Noise spectroscopy of a quantum-classical environment with a diamond qubit

Knowing a quantum system's environment is critical for its practical use as a quantum device. Qubit sensors can reconstruct the noise spectral density of a classical bath, provided long enough coherence time. Here we present a protocol that can unravel the characteristics of a more complex environment, comprising both unknown coherently coupled quantum systems, and a larger quantum bath that can be modeled as a classical stochastic field. We exploit the rich environment of a Nitrogen-Vacancy center in diamond, tuning the environment behavior with a bias magnetic field, to experimentally demonstrate our method. We show how to reconstruct the noise spectral density even when limited by relatively short coherence times, and identify the local spin environment. Importantly, we demonstrate that the reconstructed model can have predictive power, describing the spin qubit dynamics under control sequences not used for noise spectroscopy, a feature critical for building robust quantum devices. At lower bias fields, where the effects of the quantum nature of the bath are more pronounced, we find that more than a single classical noise model are needed to properly describe the spin coherence under different controls, due to the back action of the qubit onto the bath.Comment: Main text: 5 pages, 5 figures. Supplemental material: 7 pages, 7 figures, 4 table

Compact binary merger simulations in numerical relativity

The era of Gravitational Waves Astronomy was launched after the success of the first observation run of the LIGO Scientific Collaboration and the VIRGO Collaboration. The large laser interferometers incredible achievement prompted the need of extensive studies in the field of compact astrophysical objects, such as Black Holes and Neutron Stars. Today, seven years after this event, the field of study underwent a notable expansion, corroborated by the detection of a signal coming from a Binary Neutron Star merger, together with its electro-magnetic counterpart, and, more recently, by the first detections of signals coming from mixed compact binaries, i.e. Black Hole- Neutron Star binaries. In this thesis work we span our attention across different aspects of compact objects mergers, including the inclusion of new physics into the already performing numerical relativity code BAM and the study of specific systems of compact objects. We first explore the treatment of neutrinos in case of Binary Neutron Star mergers and a tool to identify and further analyze regions containing trapped neutrinos, in the hot remnant of such mergers. Neutrinos, play in fact a key role into the rapid processes that characterize the formation of elements in the dynamical ejecta expelled during these catastrophic events. In the following we explore a variety of configurations of mixed compact binary systems. After the development of the new ID code Elliptica, and the steps taken to verify its accuracy, we make use of its capability to evolve sets of physical system with various properties. Exploring the space of parameters we study different spin configurations and magnitudes of single objects and their effects on the merger dynamics

Infinite dimensional weak Dirichlet processes, stochastic PDEs and optimal control

The present paper continues the study of infinite dimensional calculus via regularization, started by C. Di Girolami and the second named author, introducing the notion of "weak Dirichlet process" in this context. Such a process \X, taking values in a Hilbert space $H$, is the sum of a local martingale and a suitable "orthogonal" process. The new concept is shown to be useful in several contexts and directions. On one side, the mentioned decomposition appears to be a substitute of an It\^o type formula applied to f(t, \X(t)) where $f:[0,T] \times H \rightarrow \R$ is a $C^{0,1}$ function and, on the other side, the idea of weak Dirichlet process fits the widely used notion of "mild solution" for stochastic PDE. As a specific application, we provide a verification theorem for stochastic optimal control problems whose state equation is an infinite dimensional stochastic evolution equation

Sul Numero Cromosomico di Matteuccia Struthiopteris (L.) Todaro

RIASSUNTOIn due esemplari di Matteuccia struthiopteris (L.) Todaro coltivati nell'Orto Botanico dell'Universita di Firenze lo studio della meiosi di molte cellule madri delle spore ha messo in luce la presenza di 39 bivalentti. Gli Autori che precedentemente hanno studiato la specie riportano n=40 oppure n=c.40

Streaming Algorithms for Diversity Maximization with Fairness Constraints

Diversity maximization is a fundamental problem with wide applications in data summarization, web search, and recommender systems. Given a set $X$ of $n$ elements, it asks to select a subset $S$ of $k \ll n$ elements with maximum \emph{diversity}, as quantified by the dissimilarities among the elements in $S$. In this paper, we focus on the diversity maximization problem with fairness constraints in the streaming setting. Specifically, we consider the max-min diversity objective, which selects a subset $S$ that maximizes the minimum distance (dissimilarity) between any pair of distinct elements within it. Assuming that the set $X$ is partitioned into $m$ disjoint groups by some sensitive attribute, e.g., sex or race, ensuring \emph{fairness} requires that the selected subset $S$ contains $k_i$ elements from each group $i \in [1,m]$. A streaming algorithm should process $X$ sequentially in one pass and return a subset with maximum \emph{diversity} while guaranteeing the fairness constraint. Although diversity maximization has been extensively studied, the only known algorithms that can work with the max-min diversity objective and fairness constraints are very inefficient for data streams. Since diversity maximization is NP-hard in general, we propose two approximation algorithms for fair diversity maximization in data streams, the first of which is $\frac{1-\varepsilon}{4}$-approximate and specific for $m=2$, where $\varepsilon \in (0,1)$, and the second of which achieves a $\frac{1-\varepsilon}{3m+2}$-approximation for an arbitrary $m$. Experimental results on real-world and synthetic datasets show that both algorithms provide solutions of comparable quality to the state-of-the-art algorithms while running several orders of magnitude faster in the streaming setting.Comment: 13 pages, 11 figures; published in ICDE 202

Know your enemy: Genetics, aging, exposomic and inflammation in the war against triple negative breast cancer.

Triple negative breast cancer (TNBC) is one of the most biologically aggressive and very often lethal breast disease. It is one of the most puzzling women malignancies, and it currently appears not to be a good candidate to a standardized, unanimously accepted and sufficiently active therapeutic strategy. Fast proliferating and poorly differentiated, it is histopathologically heterogeneous, and even more ambiguous at the molecular level, offering few recurrent actionable targets to the clinicians. It is a formidable and vicious enemy that requires a huge investigational effort to find its vital weak spots. Here, we provide a broad review of "old but gold" biological aspects that taken together may help in finding new TNBC management strategies. A better and updated knowledge of the origins, war-like tactics, refueling mechanisms and escape routes of TNBC, will help in moving the decisive steps towards its final defeat

Fair and Representative Subset Selection from Data Streams

We study the problem of extracting a small subset of representative items from a large data stream. In many data mining and machine learning applications such as social network analysis and recommender systems, this problem can be formulated as maximizing a monotone submodular function subject to a cardinality constraint k. In this work, we consider the setting where data items in the stream belong to one of several disjoint groups and investigate the optimization problem with an additional fairness constraint that limits selection to a given number of items from each group. We then propose efficient algorithms for the fairness-aware variant of the streaming submodular maximization problem. In particular, we first give a (1/2-ε)-approximation algorithm that requires O((1/ε) log(k/ε)) passes over the stream for any constant ε>0. Moreover, we give a single-pass streaming algorithm that has the same approximation ratio of (1/2-ε) when unlimited buffer sizes and post-processing time are permitted, and discuss how to adapt it to more practical settings where the buffer sizes are bounded. Finally, we demonstrate the efficiency and effectiveness of our proposed algorithms on two real-world applications, namely maximum coverage on large graphs and personalized recommendation.Peer reviewe

Changes in the microsomal proteome of tomato fruit during ripening

The variations in the membrane proteome of tomato fruit pericarp during ripening have been investigated by mass spectrometry-based label-free proteomics. Mature green (MG30) and red ripe (R45) stages were chosen because they are pivotal in the ripening process: MG30 corresponds to the end of cellular expansion, when fruit growth has stopped and fruit starts ripening, whereas R45 corresponds to the mature fruit. Protein patterns were markedly different: among the 1315 proteins identified with at least two unique peptides, 145 significantly varied in abundance in the process of fruit ripening. The subcellular and biochemical fractionation resulted in GO term enrichment for organelle proteins in our dataset, and allowed the detection of low-abundance proteins that were not detected in previous proteomic studies on tomato fruits. Functional annotation showed that the largest proportion of identified proteins were involved in cell wall metabolism, vesicle-mediated transport, hormone biosynthesis, secondary metabolism, lipid metabolism, protein synthesis and degradation, carbohydrate metabolic processes, signalling and response to stress