred to the opto-RF oscillator even in the chaotic regime.

physics.optics↗
arXiv2014

Measuring the universal synchronization properties of coupled oscillators across the Hopf instability

When a driven oscillator loses phase-locking to a master oscillator via a Hopf bifurcation, it enters a bounded-phase regime in which its average frequency is still equal to the master frequency, but its phase displays temporal oscillations. Here we characterize these two synchronization regimes in a laser experiment, by measuring the spectrum of the phase fluctuations across the bifurcation. We find experimentally, and confirm numerically, that the low frequency phase noise of the driven oscillator is strongly suppressed in both regimes in the same way. Thus the long-term phase stability of the master oscillator is transferred to the driven one, even in the absence of phase-locking. The numerical study of a generic, minimal model suggests that such behavior is universal for any periodically driven oscillator near a Hopf bifurcation point.

physics.optics↗
arXiv2007

A 2D nanosphere array for atomic spectroscopy

We are interested in the spectroscopic behaviour of a gas confined in a micrometric or even nanometric volume. Such a situation could be encountered by the filling-up of a porous medium, such as a photonic crystal, with an atomic gas. Here, we discuss the first step of this program, with the generation and characterization of a self-organized 2D film of nanospheres of silica. We show that an optical characterization by laser light diffraction permits to extract some information on the array structure and represents an interesting complement to electron microscopy.

physics.atom-ph↗
arXiv2007

Selective reflection spectroscopy of a vapour at a calcium fluoride interface

Fluoride materials exhibit surface resonances located in the thermal infrared. This makes them interesting to search for a fundamental temperature dependence of the atom-surface interaction, originating in the near-field thermal emissivity of the surface. Preliminary selective reflection experiments performed on a special Cs vapour cell that includes a CaF2 interface show a temperature dependence, yet to be analyzed

physics.atom-ph↗
arXiv2006

Quantum degeneracy of microcavity polaritons

We investigate experimentally one of the main features of a quantum fluid constituted by exciton polaritons in a semiconductor microcavity, that is quantum degeneracy of a macroscopic fraction of the particles. We show that resonant pumping allows to create a macroscopic population of polaritons in one quantum state. Furthermore we demonstrate that parametric polariton scattering results in the transfer of a macroscopic population of polariton from one single quantum state into another one. Finally we briefly outline a simple method which provides direct evidence of the first-order spatial coherence of the transferred population.

cond-mat.other↗