2,065 research outputs found

    Symmetries of Riemann surfaces and magnetic monopoles

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    This thesis studies, broadly, the role of symmetry in elucidating structure. In particular, I investigate the role that automorphisms of algebraic curves play in three specific contexts; determining the orbits of theta characteristics, influencing the geometry of the highly-symmetric Bring’s curve, and in constructing magnetic monopole solutions. On theta characteristics, I show how to turn questions on the existence of invariant characteristics into questions of group cohomology, compute comprehensive tables of orbit decompositions for curves of genus 9 or less, and prove results on the existence of infinite families of curves with invariant characteristics. On Bring’s curve, I identify key points with geometric significance on the curve, completely determine the structure of the quotients by subgroups of automorphisms, finding new elliptic curves in the process, and identify the unique invariant theta characteristic on the curve. With respect to monopoles, I elucidate the role that the Hitchin conditions play in determining monopole spectral curves, the relation between these conditions and the automorphism group of the curve, and I develop the theory of computing Nahm data of symmetric monopoles. As such I classify all 3-monopoles whose Nahm data may be solved for in terms of elliptic functions

    Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students

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    In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies. To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on). The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts. This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution. The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials. The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote students’ understanding and acceptance of quantum physics as a personal reliable description of the world

    Design of decorative 3D models: from geodesic ornaments to tangible assemblies

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    L'obiettivo di questa tesi è sviluppare strumenti utili per creare opere d'arte decorative digitali in 3D. Uno dei processi decorativi più comunemente usati prevede la creazione di pattern decorativi, al fine di abbellire gli oggetti. Questi pattern possono essere dipinti sull'oggetto di base o realizzati con l'applicazione di piccoli elementi decorativi. Tuttavia, la loro realizzazione nei media digitali non è banale. Da un lato, gli utenti esperti possono eseguire manualmente la pittura delle texture o scolpire ogni decorazione, ma questo processo può richiedere ore per produrre un singolo pezzo e deve essere ripetuto da zero per ogni modello da decorare. D'altra parte, gli approcci automatici allo stato dell'arte si basano sull'approssimazione di questi processi con texturing basato su esempi o texturing procedurale, o con sistemi di riproiezione 3D. Tuttavia, questi approcci possono introdurre importanti limiti nei modelli utilizzabili e nella qualità dei risultati. Il nostro lavoro sfrutta invece i recenti progressi e miglioramenti delle prestazioni nel campo dell'elaborazione geometrica per creare modelli decorativi direttamente sulle superfici. Presentiamo una pipeline per i pattern 2D e una per quelli 3D, e dimostriamo come ognuna di esse possa ricreare una vasta gamma di risultati con minime modifiche dei parametri. Inoltre, studiamo la possibilità di creare modelli decorativi tangibili. I pattern 3D generati possono essere stampati in 3D e applicati a oggetti realmente esistenti precedentemente scansionati. Discutiamo anche la creazione di modelli con mattoncini da costruzione, e la possibilità di mescolare mattoncini standard e mattoncini custom stampati in 3D. Ciò consente una rappresentazione precisa indipendentemente da quanto la voxelizzazione sia approssimativa. I principali contributi di questa tesi sono l'implementazione di due diverse pipeline decorative, un approccio euristico alla costruzione con mattoncini e un dataset per testare quest'ultimo.The aim of this thesis is to develop effective tools to create digital decorative 3D artworks. Real-world art often involves the use of decorative patterns to enrich objects. These patterns can be painted on the base or might be realized with the application of small decorative elements. However, their creation in digital media is not trivial. On the one hand, users can manually perform texture paint or sculpt each decoration, in a process that can take hours to produce a single piece and needs to be repeated from the ground up for every model that needs to be decorated. On the other hand, automatic approaches in state of the art rely on approximating these processes with procedural or by-example texturing or with 3D reprojection. However, these approaches can introduce significant limitations in the models that can be used and in the quality of the results. Instead, our work exploits the recent advances and performance improvements in the geometry processing field to create decorative patterns directly on surfaces. We present a pipeline for 2D and one for 3D patterns and demonstrate how each of them can recreate a variety of results with minimal tweaking of the parameters. Furthermore, we investigate the possibility of creating decorative tangible models. The 3D patterns we generate can be 3D printed and applied to previously scanned real-world objects. We also discuss the creation of models with standard building bricks and the possibility of mixing standard and custom 3D-printed bricks. This allows for a precise representation regardless of the coarseness of the voxelization. The main contributions of this thesis are the implementation of two different decorative pipelines, a heuristic approach to brick construction, and a dataset to test the latter

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Microwave-shielded ultracold polar molecules

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    Since the realization of Bose--Einstein condensates and degenerate Fermi gases, ultracold atoms with tunable interactions have become an essential platform for studying quantum many-body phenomena. Notable examples include the realization of BCS--BEC crossover and the simulation of the Bose/Fermi Hubbard model. Ultracold polar molecules could enrich the quantum gas toolbox with their long-range dipole-dipole interaction, which offers not only new opportunities in many-body physics, such as realizing the topological superfluid and the extended Hubbard model, but also applications in quantum chemistry, quantum computation, and precision measurements. However, the large number of internal degrees of freedom of molecules present a significant challenge in both cooling them to quantum degeneracy and controlling their interactions. Unlike atomic gases, a dense molecular sample suffers from fast collisional losses, preventing the implementation of evaporative cooling and the observation of scattering resonances. In this thesis, we describe how we solved the long-standing issue of collisional losses by microwave shielding, created a degenerate Fermi gas of NaK molecules, and discovered a new type of scattering resonances via which we created the first ultracold tetratomic molecules in the 100-nK regime. By synchronizing the rotation of polar molecules with a circularly polarized microwave electric field, we equip the molecular sample with a highly tunable intermolecular potential. This not only stabilizes the gas against inelastic collisions but also enables field-linked scattering resonances for precise control over scattering lengths. At long range, the molecules interact via their induced rotating dipole moments. As they approach each other, their orientations realign to produce a repulsive force, thereby mitigating inelastic collisions at close distances. With an elastic-to-inelastic collision ratio of 500, we have achieved evaporative cooling of the molecular gas down to 21 nK and 0.36 times the Fermi temperature, setting a new record for the coldest polar molecular gas to date. Thanks to the collisional stability of microwave-shielded molecules, we can directly load them into predominantly a single layer of a magic 3D optical lattice, achieving a peak filling fraction of 24%. These ultracold molecules, owing to their long lifetimes in their ground state and their long-range dipolar coupling, provide a unique platform to study quantum magnetism. With the achieved high filling fraction, we are prepared to study non-equilibrium spin dynamics such as rotational synchronization and spin squeezing. We demonstrated that the interaction between microwave-shielded polar molecules is highly tunable via the microwave power, detuning, and polarization. When the interaction potential is deep enough to host field-linked bound states at the collisional threshold, a shape resonance is induced, allowing us to tune the scattering rate by three orders of magnitude. The field-linked resonances enables controls over the scattering length in a similar fashion as Feshbach resonance for ultracold atoms, promising the realization of strongly correlated phases, such as dipolar pp-wave superfluid. It also paves the way to investigate the interplay between short-range and long-range interactions in novel quantum matters, such as exotic supersolid. Moreover, through a field-linked resonance, we associated for the first time weakly bound tetratomic molecules in the 100-nK regime, with a phase space density of 0.04. The transition from a Fermi gas of diatomic molecules to a Bose gas of tetratomic molecules paves the way for dipolar BCS--BEC crossover. With microwave-shielded polar molecules, we have realized a quantum gas featuring highly tunable long-range interactions. The technique is universal to polar molecules with a sufficiently large dipole moment, and thus offers a general strategy for cooling and manipulating polar molecules, and for associating weakly bound ultracold polyatomic molecules. Utilizing the toolbox developed in ultracold atoms, this platform possesses the potential to unlock an entirely new realm of quantum simulation of many-body physics

    Leveraging elasticity theory to calculate cell forces: From analytical insights to machine learning

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    Living cells possess capabilities to detect and respond to mechanical features of their surroundings. In traction force microscopy, the traction of cells on an elastic substrate is made visible by observing substrate deformation as measured by the movement of embedded marker beads. Describing the substrates by means of elasticity theory, we can calculate the adhesive forces, improving our understanding of cellular function and behavior. In this dissertation, I combine analytical solutions with numerical methods and machine learning techniques to improve traction prediction in a range of experimental applications. I describe how to include the normal traction component in regularization-based Fourier approaches, which I apply to experimental data. I compare the dominant strategies for traction reconstruction, the direct method and inverse, regularization-based approaches and find, that the latter are more precise while the former is more stress resilient to noise. I find that a point-force based reconstruction can be used to study the force balance evolution in response to microneedle pulling showing a transition from a dipolar into a monopolar force arrangement. Finally, I show how a conditional invertible neural network not only reconstructs adhesive areas more localized, but also reveals spatial correlations and variations in reliability of traction reconstructions

    Developing a Microwave Quantum Memory with Rare-Earth Doped Crystals

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    Rare-earth doped crystals have attracted a significant amount of attention for use in quantum systems. Available, long-lived, optical and microwave transitions has lead to proposals for quantum transduction and quantum memories, both of which are important in building large scale quantum networks. Ensembles of rare-earth spins can be coupled to superconducting resonators, and high coupling strengths (with cooperativity > 1) readily achievable. While such systems have been constructed, a useful quantum memory which exploits highly coherent transitions has not yet been developed in the microwave domain. In this thesis we couple high-Q superconducting resonators to Yb doped YSO. The spin system of Yb:YSO is explored and the main causes of decoherence are outlined, these are found to be instantaneous diffusion and spectral diffusion. In the process of this, new techniques are developed to determine decoherence sources, where nuclear spins within the YSO crystal are found to limit coherence. Two different regimes are explored to increase the coherence time. Using optimal field orientations and high magnetic field magnitudes, the coherence time is extended to (6±2) ms. While the zero field clock transition is used, along with isotopic purification, to reach the same time ((6±1) ms). Using these techniques to increase coherence, the foundations for a microwave quantum memory with Yb:YSO are laid. Cooperativities > 1 are measured for three different Yb spin systems, this allows for these spin systems to be used in memory protocols and reach unit efficiency. New pulse sequences using adiabatic fast passage are developed to provide control over the spin ensemble and for memory protocols. Finally, we use the knowledge from all of these studies to propose a system which would form the basis of an efficient, long-lived microwave quantum memory using FIB-milled Yb:YSO

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Observation of Josephson Harmonics in Tunnel Junctions

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    Superconducting quantum processors have a long road ahead to reach fault-tolerant quantum computing. One of the most daunting challenges is taming the numerous microscopic degrees of freedom ubiquitous in solid-state devices. State-of-the-art technologies, including the world's largest quantum processors, employ aluminum oxide (AlOx_x) tunnel Josephson junctions (JJs) as sources of nonlinearity, assuming an idealized pure sinφ\sin\varphi current-phase relation (Cφ\varphiR). However, this celebrated sinφ\sin\varphi Cφ\varphiR is only expected to occur in the limit of vanishingly low-transparency channels in the AlOx_x barrier. Here we show that the standard Cφ\varphiR fails to accurately describe the energy spectra of transmon artificial atoms across various samples and laboratories. Instead, a mesoscopic model of tunneling through an inhomogeneous AlOx_x barrier predicts %-level contributions from higher Josephson harmonics. By including these in the transmon Hamiltonian, we obtain orders of magnitude better agreement between the computed and measured energy spectra. The reality of Josephson harmonics transforms qubit design and prompts a reevaluation of models for quantum gates and readout, parametric amplification and mixing, Floquet qubits, protected Josephson qubits, etc. As an example, we show that engineered Josephson harmonics can reduce the charge dispersion and the associated errors in transmon qubits by an order of magnitude, while preserving anharmonicity
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