Tracking local magnetic dynamics via high-energy charge excitations in a relativistic Mott insulator

We use time- and energy-resolved optical spectroscopy to investigate the coupling of electron-hole excitations to the magnetic environment in the relativistic Mott insulator Na$_2$IrO$_3$. We show that, on the picosecond timescale, the photoinjected electron-hole pairs delocalize on the hexagons of the Ir lattice via the formation of quasi-molecular orbital (QMO) excitations and the exchange of energy with the short-range-ordered zig-zag magnetic background. The possibility of mapping the magnetic dynamics, which is characterized by typical frequencies in the THz range, onto high-energy (1-2 eV) charge excitations provides a new platform to investigate, and possibly control, the dynamics of magnetic interactions in correlated materials with strong spin-orbit coupling, even in the presence of complex magnetic phases.Comment: 5 pages, 4 figures, supplementary informatio

Practical quantum computing : a collaborative-driven quantum feature selection approach for the cold-start problem in recommender systems

LAUREA MAGISTRALEI sistemi di raccomandazione sono degli strumenti utilizzati per raccomandare degli oggetti agli utenti di una piattaforma, cercando di predire le loro preferenze. La popolarità di questi strumenti è aumentata costantemente con la crescita del Web, ed essi sono stati integrati sempre più in piattaforme che vendono contenuti ai loro utenti, come Netflix o Amazon. Per proporre raccomandazioni, questi strumenti si affidano a due principali tipi di informazione, quella contenutistica e quella collaborativa. La prima si riferisce a quelle caratteristiche che rappresentano gli oggetti di una piattaforma, come gli attori e l’anno di uscita di un film, o l’autore e il genere di un libro. L’informazione collaborativa, invece, consiste nelle interazioni passate tra utenti e oggetti ed è noto, nella letteratura dei sistemi di raccomandazione, che i metodi basati su questi tipo di informazione ottengano risultati generalmente migliori di quelli basati sui contenuti. Tuttavia, non è sempre possibile fare affidamento sull’informazione collaborativa, per esempio quando un nuovo oggetto viene aggiunto alla piattaforma. Questa situazione è chiamata scenario cold-start e, in questi casi, solo i metodi basati sul contenuto e quelli collaborativi con informazione laterale sono efficaci, considerato che l’informazione contenutistica è l’unica disponibile. Diverse tecniche sono state proposte per trattare il problema cold-start ottimizzando gli approcci basati sul contenuto. Per esempio, esistono tecniche che ponderano le caratteristiche degli oggetti secondo vari criteri. In particolare, recenti approcci basati sul machine learning hanno ottenuto risultati promettenti, stimando i pesi delle caratteristiche sulla base dell’informazione collaborativa. Sviluppando ulteriormente questo concetto, in questa tesi proponiamo un nuovo metodo di selezione delle caratteristiche in grado di incorporare l’informazione collaborativa in un modello contenutistico. Nello specifico, per affrontare efficientemente il problema ottenuto, utilizziamo il quantum annealing, uno degli attuali paradigmi di quantum computing. Il quantum annealing ha acquisito sempre più interesse industriale negli ultimi anni, grazie alla sua capacità di risolvere efficientemente problemi pratici di ottimizzazione classificati come NP-difficili.Recommender systems are tools aimed at recommending items to the users of a platform, trying to predict their preferences. The popularity of this tools has been increasing with the continuous growth of the Web, and they have been extensively implemented on platforms that sell contents to their users, such as Netflix or Amazon. In order to make recommendations, a recommender system relies on two main types of information, content and collaborative. The first refers to the features characterizing the items on the platform, such as the actors and the release year of a movie, or the author and the genre of a book. Collaborative information, instead, consists of the past interactions between users and items and it is known in the recommender systems literature that methods based on this type of information generally perform better than content-based ones. However, it is not always possible to rely on collaborative information, for example when a new item is added to the platform. This situation is called cold-start scenario and in such cases only content-based and collaborative with side information methods are effective, since content information is available, as opposed to collaborative one. Different techniques have been proposed to cope with the cold-start problem by optimizing content-based approaches. For example, feature weighting techniques, that weight item features based on various criteria. In particular, recent machine learning approaches have obtained promising results by estimating weights on the base of collaborative information. Further developing this concept, in this thesis we propose a new feature selection model able to embed collaborative information into a content-based model. In particular, in order to efficiently tackle the given problem, we apply quantum annealing, one of the current paradigms of quantum computing. Quantum annealing has been acquiring great industrial interest in recent years due to its ability of efficiently solving practical NP-hard optimization problems

Code and its image: the functions of text and visualisation in a code-based design studio

Traditionally, design learning in the architecture studio has taken place through a combination of individual work and joint projects. The introduction of code-based design practices in the design studio has altered this balance, introducing new models of joint authorship and new ways for individuals to contribute to co-authored projects. This paper presents a case study describing four design studios in a higher education setting that used code as a tool for generating architectural geometry. The format of the studios encouraged the students to reflect critically on their role as authors and to creatively address the multiple opportunities for shared authorship available with code-based production. The research question addressed in this study involved the role of code-based practices in altering the model of architectural education in the design studio, in particular the role of visual representations of a code-based design process in the production of shared knowledge

The analytical framework of water and armed conflict: a focus on the 2006 Summer War between Israel and Lebanon

This paper develops an analytical framework to investigate the relationship between water and armed conflict, and applies it to the ‘Summer War’ of 2006 between Israel and Lebanon (Hezbollah). The framework broadens and deepens existing classifications by assessing the impact of acts of war as indiscriminate or targeted, and evaluating them in terms of international norms and law, in particular International Humanitarian Law (IHL). In the case at hand, the relationship is characterised by extensive damage in Lebanon to drinking water infrastructure and resources. This is seen as a clear violation of the letter and the spirit of IHL, while the partial destruction of more than 50 public water towers compromises water rights and national development goals. The absence of pre-war environmental baselines makes it difficult to gauge the impact on water resources, suggesting a role for those with first-hand knowledge of the hostilities to develop a more effective response before, during, and after armed conflict

An Application of Reinforcement Learning for Minor Embedding in Quantum Annealing

Research in the Quantum Computing (QC) field has been soaring thanks to the latest developments and wider availability of real hardware. The strong interest in this technology has naturally spurred a contamination with the Machine Learning (ML) field. Both quantum methods to perform ML and ML methods to support quantum computation has been developed. A largely diffused QC paradigm is that of Quantum Annealers, machines that can rapidly search for solutions to optimization problems. Their sparse qubit structure, however, requires to search for a mapping between the problem’s and the hardware’s graphs before computation. This is a NP-hard combinatorial optimization task in itself, called Minor Embedding. In this work, we aim at developing and assessing the capabilities of Reinforcement Learning to perform this task

NON-EQUILIBRIUM STUDY OF CORRELATED HONEYCOMB IRIDATES WITH STRONG SPIN-ORBIT COUPLING.

Transition-metal oxides (TMO) are one of the most studied class of materials, as a consequence of their rich and exotic physical properties, mainly determined by the strong electronic correlations of the metal d electrons. In this thesis I discuss about a particular 5d compound, sodium iridates, where spin-orbit coupling Coulomb repulsion and bandwidth are in comparable energy scales. Furthermore, these systems are Mott insulators and present a zigzag antiferromagnetic transition below TN 15 K. Here we tackle the physics of sodium iridates by adopting a nonequilibrium viewpoint based on the use of ultrafast light pulses combined in the so-called pump-probe experimental configuration. The aim is to perturb the antiferromagnetic state of the system via the excitation with a pump pulse and to observe the ultrafast recovery of the ground state by means of a second delayed probe pulse. Specifically, we will measure the dynamics of the pump-induced reflectivity variation in the zigzag antiferromagnetic and normal states as a function of the probe energy and as a function of time delay between the pump and the probe pulses

Minimalist Coherent Swarming of Wireless Networked Autonomous Mobile Robots

DISA

Towards Recommender Systems with Community Detection and Quantum Computing

After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves

Quality of Life and Its Psychosocial Predictors among Patients with Disorders of Gut–Brain Interaction: A Comparison with Age- and Sex-Matched Controls

The disorders of gut–brain interaction (DGBIs) are a heterogeneous group of chronic conditions that greatly reduce patients’ quality of life (QoL). To date, biopsychosocial factors (such as gastrointestinal symptoms, alexithymia, and interpersonal problems) are believed to contribute to the development and maintenance of DGBIs, but their role in affecting patients’ QoL is still under investigation. Out of 141 patients seeking treatment for their gastrointestinal symptoms, 71 were diagnosed with a DGBI (47 females, 66.2%; Mage: 41.49 ± 17.23 years) and were age- and sex-matched to 71 healthy controls (47 females, 66.2%; Mage: 40.45 ± 16.38 years) without any current gastrointestinal symptom or diagnosis. Participants completed a sociodemographic and clinical questionnaire and a survey investigating several psychosocial risk factors. We found greater symptom severity and difficulties in identifying feelings among patients compared to controls. Further, multiple linear regression analyses evidenced that, among patients, higher expressive suppression of emotions, difficulties in identifying feelings and interpersonal problems, and a lower cognitive reappraisal of emotions predicted lower QoL. Data suggest that the QoL of patients with DGBIs is affected not only by common risk factors (e.g., interpersonal problems) but also by specific difficulties in processing and regulating emotions. The implications of these findings are discussed