283 research outputs found

    Spin Detection, Amplification, and Microwave Squeezing with Kinetic Inductance Parametric Amplifiers

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    Superconducting parametric amplifiers operating at microwave frequencies have become an essential component in circuit quantum electrodynamics experiments. They are used to amplify signals at the single-photon level, while adding only the minimum amount of noise required by quantum mechanics. To achieve gain, energy is transferred from a pump to the signal through a non-linear interaction. A common strategy to enhance this process is to place the non-linearity inside a high quality factor resonator, but so far, quantum limited amplifiers of this type have only been demonstrated from designs that utilize Josephson junctions. Here we demonstrate the Kinetic Inductance Parametric Amplifier (KIPA), a three-wave mixing resonant parametric amplifier that exploits the kinetic inductance intrinsic to thin films of disordered superconductors. We then utilize the KIPA for measurements of 209Bi spin ensembles in Si. First, we show that a KIPA can serve simultaneously as a high quality factor resonator for pulsed electron spin resonance measurements and as a low-noise parametric amplifier. Using this dual-functionality, we enhance the signal to noise ratio of our measurements by more than a factor of seven and ultimately achieve a measurement sensitivity of 2.4 x 10^3 spins. Then we show that pushed to the high-gain limit, KIPAs can serve as a `click'-detector for microwave wave packets by utilizing a hysteretic transition to a self-oscillating state. We calibrate the detector's sensitivity to be 3.7 zJ and then apply it to measurements of electron spin resonance. Finally, we demonstrate the suitability of the KIPA for generating squeezed vacuum states. Using a cryogenic noise source, we first confirm the KIPAs in our experiment to be quantum limited amplifiers. Then, using two KIPAs arranged in series, we make direct measurements of vacuum noise squeezing, where we generate itinerant squeezed states with minimum uncertainty more than 7 dB below the standard quantum limit. High quality factor resonators have also recently been used to achieve strong coupling between the spins of single electrons in gate-defined quantum dots and microwave photons. We present our efforts to achieve the equivalent goal for the 31P flip-flop qubit. In doing so, we confirm previous predictions that the superconducting material MoRe would produce magnetic field-resilient resonators and demonstrate that it has kinetic inductance equivalent to the popular material NbTiN

    Nano-film functionalized exposed core fibers enabling resonance-driven dispersive wave tailoring

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    Light sources with specific optical properties are the backbone of optical technologies such as spectroscopy or hyperspectral imaging. Yet, the creation of broadband, stable, and spectrally flat light sources, especially at low pump energies, remains a particular challenge. Supercontinuum generation (SCG) is a well-established method for broadband light generation in optical fibers. For tailorable SCG spectra, it is essential to accurately design and precisely control the dispersion of fibers with new methods. This thesis aims to explore nonlinear frequency conversion in resonance-enhanced fibers to create tunable broadband light sources with tailored properties at low pump energies. By depositing high refractive index nano-films with different thicknesses on the surface of the exposed fiber core, the dispersion of the fibers and thus the output spectrum of SCG can be tuned. Different nano-film geometries are investigated, featuring TiO2 nano-films with a uniform thickness, Ta2O5 nano-films with a gradually increasing thickness along the fiber length, and periodically structured Ta2O5 nano-films. Experiments and simulations reveal the advantages of a longitudinally varying dispersion over uniformly coated fibers concerning an enhanced spectral flatness and an enlarged bandwidth. Furthermore, periodically structured nano-films lead to multi-color tailorable higher-order dispersive waves via quasi phase-matching, which are outside of the wavelength range of classical soliton-based SCG. Resonance-based modifications of the fiber dispersion by using nano-films are a powerful new tool to efficiently shape nonlinear frequency conversion in SCG even at low pump energies. It has high technological potential for the realization of novel, ultrafast, broadband, and stable nonlinear light sources for biophotonics, environmental, life sciences, medical diagnostics, and metrology

    The assessment and development of methods in (spatial) sound ecology

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    As vital ecosystems across the globe enter unchartered pressure from climate change industrial land use, understanding the processes driving ecosystem viability has never been more critical. Nuanced ecosystem understanding comes from well-collected field data and a wealth of associated interpretations. In recent years the most popular methods of ecosystem monitoring have revolutionised from often damaging and labour-intensive manual data collection to automated methods of data collection and analysis. Sound ecology describes the school of research that uses information transmitted through sound to infer properties about an area's species, biodiversity, and health. In this thesis, we explore and develop state-of-the-art automated monitoring with sound, specifically relating to data storage practice and spatial acoustic recording and data analysis. In the first chapter, we explore the necessity and methods of ecosystem monitoring, focusing on acoustic monitoring, later exploring how and why sound is recorded and the current state-of-the-art in acoustic monitoring. Chapter one concludes with us setting out the aims and overall content of the following chapters. We begin the second chapter by exploring methods used to mitigate data storage expense, a widespread issue as automated methods quickly amass vast amounts of data which can be expensive and impractical to manage. Importantly I explain how these data management practices are often used without known consequence, something I then address. Specifically, I present evidence that the most used data reduction methods (namely compression and temporal subsetting) have a surprisingly small impact on the information content of recorded sound compared to the method of analysis. This work also adds to the increasing evidence that deep learning-based methods of environmental sound quantification are more powerful and robust to experimental variation than more traditional acoustic indices. In the latter chapters, I focus on using multichannel acoustic recording for sound-source localisation. Knowing where a sound originated has a range of ecological uses, including counting individuals, locating threats, and monitoring habitat use. While an exciting application of acoustic technology, spatial acoustics has had minimal uptake owing to the expense, impracticality and inaccessibility of equipment. In my third chapter, I introduce MAARU (Multichannel Acoustic Autonomous Recording Unit), a low-cost, easy-to-use and accessible solution to this problem. I explain the software and hardware necessary for spatial recording and show how MAARU can be used to localise the direction of a sound to within ±10˚ accurately. In the fourth chapter, I explore how MAARU devices deployed in the field can be used for enhanced ecosystem monitoring by spatially clustering individuals by calling directions for more accurate abundance approximations and crude species-specific habitat usage monitoring. Most literature on spatial acoustics cites the need for many accurately synced recording devices over an area. This chapter provides the first evidence of advances made with just one recorder. Finally, I conclude this thesis by restating my aims and discussing my success in achieving them. Specifically, in the thesis’ conclusion, I reiterate the contributions made to the field as a direct result of this work and outline some possible development avenues.Open Acces

    Neural function approximation on graphs: shape modelling, graph discrimination & compression

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    Graphs serve as a versatile mathematical abstraction of real-world phenomena in numerous scientific disciplines. This thesis is part of the Geometric Deep Learning subject area, a family of learning paradigms, that capitalise on the increasing volume of non-Euclidean data so as to solve real-world tasks in a data-driven manner. In particular, we focus on the topic of graph function approximation using neural networks, which lies at the heart of many relevant methods. In the first part of the thesis, we contribute to the understanding and design of Graph Neural Networks (GNNs). Initially, we investigate the problem of learning on signals supported on a fixed graph. We show that treating graph signals as general graph spaces is restrictive and conventional GNNs have limited expressivity. Instead, we expose a more enlightening perspective by drawing parallels between graph signals and signals on Euclidean grids, such as images and audio. Accordingly, we propose a permutation-sensitive GNN based on an operator analogous to shifts in grids and instantiate it on 3D meshes for shape modelling (Spiral Convolutions). Following, we focus on learning on general graph spaces and in particular on functions that are invariant to graph isomorphism. We identify a fundamental trade-off between invariance, expressivity and computational complexity, which we address with a symmetry-breaking mechanism based on substructure encodings (Graph Substructure Networks). Substructures are shown to be a powerful tool that provably improves expressivity while controlling computational complexity, and a useful inductive bias in network science and chemistry. In the second part of the thesis, we discuss the problem of graph compression, where we analyse the information-theoretic principles and the connections with graph generative models. We show that another inevitable trade-off surfaces, now between computational complexity and compression quality, due to graph isomorphism. We propose a substructure-based dictionary coder - Partition and Code (PnC) - with theoretical guarantees that can be adapted to different graph distributions by estimating its parameters from observations. Additionally, contrary to the majority of neural compressors, PnC is parameter and sample efficient and is therefore of wide practical relevance. Finally, within this framework, substructures are further illustrated as a decisive archetype for learning problems on graph spaces.Open Acces

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Investigating face perception in humans and DCNNs

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    This thesis aims to compare strengths and weaknesses of AI and humans performing face identification tasks, and to use recent advances in machine-learning to develop new techniques for understanding face identity processing. By better understanding underlying processing differences between Deep Convolutional Neural Networks (DCNNs) and humans, it can help improve the ways in which AI technology is used to support human decision-making and deepen understanding of face identity processing in humans and DCNNs. In Chapter 2, I test how the accuracy of humans and DCNNs is affected by image quality and find that humans and DCNNs are affected differently. This has important applied implications, for example, when identifying faces from poor-quality imagery in police investigations, and also points to different processing strategies used by humans and DCNNs. Given these diverging processing strategies, in Chapter 3, I investigate the potential for human and DCNN decisions to be combined in face identification decisions. I find a large overall benefit of 'fusing' algorithm and human face identity judgments, and that this depends on the idiosyncratic accuracy and response patterns of the particular DCNNs and humans in question. This points to new optimal ways that individual humans and DCNNs can be aggregated to improve the accuracy of face identity decisions in applied settings. Building on my background in computer vision, in Chapters 4 and 5, I then aim to better understand face information sampling by humans using a novel combination of eye-tracking and machine-learning approaches. In chapter 4, I develop exploratory methods for studying individual differences in face information sampling strategies. This reveals differences in the way that 'super-recognisers' sample face information compared to typical viewers. I then use DCNNs to assess the computational value of the face information sampled by these two groups of human observers, finding that sampling by 'super-recognisers' contains more computationally valuable face identity information. In Chapter 5, I develop a novel approach to measuring fixations to people in unconstrained natural settings by combining wearable eye-tracking technology with face and body detection algorithms. Together, these new approaches provide novel insight into individual differences in face information sampling, both when looking at faces in lab-based tasks performed on computer monitors and when looking at faces 'in the wild'

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
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